Accurate quantification of infrared remote sensing signal is important for acquisition of pollutant cloud’s information, but spectral distortions occurred in measurement may hinder the achievement of such purpose. An adaptive method based on instrumental line shape (ILS) model was established in order to compensate the contributions due to ILS distortion. Through analysis of the sources of ILS function, the ideal, inherent function as well as phase error contribution were modeled based on design parameters of a real infrared spectrometer. Furthermore, an algorithm which reconstructs ILS function from measurement was developed by using iterative optimization method, which takes root mean square between differences of simulation and measurement spectrum as cost function. The compensation result by using reconstructed ILS function on simulated spectrum suggests that differences between simulation and measurement were effectively eliminated. The analysis showed that inherent ILS may cause spectral feature broadening toward low frequency, and phase error is responsible for spectral feature asymmetry. All three sources of ILS distortion must be considered simultaneously to get accurate pollutant cloud parameter from measured spectrum. The acquisition of distortion parameters and the corresponding compensation method may be helpful for the recognition and quantification of infrared remote sensing signals.

Surface enhanced Raman scattering (SERS) is an advanced surface analysis technique that can enhance the vibrational spectrum of molecules adsorbed on or in the vicinity of metal surfaces enormously. Due to its high speed, accuracy, high sensitivity, good selectivity and minimum requirements for sample preparation, SERS technique becomes the current research hotspot and shows important application prospects in the fields of chemistry, food, biology, medical treatment, etc. However, it is known that the uniformity, reproducibility and stability of SERS active substrates are still main challenges to be overcome for the use of SERS technique as a routine analytical tool .The printing methods have the advantages of simple operation,high efficiency and low cost, which are useful for designing plasmonic nanostructures. In recent years,printing technologies have been gradually applied to the preparation of SERS substrates. By optimizing the amount of hot spots to enhance the electromagnetic field, SERS active substrate with good repeatability, high stability and strong enhancement ability can be obtained. In this work, several common printing techniques for preparing SERS substrates are reviewed,including inkjet printing,gravure printing andscreen printing. The influence of factors on SERS performance is analyzed, such as surface wettability of substrate,drying temperature, ink viscosity, surface tension and solvent. The research progresses of preparing SERS substrates by printing technologies are summarized, and the potential applications and future development are also prospected.

In this work, the coke layer on the surface of ascension pipe is investigated, and scanning electron microscope (SEM) and X-ray photoelectron spectrometer (XPS) are applied to research microscopic appearance, elemental composition and bonding state of different coke layers, and further analyze texture formation and evolution law of the coke layer. SEM analysis displays that: coke layer on the surface of ascension pipe presents different microscopic appearance, 1# coke layer presents porous structure with 0.1～1.0 μm carbon particle loosely stacked; 2# and 3# coke layer show enhanced compactness with 1.0～3.0 μm carbon particle stacked; 4# coke layer displays lots of compact patterned structure. The phenomenon indicates the formation process of the coke layer as follows: the polycyclic aromatic hydrocarbons react to form primary coke layer with particle size of 0.1～1.0 μm; primary coke layer react with each other to form compact intermediate coke layer with particle size of 1.0～3.0 μm in catalysis of metal element (Fe, et al) of dust in raw gas; intermediate coke layer further to form ultimate layer at high temperature. XPS analysis displays that, 1#—4# coke layer present C element content of 91.78%, 91.95%, 92.74% and 94.01%, O element content of 5.58%, 5.42%, 4.39% and 2.86%, corresponding to the C/O ratio of 16.45, 16.96, 21.12 and 32.87, indicating at the same time of structure change for the coke layers, oxygen-containing groups in coke layer conduct removal reaction under the condition of metal element (Fe, et al) of dust in raw gas, resulting the macroscopic increase of C/O ratio. Furthermore, peak fitting for bonding state of C element shows that 1#—4# coke layer present C—C/C—H structure content of 80.42%, 78.00%, 75.50% and 81.29%, C—O/C—N structure content of 10.22%, 11.93%, 13.54% and 9.35%, CO/CN structure content of 9.36%, 10.07%, 10.96% and 9.36%. Peak fitting for bonding state of O element shows that 1#—4# coke layer present O structure content of 20.40%, 22.21%, 19.93%, 18.36%, corresponding to —O— structure content of 24.60%, 27.80%, 31.35%, 37.82% with O2/H2O structure content of 55.00%, 49.99%, 48.72% and 43.82%. The above phenomenon indicates that the following chemical process are conducted on the coke layer: the porous structure of primary coke layer absorbs oxygen gas (O2) and water molecule (H2O), which oxidizes coke layer at high temperature. The oxidation reaction and removal reaction result in significant change of microscopic bonding state of O element in coke layer, decreasing content of O2/H2O and O structure and increasing —O— structure. The above research reveals texture formation and evolution mechanism of coke layer on the surface of ascension pipe, providing experimental and theoretical basis for solving coke problem of ascension pipe, enhancing heat exchange efficiency and decreasing energy consumption of coking enterprises.

