Heavy metal contamination has become one of the most serious environmental problems. Among these elements, cadmium (Cd) is regarded as one of the major priority pollutants in drinking water due to its high biotoxicity, non-biodegradability and persistence in the whole environment. It has hazardous effects on ecological environment and human health. The major sources of cadmium release into the environment are waste water from electroplating factories, mining activities, chemical manufacturing processes and refining processes. It is of great social significance and economic benefits to study how to remove cadmium in water/wastewater friendly and efficiently. The commonly traditional methods used for the removal of cadmium from waste water include chemical precipitation, cementation, membrane separation, ion exchange, solvent extraction, adsorption process and the like processes. Among these methods, adsorption process is generally preferred and widely used because of its high efficiency, low cost, simplicity and availability. Activated carbons are well-known adsorbents extensively used for effluent treatment in many industrial processes. Activated carbon fiber (ACF) that has the advantages of uniform micropore structure, well-developed functional groups and good adsorption properties, is a new type of activated carbon, and has been gradually applied in water/wastewater purification systems. In this study, a comparative adsorption analysis of three activated carbon fibers (net, none-woven fabric and felt) was carried out and characterized with inductively coupled plasma optical emission spectrometry (ICP-OES) as the analysis method. Several characterization techniques (specific surface area analysis, X-ray diffraction, Fourier transform infrared spectroscopy and elemental analysis) were also employed to identify the structures of three activated carbon fibers. The structures of three fibers were similar with well-developed micropore structure according to X-ray diffraction and specific surface area analysis. The activated carbon none-woven fabric fiber has the strongest polarity and the highest oxygen content in the three ACFs. And it has a lot of oxygen-containing functional groups on the surface such as hydroxyls, carboxyls and aldehydes. Besides, the adsorption of cadmium on activated carbon none-woven fabric fiber obtained the most satisfied result with 97% removal efficiency. So that none-woven fabric was chosen as the target adsorbent in the subsequent experiments. The adsorption property of cadmium ions on none-woven fabric and its influential factors such as initial solution pH value and contact time between cadmium ions and the adsorbent were examined and optimized in the next procedure. The removal efficiency and adsorption capacity vary with pH value because pH affects not only the surface charge characteristics of activated carbon fiber but also the chemical forms of cadmium in the aqueous solution. The adsorbent charge turned into negative with an increase of pH value, which is favorable for removal of cadmium due to the emerging electrostatic attractions between cadmium ions and the oxygen-containing functional groups. The removal efficiency of cadmium increased with the increase of the initial pH of the solution. Cadmium removal efficiency by none-woven fabric increased significantly in the pH range of 1～6, and slightly from pH 6 to 9. Cadmium removal efficiency from 98.04% to 99.81% was obtained from pH 6～9. At lower pH, there was electrostatic repulsion between adsorbent and cadmium ions and competitive adsorption between H+ and Cd2+. The maximum uptake value was achieved at pH>6, which might be attributed to the presence of lone pair of electrons on oxygen atoms that are beneficial to coordinate with cadmium ions to give the corresponding complex compounds. At pH>9, the removal of cadmium was the synergistic effect of adsorption on adsorbent, complex formation and precipitation formation. Based on pH influence study, the pH value of solution was adjusted to 6～7 in subsequent adsorption experiments. In the initial adsorption stage, cadmium adsorption ratio increased rapidly with the increased contact time, and 72% of cadmium was removed in the first 10 min. Then the adsorption rate lowered down with the adsorption sites of none-woven fabric filled with cadmium ions. And finally, adsorption efficiency was up to a constant after 300 min, and the adsorption capacity reached a dynamic equilibrium. Further, any increase of contact time did not show any considerable changes in percent removal of cadmium. After the adsorption conditions were optimized, the isothermal adsorption experiment and kinetic experiment of cadmium were carried out subsequently. The results showed that the saturated adsorption capacity of cadmium on none-woven fabric fiber were 3.04 mg·g-1 and 0.035 mg·m-2 when the equilibrium concentration of Cd2+ was 20.0 mg·L-1, pH was 6.0, and the adsorption time was 300 min at 25 ℃. As the concentration of cadmium in the solution continued to increase, the adsorption amount tended to be a dynamic balance. The isothermal adsorption data was simulated by Langmuir model and Freundlich model. The main assumption of Langmuir isotherm is the monolayer formation of the solute on the surface of adsorbent without interaction between the solute molecules. The Freundlich isotherm is most commonly used to explain adsorption on a surface having heterogeneous energy distribution. In Langmuir model, the linear correlation coefficient was 0.997, and Langmuir factor was 1.796 L·mg-1. In Freundlich model, the linear correlation coefficient was 0.895, and Freundlich factor was 0.918 L·mg-1, and n was 2.12. The linear correlation coefficient of Langmuir model was much higher than that of Freundlich model. The adsorption capacity calculated according to the Langmuir model was 3.07 mg·g-1, which was just approximated to the experimental data of 3.04 mg·g-1. It indicated that the adsorption system conformed to the Langmuir equation better, and it dominated that adsorption on none-woven fabric was based on monolayer adsorption. The value of Langmuir separating factor was estimated for the entire concentrations range and it was calculated between 0 and 1, confirming favorable cadmium adsorption condition. In order to investigate the adsorption processes of cadmium on none-woven fabric, the kinetic data were fitted by four dynamic models, which were pseudo-first-order kinetic equation, pseudo-second-order kinetic equation, intra-particle diffusion equation and Elovich equation. In the first 5 min of the kinetic process, the adsorption capacity of cadmium accorded with intra-particle diffusion equation fairly since the linear correlation coefficient was calculated 0.985. It indicated that the diffusing rate in the interior surface layer of the particle was the control?step in the first 5 min. However, in the 5～300 min, the adsorption kinetic data could not accord with that equation at all. The whole adsorption process of cadmium on activated carbon none-woven fabric fiber accorded with the pseudo-second-order kinetic equation approximately with linear correlation coefficient 0.999 and reaction rate constant 0.367 g·mg-1·min-1. And the adsorption capacity obtained by calculation of pseudo-second-order kinetic equation was just approximated to that obtained by experiment. The whole adsorption process accorded with the Elovich equation and the pseudo-first-order kinetic equation relatively lower. In the Elovich equation, the linear correlation coefficient was 0.981, and Elovich factors were 0.271 mg·g-1 and 0.083 mg·g-1(lg min)-1. In the pseudo-first-order kinetic equation, the linear correlation coefficient was 0.927, and reaction rate constant was calculated to be 0.008 8 min-1. And the adsorption capacity obtained by calculation of pseudo-first-order kinetic equation differed from the experimental data. So that adsorption of cadmium on ACF was based on chemical reactions, such as electrostatic interaction and hydrogen bond. In removal of heavy metals by none-woven fabric, taking a 5.0 mg·L-1 cadmium contained synthetic wastewater sample for example, the cadmium content was less than 0.10 mg·L-1 after adsorption by none-woven fabric fiber, and it met Integrated Waste Water Discharge Standard (GB 8978—1996). Besides cadmium, heavy metals such as copper, lead and chromium could be adsorbed by none-woven fabric with removal efficiency higher than 95%. None-woven fabric had poor selection of cadmium adsorption in water with variety of heavy metals. When it met electroplating waste water and mining waste water, various heavy metals could be removed with more adsorbent added in adsorption treatment. The results showed that it was suitable, simple and effective in treating water containing cadmium by activated carbon fiber due to its good effect and convenient operation. And this study provided technical assistance and theoretical support in real waste water treatment.

With the rapid development of optical communication and optical information processing technology, nonlinear optical materials have attracted considerable attention in both industry and academia. Compared with inorganic non-linear optical materials, organic nonlinear optical materials exhibit the advantages of short response time, easy processing, and high nonlinear coefficient. Particularly, 4-N, N-dimethylamino-4’-N’-methyl-stilbazolium tosylate (DAST) is an artificially-designed organic non-linear material with non-centrosymmetry and strong polarizability. Numerous theoretical and experimental results indicated that DAST is one of the most important and successful organic nonlinear materials. Owing to its high second-order optical nonlinear coefficient, large electro-optic coefficient, large birefringence difference, and low dielectric constant, faster and stronger optical nonlinear responses can be achieved by DAST. Recent spectroscopic results revealed that DAST exhibits anisotropic THz spectral features. In this article, the growth of DAST crystals, and their practical applications in THz wave generation, second harmonic generation, as well as electro-optical detection and electro-optical modulation, are systematically reviewed. Moreover, our recent results about the preparation of DAST-based composite films, THz spectra, optoelectronic properties of DAST modified by carbon nanotubes or graphenes, and novel DAST-based metamaterials, are simultaneously presented. These results suggest a new perspective on DAST and its potential applications in the future. In addition, we have proposed some new ideas about DAST, such as DAST crystal growth induced by an electric filed or self-assembling monolayer, and frequency conversion efficiency of DAST crystal improved by quasi-phase-matching method. The DAST results systematically summarized in this review are helpful for promoting further studies on DAST-based materials and their applications in electro-optic modulators, THz detectors, frequency converters, etc, and consequently, the applications of DAST in optical communications, optical information processing, military technology and other important fields can be further expanded.

Time-dependent absorption spectroscopy, such as cavity ring-down spectroscopy (CRDS) and cavity attenuated phase-shift spectroscopy (CAPS), is a new type of absorption spectroscopy technology developed in recent thirty years. It has the advantage of high detection sensitivity, fast response, and not being affected by the fluctuation of light source intensity. Traditional absorption spectroscopy is based on the Lambert-Beer law, such as direct absorption spectroscopy (DAS), wavelength modulation spectroscopy (WMS), cavity enhanced absorption spectroscopy (CEAS) and so on. The weak absorption of material is hard to be measured once the background light signal is strong. And the instability of the light source also brings some limits to the detection. The time dependent absorption spectroscopy can make up for the shortcomings of traditional absorption spectrometry to a large extent because of its characteristics of not being affected by the fluctuation of light source intensity, but it also has its own limitations. First of all, CRDS and CAPS are not theoretically unified. And the existing theory can only apply to Pulse-CRDS with short pulse light where the pulse width is far less than the time constant of the resonant cavity itself. For long pulse-width light source or low reflectivity (less than 99.9%) cavity, the existing theory will no longer apply. As for CAPS, the modulation signal of the light source must be periodically sinusoidal or square-wave modulated signal. And there are no other types of periodic modulation signals and aperiodic signals mentioned. In view of the limitations of the time-dependent absorption spectroscopy mentioned above, we present a novel analytical method on time-dependent spectroscopy in this paper. The resonant cavity is regarded as a first-order sensing system. We use the first-order transfer function to unify the theory of time-dependent absorption spectroscopy and prove the consistency between the existing theoretical results and the derivation results under the novel method on the formula derivation. For Pulse-CRDS, we use Gaussian pulse light to derive the expression of transmitted light intensity under first-order sensing theory and simulate a series of different pulse widths γ, resonant cavity time constants τreal, and fitted time constants τanal. After analysis and comparison, we find the deviation of τanal and τreal is less than 1% when γ0.3τreal. In order to make Pulse-CRDS used with long pulse-width light, a correction function is given in this paper. And the error of the corrected ring-down time is less than 1% when the pulse-width is 0.3 times greater than the ring-down time. For CAPS system, we build an experimental platform with LED light source centered at 405nm and square wave modulated. Then we measure the phase difference and the peak value at different frequencies. The time constants τ calculated respectively by the phase-frequency and amplitude-frequency characteristic derived from the first-order transfer function are approximately the same, 7.24 and 7.25 μs with residual ranges of ［-0.01, 0.02］ and ［-0.02, 0.025］ respectively. The result shows that the theory of the first-order sensing system is fully applicable to the signal analysis of time-dependent spectroscopy. And the theory of first-order sensing system also makes the theory of time-dependent spectroscopy unified.

Radiation thermometry techniques have been developed from monochrome thermometry for single-point temperature measurement to multi-spectral thermometry for 2D or 3D temperature field measurement in the past few years with the development of radiation measurement sensors, but it is difficult to overcome the temperature deter error resulting from the modeling of emissivity. Determining emissivity behaviors is difficult and important for decreasing temperature errors and a general method is required to avoid the influence of emissivity behaviors. Dual-wavelength thermometry techniques have been developed to determine the temperature on a gray-body surface, and a series of compensation algorithms and wavelength choosing algorithms have been proposed to decrease the temperature error in dual-wavelength thermometry but they still have been affected dramatically by emissivity behaviors. Sometimes the error of dual-wavelength thermometry using the ratio method is greater than that of monochrome thermometry. Multi-wavelength thermometry has been widely used, but the number of measurement channels and emissivity behaviors still have important influences on temperature errors. The direct emissivity constraint algorithm and the emissivity constraint algorithm using a relaxing factor for radiation thermometry have been developed in this paper to determine the true temperature approximately. There is equivalence between the two algorithms as the emissivity constraint is the same. The emissivity constraint algorithm using a relaxing factor utilizes the least square algorithm instead of the ratio method to determine the true temperature. The error equation of the radiation thermometry algorithm with emissivity constraint using a relaxing factor has been deduced. It can be found that decreasing the value of λT can decrease the relative error of temperature as the signal-to noise ratio for each channel is big enough to keep accurate signals. Emissivity behaviors have an important influence on temperature errors, and the obvious emissivity variation with wavelength within the constraint interval can make temperature errors decrease. The direct emissivity constraint algorithm shows that increasing the number of channels may decrease temperature errors. It is obvious from the two methods that decreasing the length of the shrunk range of emissivity can decrease temperature errors dramatically.

Fluorescent nanomaterials not only have the advantages of nanomaterials, but also have excellent optical properties. They are widely used in fluorescent labels, ion recognition, fluorescence immunoassays, optical imaging and medical diagnostics. Therefore, researches on the preparation, structure analysis and fluorescence characteristics of fluorescent nanomaterials have received much attention. In order to obtain Si-based fluorescent nanomaterial with high luminous intensity, high fluorescent quantum efficiency and better controlling of preparation process, the effects of Si nanowires on the luminescence properties and the optical stabilities of the samples were investigated. First, based on the solid-liquid-solid growth mechanism, Si nanowires were grown under the conditions of reaction temperature of 1 100 ℃, N2 gas flow rate of 1 500 sccm and growth time of 15～60 min. The Si nanowires with different lengths and distributions were grown on single-crystal Si(100) substrates with “polishing” and “pyramid” texture surfaces respectively. And the Si nanowires with density of about 108 and 1010 cm-2 were grown with Au or Au-Al alloy film as metal catalyst. Then, a series of fluorescent nanometers SiNWs:Tb3+ samples were prepared using L4514 automatic temperature control tube heating furnace based on high-temperature solid-state method under the temperature 1 100 ℃, doping time 60 min, N2 gas flow rate 1 000 sccm, different Si nanowire substrates and high-purity Tb4O7 (99.99%) powder as doping agent. Next, at room temperature, the photoluminescence characteristics of different samples were measured by the Hitachi F-4600 fluorescence spectrophotometer with the fixed excitation light wavelength of 243 nm, the excitation light slit of 2.5 nm, the emission light slit of 2.5 nm, the scanning wavelength range of 450～650 nm and the PMT voltage of 600 V. Finally, the optical stabilities of the fluorescent nanomaterial was experimentally tested, such as time stability (0～30 d), temperature stability (300～500 K), acid and alkali stability (pH 1 and pH 11), anti-photobleaching stability (0～120 min), etc. Besides the water solubility and the dispersibility were tested. The results showed that SiNWs:Tb3+ produced strong green light emission with Si nanowires substrate which was prepared with the growth time 30 min, the “pyramid” texture surface and Au as the metal catalyst. The peak of green emission was 554 nm, which belongs to energy level transition 5D4→7F5. At the same time, three bands appeared at wavelengths of 494, 593 and 628 nm, which belong to the energy levels transitions 5D4→7F6, 5D4→7F4 and 5D4→7F3, respectively. In addition, the sample was shown to have excellent optical stabilities such as time, temperature, acid-base, anti-photobleaching, good water solubility and dispersibility. When the temperature is increased to 500 K, the light emission intensity is reduced by only about 8.9%. The green light luminescence intensity of the sample is not attenuated when irradiated with an UV light source with a wavelength of 365 nm and a power of 450 W for 120 min. In the strong acid solution of pH=1, no attenuation was found in 120 min and the attenuation was less in 15 min in the strong alkaline solution of pH 11. Subsequently, the luminous intensity showed a slow decline, but the luminous intensity of the sample became extremely weak after 60 min. The analysis showed that there is a layer of SiO2 coating on the surface of SiNWs:Tb3+, and NaOH solution easily reacts with SiO2. As time increases, the SiO2 layer is destroyed, so the luminescence intensity of the sample decreases. There was no deposit found when the sample dissolved in water for 30 days. At the same time, the luminance of solution was uniform. After studying the process conditions such as preparation temperature, gas flow rate and doping time, the influence of Si nanowires on Tb3+ green light emission was studied in depth. The material showed good optical stabilities, water solubility and dispersibility. It has a certain application value as a fluorescent marker.