With the continuous development of light emitting diode (LED) technology, high-power LEDs with various wavelengths have been developed, therefore we can design a LED-based QE instrument using LED with various wavelengths as monochromatic light source instead of expensive monochrometers. Compared with the traditional QE instrument, there is no filter required in the LED-based QE instrument, so it is not necessary to rotate the filter wheel for a grating monochrometer to avoid the influence of high-order spectrum. Without mechanical movements, which reduces the failure rate and speeds up the measurement. Several LEDs are welded on a printed circuit board (PCB) as the discrete light source. However, it is impossible to converge light as the traditional QE instrument using ellipsoidal mirror, lens or concave mirror. Therefore, the discrete light from the LED board is collected and homogenized into a small spot by a light pipe formed with highly reflective reflectors, which can solve the difficulty in converging discrete light sources and the high utilization ratio of light is achieved. By measuring the peak intensity, full width at half maxima (FWHM) and stability of LED, and further comparing it with the traditional QE instrument using halogen lamp and xenon lamp as light source, it is found that the measurement accuracy of QE is positively correlated with the peak intensity of monochromatic light. The higher the peak intensity is, the higher the measurement accuracy is. Furthermore, it is also observed that the measurement accuracy has no obvious correlation with FWHM in the range of 2.5~9.5 nm. The QE of the same solar cell is measured by LED-based, halogen lamp-based and xenon lamp-based QE instrument, respectively. The integrated current is further calculated based on QE in the same wave range, and compared with that obtained from the advanced xenon lamp-based QE instrument, it is found that the relative error of the LED-based QE instrument is only 0.34%, which is equivalent to the accuracy of the halogen lamp-based QE instrument. Based on integrated current conditions, the measurement accuracy has no obvious correlation with the FWHM of LED varies from 8.3~55.7 nm. Moreover, the instability of LED is 0.4%, which is between xenon lamp and halogen lamp. Based on these three aspects, it can be concluded that LED can be used as the monochromatic light source for QE measurement.

Primary open angle glaucoma is a common blind eye disease. Elevated intraocular pressure is the most important risk factor for the occurrence and development of primary open-angle glaucoma. It is caused by the lesion of aqueous humor outflow system and the increase of aqueous humor outflow resistance in trabecular meshwork pathway. It has been shown that TGF- β in aqueous humor can make trabecular cells fibrosis and induce excessive proliferation of trabecular cells, thus hindering the outflow of aqueous humor, leading to the occurrence of primary open-angle glaucoma. POAG is a covert disease with slow progression and no symptoms in the early stage. It is often found only when the visual field is significantly impaired in the late stage. Therefore, the early diagnosis of it is particularly important. Synchrotron radiation infrared microimaging combined with high luminance and high resolution synchrotron radiation source with Fourier transform infrared spectrometer and infrared microscope can realize cell detection. It is very important to obtain cellular change information from molecular level and to understand the pathogenesis of disease and the early diagnosis of disease. Although there are many research reports on infrared spectroscopy in biomedical field, the application of infrared spectroscopy microscopic imaging technology to the study of biomedical systems such as cells is still an area in urgent need of development. At present, no infrared spectroscopy has been found for the detection of trabecular meshwork cells. In this paper, TGF-β was used to induce rat trabecular meshwork cells into myofibroblasts in vitro, simulating the process of trabecular meshwork cells fibrosis. The meshwork cells and the myofibroblasts induced by TGF-β were studied by synchrotron radiation infrared microscopy and spectral analysis, and the feasibility of using synchrotron radiation in the early diagnosis of primary open-angle glaucoma was discussed. Study have shown that the elastin in myofibroblasts was significantly higher than that in trabecular meshwork cells, and 95% of elastins were nonpolar amino acids. Comparing the IR spectra of the two kinds of cells, it was found that the stretching vibration of CH3, CH2 and CH of myofibroblasts at 2 934, 2 900 and 2 845 cm-1 was stronger than that of trabecular meshwork cells, which might be due to the increase of intracellular elastin induced by TGF-β. In this paper, we detected the excessive proliferation of trabecular meshwork cells at the cellular level, which laid a foundation for obtaining the infrared spectrum of cells directly and detecting the proliferation of trabecular meshwork cells in the future, and then for detecting diseases such as primary open angle glaucoma. It is concluded that synchrotron radiation infrared spectroscopy and microscopic imaging are expected to be new methods for detecting POAG and provide a basis for real-time clinical detection of glaucoma by portable infrared microspectrometer.