Experimental and theoretical investigations of the terahertz (THz) absorption spectra of two typical monosaccharides and disaccharides (D-glucose and lactose monohydrate), which have similar structures, were carried out by using terahertz time-domain spectroscopy (THz-TDS) and density functional theory (DFT). Firstly, the THz absorption spectra of D-glucose and lactose monohydrate were measured in the frequency range from 0.3 to 1.7 THz by THz-TDS system, and it was found that although the composition of lactose contained glucose, the THz-TDS was very sensitive to the structural changes of carbohydrates. The two carbohydrates showed their special THz fingerprint absorption characteristics in the measured THz band, respectively. Secondly, the vibration frequencies of these two carbohydrates in the THz band were calculated by using the DFT method, and the simulation results, including the unit cell configuration of D-glucose and the isolated-molecule and unit cell configuration of lactose monohydrate, were obtained. At the same time, combining molecular vibration animation displayed by GaussView and potential energy distributions (PED) analysis, the vibrational modes of these two carbohydrates in the THz band were assigned in detail. It was found that the vibrational modes of lactose monohydrate were closely related to the vibrational modes of hydroxyl (—OH), hydroxymethyl (—CH2OH) and glycosidic bond. The absorption peaks of D-glucose at 1.44 THz and lactose monohydrate at 1.38 THz were mainly caused by intermolecular interactions (hydrogen bonds and van der Waals forces), especially the strong hydrogen bonds. Finally, using the reduced density gradient (RDG) analysis, the type and intensity of the intermolecular interactions of D-glucose and lactose monohydrate were visualized. The experimental results indicated that the THz-TDS technique has a keen perception for the subtle changes in the structure of carbohydrates, which provides an effective method for investigation of intermolecular interactions and detection of carbohydrates.

Atmospheric temperature, water vapor, surface skin temperature and surface emissivity are the intrinsic information of the atmosphere and surface of earth. Retrieval of atmospheric temperature profile and water vapour profile is important for accurate weather forecasting and climate change research by using satellite infrared data, at the same time, the retrieved surface skin temperature and surface emissivity spectra were used to study the growth of plant and crop yield, evaporation and circulation of surface water, energy balance, surface composition and physical properties, climate change and global environment. In this paper, considering the atmosphere and the ground as a whole system, the retrieval method for simultaneous retrieval of atmospheric temperature profiles, water vapour profiles, surface emissivity, and surface skin temperature was established. simultaneous retrieval were performed by using hyperspectral infrared satellite Atmospheric Infrared Sounder data (AIRS) in China’s Xinjiang region for two typical desert and snow features. Firstly, infrared radiation transmission equation of earth-atmosphere system was linearized. Then, it was proposed that atmospheric profile and surface emissivity can be structured by Empirical Orthogonal Functions (EOF) to effectively reduce the inversion variables. The physical simultaneous retrieval algorithm could be developed finally. In the retrieval process, the first guess values were obtained through National Centers for Environmental Prediction (NCEP), and the optimum solution could be obtained by Newton iteration method. The observation area covered the Taklamakan desert and Junggar basin in Xinjiang, China. The latitude of the Tazhong observation station is 38.98 degrees and the longitude is 83.64 degrees, which is located in the hinterland of the Taklimakan desert in central tarim basin. The latitude of National field science observation station of fukang desert ecosystem is 44.2 degrees and the longitude is 87.9 degrees, which is located in Junggar basin. The Tazhong observation station and National field science observation station of Fukang desert ecosystem were selected to be the retrieval of the ground verification point. These stations were selected to be the retrieval of the ground verification point. The results showed that the surface temperature in the Taklamakan desert is significantly higher than that in Junggar basin, which is consistent with the actual situation. According to the retrieval of the surface emissivity distribution at 8.6 and 13.4 μm, it could be seen that the desert surface emissivity is significantly lower than the emissivity of snow at 8.6 μm, and retrieval of the two kinds of ground infrared emissivity spectrum is consistent with the laboratory measurement of emissivity spectra by comparing the retrieved surface emissivity and the jet propulsion laboratory measurement of desert and snow emissivity data between 6～15 μm. The atmosphere and ground were considered as a whole system, the surface emissivity was added in the retrieval in this paper. Through comparison and analysis of retrieved two kinds of ground atmospheric profile with the local meteorological sounding values and traditional method retrieval, the research showed that the retrieval accuracy of atmospheric temperature and water vapour profile is improved, especially the improvement is obvious in the boundary layer. At the same time, the analysis showed that the improvement of the retrieval precision of the atmospheric profile in the desert region is higher than that in the snow region. Because the surface emissivity changes within the spectrum is larger in the desert area, while the surface emissivity changes within the spectrum is smaller in the snow area. It is important that using the proposed approach can simultaneously retrieve atmospheric temperature profiles, water vapour profiles, surface emissivity and surface skin temperature. The retrieval precision of the atmospheric temperature profile and water vapour profile in the desert area can be improved more effectively compared with snow region. This paper can provide the service and support for the numerical weather forecast and the future hyperspectral infrared satellite application in China, which is of great significance.

When facing complex sea-sky background, island-shore background, bad weather, bright waves or decoys interference and other complex conditions, the detection rate, false alarm rate, detection distance or other performance indicators of the existing ship target detection system based on a single wide-wave infrared image will be affected. Considering the above problems, the detection method for ship target at sea based on multi-spectral infrared images was studied in this paper. Through the data acquisition system for multi-spectral infrared images, 107 groups of 5 medium-wave infrared images were collected actually. The spectrals from 1 to 5 were 3.7～4.8, 3.7～4.1, 4.4～4.8, 3.7～3.9 and 4.65～4.75 μm respectively. The sample data set was constructed by annotating the multi-spectral images manually, which was made up of 298 ship targets and 353 non-ship targets. Firstly, PCA transform was adopted to reduce the dimension of multi-spectral infrared images and selective search algorithm was adopted to generate the initial target candidate regions. In order to solve the problem that there are too many obvious non-ship target regions, the integral image was used to calculate the local contrast of the initial candidate regions and the ship target candidate regions were located according to the geometrical and grayscale features of infrared ship target. Secondly, each ship target candidate region was extended to incorporate the local context information. For the 5 spectral images corresponding to each ship target candidate region, dense SIFT feature of each spectral image was extracted. PCA was applied to SIFT feature, reducing its dimensionality from 128 to 64. Then the spatial and spectral position distribution information of each SIFT feature was added to the feature vector. Based on the Gaussian mixture model, the feature vectors of each candidate region were encoded to Fisher vector representation. Finally, linear SVM classifier was used to recognize ship targets. Experiment of the generation of ship target candidate regions showed that the proposed constraint method based on geometrical and grayscale features of infrared ship target can effectively overcome the shortcomings of selective search algorithm and quickly locate the ship target candidate regions from the initial target candidate regions. Experimental results on 25 groups of multi-spectral images showed that the generation of ship target candidate regions takes 0.353 s totally, while locating the ship target candidate regions takes only 0.005 s. Test of target recognition on 100 positive and negative samples showed that the recognition rate of the proposed target recognition algorithm reached 0.97, which is significantly higher than the target recognition rate based on single-wave infrared image. The proposed target recognition algorithm integrates the feature information of the multi-spectral target images and applies Fisher vector to extract the deep layer information in the gradient statistical features of the multi-spectral target images. Experimental results on 25 groups of multi-spectral images showed that the proposed ship target detection method can detect the ship targets at sea in different scenes such as sea-sky background, island background and bright waves interference. The locations of the ship targets are accurate and the ship recall rate reaches 0.95. The average detection time of each group of multi-spectral images is 1.33 s. The study results showed that with considering the radiation difference between the ship target and its local ocean background in the infrared image and the effective fusion of the radiation characteristics of ship target in multi-spectral infrared images, the divisibility of ship target can be enhanced, which results in the improvement of recognition rate and detection rate of ship target. This study provides new technical support for ship target detection based on multi-spectral infrared images.

Identification of wood species is an important part of wood processing and commerce. The traditional methods of wood species identification mainly include microscopic detection and wood texture recognition which are complex, time-consuming and costly. They cannot meet the current needs. Near infrared spectroscopy (NIRS) of wood combined with pattern recognition methods were used to identify wood species. NIRS combined with three kinds of pattern recognition methods including principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and soft independent modeling of class analogy (SIMCA) were used to identify fifty-eight wood species. Five spectral preprocessing methods including 5 point smoothing, standard normal variable (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SG 1st-Der) and wavelet derivative (WD) were used to spectral transform. The correct recognition rate (CRR) of calibration and test sets were used for evaluation index of models. The results showed that the wood species could not be identified by using the first three principal components. In PLSDA model, the CRR values of calibration and test sets for original spectra model were the highest, which were 88.2% and 88.2%, respectively. The CRR values of calibration and test sets for 5 points smoothing model were 88.1% and 88.2%. The CRR values of calibration and test sets for SNV model were 84.4% and 84.5%. The CRR value of calibration and test sets for MSC model were 83.1% and 84.2%. The CRR values of calibration and test sets for SG 1st-Der model were 81.8% and 82.7%. The CRR values of calibration and test sets for WD (the wavelet basis is “Haar” and the decoposition scale is 80) model were 87.3% and 87.2%. In PLSDA models, the original spectra model had the best results compared to others. In SIMCA model, the CRR values of calibration and test sets for original spectra were 99.7% and 99.4%. The CRR values of calibration and test sets for 5 points smoothing were 100% and 100%. The CRR values of calibration and test sets for SNV model were 99.5% and 99.1%. The CRR values of calibration and test sets for MSC model were 99.0% and 98.4%. The CRR values of calibration and test sets for SG 1st-Der model were 98.4% and 99.0%. The CRR values of calibration and test sets for WD model were 100% and 100%. Compered to others spectra processed by 5 points smooting and WD had a best results in SIMCA models, the CRR values of calibration and test sets were 100%. Three kinds of pattern recognition methods combined with five spectral preprocessing methods were used to classify 58 kinds of wood. It could be concluded that the PCA method can’t explicitly classify 58 wood species because of complex properties of wood leading to the scatters of each wood species interwined with each other in PCA distribution diagram. The PLSDA model of original spectra could get a better result with the CRR value of 88.2% and 88.2% for calibration and test sets, respectively. The best SIMCA models were constructed by 5 point smoothing or WD preprocessing methods with the CRR of 100% for calibration and test sets. However, the factor of the WD-SIMCA model was smaller than 5 point smoothing method, and the model was more parsimonious, so WD-SIMCA model was an optimal model. The paper showed that spectral preprocessing methods can improve the accuracy of identificationof wood species, and SIMCA supervised pattern recognition method can be used to build effective identifying model and NIR combined with pattern recognition method can provide a rapid and simple method for identification of wood species.

To acquire the high-accuracy of samples from the computer color matching, a novel color matching method was proposed in this paper which combines chemical component analysis with computer color matching. The kernel of this method is to select the inks that are most similar to the printing inks of target colors, thus the high-accuracy color matching is achieved. This method can provide a new referential direction for future development of computer color matching. The verification of color matching effects was conducted using inks that were similar to target colors in composition. The target colors were firstly printed by printing inks, and then the color matching was carried out using the same printing inks to maintain the consistency between target colors and test colors of printing inks. In this paper, three different brands of printing inks were used to match colors with target colors, and the accuracy and efficiency of computer color matching from those printing inks were compared intuitively. The target colors were printed using three different colors Silian ink in arbitrarily proportions and volume, and they were obtained by using IGT-C1 printability tester (IGT Inc.,Netherlands), and these target colorsranges included secondary and tertiary colors, with each color range having three samples, respectively. Three primary printing inks Cyan, Magenta and Yellow of three brands of printing inks—Silian, Dongyang and Mudan were used as basic inks to establish the fundamental database by X-Rite color matching software (X-Rite Inc., America), and then different target colors were matched with foundational database of three brands of printing inks respectively. The results showed that the color matching accuracy of the Silian ink outperformed the other two brands of printing inks because of the fact that Silian ink was used as the printing inks of target colors, and the holistic color differences of Silian ink were the smallest among the three brands of printing inks and the color differences less than 1.0 were achieved just after one or tw-ice corrections. The smallest color difference was acquired even 0.36 and from the spectrum matching, which implied that Silian ink almost achieved isomeric match with the target color. This experiment has verified the feasibility of the emphasis of component analysis—computer color matching method, which picks the most similar inks to the printing inks of target colors so that we can achieve a high-precision color matching. The chemical analytical tool assessing the difference between printing inks of target colors and color-matching test colors. To distinguish the printing inks of target colors and color-matching test colors from the component levels, the infrared spectral similarity was used as an analytical tool in this paper. The spectra of printing inks of three colors of the three brands were measured by the Thermo Nicolet 6700 Fourier transform infrared spectrometer (Thermo Fisher Scientific Inc., Waltham, USA), and the infrared spectral similarities of the printing inks of target colors and color-matching were all obtained and then their average similarities were also calculated by the OMNIC software. Through the comparison between the infrared spectral similarities of different brands of printing inks and the precision of computer color matching experiment, the rationality and validity of the infrared spectra as a chemical analytical discriminant tool to evaluate similarity between inks of target colors and color-matching colors was verified. The results indicated that the similarity between spectra of Silian inks and printing inks of target colors was the highest and even reached 100%, while Dongyang inks offered a high similarity of 86.53%, and Mudan inks provided the lowest similarity of 64.63%. The results showed that when the number of correction was the same, taking color difference as the criteria of judgment, the color differences of color matching for Silian ink were the smallest and it meant this ink provided the highest accuracy of color -matching; and Dongyang ink took the second place, and its color differences were about twice as large as Silian ink; and the Mudan ink showed the highest color differences, and its color differences were three times more than those of the Silian ink. The result of computer color matching experiment was consistent with infrared spectral similarities results, and the principle suggested that the higher the infrared spectral similarity between printing inks of target colors and test colors was, the easier high precision color matching sample could be received. Conclusion and Prospect: The feasibility of the new color matching method which combines the component analysis and computer color matching to gain the high-accuracy color-matching samples was proved by experiments and result analysis. Use the infrared spectral similarity as an analysis tool to distinguish the difference in components between the printing inks of target colors and test colors is feasible, which can be an effective criterion for determining the color matching accuracy. Future research will focus on probing into the correlative numerical relationship between infrared spectral similarity and color-matching precision, and further seeking more effective chemical analytical method to estimate the componential relationship between printing inks of target colors and color-matching test colors.

In the quantitative modeling of near-infrared spectroscopy data, the high redundancy and high noise of the data severely affect the robustness and accuracy of the modeling. Therefore, this paper presents a feature-based spectroscopy combined with improved Particle Swarm Optimization (PSO) Method of choosing. First, we measure the importance score of each feature through mutual information, and then sort the features according to the importance of the features in descending order. This effectively avoids the problem of losing important information caused by using the principal component reduction method. Secondly, the concept of jump degree is introduced and a method of feature stratification is constructed. Similar features of similar importance are merged into the same feature subset, and the descending ordered feature set is segmented into different feature subsets, avoiding the screening uncertainty caused by artificially setting the score of feature importance score during feature process. Finally, the particle swarm optimization algorithm with fast convergence rate and few control parameters is used as the optimal feature subset optimization method. At the same time, particle swarm optimization is improved in two aspects: The chaotic model is introduced to increase the diversity of the population and improve the global searching ability of PSO, so as to avoid getting into local optimum. The number of features is introduced into the fitness function, and the influence of the number of features on the fitness function is adjusted by the penalty factor in the early iteration to improve the adaptability of the algorithm. The stratified data is collected as a feature subset and then added as a modified particle swarm optimization algorithm to select the high-resolution feature subset. In this paper, the nicotine index as an example of the feature selection process is described, using Nicolet company Antaris II near infrared spectrometer near infrared spectrum data acquisition, spectrum scanning range is 4 000~10 000 cm-1. First, we use the mutual information theory to calculate the importance score of 1 557 features of the whole spectrum on the quantitative modeling of the index to be measured, and take the average of 30 experiments. Secondly, all the features are sorted in descending order of importance scores to calculate the jumping degree of all the features. According to the jumping degree, the critical points of the feature stratification are searched, and the features are divided into different feature layers to construct a feature containing 8 feature subsets set S={S′1, S′2, S′3, S′4, S′5, S′6, S′7, S′8}. Then, the feature subset is in turn {S′1}, {S′1, S′2}, {S′1, S′2, S′3}, …, {S′1, S′2, S′3, S′4, S′5, S′6, S′7, S′8} as a candidate for initial particle swarm. With R/(1+RMSEP) as the evaluation criteria of the pros and cons of feature subsets, each iterative experiment 50 times, the ratio of the largest feature subset is the optimal feature subset. In order to verify the effectiveness of this algorithm, we select representative tobacco near-infrared spectral data as a training set and a test set, establish a PLS quantitative model of nicotine and total sugar, and compare with the full-spectrum, stratified characteristic spectrum, particle swarm algorithm selected by the characteristic spectra. The simulation results show that the modeling correlation coefficients R of nicotine and total sugar selected by this algorithm are respectively 0.988 5 and 0.982 2, RMSECV of mutual verification are 0.098 4 and 0.889 3 respectively, RMSEP of prediction root mean square error are 0.901 6 and 0.100 7 respectively, Accuracy are significantly higher than the other three methods. From the selected number of features, the proposed algorithm has the least number of selected features, effectively eliminating the weak correlation and noise and redundant information in the original feature set, minimizing the number of main factors of the model and reducing the complexity of the model, and the model is steadier, more adaptable.