Fourier transform infrared spectroscopy (FTIR) has the advantages of a low test sample requirement, high sensitivity to characteristic groups and simple sample preparation and analysis. Inductively coupled plasma mass spectrometry (ICP-MS) is important because of its high detection rate sensitivity to trace elements, low detection limit and ability to analyse multiple elements simultaneously. Synergistically, the use of FTIR and ICP aids in the rapid identification of chemical elements and groups of functional medical materials, thereby providing new design ideas and theoretical basis for the development of bionic medical antibacterial materials. Hydroxyapatite (HA) is used in thin film materials because of its excellent bone conduction and osteoinductive properties. Titanium-implanted surface HA film is currently in the clinical application stage, but the brittleness and lack of antibacterial properties of HA often lead to implant failure. Thus, a bone-promoting functional coating with good wear resistance and excellent bacteriostasis must be developed to address these limitations. This paper presents a method for preparing a bone-promoting coating on the surface of titanium with good abrasion resistance and excellent bacteriostasis. The antibacterial ion sustained release law and biological activity of the coating were studied. For the first time, a gelatin, silver (Ag) and magnesium (Mg) ion-modified hydroxyapatite (Mg-Ag-HA/gelatin) antibacterial coating was prepared on the surface of industrial pure titanium. Ag was introduced into the HA coating to improve its antibacterial properties, while Mg was added to improve the biocompatibility of industrial pure titanium. Gelatin could simultaneously improve the biocompatibility and mechanics of HA. The release and sustainability of Mg and Ag in the coating were determined using ICP-MS. Morphology, Ca/P, chemical structure and crystal structure of deposited Mg-Ag-HA/gelatin were characterized using FTIR, scanning electron microscopy, electron diffraction spectroscopy and X-ray diffraction. Results showed that a Ca-COO chemical bond formed between the carboxyl group of gelatin and the calcium ion of HA. Gelatin and Mg-Ag-HA formed an organic-inorganic composite coating, and Mg and Ag were successfully introduced and evenly distributed into the HA lattice. After simulated body fluid immersion, a new calcium-deficient HA was formed on the surface of the Mg-Ag-HA/gelatin-coated samples, and new Mg, Na and Cl were detected in the spherical apatite. Results showed that the new composite coating has good biological activity. SEM and laser confocal experiments showed that mouse MC3T3-E1 cells adhered well on the film and had good morphology. The composite coating did not manifest cytotoxicity. The addition of gelatin greatly reduces the release rate of Mg2+ and Ag+ in the composite coating, improves the physiological stability of the composite coating and guarantees the long-term antibacterial function of the coating. As a titanium-based coating material, Mg-Ag-HA/gelatin has good antibacterial ion release ability and excellent biocompatibility, which provides a new idea for the development of new anti-infective surgical implants.

Tunable diode laser absorption spectroscopy (TDLAS) technology is widely used in the industrial production and environmental pollution monitor due to its high resolution, high sensitivity and fast measuring speed. The second-harmonic signal of wavelength modulation spectroscopy (WMS) is often used as the detection signal for gas concentration inversion. The detection performance of TDLAS is closely related to the system parameters, such as the time constant of lock-in amplifier, scanning amplitude, scanning frequency, modulation amplitude and modulation frequency. The selection of each parameter in practical measurement is mostly according to the spectral line shape characteristics, in which method the relevance between each parameter is not considered completely. Since the signal sampling and processing affect the spectral line in the frequency domain, the mechanism of influence between parameters is interrelated. However, the influence of each parameter on the frequency domain of the signal was rarely researched. Aiming at this problem, the influence of modulation parameters on second harmonic signals was observed by experiment based on a certain theory in the present paper. The influence of each parameter on signal line shape, frequency characteristics and introduced noise was obtained when only one parameter was changed with all other parameters unchanged. Then the determinant of multi-parameter joint changes on spectral frequency band was analyzed and verified. Compared with traditional methods based on time-domain features, spectrum analysis from frequency characteristics has the advantages not only similar to the principle of spectrum signal acquisition, detection and processing, but also intuitive to get the influence of various parameters on the main frequency band and the attenuation trend of different frequencies signals. The basic selection method of each parameter was summarized based on spectral frequency characteristics. Firstly, the time constant and the scanning parameters should be reasonably set with the judgment criteria of the relationship between the spectral frequency band and the cutoff frequency, which is determined by the time constant of the lock-in amplifier. The appropriate time constants and scanning parameters should be set so that the signal frequency band is close to but not intersected with the cut-off frequency, the in-band components are not attenuated, and the out-band noise is maximally suppressed. Then, the modulation parameter should be determined to maximize the main frequency component amplitude of the spectrum on the basis of the function of lock-in amplifier and the spectrum signal noise ratio. Finally, the sampling rate should be determined according to the system requirements. When the sampling points of one period remain unchanged, the detection accuracy is relatively improved at low scanning frequency but the sampling time increases, on the contrary, the sampling speed increases and the detection accuracy decreases at a higher scanning frequency. The interrelated parameters should be adjusted simultaneously to reduce the influence of the hardware limit on the parameter optimal selection. In this paper, the optimal second harmonic signal can be obtained by parameter selection in the premise of sufficiently considering the system detection requirements and hardware condition limitations, which provided experimental basis and reference method of parameter optimization for the practical application of such technology.

Tenderness is one of the most important attributes of pork eating quality. The tenderness of pork depends on the complex physical and chemical characteristics of pork tissue. And a rapid, non-destructive detection method is urgently in need. This paper reports the feasibility of spatially resolved hyperspectral imaging technique for nondestructive detection of pork tenderness. First, the spatial resolved scattering images of 54 pork longissimus dorsi muscle were collected by hyperspectral system on line-scanning mode. The region of interest (ROI) was selected and the diffused spatial profile of incident light was extracted on the surface of the pork sample. The diffused spatial profile was fitted non-linearly by 4-parameter Lorentzian distribution function. The goodness of fit was R2>0.992, and the residual analysis showed that the 4-parameter Lorentzian function could describe the spatial distribution of light intensity on meat surface. Four morphological parameters of spatial resolved spectrum at wavelength of 480～950 nm were extracted: asymptotic value a, peak value b, full width at half of the peak value c (FWHM) and slope at half of the peak value d. Partial least squares regression (PLSR) models were established to relate each parameter spectra and Warner Bratzler shear force (WBSF) values of pork samples respectively. The results showed that all parameters spectra contained pork tenderness information, in which the peak parameter b had the best prediction results, with determination coefficient of calibration set R2c of 0.674, the root-mean-square error SEC of 8.396N, the determination coefficient of prediction set R2p of 0.610, and the root-mean-square error SEP of 8.643N. In order to improve the accuracy and stability of the prediction model and realize the information fusion of multi-parameter spectra, PLSR analysis was firstly used to extract the latent variables in each parameter spectrum, which have high relative variance contributionto pork tenderness. Then, the latent variable scores were combined as the characteristic variables of the parameter spectra, and multiple statistical regression analysis was performed to relate the characteristic variables and the WBSF values of pork samples. In order to avoid data redundancy, PLSR algorithm was secondly used to reduce and transform the characteristic variables of the parameter spectra. Using the cross validation method, the first two - dimensional factor scores were selected to establish the calibration model. The variance interpretation rate of the pork WBSF value from the first factor was 92.28%. Compared with the PLSR model built by the single-parameter spectrum, the prediction results of the multi-parameter spectra model have been greatly improved, with R2c of 0.923 and R2p of 0.800, SEC of 4.083N and SEP of 5.655N respectively. The results show that all regression coefficients are very significant (p<0.01). In this study, the multi-parameter information fusion method was adopted to provide an idea for the application of spatial resolution spectroscopy in the nondestructive testing of pork tenderness. This method decomposed the spatial resolved spectra into 4 morphological parameters effectively, and achieved the information extraction and fusion of different parameter spectra, providing technical support for the development of non-destructive rapid detection equipment for pork tenderness based on spatial resolved spectroscopy technology.