At present,the wood grades of the national musical instrument Chinese zither panel mainly rely on the personal experience of the musical instrument technician.This method relies on experienced technicians and is susceptible to subjective judgment. In view of this situation, we use the Paulownia wood used to make the Chinese zither panel as an experimental sample. We propose a method of using near-infrared spectroscopy and the improved BP neural network to rapidly identify different grades of Chinese zither panels .Because Near-infrared spectroscopy can characterize a number of material structure and composition information, with the low cost of measuring instruments and many measuring accessories,we conduct an experimental analysis of the near-infrared spectral data of Paulownia panel. In the experiment, spectral denoising is first performed to eliminate system errors and improve spectral resolution, regarding the root mean square error and the square sum of signals as the evaluation criterions of various pretreatment methods.Therefore ,the first derivative is selected as the final pretreatment method, and 15 is the appropriate filter denoising window size.The principal component analysis is then used to compress the data and the Mahalanobis distance is used to eliminate the modeling set’s abnormal samples to create a more representative modeling set. Then, an unsupervised clustering is used to analyze the Paulownia panel grades , which proves the feasibility of grade classification.Since H2O has a large absorption in the near-infrared spectral region, according to experimental spectral analysis results,we do not consider the fundamental frequency vibration band (5 396 to 4 978 cm-1) and the first overtone vibration band (6 800 to 7 000 cm-1),but consider only the remaining near-infrared spectral band. Different spectral bands are combined, and seven bands are used as input to the neural network to carry out the panel grade recognition.We also improve the traditional BP neural network model. The learning rate of BP neural network is set by an adaptive optimization strategy to speed up the traditional neural network’s training rate.At the same time, the cross entropy function is used as the cost function to speed up the updating of the weight.The Relu function is selected as the transfer function between the input layer and the hidden layer, which improves the training speed of the model and effectively prevents over-fitting.The Softmax function is chosen as the transfer function of the last layer to reduce complex calculations. By this way, the final BP neural network is constructed.The amount of spectral information that can be extracted by different principal component variables is different.We adjust the input of the BP neural network model by increasing the number of principal components and adjusting the spectral band interval.When the number of principal components is 11 and the spectral intervals are 10 000 to 7 000 cm-1 and 4 976 to 4 000 cm-1, the unknown sample’s recognition rate reaches 99.7% , and the selected spectral range covers all the characteristis of C—H bond and other bond information.The results show that near-infrared spectroscopy combined with BP neural network can effectively identify different grades of Paulownia panel, thereby reducing manual detection errors, shortening the processing time, and better meeting the needs of the instrument market.

The early identification of sclerotinia sclerotiorum rot of oilseed rape was observed from the canopy and leaf scale by using the thermal infrared imager based on the UAV simulation platform. The thermal infrared image data were obtained from the canopy scale, and the temperature values of the canopy scale were extracted, and the rape leaves were monitored for acquiring the physiological index. Then, the average temperature and the maximum temperature difference were used to compare the healthy and diseased samples and one-way ANOVA was also used. The results showed that the difference in the maximum temperature difference between healthy and diseased plants was obvious, and the difference in the average temperature between healthy and diseased plants was obvious with the number of days. The single factor analysis of variance showed that there was significant difference (p<0.01) between the maximum temperature difference at the first day after rape infection. Furthermore, the physiological indexes (stomatal conductance, photosynthetic rate, carbon dioxide concentration and transpiration rate) of rapeseed were analyzed with the number of days. The changes could be used to detect the correlation between physiological index and temperature Sexual analysis. The results showed that there was a significant correlation between photosynthetic rate, carbon dioxide concentration and transpiration rate and temperature. The temperature information of the healthy and diseased areas in the diseased leaves of the samples was obtained. The thermal infrared image could visually identify the disease infection process and use the pixel value to estimate temperature difference between the health and the affected area. The healthy and diseased samples were identified by maximum temperature, minimum temperature, average temperature and maximum temperature difference. The results were compared with the single factor analysis of variance (ANOVA), showing that the maximum temperature, the minimum temperature, the maximum temperature difference and the average temperature in the healthy and infected areas were significantly different, and the lesion area temperature was higher than that of the healthy area. The single factor analysis of variance showed that there was significant difference (p<0.01) in the maximum temperature difference at the first day, and the early identification of sclerotinia sclerotiorum could be realized.

Soluble solid content (SSC), including sugar, acid, fibrin and mineral components, is a comprehensive index for evaluating the fruit maturity and quality, which can affect the taste, flavor and shelf life. Non-destructive and rapid detection of SSC in watermelon is very important for determining the maturity and monitoring the internal quality during storage and transportation, and is helpful to improve production efficiency and market competitiveness of watermelon. For the rapid and non-destructive near infrared (NIR)-based detection of the watermelon SSC, many researchers have used near infrared diffuse transmission method, which requires high light energy and high power transmission, and high power transmission will affect the internal quality. In contrast, the number of researches on near infrared diffuse reflectance method are relatively smaller. It has the advantages of low light energy and low cost, which is in favor of miniaturization and portability of the instruments, and will avoid the fruit quality changes caused by high power transmission. In this study, the greenhouse watermelon was used as the research object, and the near infrared reflectance spectra were collected in the watermelon stem, navel and equator at near 976, 1 186 and 1 453 nm by using JDSU portable near infrared spectrometer. The models between watermelon SSC and near infrared reflectance spectroscopy were established by using partial least square regression (PLSR). Firstly, the sample collection of different parts in the watermelon was divided based on the joint x-y distances (SPXY) method, with SSC as y variables and spectral as x variables. The samples distances were calculated by using x and y variables, and the watermelon samples were divided into 51 calibration sets and 15 prediction sets. The SSC of the calibration sets has a wide distribution range, which covers that of the prediction sets, and can increase the diversity and representativeness of samples and help to build a stable and reliable prediction model. Secondly, the prediction accuracy of quantitative models between the near infrared reflectance spectroscopy and SSC in different detection positions was investigated, and higher correlation and better prediction performance was found in the equator position with prediction correlation coefficient of 0.629 and root mean standard error of prediction of 0.49%. The accuracy of the models between SSC and near infrared spectra information in different watermelon positions was related with the spectrum collection ways and the differences in growing area, variety and maturity. Therefore, the determination of the detection position in the watermelon should be based on the actual situation in the model-building process. Finally, in order to improve the prediction accuracy of the models built for the watermelon equator, the spectra should be pre-processed with the model built for the watermelon equator, and then normalize the results, based on which we can obtain the best prediction model of PLSR. The prediction correlation coefficient was 0.864 and the root mean standard error of prediction was 0.33%, showing higher correlation and improved prediction accuracy. In conclusion, the results indicated that the SSC of the greenhouse watermelon can be accurately predicted based on detecting the equator position by near infrared reflectance spectroscopy. Therefore, it has the potential for improving the rapid and non-destructive testing technology and developing small and portable equipment to detect watermelon SSC by near infrared spectroscopy.

Pretreatment is an effective way to improve the utilization efficiency of the corn stover biotransformation. The conversion rate is directly related to contents of the cellulose and hemicellulose in corn stover during the bio-refinery conversion to biofuels. To achieve an effective control for the corn stover bio-refining process after the pretreatment, the near infrared spectroscopy (NIRS) was used to quickly detect contents of the cellulose and hemicellulose, solving the problems of being time consuming and high-cost in the traditional chemical analysis method. To improve the efficiency and precision of the NIRS detection, the genetic simulated annealing algorithm (GSA) based on genetic algorithm (GA) combined with simulated annealing algorithm (SA) was presented for optimizing the characteristic wavelength variables of NIRS. In the GSA, firstly, the number of the NIRS wavelengths was used as the code length for binary coding; secondly, the root mean square error of cross-validation (RMSECV) of the partial least squares (PLS) regression model was used as the objective function; thirdly, the fitness function was designed combining with the temperature parameter; and last, the selective replication of the perturbation solution was realized based on the Metropolis criterion. Therefore, GSA can effectively improve the search efficiency at the later stage of evolution while avoiding premature convergence. 120 samples of corn stover were prepared by using the pretreatments of alkaline, biology, and the combination of alkaline and biology. The contents of cellulose and hemicellulose were measured using the wet chemistry methods. The NIRS were collected using the Nicolet Antaris Ⅱ Fourier near infrared spectrometer. The spectrum was pretreated by 7 points Savitzky-Golay smoothing combining with multivariate scattering correction and standard normal variate transformation. The samples were divided into correction set and validation set by using Kennard-Stone algorithm at a ratio of 3∶1. The GSA is used for the characteristic wavelength variables optimizations of the NIRS whole wavelengths (Full-GSA), the synergy interval partial least squares selected spectral region (SiPLS-GSA), and the backward interval partial least squares selected spectral region (BiPLS-GSA), respectively. And then, the optimized results of the characteristic wavelength variables were evaluated by the PLS regressive model with the validation set. In Full-GSA, 1 557 wavelength points were used as chromosome genes in whole wavelengths, 118 cellulose characteristic wavelength points and 164 hemicellulose characteristic wavelength points were selected after 16 executions. In SiPLS-GSA, the cellulose and hemicellulose wavelength points of spectral region optimized by SiPLS were 388 and 160, respectively, and 157 cellulose characteristic wavelength points and 148 hemicellulose characteristic wavelength points were gotten after the further optimization by GSA. In BiPLS-GSA, the cellulose and hemicellulose wavelength points of spectral region optimized by BiPLS were 358 and 180, respectively, and 130 cellulose characteristic wavelength points and 153 hemicellulose characteristic wavelength points were selected after the further optimization by GSA. It was shown that not only the number of wavelengths was significantly decreased after the optimization, but also the performance of regressive model was obviously better than that of the whole wavelengths. The best performance of regressive model for cellulose characteristic wavelengths was obtained by Full-GSA, and the best performance for hemicellulose characteristic wavelengths was obtained by SiPLS-GSA. The mean relative error (MRE) values of validation set for cellulose and hemicellulose in the best model were 1.752 4% and 2.020 8%, which were decreased by 13.636 6% and 25.368 4% compared with the whole wavelengths, respectively. The GSA combining with temperature parameters to design the fitness function is suitable for the NIRS characteristic wavelength selection of the cellulose and hemicellulose contents in corn stover, and has a good global search capability. The encoding scheme of GSA using each wavelength point in whole wavelengths as chromosome gene is suitable for the characteristic wavelength selection of NIRS whole spectrum. GSA is also suitable for the characteristic wavelength selection of the spectral region optimized by SiPLS and BiPLS, and the selection of wavelength points in the optimized spectral region can also be achieved effectively.

To study the variation trend of major chemical composition of wheat seeds during short-time natural aging, the nondestructive technology based on near infrared spectroscopy (NIR) and support vector machines (SVM) is applied to evaluate the natural aging stage at the same time. There are 45 wheat samples collected in the experiment. The samples are scanned at the beginning and after natural aging for 4 months, 7 months and 9 months respectively by VERTEX 70 Fourier transform infrared spectrometer in large sample cup rotation sampling mode. The spectral standard deviations of each sample at four natural aging stages are calculated firstly. The standard deviations represent the statistical quantity of data dispersion. The obvious variation regions are screened according to the standard deviations calculated from the spectrums of 4 aging stages. To avoid abnormal discrete degree value caused by accidental factors, the averages of 45 samples spectrum discrete degree are calculated. The spectral peaks are mainly distributed in the area of 8 362, 6 950, 7 563, 5 319, 4 998 and 4 478 cm-1 according to the standard deviation. The region nearby 6 950 cm-1 reflects stretching vibration of O—H in liquid water, and the standard deviation value is greater. This illustrates the moisture changes remarkably during natural aging stage. The region nearby 5 319, 4 998 and 4 478 cm-1 reflect vibration information of primary amide, secondary amide and amide in protein. The standard deviation values at these peaks are all lower than the value of 6 950 cm-1, so the protein changes more slowly than moisture during aging stage. The region nearby 8 362 and 7 563cm-1 reflect secondary vibration information of C—H and the he standard deviation value is greater. There are C—H group in protein, starch, etc. of wheat seeds. It shows that comprehensive changes of protein, starch and other components are relatively strong. According to the above analysis, the multi-classification model has been built based on NIR and SVM to determine the 4 types natural aging stages. The sample set is divided into two parts randomly according to the ratio of 3∶1. The number of train sample is 135 and the number of test sample is 45. The best parameters of SVM are selected by grid searching. While the kernel function is RBF function, the penalty parameter is 8 and kernel parameter is 0.008 974 2, and the recognition rate of training set and test set reach to 99.26% and 99.78%. The results show that NIR technology combined with SVM can be applied to determine the natural aging stage of wheat seeds, which also provides a convenient and fast tool to monitor physiological characteristics changes during wheat seeds storage.

Visible-near infrared spectroscopy based chronology classification of ancient ceramic method has been proposed to make the identification more objective and accurate. Yaozhou kiln exists in many dynasties and it has great similarity between different dynasties. Therefore, age identification of Yaozhou kiln faces great challenges. Taking Yaozhou kiln as the research object, some multi-spectral data of ancient ceramic from different dynasties are gotten from ultraviolet-visible near infrared spectroscopy analyzer. To avoid the first-order and second-order differential missing intermediate transition information, a fractional-order differential preprocessing method is proposed to suppress and eliminate the background information and noise from spectral data. The experimental results show that the classification accuracy of Yaozhou kiln in different dynasties is only 84.8% when the differential processing is not performed (0th order), while the classification accuracy based on different fractional differentials is obviously higher than that of 0th order. And the optimal order is 0.7. Then, a deep belief network based ancient ceramic classification method is proposed. First, stacked restricted Boltzmann machine (RBM) is employed to extract some high-level features during pre-training stage. The results show that the correlation coefficient between the features before RBM dimension reduction is 0.885 7, while the correlation coefficients after dimension reduction by the first and second RBM are 0.544 6 and 0.391 5 respectively, which means the redundancy is obviously cut back. Then some weight and bias values trained by RBM are used to initialize BP neural network. The whole deep belief network is fine-tuned by BP neural network to promote the initiative performance of network training and overcome local optimal limitation of the neural network due to the random initializing weight parameter. Experimentally, the optimal number of RBMs in depth belief network is 2, and the optimal number of RBM hidden layer units is 100. Meanwhile a dropout strategy is put forward to randomly ignore neurons of some hidden layers to reduce interdependence between features in the network training process and prevent over-fitting from some small data. When the ratio of Dropout is 0.45, the classification accuracy is highest. According to the method mentioned in this paper, the chronology classification accuracy in Yaozhou kiln is 93.5%, and accuracy of Yaozhou kiln in the Five Dynasties is highest, reaching 96.3%. Comparisons with some chronology classification methods highlight the superior performance of the developed method.

High temperature coal tar pitch is a kind of high quality raw material to produce carbon materials. However, coal tar pitches without refining treatment was not suitable to produce such high quality carbon materials. Actually, refining treatment is an important way to adjust the molecular weight distribution, aromatic structure, and aliphatic side chain structure of coal tar pitch. Refining treatment is a precondition to produce carbon materials which is easy to graphitize with coal tar pitch as the raw material. In this study, four kinds of refined pitches numbered as RP-1, RP-2, RP-3 and RP-4 have been obtained by two kinds of preparation methods with the medium pitch and modified pitch as the raw materials, respectively. The refined pitches have been studied by Fourier Transform infrared spectroscopy (FTIR) and curve-fitting analysis in order to gain additional information on the comparation of these four refined pitches. The curve-fitted data provide quantitative evidence of aromaticity(Iar), length of aliphatic chain(CH3/CH2), distribution of OH groups, and oxygen-containing functional groups with different refined pitches. The results have showed that: these four kinds of refined pitches has a larger aromatic condensation degree. RP-3 has the highest aromaticity index of 0.9. RP-4 has the Index of the branched chain of 0.07, which means that RP-4 have a long aliphatic chain. The distribution of OH groups in refined pitches was significantly different. The results can provide a theoretical support for the selected raw materials in the preparation of carbon/graphite materials.

Grayish green nephrite is named for a kind of nephrite belonging to green nephrite type, but with appearance similar to gray nephrite. Although their appearance is similar, the price of grayish green nephrite is much higher than that of gray nephrite. Thus a phenomenon appears that some dealers tell their consumers green nephrite while selling gray nephrite. In addition, some jade materials with such appearance appear in some unearthed jade artifacts, but their types can not be accurately identified. This makes it particularly important to quickly and accurately identify grayish green nephrite and gray nephrite. In this study, representative grayish green nephrite and gray nephrite samples were investigated using ultraviolet-visible spectroscopy, Fourier transform infrared spectroscopy and electron microprobe analysis, and all the characteristics were yielded. By comparing the features between them, it can be found that there is no significant difference in the UV-Vis reflection spectra of both types of samples. However, the differences in the infrared spectra of them are recognizable. In order to explore more effective identification features, the reflection and transmission methods were used to obtain infrared spectra. The infrared spectra of both types of samples were generally the same, with the following distinguishable differences. The peak or shoulder around 1 050 and 1 018 cm-1 and the broad shoulder near 411 cm-1 occur in the reflection spectra of grayish green nephrite which do not appear in those of gray nephrite. The shoulder around 453 cm-1 and the peak near 401 cm-1 exhibit in the transmission spectra of the gray which do not exist in those of the grayish green. The above findings can be used as spectral characteristics to identify grayish green nephrite and gray nephrite. The intensity of the OH stretching vibration bands at 3 674, 3 661 and 3 643 cm-1 after Beer-Lambert Law transformation of the infrared transmission spectra and the Mg and Fe2+ content in the M1 and M3 sites are well correlated. The Mg(M1+M3)# (Mg/(Mg+Fe2+) in the M1 and M3 sites) ratio calculated by the relationship between the above two can be used to distinguish between grayish green nephrite and gray nephrite using their infrared transmission spectra. Mg(M1+M3)# ratio in grayish green nephrite (0.871~0.892) is smaller than that of gray nephrite (0.927~0.949). Moreover, the result of electron microprobe analysis showed that there are some differences in chemical composition between them. Mg content in grayish green nephrite (4.45~4.53) is less than that of gray nephrite (4.66~4.78), and Fe2+ content in the grayish green (0.28~0.49) is larger than that of the gray (0.10~0.23). However, Mg and Fe2+ content between them are not much different from each other, suggesting that the difference in infrared spectra may be related to the physicochemical conditions during crystallization besides having a certain correlation with the composition (the genetic types of grayish green nephrite and gray nephrite are ultrabasic rock type and dolomitic marble type, respectively). The above infrared spectrum identification features not only have important gemological significance for identification of grayish green nephrite and green nephrite, but also have potential application value for discriminating origin and analyzing occurrence of some ancient jades with the similar appearance to the studied nephrites.

At present, underwater temperature measurement of seawater is a hot research topic, because knowledge about seawater temperature is of great importance in many fields. The laser Raman spectroscopy is a feasible method for measuring the vertical profiling of seawater temperature in large water areas. However, the real-time remote sensing of underwater temperature has not been reported. In this paper, a low-cost and practical Raman Lidar seawater temperature remote sensing system is constructed, and a real-time spectra acquisition and temperature determining software system is developed. Firstly, a background subtraction algorithm which combines the spatial accumulation of the array CCD with the exposure time integral is used to effectively enhance the signal to noise ratio of Raman spectra and improve the detection sensitivity of this Raman Lidar system. Usually the Raman spectra measured on-site are in low signal to noise ratio and baseline drift conditions. In this case, the “area ratio” (i. e. the ratio of the integrated Raman spectrum at low wavelength to the integrated Raman spectrum at higher wavelength) is a good temperature indicator. In this paper, we comprehensively studied the influence of Raman spectra area ratios split positions and fitting methods on the temperature measurement accuracy. More than 500 groups normalized Raman spectra at different temperatures are experimentally measured in the process of water temperature rising continuously. The area ratio SHB/SNHB and SNHB/SHB are used as the spectra characteristics to relate with the water temperature respectively, and both linear and second-order polynomial fitting algorithm are analyzed. The results show that the split positions have a great influence on area ratio variation range, and the fitting order has a great influence on the accuracy of fitting relationship between area ratio and seawater temperature. Both of them will eventually affect the water temperature measurement error. In order to objectively and directly reflect the influences of different area ratio methods, split position and fitting order on the water temperature measurement error, we further analyze the temperature measurement error at different conditions. The results show that the temperature measurement error is less affected by the split position, while is greatly influenced by the area ratio method and the fitting order. For the same split position and the same area ratio method, the results using order polynomial fitting are better than that using linear fitting. The results also show that linear fitting thearea ratios SHB/SNHB with water temperatures is a good choice, because it can obtain good measurement accuracy, and at the same time it has the advantage that the fitting parameters are simple and easy to be adjusted. Furthermore, the influences of different area ratio method and split position on the anti-interference of the system are studied. The results show that the anti-interference of SHB/SNHB method reduces with the decrease of the split wavelength, while the anti-interference of SNHB/SHB method enhances with the decrease of the split wavelength. The research results are used to inform the parameter setting of water temperature determining method, and improve Raman Lidar system temperature measurement accuracy. Considering all these results above, we choose the large wavelength 649.3 nm as the split location to calculate the Raman spectra area ratios SHB/SNHB, and linear fitting them with the water temperatures. Finally, the continuous temperature measuring performance of this Raman Lidar seawater temperature remote sensing system is verified experimentally. The experiment results show that the temperatures measured by Raman Lidar system are in good agreement with that by synchronous temperature sensor which is dipped in the sample tap-water and connected to the computer. The maximum measurement error is about ±0.5 ℃, and the standard deviation of measurement error is about 0.21 ℃.

Preparation of high-quality noble nanostructure substrate is vital for the application of surface-enhanced Raman scattering (SERS) technology in ultrasensitive bioassay. In our work, based on the improved Langmuir-Blodgett method, the gold nanorods were extracted from colloid to the interface between the colloid and toluene with the help of ethanol, and fixed by polymethyl methacrylate (PMMA), then a uniform and dense array of two-dimensional domain-like nanostructure was formed in a large area. Next, the plasma clean technology was used to treat the fabricated substrate for enhancing its SERS performance due to the exposed surface of the Au NRs. The experimental results showed that the Au NRs/PMMA substrate exhibited the excellent SERS characteristic and its enhancement factor (EF) achieved 5.49×106 under irradiating of 785 nm laser. In addition, the highly sensitive label-free quantitative detection of tumor maker, prostate specific antigen (PSA), was developed by using Au NRs/PMMA substrate. In the experiments of label-free detection, the Raman characteristic peaks of the PSA were first acquired by comparing the SERS spectra of the PSA standard solution and new-born battle serum solution, and they were mainly located at 823, 1 080, 1 385, 1 586 and 1 640 cm-1. Following, the SERS spectra of PSA standard solution, clinical male serum samples and female serum samples were measured and analyzed to screen the Raman characteristic peaks of PSA associated only with serum PSA levels, and they were located at 649, 680 and 1 640 cm-1. Furthermore, the SERS spectra of α-fetoprotein (AFP) belonging to the glycoprotein same with PSA and human kallikrein 2 (hK2) homologous with PSA were separately measured as two controls, and the extremely specific Raman characteristic peaks of PSA located at 1640 cm-1 were determined and applied in the detection of clinic serum samples. Subsequently, the does-repose curve was obtained by the relationship of the intensities of the Raman peaks at 1640 cm-1 and the PSA concentrations in the standard solutions. Lastly, the PSA concentrations in the clinical serum samples were detected based on the SERS-based label-free detect proposal. It demonstrated that SERS-based label-free detection not only exhibits a well consistency of test data when compared with that of the chemiluminescent immunoassay (CLIA), but also higher sensitivity, and its limit of detection as low as 0.06 ng·mL-1 in the range of 0.1 mg·mL-1～0.1 ng·mL-1. Therefore, it reveals that the proposed protocol has a significant application potential for the quantitative detection of tumor marker.

Pressure can lead to protein fold and denaturation. As for the element of protein, the study of amino acid under high pressure has attracted attention of scholars in recent years. The properties of some amino acids among 20 common amino acids have been studied by using high-pressure Raman spectroscopy techniquewith the maximum pressure reaching to 30 GPa. In order to investigate the structural change of L-serine (C3H7NO3) under ultra-high pressure, L-serine crystal was studied at room temperature by in-situ high pressure Raman spectroscopy, and it was submitted to pressure up to 22.6 GPa. The results showed that a new peak appears at 102 cm-1 when the pressure reaches 2.7 GPa, and the peak splits at 1 123 cm-1 (NH3 antisym rocking). Furthermore, a new peak appears at 574 cm-1 as the pressure reaches 5.4 GPa, and the original peak at 164 cm-1 disappears. While the pressure reaches 6.0 GPa, new peaks appear at 226, 456, 770, 2 968 cm-1 (CH2 stretching). And one peak splits at 877 cm-1, which produces a new peak at 894 cm-1. When the pressure reaches 7.9 GPa, new peaks appear at 145, 151 and 2 946 cm-1. With the pressure reaching 11.0 GPa, the vibration peak at 249 cm-1 begins to split and a new peak appears at 241 cm-1, which is located at 2 956 cm-1 (CH2 Stretching) while the original peak at 391 cm-1 and 431 cm-1 disappears. When the pressure reaches 17.5 GPa, a new peak emerges at 200 cm-1. By further analyzing the Raman spectroscopy, many Raman peaks have inflexed at pressure of 1.37, 2.2, 5.3, 7.46, 11.0 and 15.5 GPa. These results showed that the crystals undergo seven structural phase transitions, which are in the pressure range of 0.1~1.37, 2.2~2.7, 5.3, 6.0, 7.46~7.9, 10.1~11.0 and 15.5~17.5 GPa, respectively. Moreover, a new phase transition that was found at 6.0 GPa has never been discussed before. This Structural change may be caused by rearrangement of molecules, which is induced by pressure. And molecular rearrangement leads to hydrogen bond rearrangement, which causes new CH2 stretching vibration peak. The Raman spectra in the range of 10.1～11.0 GPa focus on the low wavenumber, which is assigned to the low-energy vibration such as crystal lattice vibration and the new CH2 stretching vibration. So the crystal lattice vibration of L-serine crystal change within 10.1~11.0 GPa, which causes the new hydrogen bond of L-serine crystal to change its structure. No direct evidence of structural phase transition has been found in the 15.5~17.5 GPa pressure range, except for the inflection point at 17.5 GPa. Therefore, it is speculated that L-serine crystals undergo a structural phase transition.

The blood contains a variety of biological information such as hormones, enzymes, antibodies and so on. The detection and identification of numerous biological information in the blood can be used to determine and trace the origin of the blood species. Therefore, the development of blood analysis is of great significance to the fields such as criminal cases detection, species identification, disease prevention and so on. At present, the traditional blood detection methods are mostly microscopic observations, immunoassays and DNA/gene detection methods. These techniques can cause irreversible damage to blood samples and have problems such as long analysis cycle, complicated structure apparatus and high test prices. With the rise of laser technology, as a non-linear scattering spectroscopy, Raman spectroscopy is used in blood detection techniques. In blood detection techniques, Raman spectroscopy is usually combined with confocal microscopy to collect blood samples that have been coated on glass slides or in transparent containers because of its advantages like being fast, nondestructive, ect. However, complex optical systems and expensive experimental setups limit the widespread use of this technology. To provide a simple and easy-to-use method for detecting blood Raman, the Raman signal of human whole blood was collected and analyzed by a capillary-based Raman spectroscopy. Blood samples were sampled by siphon effect of the capillary. Compared with the loading of the carrier, the blood sample had the advantages of simulating human blood vessels, maintaining blood activity, reducing the degradation effect of oxygen on blood components and reducing the laser burns on blood samples. In order to avoid the fluorescence interference in the region of strong fluorescence of visible light, a 360 nm ultraviolet laser was used as an excitation light source to prevent the interference of the visible fluorescence signal. The integration time was set to 800 ms, which effectively avoided the burns on the blood sample caused by the laser irradiation for a long time which would affect the stability and authenticity of the experimental data. The average number was 2 times, aiming at avoiding the impact of inaccurate data caused by a single measurement. Spectral scanning range was 500~1 800 cm-1. The results showed that this range can better avoid the interference of the stronger fluorescence region of the visible light portion. The spectral signal at this time was processed by filtered denoising and baseline correction. Firstly, a 5-order discrete wavelet transform filter was used to decompose the signal at the first layer. The high-frequency noise signal was filtered out, and the low-frequency effective signal was retained to remove the spurious signal, and the effective signal of the spectrum was extracted. Secondly, baseline correction for the use of fourth-order polynomial fitting base and subtraction, aiming at achieving human whole blood capillary Raman peak signal extraction. Finally, the spectra obtained by inspecting the SDBS database and human blood samples were measured by reishaw confocal Raman spectroscopy to verify that some of the measured signals were Raman signals of several amino acid components in the human body. Experimental studies have found that the capillary-based Raman experimental system is more stable and repeatable than the conventional Raman probe system, and can effectively extract Raman spectrum signals in human whole blood, and its high Accuracy of confocal Raman microscopy system is cheaper, simpler and easier to be popularized, but the signal SNR and the peak intensity of the effective signal remain to be further improved. It is a possible solution to detecting human blood Raman signal.

Four kinds of bioagents simulants, Pantoea agglomerans (Pan), Staphylococcus aureus subsp. Aureus (Sta), Bacillus globigii (BG) and Escherichia coli (EH) were cultured and the strains growth curves were obtained, and the generation time were 0.99, 0.835, 1.07 and 1.909 h respectively. Two wavelengths at 266 and 355 nm of a designed short range fluorescence lidar were engaged for the measurements of two dimensional fluorescence spectra in the amino acid band and NADH band of the biological warfare agents, respectively. The simulants were diffused homogeneously inside a controlled fluorescence measurement chamber and interogated by the lidar. The 2 dimensional fluorescence spectra of four kinds of vegetative bacteria, BSA and OVA(as simulant of toxin agents) were obtained with resolution of 4 nm. The fluorescence spectra of Pan, Sta, EH and BG, BSA and OVA were consistent with the standard fluorescent component tryptophan in the amino acid band with FWHM of 60 nm, but the central wavelength of the fluorescence spectra of these simulants blue/purple shifted obviously as affected by the external biochemical environment, concentration and ratio of different bacterial internal fluorophores, so the energy level between the excited state and the ground state of the fluorescence molecule increased. Accordingly, weak NADH fluorescence spectra with 100 nm FWHM inside the four vegetative bacteria aerosols were also detected, but Raman scattering contribution of water and nitrogen could not be effectively extracted. The second-order derivative fluorescence spectra of the four simulants showed that the high-order processing and recognition of the fluorescence spectrum was feasible.

In this article, a Raman spectroscopy classification method combining the fluorescence background rejection based on graphitized carbon black (GCB) preprocessing and modified hierarchical clustering analysis (HCA) has been put forward, by which 39 fuel samples have been classified correctly and automatically into six types of 0# automobile diesel fuel, 0# general diesel fuel, 97# gasoline, 93# gasoline and 3# jet fuel. GCB preprocessing, during which 50mg GCB is used to filter 0.75 mL fuel sample once, has no influence on those samples which have no fluorescence background such as 3# jet fuels and gasolines and can reject effectively the weak fluorescence background of gasoline and automobile diesel fuel samples and strong fluorescence background of gasoline and general diesel fuel samples. Firstly, the Procrustes distance in the Procrustes analysis (PA) was adopted to measure the similarity between the samples for the classical HCA which was regarded as the modeling stage in the modified HCA algorithm. And the centroid vectors belonging to the different clusters were calculated according to the results of the HCA. Secondly, the types of the unknown test samples could be determined by calculating and comparing the Procrustes distances between the test samples and the centroid vectors of the clusters. The “leave-one-out” cross validation results based on the 39 samples belonging to 6 classes have shown that the GCB preprocessing is an effective fluorescence rejection method for Raman spectra of light fuels which can be applied to qualitative and quantitative analysis.