The qualitative and quantitative analysis of dangerous liquid mixtures by Raman spectroscopy has always been a difficult problem in field application. To solve this problem, this paper aoalyzes the changes of peak position, peak value and peak shape of Raman spectra after mixing, and innovatively constructs the mapping relationship from mixture components to mixture Raman spectroscopy. The mapping relation describes that the Raman characteristic peak response of the mixture is only related to the Raman characteristic peak response of each component and the mixing ratio of each component in the mixture. Based on the inverse matrix calculation, the mixing ratio of each component can be inversely deduced from the Raman spectra of the mixtures. So, in this paper the qualitative and quantitative identification method of dangerous liquid mixtures is proposed. The main steps include: First, collected Raman spectroscopy. Second, processed spectral data and obtained Raman characteristic peaks. Third, calculated the positive and negative matching coefficient between database standard samples and test samples. Finally, if the matching coefficients of both positive and negative characteristic peaks were high enough to satisfy certain threshold conditions, the test samples could be identified as a certain purity. If not a purity, the test samples would be analyzed as a mixture. In this part, the compositions whose negative matching coefficient of Raman spectra characteristic peaks is high will be determined as the compositions of the mixture, and proportion of mixture components is calculated. In the experimental part, acetone, toluene, trichloromethane, ethanol and their mixtures were selected to study. When the mixture was mixed with acetone and ethanol at a ratio of 3∶7, the calculated values of the mixtures by calculation using the method proposed in this paper were 0.245 7 for acetone and 0.706 0 for ethanol. When the mixture sample was composed of toluene and trichloromethane in a ratio of 3∶7, the calculated values of the mixtures were 0..323 4 for toluene, 0.763 0 for trichloromethane. When the mixture sample was composed of acetone, toluene and ethanol in a ratio of 4∶3∶3, the calculated values of the mixtures were 0.795 9 for acetone, 0.303 5 for toluene and 0.287 5 for ethanol. The results show that the calculated values of the mixed components were basically in agreement with the actual values, and the qualitative and quantitative identification method of Raman spectroscopy for dangerous liquid mixtures can accurately determine the composition of each mixture and the proportion of each component in the mixture from the mixed Raman spectroscopy when the components of dangerous liquid mixture are two or three. It has great application value for the field identification of dangerous liquid mixtures.

In order to further reveal the mode transition of the hollow cathode discharge, and especially to explore the formation mechanism of self-pulse in the hollow cathode discharge, the optical characteristics of the discharge in different modes are studied in air by using a cylindrical hollow cathode. The V-I curve, luminescent image, and the waveform of the self-pulses are measured in different discharge modes. The experimental results show that with the increase of discharge current, the discharge can be divided into Townsend mode, self-pulsing discharge mode, normal glow discharge mode and abnormal glow discharge mode. Although the applied power source is a DC power source, the current and voltage periodically change with time during the self-pulse discharge phase. Results show that the optical characteristics at different modes are different. During the process transiting from the Townsend discharge to the self-pulsing discharge mode and from the self-pulsing mode to the normal glow discharge mode, there is a sudden change in light intensity at the region both in radial center of the hollow cathode and near the axial aperture. The emission spectra at different currents are measured in the range of 200 to 700 nm. The results show that the emission spectra mainly locates at the wavelength range of 330～450 nm, mainly including the second positive band system of nitrogen molecules (C3Πu→B3Πg) and the first negative band system of nitrogen molecular ions (B2Σ+u→X2Σ+g). The intensity of the first negative band system of nitrogen molecular ions is strong. Because the excited energy is high for B2Σ+u, which indicates that the hollow cathode discharge is more likely to obtain highly excited particles and high energy electrons than other types of discharge. In addition, a weak spectral band is located at 650～700 nm, which is the first positive band system of nitrogen molecules (B3Πg→A3Σ+u). On this basis, according to the theory of diatomic spectroscopy emission and the three spectral bands of the second positive band system of nitrogen molecules, the molecular vibrational temperature of nitrogen under different currents is calculated by using the emission spectrum of the second positive band system. The results show that the molecular vibrational temperature is about 3 300 K in the present, and it increases with the increase of discharge current, and there is a sudden increase when the pulse disappears. Since the electron energy and electron density are closely related to the molecular vibrational temperature, the results also indicate that the average electron energy and electron density increase with the increase of the discharge current. When the pulse disappears, the average energy and electron density appear to increase drastically. Finally, the formation mechanism of self-pulse in hollow cathode discharge is discussed. The results show that self-pulse discharge originates from the transition of discharge modes.

To enhance the efficiency of cathode electron injection of organic light-emitting diode (OLEDs), we designed a new hybrid modified layer (HML) with the structure of Bphen: LiF/Al/MoO3, which was applied to ITO/NPB/Alq3/Al. The traditional material LiF is used for the electron injection layer of the reference device. The results show that using HBL at the interface between organic and cathode is very effective. We have measured the electroluminescence (EL) spectra of the device. The EL peak of the device is 534 nm, which indicates that it is from Alq3. It can be seen that the structure design of the multilayer modified layer does not change the luminescence spectrum of the device. Bright green emission can be obtained from the optimized EL devices. Compared with traditional device based on LiF, the performance of the single unit with hybrid modification layer has better luminous properties and efficiency. Present research work shows that when the optimum parameter of the hybrid layer is Bphen∶LiF(5 nm; 6%)/Al(1nm)/MoO3(5 nm), the maximum current efficiency and the maximum power efficiency of the device are 4.28 cd·A-1 and 2.19 lm·W-1, respectively, which are 25.5% and 23.7% higher than those of the reference device. Current-voltage characteristics demonstrate that the hybrid interfacial layer can promote electron injection, thus increasing the current efficiency and reduced their operating voltage slightly of OLED. We systematically analyze the improvement of device performance from two aspects. On the one hand, LiF can fill the electron trap of Bphen to enhance the current injection, moreover, HML can also block hole transport and reduce the hole current. On the other hand, based on charge balance factor theory, HML enhances the injection of electrons and increases the charge balance factor, which improves the balance of carriers in the device. The experimental results show that the cathode hybrid layer can improve the performance of the device.

To improve the selectivity of carbon dots to the photochemical recognition of mercury ions and the feasibility of its detection methods, a new kind of Nitrogen-Sulfur codoped carbon dots(NS-CDs)material with high fluorescence was prepared by thermal decomposition method and using citric acid and sulfanilamide as raw materials. Its structure and optical properties were characterized by infrared spectrometer, UV-Vis absorption spectrometer, transmission electron microscope, elemental analyzer, fluorescence spectrometer, etc. The results showed the quantum dot had graphite like structure, high water solubility and dispersibility, and its average particle size was about 4.78 nm. There are the following absorption peaks in the infrared spectra of NS-CDs: N—C and O—H bond vibrational absorption peaks at 3 446 and 3 261 cm-1, C—H bond vibrational absorption bands at 2 966 and 2 923 cm-1, the CC double bond vibrational absorption peak of the benzene ring skeleton at 1 630 and 1 570 cm-1, shear vibration peak of —CH3 at 1 388 cm-1, the vibrational absorption peaks of C—N, C—S, C—O, C—O—C and —SO-3 bonds at 1 268, 1 192, 1 146 and 1 071 cm-1, characteristic absorption peak of epoxy group at 912 cm-1 and the absorption band of N—H bond deformed vibration absorption band at 739 cm-1. It can be seen that the carbon dot contains not only the skeleton structure of benzene ring, but also the bonding structure in which N, S and other elements participate. An obvious and wide diffraction characteristic peak of (002) crystal plane appeared at 21.4° in NS-CDs. Its lattice distance (0.41 nm) was slightly larger than that of graphite (0.34 nm). The C, N, S and O element content of NS-CDs is 68.72%, 7.37%, 6.24% and 17.67%, respectively, which are consistent with the results of IR analysis. NS-CDs has a strong absorption peak at 309 nm, due to π→π* electron transition of CC bond, and a long tail in the visible region, and an absorption shoulder peak caused by the n→π* electron transition of the CO bond at 335 nm. When the excitation wavelength is less than 390 nm, the fluorescence emission peak of NS-CDs increases gradually with the increase of excitation wavelength, and the fluorescence intensity is the strongest at 390 nm, and weakens with the increase of excitation wavelength when the excitation wavelength is greater than 390 nm. At the same time, it is found that the emission peak gradually shifts red with the increase of excitation wavelength. When NS-CDs solution is diluted gradually, the optimal excitation peak shifts blue from 390 to 360 nm. When pH12.0, the fluorescence intensity of NS-CDs decreases sharply, so PBS buffer solution (pH7) was used to detect metal ions. Of the 16 metal ions, only Hg2+ has an extremely significant effect on the fluorescence intensity of NS-CDs, which completely quenches its fluorescence. Due to the high selectivity of NS-CDs to Hg2+ and the strong fluorescence quenching effect of NS-CDs by Hg2+, a new fluorescent chemical recognition method for Hg2+ by NS-CDs was established. The linear equation of the recognition method is y=5.559 02x-13.860 39, with a linear concentration range of 1×10-3~1×10-9 mol·L-1, R2 of 0.994 7 and the detection limit of 7.11×10-3 nmol·L-1. Its relative standard deviation is less than 9%, and it has high detection precision and recovery rate for practical samples, so it can be used for the detection of Hg2+ in real water samples with a good application prospect in the field of biological and environmental analysis.