To conduct green, rapid and accurate detection of organic pollutants in water, the current paper proposes a detection method of Chemical Oxygen Demand (COD) based on fluorescence multi-spectral fusion. The experimental samples consist of 53 actual water samples including inshore seawater and surface water. The physicochemical valuesof COD of the samples are obtained by standard chemical methods. A fluorescence spectrophotometer is used to collect the three-dimensional fluorescence spectra of the samples, and the spectral data are processed and modeled. The three-dimensional fluorescence spectrum is spread at the excitation wavelength in the excitation wavelength range of 200～330 nm and the emission wavelength range of 250～500 nm, with excitation wavelength interval being 5 nm, and the emission wavelength interval 2 nm. With the ant colony optimization-interval partial least squares (ACO-iPLS) as the feature extraction algorithm and the least squares support vector machine algorithm optimized by particle swarm optimization (PSO-LSSVM) as the modeling method, the prediction model of fluorescence emission spectral data at a single excitation wavelength, the fluorescence multi-spectral data-level fusion (Low-Level Data Fusion, LLDF) model and the fluorescence multi-spectral feature-level fusion (Mid-Level Data Fusion, MLDF) model are built respectively, and the prediction effects of various models are compared. The results show that there exist some differences in the prediction effect of the models for the fluorescence emission spectrum data at different excitation wavelengths. The prediction model of the fluorescence emission spectrum data at the excitation wavelength of 265 nm is optimal, with determinant coefficient (R２ｐ) and the root mean square error in prediction (RMSEP) of the calibration set being 0.990 1 and 1.198 6 mg·L-1 respectively. For fluorescence multi-spectral data-level fusion models, fluorescence emission spectra at excitation wavelengths of 235, 265, and 290 nm (abbreviated as: LLDF-PSO-LSSVM) have the best prediction effect, with the results of R2p and RMSEP being 0.992 2 and 1.055 1 mg·L-1 respectively. For fluorescence multi-spectral feature-level fusion models, fluorescence emission spectra at excitation wavelengths of 265, 290, and 305 nm (abbreviated as: MLDF-PSO-LSSVM) have the best prediction effect, with the R2ｐbeing 0.998 2 and the RMSEP being 0.534 2 mg·L-1. A comprehensive comparison of various modeling results shows that the model of MLDF-PSO-LSSVM has the best performance, indicating that the multi-spectral feature-level fusion model based on fluorescence emission spectrum data is more accurate and more effective for predicting COD of water quality.

The traditional fluorescence spectroscopy has been used for the detection of polycyclic aromatic hydrocarbons (PAHs) in soil. However, due to the complexity of soil system and the diversification and trace of PAHs pollutants, the traditional fluorescence spectroscopy cannot effectively extract the characteristic information of PAHs in soil. In order to solve the above problem, a new detection method of PAHs in soil was proposed and established based on two-dimensional (2D) correlation fluorescence spectroscopy. The typical PAHs pollutants of anthracene and phenanthrene in soil were used as research targets, and 38 mixture samples (the concentration of anthracene and phenanthrene in soil were between 0.000 5 and 0.01 g·g-1) were prepared. Three-dimensional (3D) fluorescence spectra of all samples were collected in the excitation wavelength range of 265～340 nm within an interval of 5 nm and in the emission wavelength range of 350～500 nm within an interval of 1 nm. And synchronous 2D correlation fluorescence spectra of all samples were calculated based on one-dimensional (1D) fluorescence spectra under the excitation perturbation. The characteristics of 3D fluorescence spectrum and synchronous 2D correlation fluorescence spectrum of the mixture of anthracene and phenanthrene were studied in soil (anthracene: 0.005 g·g-1, phenanthrene: 0.005 g·g-1). In the synchronous 2D correlation fluorescence spectrum, four auto-peaks were observed at 398, 419, 444 and 484 nm along the main diagonal. Among them, the fluorescence peaks of 398 and 484 nm came from the phenanthrene in the soil, and the fluorescence peaks of 419 and 444 nm came from the anthracene in the soil. At the outside of the main diagonal line, there were negative cross peaks between anthracene and phenanthrene fluorescence peaks, which further verified that the sources were different. At the same time, there were two cross peaks at (408, 434) nm and (434, 467) nm, and the peaks at 408 and 434 nm were assigned to phenanthrene and 467 nm was assigned to anthracene in soil. It was pointed out that, compared with traditional 3D fluorescence spectroscopy, 2D correlation fluorescence spectroscopy could not only extract more characteristic information (the characteristic peaks of 408 and 467 nm are not represented in the 3D fluorescence spectrum), but also provide the relationship between fluorescence peaks, and effectively analyse their sources. On the basis of the characteristics of the 2D correlation fluorescence spectra, the N-way partial least squares (N-PLS) models for detecting the contaminants of anthracene and phenanthrene in soil were developed based on synchronous 2D correlation fluorescence spectral matrices (38×151×151). For anthracene in soil, the correlation coefficients r were 0.986 and 0.985 in calibration and prediction set; the mean square root error of calibration (RMSEC) and the root mean square error of the prediction (RMSEP) were 4.33×10-4 and 5.55×10-4 g·g-1, respectively. For phenanthrene in soil, the correlation coefficients r were 0.981 and 0.984 in calibration and prediction set; the RMSEC and the RMSEP were 5.20×10-4 and 4.80×10-4 g·g-1, respectively. In order to compare, the N-PLS models for quantitative analysis of anthracene and phenanthrene in soil were established based on a 3D fluorescence spectral matrices（38×16×151）. For anthracene in soil, the correlation coefficients r were 0.981 and 0.972 in calibration and prediction set; the RMSEC and the RMSEP were 5.09×10-4 and 6.74×10-4 g·g-1, respectively. For phenanthrene in soil, the correlation coefficients r were 0.957 and 0.956 in calibration and prediction set; the RMSEC and the RMSEP were 7.36×10-4 and 7.77×10-4 g·g-1, respectively. It was pointed out that, for the detection of anthracene and phenanthrene in soil, the correlation coefficients r, RMSEC and RMSEP of N-PLS models were better based on 2D correlation fluorescence spectra than 3D fluorescence spectra. The results showed that the direct detection of PAHs contaminants in soil based on 2D correlation fluorescence spectroscopy is not only feasible, but also can provide better analysis results. This study provides a theoretical and experimental basis for direct detection of PAHs in soil by laser induced fluorescence combined with 2D correlation technology, having a good application prospect.

Existing AOTF tuning relationship mostly ignores the optical rotation effect, but it will effect the accurate design of acousto-optic tunable filter, and will further influence its filtering performance, so the relationship between the specific rotation rate and diffraction wave length was deduced, then the AOTF tuning relationship considering the optical activity was acquired; aiming at temperature drift of AOTF diffraction wave under different temperature, therefore affecting the spectral resolution of AOTF, this article starting from the ultrasonic velocity influenced by temperature, the relationship between the wavelength and driving frequency under different temperatures was gained by analyzing, and the new method of frequency correction tracking diffracted wavelengths was put forward under different temperatures. According to AOTF tuning relationship considering the optical rotation, and considering the real-time tracking temperature, repeatedly conversion driving frequency will easily cause damage to the system, and will further affect the efficiency, so we adopted 10 ℃ as a period of temperature, within a period of using a driver frequency to adjust AOTF according to the medium temperature, and every period has the corresponding relation of driving frequency-diffraction wavelength. The paper described the specific measures for the implementation and made the corresponding experiment. The results showed that compared to no frequency correction with normal room temperature, the diffraction wavelength was close to the target wavelength operated by the frequency correction under the corresponding temperature, and the error of the correction method is 1 order of magnitude lower than that without correction. The method provides an important basis for high accuracy spectrum measurement with AOTF under different temperatures, which has important practical value.

Formaldehyde (HCHO) is one of the main pollutants of indoor air. Exposure to excessive formaldehyde for a long time will cause serious damage to human eyes, skin and respiratory organs, and even lead to loss of the function of nervous system［1］, as well as ear, nose and laryngeal cancer[2]. Therefore, a rapid, efficient and accurate detection of gaseous formaldehyde is of great significance to human health. So far, there have been a lot of techniques that can be used for the detection of gaseous formaldehyde, such as gas chromatography (GC)[3] and high-performance liquid chromatography (HPLC)[4]. Although chromatographic apparatus may provide detection limits of several μg·m-3, they are time-consuming and not suitable for real-time and continuous monitoring of formaldehyde concentration because of their weight and bulk. Semiconductor gas sensors based on gas-sensitive films provide a good alternative in indoor formaldehyde monitoring due to their advantages of high stability, short response time and continuous monitoring. However, high detection limit (>300 μg·m-3) and poor selectivity is considered to be a great limitation for such sensors[5]. Enzyme-based biosensors usually have a good sensitivity and selectivity, but their thermal stability is usually poor, which seriously restricts their application[6]. Colorimetric and fluorescence methods are widely applied in the design of formaldehyde gas sensors because of their fast response, high sensitivity, low detection limit, good selectivity, simplicity and low cost［7-9］. These methods are based on the combination of probes and formaldehyde which can generate new substances, resulting in the change of absorption spectrum or fluorescence enhancement, so that ones may achieve quantitative detection of formaldehyde. Descamps et al developed a portable formaldehyde detector by using 4-amino-3-penten-2-one (Fluoral-P) as a probe. Fluoral-P is a reagent with enaminone structure which can selectively react with formaldehyde to form cyclic compound 3,5-diacetyl-1,4-dihydrolutidine (DDL). Since the absorption band of Fluoral-P is far apart from the absorption band of DDL, and a fluorescence peak with large Stokes shift can be produced after combined with formaldehyde, it is widely applied for the detection of formaldehyde. However, Fluoral-P is extremely unstable and easy to hydrolyze to form acetylacetone and ammonia under the presence of water, which severely limits its application in formaldehyde detection[10].In this work, we have studied the optical and chemical properties of the interaction between 4-amino-1,1,1-trifluorobut-3-en-2-one(3F-FP), a derivative of Fluoral-P, and formaldehyde solution by UV-vis absorption steady state fluorescence spectroscopy and gas chromatography-mass spectrometry(GC-MS). We found that the hydrolysis rate of Fluoral-P is k＝1.555 9×10-5 L·mol-2·s-1, however, 3F-FP has very low hydrolysis rate(close to 0) and shows excellent stability in water environment. Meanwhile, 3F-FP can react with formaldehyde to form cyclic compound 6F-DDL, and a new absorption band appears at 430 nm and the fluorescence peak intensity at 489 nm also gets a significant enhancement and the enhancement factor is 12. The fluorescence growth rate is k＝7.881×103 h-1. In the following work, we will use porous glass as the carrier for 3F-FP[11], by which the concentration of 3F-FP and contact surface area between the 3F-FP probe molecule and formaldehyde can be increased, leading to further increase of the fluorescence growth rate. In conclusion, 3F-FP has shown good application prospects in the field of gaseous formaldehyde detection.

China is rich in turquoise mineral resources, and is one of the major producing countries in the world. As a kind of famous jade, turquoise is deeply loved by people with its unique colour of green and structure, which also leads to a large number of optimally treated products and imitations in the market. Compared to the study of turquoise and its imitations, the current researches on turquoise and its imitations need further data accumulation. In this paper, natural turquoise and its typical imitations from Zhu County in Hubei province were studied. Using optical photograph, field emission scanning electron microscope and energy spectrum, infrared and Raman spectra, a comparative study was conducted from the perspective of color, microstructure and microstructure. Research results showed that natural turquoise’s color varies with, the “white-light blue-light green-yellow-green-blue” series of change; the crystal particles are very small, and they are micro-sized to nano-scale, and short columnar and lamellar grains can be seen. The colors of turquoise’s imitations are single, often for the green, and it’s mostly scattered granular aggregate, and its size and appearance are not uniform. Natural turquoise mainly contains Cu, Al, P, O and other elements, and occasionally contains a small amount of Zn, Fe and other elements, therefore, it is mainly copper and aluminum phosphate minerals. Most particles of the turquoise imitations mainly contain of C, O, Mg and occasionally contain a small amount of Ca element, therefore, it’s mainly composed of magnesium carbonate magnesite. In the comparative study of infrared spectrum, the infrared spectrum of natural turquoise 3 083～3 509 cm-1 area contains a large number of infrared absorption peaks corresponding to the ν(OH) and ν(H2O). There were infrared absorption spectrum peak corresponding to νas (CH2), which is related to dyeing. These infrared absorption spectrum peaks are also the effective Raman fingerprint peaks of natural turquoise and turquoise imitation. In the study of contrasting Raman spectrograms, there were usually scattering peaks of ν(OH), ν(H2O), ν(PO4) at ～3 470, ～3 270, ～1 039 cm-1 related to natural turquoise when the turquoise imitation was not. So, they are all effective Raman fingerprint peaks between natural turquoise and turquoise imitation. Based on color, microstructure, microstructure and vibration spectrum, natural turquoise and its imitations can be distinguished. At the same time, this kind of method also contributes to distinguishing the other kinds of jade and the treatment ones.

Carbon dioxide, the major constituent of the atmosphere and burnt gas, has great significance in high sensitive detection in physics and chemistry as well as in the life sciences applications. The existing CO2 detection methods have some defects, which makes the detection difficult to meet the need of the national defense scientific research, energy and chemicals as well as the clinical human breath analysis. Quartz-enhanced photoacoustic spectroscopy (QEPAS) technology invented lately has the advantages of high selectivity, compactness and immunity to environmental acoustic noise. Based on the fact that the QEPAS detection sensitivity scales linearly with excitation laser power, a power boosted QEPAS sensor for CO2 detection is developed. The sensor is based on QEPAS with an erbium-doped fiber amplified 1 572 nm distributed feedback (DFB) laser. In order to reduce the sensor background noise to the thermal noise of the quartz tuning fork, wavelength modulation spectroscopy and the harmonic detection technique were employed. In order to optimize the sensor performance, the laser wavelength modulation depth was optimized at normal atmosphere. A 3.5 ppm detection limit was obtained in the condition of 1 495 mW laser power, 0.33 cm-1 modulation depth and 0.833 Hzdetection bandwidth. The corresponding normalized noise equivalent absorption coefficient was 1.01×10-8 W·cm-1·Hz-1/2.

Soil organic matter is an important part of soil , and plays an important role in soil fertility, environmental protection and sustainable development of agriculture .As the main body of soil organic matter, humus has an effect on a series of properties and forms of soil. Its quantity, composition and properties can reflect certain conditions and processes of soil formation, and it is an important index of soil fertility. Humus is a complex mixture of species, and there is no definite molecular weight and only molecular weight distribution. Its molecular composition and chemical structure are still not clear until now, which largely limits the further research of humic acid. Due to the uncertainty of the composition of the humus molecules, there are some limitations of various methods. Optimizing the search for a more accurate and reliable humic acid characterization method has become a hot spot in the current research. Fertilization modifies the composition and structure of humic acid in soil, but the short-term influence degree is difficult to be detected by conventional methods. This paper uses 38 years of long-term black soil positioning test, through the separation and purification of humic component HA, thermal properties analysis, fourier transform infrared spectroscopy, 13C nuclear magnetic resonance spectroscopy and fluorescence spectroscopy analysis technology. The effects of organic fertilizer and inorganic fertilizer on the molecular structure of HA in black soil were analyzed from the point of view of material structure. The results showed that the mechanism of organic fertilizers application (MNPK) and CK (no fertilization) to increase crop yields was that MNPK fertilization increased soil organic carbon content compared with CK, at the same time, the HA content of humus could be extracted from soil, and the content of aliphatic C in soil HA increased and the structure was simplified. The lipids in soil organic matter were mainly fat, wax and resin, which had a significant influence on the decomposition of soil organic matter, thus affecting nutrient release and plant growth, and the increase of soil nutrients promoted the increase of maize yield under MNPK fertilization. The analysis showed that the total reaction heat of MNPK fertilization treatment soil HA, 2 920/1 720 and 2 920/2 850 value was higher than CK, and 13C nuclear magnetic resonance spectroscopy showed that the molecular structure of HA in black soil was obviously aliphatic after MNPK fertilization, and increased the soil aliphatic C content of HA, reduced the aromatic C content. Fluorescence emission spectra showed that the f450/500 value of MNPK-treated HA increased significantly compared with the control, indicating that the degree of aromatization of soil HA decreased with the application of organic fertilizer. The above analysis results showed that: after the application of organic and inorganic fertilizers, the molecular structure of HA in the black soil became aliphatic and simplistic; the combination of multiple spectroscopic techniques could mutually verify the accuracy of the results. At the same time, the test results also proved that once organic fertilizer was applied in the rotation period, the soil organic carbon and the Aliphatic C content of soil HA could be increased, and the crop yield and soil fertility could be increased.

Leucomalachite Green (LMG) is a major metabolite of malachite green (MG). It has a long residence period in edible fish tissues. At present, the use of MG has been banned in some countries for its increased risk of carcinogenesis, mutagenesis and other adverse effects to human health. However, MG is still widely used in aquaculture, aquatic transport and storage for its low price. The interaction between LMG and bovine serum albumin (BSA) under simulative physiological conditions was investigated by spectroscopy. Two spectroscopic approaches (fluorescence and circular dichroism) and two different experiments were used for monitoring the biological dynamic process. Qualitative and quantitative information was obtained with the resolution of the data matrices by chemometrics method - multivariate curve resolution-alternating least squares (MCR-ALS). Atomic force microscope (AFM) was executed in order to verdict the particle morphology and dimensions of the LMG-BSA conjugates. The root mean square (RMS) roughness of the individual BSA molecule was found to be (1.24±0.28) nm. The BSA molecule particle was observed to be looser on the mica substrate upon interaction with LMG. The RMS was changed to be (13.47±0.53) nm for the LMG-BSA interaction. The calculated result of circular dichroism (CD) spectra revealed that the α-helical content for the LMG-BSA complex was 42.5%, which has a slight decrease compared with the free BSA (46.3%). The results of AFM and CD spectra showed that the binding of LMG to BSA induced micro-environmental and conformational changes of BSA molecules. In order to identify the LMG-binding site on BSA, site marker competitive experiments were carried out, using drugs which were specifically bound to site Ⅰ (warfarin) and site Ⅱ (ibuprofen) on BSA. The binding constant of the system with warfarin (1.88×106 L·mol-1) was almost 70% of that without warfarin (2.65×106 L·mol-1), while the constants of the systems with and without ibuprofen had only a small difference, indicating that LMG was bound to site Ⅰ of BSA. The molecular docking gave more intuitive understanding of the binding of LMG and BSA. It was recognized that LMG binds within the sub-domain ⅡA pocket in domain Ⅱ of BSA. These values showed that hydrophobic forces were the main interactions in the binding of LMG to BSA and the stabilization of the complex. It can be expected that the study will have great significance in helping to further clarify the metabolism and distribution of LMG in vivo and the mechanism of toxicological effects and pharmacokinetics from molecular level.

Pest detection algorithm research is an important guarantee to precisely and rapidly monitor the forest pest and forest protection and quarantine. Based on the external morphology of the host and its internal physiological phenomena, taking the leaf loss (LL), relative chlorophyll content (RCC), relative water content (RWC), and the three spectral values of the characteristic wavelengths (ρ733.66～898.56, ρ′562.95～585.25, ρ′706.18～725.41) as the experimental data which were randomly divided into experimental group (63) and verificantion group (37) with 5 repeated tests, then the models of Fisher discriminant analysis, random forest and BP neural networks for pest levels were constructed. The detection accuracy, Kappa coefficient and R2 were used to comprehensively compare the detection effects of these three algorithms. The results showed that the detection accuracy of Fisher discriminant analysis, BP neural networks and random forest were 69.19%, 65.41% and 83.78%, and Kappa coefficient were 0.576 9, 0.532 4 and 0.778 8, and R2 were 0.722 2, 0.582 6 and 0.870 9. Overall, all of these algorithms have the capability of pest detection, among which, the detection effect of the random forest is the best, and Fisher discriminant analysis is secondly, and BP neural networks is thirdly. Besides, the accuracy of random forest detection is superior to that of Fisher discriminant analysis and BP neural networks in non-damage, mild damage and severe damage, but these three methods have insufficient detection accuracy for moderate damage level. The results could be a reference tothe selection of detection algorithm in P. chao and other types of diseases and insect pests, building a strong foundation for further study.

Bursaphelenches xylophilus disease, also known as pine wilt disease, is a fatal one caused by the parasitism of pine wilt nematode in pine trees. It’s difficult to prevent and control the disease because of rapid infection and spread. The recognition and estimation of the disease play a significant role in the protection of forest resources and ecological environment in China. Studies have shown that the chlorophyll and water content in Pinus Massoniana will reduce gradually when the pest degree deepens and the spectral reflectance of Pinus Massoniana in different pest degree appears to be greatly different. Therefore, the spectral analysis technique has unique advantages in pest degree estimation. In this paper, the variation regularities of the spectral characteristic parameters were studied for the samples with different pest degrees. Then the measured spectral characteristic parameters were taken as independent variables and the quantization of samples’ disease degree as dependent variables to construct an estimation model for the pest degree with the help of linear regression equation. Valuable efforts made on the spectral characteristic selection and the evaluation model could provide significant guidance for the estimation of Bursaphelenchus xylophilus disease, as well as providing scientific support and application reference for related research and local precision agriculture.Firstly, the variation of the spectral reflectance in the green, red and near infrared bands was studied; six spectral characteristic parameters indicating the degree of pest damage were conducted, including the peak reflectance and their corresponding wavelengths (positions) of the green, and red bands, as well as the slope and position of the red edge; the correlation between spectral characteristic parameter and pest degree was analyzed. Next, the liner models for estimating the pest degree of Pinus Massoniana samples were constructed. The steps consisted of (1) calculating the reflectance of spectral characteristic parameters such as green peak (RGP), reflection of red band (FRB) and red edge slope (RES) for samples in healthy, mild, moderate and severe pest degree; (2) quantizing the pest degree of these samples; (3) taking the measured spectral characteristic parameters as the independent variables and the quantitative value of the pest degree as the dependent variables, and constructing the pest degree estimation models with the linear regression equation. In the experiments, the Pinus Massoniana samples from Yongsheng Forest Farm and the area of Maohe Zhai in Fuling District of Chongqing were investigated and Pinus Massoniana trees belonging to healthy, infected and completely dead categories were tested and monitored separately and randomly. An ASD field spectrometer, FieldSepc4 with a range of 350 to 1 100 nm and a resolution of 1nm, was used to collect spectral data for Pinus Massoniana samples. 70 records of effective spectral data for Pinus Massoniana trees collected were divided into five levels, i.e. healthy, infected mildly, moderately, severely as well as dead according to the different pest levels. Spectral data was then processed by Matlab software to generate the spectral reflectance curves. The spectral characteristic parameters with wavelength covering the green region (510～580 nm), the red region (620～680 nm) and the near infrared region (680～780 nm) were calculated and the estimation models for pest degree were constructed. The results demonstrated that: (1) the spectral characteristics for dead samples such as green peak and red band disappear, at the same time, the steep uptrend of the red edge is leveled. For the remaining kinds of samples, the spectral parameters RGP, FRB and RES are negatively correlated with the pest degree. The deeper the pest degree is, the smaller the parameter is, that is Health (RGP)>Mild (RGP)>Moderate (RGP)>Severe (RGP), Health (FRB)>Mild (FRB)>Moderate (FRB)>Severe (FRB) and Health (RES)>Mild (RES)>Moderate (RES)>Severe (RES); (2) with the deepening of pest degrees, GPP moves towards the longer wave direction called “red shift” in the green peak position while RBP and REP move towards the shorter wave direction called “blue shift” in the red valley position as well as the red edge position; (3) compared with univariate linear estimation models, the bivariate models generate higher correlation coefficients, but smaller estimation error and residual. In the experiment, the two Pinus Massoniana trees were estimated. The results for the bivariate linear estimation models were PD=2.990 7 and PD=3.679 and corresponded with the actual observations. In our following research, the correlation analysis on the spectral characteristics will be extended to the 1 100～2 500 nm bands.

In order to further understand the spectral characteristics of different shapes and different colors of scleractinian coral, reflectance spectra of two common scleractinian coral species (brown schistose Turbinaria peltata and blue gray massive Platygyra daeda) were measured using a fiber spectrometer in the sea area of Lu Huitou Sanya Bay in the north-western South China Sea (SCS). On the morning of July 22, 2015, 7 samples for each species were collected. The size of the samples was ~6 cm, and was fostered to laboratory reef aquariums, in which water temperature was controlled ~26 ℃. The reflectance spectra were measured after the samples were fostered for more than 4 hours in the reef aquarium, and weather condition was sunny day without clouds. Using a spectrometer (USB2000+), band 200～850 nm, resolution 1.34 nm, step length 0.6 nm, and field angle 25 degrees, the sample was placed on the cylinder platform, and ~26 ℃ seawater was continuously injected to ensure the water temperature was constant. The extra seawater automatically spilt from the upper edge of the aquarium to exclude the “converging phenomenon” of the refraction of light into the water body. The black nylon cloth was adhered to the inter side wall of the aquarium to avoid the reflection effect. The distance between the probe and the sample was 5 cm, and the reflectance was the average of repeated 10 times measurements. The light source was the sun light, and the calibration of the spectrometer was carried out every time before the measurement, and reflectance spectrum in the visible light band was used to analyze the character of the reflectance of the samples. Then for spectral reflectance analysis, derivative spectroscopy was used to study the difference of the reflectance spectrum of them. The results showed that the reflectance of two kinds of coral is obviously different from each other in the visible wavelength range. The reflectance of Platygyra daeda was significantly higher than that of Turbinaria peltata with significant differences, and a similar high reflectance appeared only near 700 nm. The reflectance of Turbinaria peltata was between 4% and 15% with significant peak and trough. In 400～450 nm area reflectance of Turbinaria peltata showed a relatively lower value of about 4%～5%; after 480 nm it soared to around 10%, and obvious peaks appeared at 502, 578, 604 and 652 nm, and the main peaks at 604 nm, and 578 and 652 nm were the two shoulders; obviously the trough appeared at 670 nm, then surged to 36% at 700 nm. The reflectance of Platygyra daeda was between 6% and 16% with no obvious peak and trough. The reflectance values were relatively lower around 6% near 400～420 nm, increased sharply to about 15% with large characteristic peaks near 486 nm, at 486, 577, 607 and 650 nm there were four distinct peaks; with 607 nm as the main peak, 577 and 650 nm were the two shoulders; a significant trough appeared near 415 nm, and a less obvious trough appeared at 667 nm, then the reflectance increased significantly to about 37% at 700 nm. Derivative analysis results showed that the distinguishable bands of Turbinaria peltata and Platygyra daeda were as follows: first order derivatives are mainly in 483.7～492.6, 496.2～500 and 533.5～540.5 nm bands. The second order derivatives are mainly in 414～422.7, 499.4～504, 520.2～523.3, 534.2～536.6, 557.5～561 and 671.8～675 nm bands. The fourth order derivatives are mainly in 414～417.6, 427.4～430.3, 433.4～436.5, 452.3～455.5 and 657.1～659.1 nm.

In this paper, the photoluminescence detection of quantum dot photodetector arrays is studied. The quantum dot detector adopts AlAs/GaAs/AlAs dual-barrier structure. In the wide GaAs well, there are InAs quantum dots (QDs) and In0.15Ga0.85As quantum Well (QW), and a simple device model for analysis is built. Under the irradiation of 632.8 nm He-Ne laser at room temperature, when the optical power is 0.01 pW and the bias voltage of the device is -0.5 V, the integration time is 80.2 μs and the voltage response rate is 7.0×1011 V·W-1, which has a very high sensitivity. At the temperature of 300 K, this quantum dot detector can detect the very weak light whose power is less than 10-14 W. The high-sensitivity spectrometer and molecular hyperspectral system developed with this kind of quantum dot photodetector are used to detect biological tissue samples. A spectroscopic system for mutual verification and mutual calibration of biological tissues is developed.

The soil profile is one of the core topics in pedogenesis research, but traditional pedological observations of soil profiles rely on the use of visible light and a toolbox that has not changed in the past decades. The imaging spectroscopy can provide high-resolution spatial and spectral soil profile information, which gives continuous depth functions of soil properties and compensates for the large gap between the sampling depths of reflectance spectroscopy. The objective of this study is to analyze the classification of soil horizon in a profile by investigating the spectral data of imaging spectroscopy collected in the laboratory. The support vector machine (SVM) method was used to classify the spectral data, and the feasibility and influence factors of the imaging spectroscopy for classification were studied. Firstly, the morphological characteristics of the average spectral curves of each horizon in sample profile were analyzed qualitatively. Secondly, depth dynamic and scatter plot of principal components were qualitatively analyzed to explain the feasibility of horizon classification using profile imaging spectroscopy. Finally, One thousand times computations were carried out to reduce the classification error by partitioning random dataset and building prediction model. The prediction results can quantificationally testify the feasibility and the influence factors were discussed by the percentage of wrong classification in prediction results. The results indicated that the four average spectral curves in sample profile differed and reflected the variation in depth derived from pedogenic processes. The principal components of the imaging spectral data showed the continuous change in the depth direction of the soil profile and the grouping feature in scatter plot, which proved that imaging spectroscopy reflected the difference between the genetic horizons and can be used for the horizon classification. The average accuracy of classification prediction reached 93.08%. Moreover, it was found that the sample with similar scattering distribution and locating transition region were classified in wrong classes easily. This study provides a theoretical basis for horizon classification, and proves that imaging spectroscopy is a potential technology for mapping soil profile.

Remote sensing retrieval has been widely used for dynamic monitoring of the physical and chemical properties of regional soil, but there are few studies on the areas with low organic carbon content and uneven underlying surface which have unremarkable soil spectral characteristics. The cinnamon soil belt in Loess Plateau has multiplicity topography, widely distributed hills and low organic matter content. Large areas soil degradation caused by mining activities has resulted in the fact that soil spectroscopy characteristics are strongly disturbed, which has some inhibiting effect on the remote sensing retrieval accuracy of soil organic carbon content at the regional scale. Based on cinnamon soil belt with typical coal mining subsidence area in Shanxi Province as an example, this research used the surface reflectance and outdoor sample data from the field of coal mining subsidence area to retrieve soil organic carbon content. Conducting comparative experiments on the atmospheric correction methods of the Landsat8 OLI image in the study area by the FLAASH model and the 6SV model combined with high spatial and temporal resolution aided meteorological data to analyze the effect on soil spectral curve and organic carbon content in the mining subsidence area of the cinnamon soil belt and recognize sensitive bands. Multiple linear regression(MLR), BP neural network(BP) and partial leas squares regression(PLSR) model were established to retrieve soil organic carbon content by using the original spectral reflectance R and mathematical transformation forms such as R, log (1/R) and R′. The results showed that the atmospheric correction effect of the 6SV model was better than that of the FLAASH model which could effectively eliminate the interference of atmosphere and topography to reflectance. The reflectance of visible light decreased and the near-infrared rose obviously. The soil reflectance spectra of different organic matter content was clear. The bands of 640~670, 850~880, 1 570~1 600, 2 110~2 290 nm were highly indicative of soil organic carbon content. Compared with multiple linear regression (Coefficient of determination R2 was 0.765) and BP neural network (R2 was 0.767), the partial least-squares regression model had the highest retrieval accuracy (R2 was 0.778). It was found that the 6SV atmospheric correction model and partial least squares regression modeling combined with aided meteorological data which had high spatial and temporal resolution could significantly improve the retrieval accuracy of soil organic carbon in the mining subsidence area of the cinnamon belt. The soil organic carbon content in the study area from 2013 to 2015 was retrieved based on this model. Results showed that the soil organic carbon content in the middle of the study area was higher than that in both sides, and the soil organic carbon content was restored by reclamation. The results can be used to reveal the spatial-temporal distribution of soil organic carbon in the mining subsidence area of the cinnamon belt in the Loess Plateau, providing theoretical and technical support for improving regional soil spectral analysis, land reclamation evaluation, establishment of carbon flux observation network in mining subsidence area of the cinnamon belt and estimation of soil carbon pool, which provides the basis for the ecological sustainable development of the cinnamon belt in the regional and global scales.