Water content of rock is an important index to affect the physical, chemical and mechanical properties of rock. In geotechnical engineering, tunnel engineering and other fields, water content is the key factor to induce disaster and disease. Compared with the traditional method, the determination of rock water content by using the feature of NIR spectrum (NIRS) has obvious advantages of nondestructive and quantitative analysis, and the difficulty and key is the feature selection of NIR spectrum. In order to solve this problem, laboratory experiments were carried out to study the feature selection of near infrared spectra of rock under different water content. The Filter method of feature selection, using the inherent characteristics of the sample data, evaluates the importance of the feature, enhances the correlation between the feature and the class, and reduces the correlation between the features, so it has the advantages of low complexity, being intuitionistic, high efficiency and strong universality and accords with the characteristics of the data studied in this paper. Therefore, this paper selects the Filter type dependency metric for feature selection. In the laboratory experiment, 11 kinds of sandstone samples with different moisture content were prepared, and 44 NIR spectra were collected respectively at 4 test points on the front, behind, left and right sides. Then, the first derivative method was used to preprocess the spectrum. Based on this, the spectral characteristics were analyzed at 1 400 and 1 930 nm, and six initial characteristic variables (the peak area, peak height, width of half height, width of left shoulder, width of right shoulder, the ratio of the width of the left shoulder to the width of the right shoulder )were extracted respectively. Considering the different dimensions and variation range of the six initial characteristic variables, the original data were normalized to eliminate the influence of different dimensions and variation ranges. And then, according to the principle of independent variable selection, redundant variables with strong linear correlation between independent variables were removed. Then, used the statistical correlation coefficient in the dependency metric as the measure of correlation degree, and the correlation among the initial characteristic variables and the correlation between the initial characteristic variables and water content were analyzed. The optimal characteristic variables at two strongly correlated spectral segments were obtained. Finally, multiple regression models were constructed at the strong correlation spectral segments, and the models were tested and analyzed. The results showed that: (1) the characteristics of the near-infrared spectral absorption peaks around the wavelengths of 1 400 and 1 930 nm are significantly correlated with the rock water content; (2) the peak height, right half width and left half width at the wavelength of 1 400 nm have linear correlation with the water content obviously, and the peak height and right half width at the wavelength of 1 930 nm also have linear correlation with the water content obviously; (3) the multiple linear regression model can accurately express the correlation between the water content and the near-infrared spectrum, and the model can be used to predict the water content of water-bearing rock based on the characteristics of near-infrared spectrum. It provides basic modeling data for dynamic monitoring and evaluation of rock water content by using near infrared spectrum analysis technology.