The detection of plant water deficit based on hyperspectral technology is the current research hotspot. Fescue is one of the major herbaceous plants which have the maximum usage in northern China, and its growth has a large demand for water, and it will have a series of changes in physical characteristics (color, texture, shape, etc. ) and physiological characteristics under the condition of water deficit. By studying the establishment of plant water content detection model based on hyperspectral technology, the rapid non-destructive monitoring and assessment for the plant water deficit can be achieved, and the plant water status can be diagnosed comprehensively and reliably. The research can provide important basis for predicting the physiological response and change of common herbaceous plants in the North under future climate change. Fescue was sampled to carry out pot control simulation research under constant temperature and humidity conditions. The experiment involves two variables of CO2 concentration (CX) and soil water holding capacity (WX). Two CO2 gradients were set, 400 and 700 μmol·mol-1, respectively. Three water holding capacity treatments were carried out at each CO2 gradient, 100%, 40% and 20% of water holding capacity respectively. And then an ASD Field spec Hand Held spectrometer was used to measure the spectral reflection parameters of the fescue at 10:00—14:00 per day, including Spectral Reflectance (Ri), First Derivative Spectrum (Dλi), Red Ddge Magnitude (Dλr), Red Edge Position (λr), Red Valley Absorption Depth (D), Red Edge Area (Sr), Photochemical Reflectance Index (PRI), Chlorophyll Index (Rch), Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Normalized Difference Spectral Index (NDSI), Ratio Spectrum Index (RSI), Fractal Dimension (Fd), etc. Regression analysis and statistical model building were applied to analyze the quantitative relationships between spectral parameters and physiological parameters. The complex relationships between spectral characteristic parameters and Fescue leaf water content were analyzed using statistical methods to extract optimal spectral characteristic parameters and subsequently to establish the estimation models of spectral characteristics and water deficit. The results showed that Normalized Difference Vegetation Index (NDVI), Chlorophyll Index (Rch), and Fractal Dimension (Fd) were significantly correlated with leaf water content at the 99% confidence level. So, we reckoned that the three spectral characteristic indicators are the most effective parameters for monitoring water deficit of Fescue. There are good linearities between leaf water content and spectral characteristic parameters, and the formula of detection model is, Y=-0.125XRch+1.714XNDVI-0.023XFd+0.018, and the model test is significant at the 99% confidence level. The results can provide the technical supports for rapid non-destructive monitoring of the drought degree of Fescue, and provide a scientific guidance for large scale irrigation and management of Fescue.

The β-carotene, with carbon-carbon single and double bonds (C—C, CC), is a typical linear polyenes, which widely exists in plants and has important biological function and plays an important role in investigating the π-electron conjugated properties. According to the Andreas’s study, when the exciting wavelength falls in electron absorption band, it will produce the resonance Raman effect and the Raman intensity can enhance 106 times. The Raman spectra of different parts of the carrot, white radish and green radish and the β-carotene are measured by using the resonance Raman spectroscopy, finding that the Raman spectra of carrot match well with β-carotene due to a high β-carotene content in carrots. Studies from Gellerman et al. show that the sample concentration is directly proportional to Raman peak intensity, which is clearly seen from the β-carotene Raman spectrum: the Raman intensity of three kinds of radish vertical root head to taproot and lateral skin to core gradually decrease, and the Raman intensity of C—C of the green and white radishes are lower and occur peak splitting. Calculating to intensity ratio of carbon-carbon single and double bonds to the carbon-hydrogen (C—H), the variation rates of ICC/IC—H of different measuring parts (horizontal and vertical) of three kind radish are close: the rates of change of epidermis and root core of carrot are A1=0.213 3 and A2=0.215 9, and the outside and inside of green radish are B1=0.219 1 and B2=0.211 4, and the outside and inside of white radish are D1=0.223 9 and D2=0.224 1; However, variation rates of IC—C/IC—H with the different measuring parts are greatly different: the carrot are a1=0.212 1 and a2=0.232 4, and the green radish are b1=0.263 5 and b2=0.268 7, and the white radish are d1=0.369 0 and d2=0.304 9. It is found that Raman intensity ratios of C—C to the C—H of three kinds with the different parts are greatly different, but the ratios of CC to C—H has similar distribution. This is due to the low levels of β-carotene in green and white radishes, the vibrational peak of C—C occurs peak splitting, namely, two vibrational peaks appear at 1 130 and 1 156 cm-1. As the amount of β-carotene decrease, the intensity of C—C peak reduces, and the intensity of new peak is induce, making the peak intensity of the original peak greatly reduce. This is consistent with the results of IC—C/IC—H. Therefore, using the Raman intensity of C=C to analyze the β-carotene content of different parts is more accurately. Furthermore, studies of the content of the β-carotene in different parts of radish can help to provide a good theoretical basis for daily consumption and dietary nutrition.

Haploid breeding technology is a new method for maize breeding, which can effectively shorten the cycle of homozygous lines and improve the breeding efficiency. The technology needs to select enough haploid grains first, and the haploid grains only account for 0.05%～0.1% of the mixed grains without artificial intervention. Even with the biological induction technology, the number of haploid grains is generally less than 10%. High-speed and accurate identification of haploid grains can meet the!needs of engineering breeding. However, molecular biology and morphological identification methods commonly used in practical work are time-consuming, costly and destroying samples. It is difficult to select Maize Haploid grains efficiently and accurately. Relevant studies have proved that there are obviousoil content differences between haploid and diploid of high-oil maize. At present, low-field nuclear magnetic resonance technology can be used to detect oil content of maize and identify haploid according to its oil content. However, nuclear magnetic resonance (NMR) instrument has some weaknesses, such as high price, difficult maintenance, slow speed and low efficiency. It takes 4 seconds for each single-grain sorting. It cannot meet the needs of large number identification for engineering breeding. Using VIAVI near infrared spectrometer (NIRS) can achieve the detection speed of 0.25 seconds for each maize. The NIR technology is faster, cheaper and easier to maintain. The NIR identification method can replace the method of NMR. Qualitative identification of haploid by NIRS has achieved some results, but currently there are relatively few maize varieties collected in the study. The study only establishes models for haploid of one variety, and classifies haploid of that variety. There are no studies on identification of multiple hybrid haploids at home and abroad, but engineering breeding urgently needs a method to identify multiple varieties of maize haploids. In this paper, a method for identifying haploids based on deep belief network is proposed. DBN is a multi-layer deep neural network. Each layer is composed of a restricted Boltzmann mechanism. By using layer-by-layer training strategy, the problem that traditional neural network training methods are not suitable for multi-layer training can be solved. The comparative experimental results show that the identification model of multiple varietieshaploid established by DBN method has high classification performance and can meet the requirements of maize engineering breeding accuracy.

In order to quickly detect the water content of potato leaves and explore the change of leaf water content under drought stress, the hyperspectral imaging technology was utilized to detect and visualize the moisture content of potato leaves in this paper. 71 leaves were collected and the water gradient of the leaves was controlled by a drying method. A total of 355 samples were obtained. The hyperspectral sorting instrument was used to collect potato leaves spectral and image data of 862.9~1 704.2 nm (256 wavelengths). The water content was measured by weighing method. According to a certain proportion, Sample Set Partitioning Based on Joint X-Y Distance (SPXY) algorithm was used to divide the sample into a model set and a validation set. For the calibration set, the feature wavelengths were extracted by using Coefficient Analysis (CA) and Random Frog (RF) algorithms respectively, and Partial Least Squares Regression (PLSR) models were established respectively. The calibration set and validation set determines coefficient R2 and the RMSE (Root Mean Square Error) were used as the evaluation index. The gray image of the potato leaves water content was calculated using the results of the detection model. The visualization analysis of potato leaves water content was realized based on the pseudo color image transformation and segmentation. The average reflectance of each sample leaf was calculated by ENVI software, obtaining a total of 355 sample’s spectral data. According to the proportion of 2∶1, the total samples were divided into calibration set (240 samples) and validation set (115 samples) by SPXY algorithm. Spectral characteristics of the collected data were analyzed. Two algorithms, CA and RF, were used to select 15 characteristic wavelengths, respectively. Based on the CA, the selected 15 wavelengths with the correlation coefficient higher than 0.96 were 1 406.82, 1 410.12, 1 403.62, 1 413.32, 1 416.62, 1 419.82, 1 400.32, 1 423.12, 1 426.32, 1 429.62, 1 432.82, and 1 441.12, 1 493.32, 1 442.52 and 1 445.8 nm. Based on the RF algorithm, the 15 feature wavelengths that the selected probability higher than 0.3 were 1 071.62, 1 041.12, 1 222.52, 1 465.22, 1 397.02, 1 449.02, 1 034.32, 1 523.22, 976.42, 1 172.52, 979.82, 1 165.82, 1 037.72, 1 426.32 and 869.8 nm. The PLSR model was established using the characteristic wavelengths filtered by the CA and RF algorithms, marked RF-PLSR and RF-PLSR model respectively. The water content of potato leaves was analyzed visually using the more precise model. First, the each pixel water content of the potato leaf image was calculated to obtain a gray image. Then, the gray image was pseudo-color transformed to draw a visual color image of the leaf water content. In order to reflect the change of potato leaves water content in the drying process, HSV model was used to segment the pseudo-color image of sample leaf. The segmentation image results, showing the proportion of leaf area in a certain water content interval, were obtained. The results showed that the 15 wavelengths selected by the CA algorithm were in the range of 1 400.3 to 1 450.0. The calibration accuracy of CA-PLSR was 0.975 5, and the RMSEC (Root Mean Square Error of Calibration) was 2.81%, and the validation accuracy was 0.933 2, and the RMSEV (Root Mean Square Error of Validation) was 2.31%. The range of characteristic wavelengths selected by the RF algorithm, with local “peak valley” characteristic, was wider than that of the CA algorithm. The calibration accuracy of RF-PLSR model was 0.983 2, and the RMSEC was 2.32%, and the validation accuracy was 0.947 1, and the RMSEV was 2.15%. The RF-PLS model is selected to calculate the water content of each pixel in the potato leaf images. According to the pseudo-color image, it could be seen intuitively that the water content gradually decreased with the drying time increasing. From the perspective of leaf tissue structure, with the strengthening of water stress, the leaves began to lose water from the edge and gradually spread to the middle, in which the water content of leaf stems and veins was higher than that of other parts. The pseudo-color image was segmented by HSV model based on the color difference of the image. The proportion of pixels with water content greater than 90%, 80%, and 70% in the leaf pseudo-color image to the entire leaf image. Using the hyperspectral imaging technology can realize the water content detection and distribution visualization of potato leaves, which provides a new theoretical basis for the potato growth analysis and potato leaf water content analysis.

Hyperspectral imaging technology is profoundly applied into the fields of agriculture, medicine and remote sensing due to its high spectral resolution, merged image-spectrum, and fast non-destructive testing. While the method used now has the defects of long-term testing period, poor efficiency and sorting asynchrony. Spectral image can identify and classify the target garbage by establishing a recognition and classification model and analyzing reflectance spectrum information based on the facts that different materials of domestic garbage, due to their different molecular structures, will absorb different wavelengths of light and the hyperspectral image can obtain the spatial information and the reflectance spectral information from different-wavelength illumination of the target garbage. Collected recyclable garbage samples of common paper, plastic and wood materials, including plastic bottles, food packaging bags, plastic toys (jewelry) pieces, disposable chopsticks, ice cream bars, wooden furniture pieces, wooden boxes, waste textbooks, advertising paper, office paper and other items, 30 in total. And cleaned and cut them to avoid the influence of sample surface stains on the sample reflectivity. Hyperspectral imaging systems were used to acquire hyperspectral images of the sample in the near-infrared (780 ～1 000 nm) formed 18 training samples and 12 test samples. Pre-processed the collected sample image by de-noising and black-and-white correction inversion of reflectivity information. Then analyzed the region of interest of training samples by principal components analysis. The characteristic band extracted were 795.815, 836.869, 885.619, 916.409, 929.239, 934.37, 957.463, 972.858, 988.253 nm; Next, matched and categorized the characteristic band of the ROI with reference spectra of the three types of garbage from the characteristic band by spectral angle mapping. The result illustrated that the classification precision of paper (A class), plastic (B class) and wood (C class) were 100%, 98% and 100% respectively and the average was 99.33%; at last, sorted the test samples by Fisher linear discrimination. The classification precision of class A, B, C were 100%, 100% and 97% respectively and the average was 99%. After a series of testing and classification by SAM and Fisher as the narrated above, the results showed that aforesaid manipulation of hyperspectral image for recyclable garbage by SAM can get more accurate results which is 99.33%. meanwhile, the research can testify that it’s feasible to apply the scheme of hyperspectral imaging to assort garbage, which is significant to methodically and automatically recycle garbage in the future.

In order to investigate the feasibility of reflectance spectroscopy for the detection of chlorpyrifos pesticides in water, indoor and outdoor spectral data of chlorpyrifos samples in two different concentrations were obtained using a hyperspectral acquisition system composed of ASD’s FieldSpecPro Spectrometer. The partial least squares (PLS) and principal component analysis (PCA) algorithms were used to establish quantitative models for spectral data of chlorpyrifos samples. The results showed that the predictable ability of the model is significantly reliable. Correlation analysis (CA) was used to calculate the correlation coefficient to select the characteristic wavelength of the spectrum of chlorpyrifos samples. The characteristic wavelengths of indoor and outdoor experimental spectra with concentration ranges of 5～75 mg·L-1 were 388, 1 080, 1 276 and 356, 1 322, 1 693 nm, respectively. And the characteristic wavelengths were 367, 1 070, 1 276, 1 708, and 383, 1 081, 1 250, 1 663 nm in the range of 0.1～100 mg·L-1 experiments. The PLS algorithm was used to establish a quantitative model of the sample characteristic wavelength spectral data. Compared with the full-band model, the calibration set determination coefficient (R2C) of the PLS characteristic wavelength model with concentration range of 5～75 mg·L-1 was increased to 0.987 5 and 0.999 2 in the indoor and outdoor experiment, respectively. And the prediction set determination coefficient (R2P) was increased to 0.989 4 and 0.994 4, respectively. The root mean square error of the calibration set (RMSEC) was reduced to 2.841 and 0.714, respectively. The root mean square error of the prediction set (RMSEP) was reduced to 1.715 and 1.244, respectively. The R2C of the characteristic wavelength PLS model with concentration range of 0.1～100 mg·L-1 in the indoor and outdoor experiment was increased to 0.998 3 and 0.998 8, respectively. The R2P was increased to 0.998 4 and 0.999 0, respectively, and the RMSEC of the correction set was reduced to 1.383 and 1.186, respectively, and the RMSEP of the prediction set was reduced to 1.510 and 1.229, respectively. The ratio of standard deviation of the validation set to standard error of prediction (RPD) were increased, especially for experiments with a concentration range of 0.1～100 mg·L-1. The RPD value increased to 21.7 significantly, indicating that the quantitative model based on the characteristic wavelength has higher accuracy of prediction ability. However, comparative experiments with different concentration ranges show that the relative error of the low-concentration chlorpyrifos solution prediction by the ASD spectrograph is large and there is an objective detection limit. In order to ensure that the characteristic wavelengths of chlorpyrifos pesticides under different experimental conditions are analyzed and the universality and robustness of the model are enhanced, four bands are selected according to the characteristic wavelengths, that is, 351～393, 1 065～1 086, 1 245～1 281 and 1 658～1 713 nm used as characteristic bands. The characteristic band model has a total of 38 wavelength variables. Compared with the 432 wavelength variables of the full-band model, the model variable was reduced by 91.2%. The R2C of indoor and outdoor experimental PLS models with concentration range of 5～75 mg·L-1 were 0.993 7 and 0.987 8, and R2P were 0.979 8 and 0.998 2, and RMSEC were 1.69 and 2.516, and RMSEP were 1.987 and 0.659, respectively. The R2C values of the experimental PLS model with concentration range of 0.1～100 mg·L-1 were 0.988 2 and 0.980 7 for the indoor and outdoor experiments, and the R2P were 0.939 1 and 0.993 6, and the RMSEC were 3.345 and 3.942, and the RMSEP were 8.996 and 2.663, respectively. All of the model RPD values were more than 2.5 and met the quantitative analysis conditions. Therefore, the hyperspectral system of the paper for the rapid detection of chlorpyrifos pesticides in indoor and outdoor environments has a certain feasibility. The results of this study have practical application value for the rapid detection of non-point source pollutants such as organic phosphorus pesticides, which can provide a theoretical basis for the development of an instrument for the rapid detection of organophosphorus pesticides in farmland water.

Steel slag tailings are the main solid waste in metallurgical industry, with the production of 15%~20% of crude steel. The utilization ratio is quite low and only reaches 10% of steel slag tailings production due to limited technology. Meanwhile, steel slag tailings are disposed by direct stacking and landfill in general since the management system is not perfect, which pollutes land source, underground water source and air quality. Recycling of solid waste is one important method to achieve sustainable development of resources. The main chemical composition, i. e., CaO, SiO2, Al2O3, MgO, Fe2O3, MnO, f-CaO, etc. and mineral composition, i. e., tricalcium silicate, dicalcium silicate, monticellite, dicalcium ferrite, etc. of steel slag tailings are almost similar to that of cement clinker, making them as the cementitious material with potential cementitious activity. In this paper, steel slag tailings were stimulated physically by mechanical grinding to obtain powder with various sizes. Then, mortar samples of steel slag tailings were prepared in light of Steel slag powder used for cement and concrete (GB/T 20491—2006) and Method of testing cements-Determination of strength (ISO) (GB/T 17671—1999). Then, the impact of steel slag tailings powder at different size, i. e., A40, A60, A80, A100 and A120, on cementitious activity was investigated experimentally, as well as the impact of hydration time on cementitious activity, namely at 3, 7 and 28 d. After that, the size distribution of steel slag tailings powder was characterized by LPSA, and mineral composition and steel slag tailings mortar were tested by XRD and microstructure of steel slag tailings mortar was tested by SEM in order to obtain physical excitation mechanism of steel slag tailings. The results showed that the cementitious activity of steel slag tailings powder presented an increasing trend followed by a decreasing trend as size of steel slag tailings powder decreased. When grinding time was 80 min, the maximum activity occurred at A80. The activity index showed as 67.55%, 71.96% and 73.61% for 3d, 7 and 28 d respectively. As the powder size decreased, the characteristic peak of RO phase in XRD analysis kept a steady intensity while characteristic peak intensity of Ca2SiO4 and Ca3SiO5 increased first but decreased afterwards. The hydration reaction of Ca3SiO5 and Ca2SiO4 could generate Ca(OH)2 and C-S-H gel with good cementitious activity. Small amount of Ca2SiO4 could be found in A80 steel slag tailings powder while large amount of Ca3SiO5 existed, both of which could be used to generate Ca(OH)2 and C-S-H gel. They can improve the mechanical property of A80 steel slag tailings mortar slightly at early period of 3~7 d but greatly at later period of 7~28 d. When hydration time reached 3 d, small amount of hydration products and many micro particles existed in A80 steel slag tailings mortar. For 7 d, the abovementioned hydration products increased to a great extent and larger particles formed. At 28 d, lots of hydration products were generated and there was rarely dispersed micro particles. Under this condition, recycling of solid wastes could be accomplished so as to increase benefit and would not pollute environment severely any more.

Jingdezhen blue and white porcelain is one of the most representative ceramic types in China, which is famous for its high academic and economic values. However, it is hard to discriminate blue and white porcelains of different time periods from their appearance, and how to solve this problem quickly and accurately is a major challenge to preservation of cultural relics. As a totally nondestructive technique, hyperspectral remote sensing has been successfully applied in pigment analysis of historic frescoes and paintings. In this study, 28 Jingdezhen blue and white porcelain samples of different time periods were collected, and their reflectance spectra of both bodies and cobalt blue materials were measured by ground spectrometer. The typical spectral features of blue and white porcelain were summarized, and the change trend of spectral feature parameters for cobalt blue material was analyzed. The study indicated that cobalt blue material has abundant spectral features in visible to near infrared bands, and the spectral feature parameters of different cobalt blue material types showed obvious difference. Hyperspectral remote sensing has significant potential in the cohort analysis of Jingdezhen blue and white porcelains.

Stellar spectra classification is one of hot spots in astronomical techniques and methods. With continuous operation and improvement of observation apparatus, hundreds and thousands of spectra were obtained by researchers, which presented challenges to process them manually. In view of this, data mining algorithms have attracted more attentions, and have been utilized to deal with the spectra. Neural networks, self organization mapping, association rules and other data mining algorithms have been utilized to classify the stellar spectra in recent years. In these algorithms, Support Vector Machine (SVM) is much more popular due to its good learning capability and excellent classification performance. The basic idea of standard SVM is to find an optimal separating hyper-plane between the positive and negative samples. SVM as a convex programming problem has a unique optimal solution, which can be posed as a quadratic programming (QP) problem. In order to further improve the classification efficiency, Twin Support Vector Machine (TSVM) has been proposed. It aims at generating two non-parallel hyper-planes such that each plane is close to one class and as far as possible from the other one. The learning speed of TSVM is approximately four times faster than that of the classical SVM. TSVM receives many attentions since it shows low computational complexity, and many variants of TSVM have been proposed in literatures. During the process of stellar spectra classification, the classification model is built based on the observation data. The key step is to manually label the spectra, which is time-consuming and painstaking. Therefore, how to construct the spectra classification model based on the labeled and unlabeled spectra is a problem deserving study. In order to effectively classify the stellar spectra, Twin Support Vector Machine with Unlabeled Data (TSVMUD) is proposed in this paper. In TSVMUD, the stellar spectra are firstly divided into two parts, one is for training, and the other is for test. Then, the proposed method TSVMUD is utilized on the training data and the classification model is obtained. At last, the spectra in the test dataset are verified by the classification model. TSVMUD not only preserve the advantage of low computational complexity, but also improve the classification efficiency by taking both the labeled and unlabeled data into consideration. The comparative experiments on the SDSS datasets verify that TSVMUD performs better than the traditional classifiers, such as SVM, TSVM, KNN (K Nearest Neighbor). However, some limitations exist in TSVMUD, for example, how to deal with the mass spectra is quite difficult to solve. Inspired by random sampling, we will research the adaptability of our proposed method in the big data environment based on big data technologies.

Extreme Ultraviolet (EUV) spectroscopic observation is one of the most important approaches in diagnosing the basic physical phenomena in the solar atmosphere. However, the designs of many instruments used for visible wavelengths cannot be applied for EUV because of its much shorter wavelength. Conventional solar EUV imagers and spectrographs have their own limitations: Where as we cannot get spectral information from a EUV imager, it takes too long time for a single slit spectrometer to scan an area, which makes it difficult to catch the dynamics of highly transient solar activities. These limitations make the high resolution observation of solar activities and the research of its mechanism very difficult. We cannot observe the acceleration process of CME (coronal mass ejections) in inner coronal and cannot connect the CME observed by visible light with the activity area observed by EUV directly. Moreover, we cannot get the line-of-sight velocity of the solar activities, so it is difficult to find the source area of CME. In this paper, we present the design of a new type of solar EUV spectral imager with extra high spectral resolution. It can get the full-disk EUV image of the Sun with additional information on spectral line profile. So we can get the line-of-sight velocity of plasma in low coronal and the velocity map of the full coronal disk. Combining the spatial and spectral information, we can identify the movement corresponding to the configuration evolvement of the plasma. Because there is no slit and movement assembly, the imager can get high temporal resolution data of the whole solar disk to capture the rapidly transformation of solar activities. The new imager adopts a kind of slitless spectral imaging design, which means to project a narrow band spectrum data from different angle to a plate detector and invert to get the spatial image and spectral information. The biggest difference between the multi-order spectral imaging and the traditional spectrograph is that there is no scanning slit in former. These give the new imager the advantage which can get the spatial and spectral information in a wide field of view simultaneously. Considering the limitations of the EUV band and space application, it is impossible to get many orders image like the medical CT or telescopes response to visible light. Based on the multi-order imager principle, we proposed a five-order spectrograph. The optical system of the new imager consists of a reflect mirror, a grating and five CCD detectors. The dispersed lights after the grating are received by five detectors. Four detectors receive ±1 and ±2 orders of diffraction, and another one receives 0 order image with spatial resolution information. The spectral information can be obtained by inversion with five orders spectral images. The paper introduces the design of the optical system based on a varied line space (VLS) grating and the inversion algorithm, which will improve the instrument efficiency and image qualitywith limited volume and weight. The spatial resolution will be 1.8 arcsec·pixel-1 and the spectral resolution will be 7.8×10-3 nm·pixel-1. With this technology, we can get the full solar disk velocity map aswell as the intensity map simultaneously, which is suitable for space application.

According to the fundamental absorption properties of ethane (C2H6) near 3.3 μm, a mid-infrared C2H6 sensor based on a wavelength modulation spectroscopy (WMS) technique was developed using a room temperature, continuous-wave (CW) interband cascade laser (ICL) emitting at 3.34 μm and a dense multi-pass gas cell (600 mL) with a 54.6 m optical path length. The principle of gas detection using spectral absorption method based on wavelength modulation spectroscopy and two harmonic (2f) detection technology is introduced in detail. Selection details of the target ethane absorption line are also given. The use of this technology reduces the influence of optical power drift on the system, making the minimum detection limit (MDL) and stability performance of the system get promoted. Ethane sensing system is introduced in detail through optical and electrical modules combined with the scheme. The application of self-developed software and hardware units as well as commercial instruments and their model are described for the reference to others, and physical map of the sensor optical core is also given. Moreover, the pressure and modulation depth are optimized in order to match the wavelength modulation of laser and absorption linewidth based on gas pressure. The curves of the modulation amplitude corresponding to the peak value of 2f signals and the modulation depth corresponding to modulation depth are also drawn accordingly, and finally the appropriate pressure and modulation depth are determined to be 100 Torr and 0.074 cm-1, respectively. The corresponding modulation amplitude is ~0.026 V at that point. In addition, the work of system sensitivity estimation is conducted by using 136.8 nmol·mol-1 C2H6 standard gas based on the optimized air pressure and modulation depth. The parameters setting of ICL scanning and modulation signals, phase-locked amplification as well as data acquisition are introduced in details, and pictures recorded by oscilloscope are also given. In this case, the system MDL is estimated to 33 nmol·mol-1 by comparing 2f signal acquired by DAQ and background noise signal. Finally, the fitting curves and its correlation information are described by carrying out ~5 minute system calibration tests, respectively, by using 9 different C2H6 standard gases from 20～400 nmol·mol-1. Moreover, 2 hours system stability test was conducted by using 48 nmol·mol-1 C2H6 sample. The result shows that this system works steadily and a minimum detection limit (MDL) of ~0.81 nmol·mol-1 is achieved with a measurement time of 4 s. The MDL is further improved to 0.36 nmol·mol-1 with a measurement time of 63s, based on an Allan deviation analysis for the C2H6 sensor operation.

Model transfer is a key common technical problem in the near infrared spectral analysis technology. By seeking feasible mathematical methods between the two instruments that have the same working principle, we can make the model which was set up on one instrument be applied to another one. In this paper, with 150 flue-cured tobaccos as test samples, with two Bruker MPA near infrared spectrometer and one Thermo Antaris near infrared spectrometer as the research object. We obtained spectral data by integrating sphere diffuse reflectance. Processed and analyzed the spectral data by using the first derivative and standard normal variate (SNV) transformation, and calculated the value of residual error between different instruments, first moment, signal probability density and maximum signal to noise ratio(SNR) and so on. Also, we established mathematical model of total sugar content by partial least squares (PLS) to test the effect of model transfer. The results showed that first derivative could reduce the first moment and transfer the deviation between different instruments into the standard Gaussian distribution, but at the same time, it could also put SNR down. SNV could also reduce the first moment and even could do better than first derivative, and it could increase SNR significantly, but SNR could not transfer the deviation into standard Gaussian distribution, which would need other ways to make up for it. The combination method of the first derivative and SNV can retain the advantages of both, and make up for the disadvantages of individual treatment, and it can settle the model transfer problem caused by different instrument types and different using time between different instruments that work in the principle of Fourier, which is based on the integrating sphere diffuse reflectance. This method is an ideal model transfer method without the prototype.

Powdery mildew and stripe rust are two of the most prevalent and destructive wheat diseases causing severe decreases in wheat yield in China. It is necessary to quantitatively identify different diseases for spraying specific fungicides. In this study, a line-scanning hyperspectral imaging system (ImSpector V10E) was utilized to capture spectral and imagery information of wheat leaves infected by powdery mildew, stripe rust and normal leaves. Based on 320 hyperspectral images, strong spectral reflectivity responses were discovered at the bands of 550～680 nm in the wheat leaves infected with powdery mildew and stripe rust after the savitzky-golay （SG） smoothing method. To reduce the dimensionality of the spectral matrix, 3, 6 and 30 variables were extracted as sensitive wavelengths from full spectra for different diseases using X-loadings of principal component analysis (PCA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS), respectively. Least squares support vector machine (LS-SVM) and extreme leaning machine (ELM) were applied to build identification models using full spectra and sensitive wavelengths extracted by X-loadings of PCA,SPA and CARS to distinguish powdery mildew, stripe rust and normal leaves. The accuracy rates of all the models in the calibration set and test set were above 94.58%. Among these models, the ELM classification model combined with X-loadings of PCA had the best performance, with accurate identification rates of 99.18% on the calibration set and 100% on the test set. Moreover, this model was simple in structure with only three variables (560, 680 and 758 nm). Meanwhile, the microstructure of three kinds of wheat leaves were also studied. Although the infection mechanisms of these two diseases were slightly different, they both destroyed the mesophyll cells, reduced chlorophyll content and photosynthesis markedly. The string of changes leaded to weakened light absorption but increased reflectivity in the visible light band. Thus, the results indicated the potential for the rapid and non-destructive detection of wheat diseases by hyperspectral imaging, which could help to develop online multispectral detection system for different kinds of plant diseases.

The Molecular Structure of 4-Amino-3-phenylbutanoic acid conformers have been studied in the gas phase. Natural Bond Orbital Analysis (NBO) and Mulliken analysis of atomic charges of 4-Amino-3-phenylbutanoic acid have been performed by DFT level of theory using B3LYP/6-311++G(d,p) basis set. The atomic charges, electronic exchange interaction and charge delocalization of the molecule have been performed by Natural Bond Orbital(NBO) analysis and Natural Population Analysis(NPA) have been constructed at B3LYP/6-311++G(d,p) level.

Arsenic (As) pollution not only affects soil fertility and crops growth, but also exposes to humans through air, soil, water and food, posing a major threat to human health. Mineral exploitation is one of the most important sources of As environmental problems. In this study, the nine villages (S1—S9) in the vicinity of a Pb-Zn mining area in Yunnan province were chosen as study area, and the county town 20 km away from mining was selected as reference area (S10). The samples including 76 cultivated soils, 306 crop/vegetables and 86 human hairs were collected. Microwave digestion was used to pretreat these samples by controlling the three variables of acid dosage, temperature and duration to get the optimal solution of muti-media. The Inductively Coupled Plasma Mass Spectrometry (ICP-MS) were used to determine the contents of As in order to investigate the As pollution level and human health risk in muti-media. The results will provide a reference for policy decision on the prevention and treatment of As pollution caused by mining. Results showed that: (1) after these samples were pretreated by microwave digestion, As detection limits for soil, crops and human hair ranged from 0.01 to 0.12 μg·L-1, and accepted recoveries ranges were 92.43%～112.23% (soil), 97.88%～114.72% (crop/vegetable) and 91.44%～109.65% (hair), respectively, and relative standard deviation was less than 5% with satisfactory results. (2) The mean content of As in soil was 70.66 mg·kg-1, which was 3.84 times higher than that of the background value in Yunnan province. According to GB 15618—1995 of “soil environmental quality standard” (Grade Ⅱ) and the single-factor pollution index (Pi), cultivated soil was severely polluted by As. Furthermore, the highest As content was detected in S1, which might be closely related to the mining, smelting and transportation for many years. In crop/vegetables samples, the mean As content of tuber-vegetables was 1.75 mg·kg-1, followed by leafy-vegetables (0.77 mg·kg-1), which was higher than the maize (0.52 mg·kg-1) and root-vegetables (0.51 mg·kg-1). Based on the maximum permissible standard set by China, the excessive rate of As content in crops was 80.64%. (3) The potential health risk assessments of As exposed to multiple pathways among local residents were evaluated by the hazard index (HI), the total carcinogenic risk (TCR), the target hazard quotient (THQ) and carcinogenic risk (CR), respectively. The total non-carcinogenic risk of As for adult and child were 1.13～1.20, which was unacceptable risk. The carcinogenic risk was as high as 10-3, exceeding the general risk acceptable level (10-4) recommended by the United States Environmental Protection Agency (USEPA). Besides, diet was the dominant exposure pathway. The THQs (>1) and CRs (>10-4) of As in vegetables showed that a potential risk did exist, and the TCR of As for child was higher than that for adult. In view of high As risk of vegetables grown in local area, so we suggested that the nonlocal foods were input to avoid health risks of As pollution and those crops whose edible parts are not easy to accumulate As should be largely planted. (4) The contents of As in hair samples in mining area were 0.97 μg·g-1, which was 4.41 times higher than that in S20 km (0.22 μg·g-1) (p<0.05) and beyond the recommended standard of the Ministry of Health (0.6 μg·g-1). The contents of As from male were higher than that from female, and the Group Ⅱ (19～40 years) were higher than Group Ⅲ (≥41 years). It meant that the males in 19～40 years who acted as the major participant in mining and smelting activities were more vulnerable to exposure to As than others. (5) This study provided a powerful basis for the As pollution level in muti-media of the mining area, but also for the assessment of As exposure risk to local human health.