The atmospheric complex pollution becomes more and more serious, which results in atmospheric enhanced oxidation and accelerated transformation of gases into particles. The properties of atmospheric particles are determined by their radius range and number size distribution. A novel spectral method was developed for real-time, online and simultaneous determination concentrations of near-ground atmospheric pollution aerosols basing on differential optical absorption spectroscopy (DOAS), combining double optical path technique. The broadband xenon arc lamp is as light source of double optical path DOAS system. The differential optical absorption spectra are attained accurately using this system. The aerosol extinction coefficients are obtained by removal the contribution of polluted gas and Rayleigh scattering. Based on the kernel function criterion, the aerosol physical properties were retrieved using Mie scattering theory of uniform spherical particles. The aerosol size distribution and number density spectra distribution are retrieved by using aerosol extinction coefficients which have been got by DOAS system. The volume size distribution was retrieved by a step function (histogram). The number size distribution can be retrieved with the relationship the volume size distribution and the number size spectra. The inversion method was applied in the field experiment. The particle extinction coefficient was obtained from 300 to 650 nm. With the extinction coefficient, the number size distribution was retrieved. The particle radius ranges from 0.1 to 1.25 μm. The study can be used to analyze atmospheric particulate mico-physical properties. Inversion method and remote sensing system are used in the field campaign. The research results not only can provide technique support to controll the haze weather, but also study atmospheric heterogeneous gas/particle chemical reaction to provide the raw data. The research will also promote further development and application of the DOAS technique.

Oil spill has become one of the most serious problems in global environmental pollution and brings a serious threat to the marine ecological balance and human health. Therefore, it is of great importance to study efficient oil spill detection methods to protect the marine ecological environment. As three-dimensional fluorescence spectra technology has advantages of getting oil spill fingerprints, it has become an important analytical method in the field of oil spill identification. A satisfactory oil spill identification effect was obtained by combining 3D fluorescence spectra technology with the parallel factor (PARAFAC) analysis algorithm. The applicable concentration range for different oils should be determined before the implementation of PARAFAC algorithm. Besides, PARAFAC is sensitive to number of components. The selection of number of components directly affects qualitative and quantitative analysis results. The method of 3D fluorescence spectra technology combined with PARAFAC is limited in real sea surface oil spill due to above reasons. The composition of oil spill is extremely complex, in which each component not only has a uniform concentration linear range but also is affected by the fluorescence quenching. Due to different content of components, the three-dimensional fluorescence spectra of the oil spill sample (sample is not diluted) are quite different. Some algorithms (such as parallel factor analysis) that resolve the three-dimensional fluorescence spectra are no longer applicable. With the change of the type and content of the sample components, the change rule of the three-dimensional fluorescence spectra image characteristics is also obvious. Therefore, a novel detection method for oil spill based on 3D fluorescence spectra technology and digital image recognition is proposed in this paper. Firstly, three types of mixed oil samples were formulated. Each type of mixed oil was directly mixed with two types of five mineral oils (gasoline, diesel, jet fuel, engine oil, lubricating oil) at different volume ratios. The three-dimensional fluorescence spectral of samples were obtained by FS920 fluorescence spectrometer. The corresponding three-dimensional fluorescence derivative spectral grayscale image was obtained by preprocessing of derivation and graying. Then, the digital image features such as color, texture and shape of three-dimensional fluorescence derivative spectral grayscale image were extracted. Finally, the classification and quantitative models of samples were established by fisher discriminant and stepwise regression respectively. The classification model has good classification and recognition effect on three types of mixed oil samples. The linear correlation coefficient R of the quantitative model is greater than 0.99. The significance test p-value of the quantitative model is less than 0.05. The results show that the digital image characteristics three-dimensional fluorescence spectral can be effectively extracted by our method and used for the qualitative and quantitative analyses of oil samples. The study provides a simple and accurate identification method for sea surface oil spill.

The quantitative analysis of the proportion of mixed source oil is of great significance to the determination of the contribution of oil source in different accumulation periods. In order to establish a fast and effective method to determine the contribution of mixed oil, micro-fluorescence spectroscopy was addressed to quantitatively determine relative contribution. Taking Wangjiagang area in the Dongying Depression as an example, biomarkers, including Gammacerane/C30Hoxane, Ts/(Tm+Ts), Ts/Tm and C2920S/(20S+20R), are used to fine classification of petroleum groups and oil source correlation of Es4 crude oil, and the type and maturity of crude oil are constrained. The mature(from well X1) and low-mature(from well X2) crude oil from the upper Es4 in the Dongying Depression were selected as two typical end members for mixing experiment. On the basis of verifying the reliability of components of the end member, the mass proportions of mixed oil(X1∶X2) are respectively 0∶10, 2∶8, 4∶6, 6∶4, 8∶2 and 10∶0. The relationships of maturity of mixed crude oil, contribution of end member and fluorescence spectrum parameters were analyzed. The results show that the fluorescence spectra of end-component are mainly three-peak type, and the mixed crude oil inherits the spectral spectrum shape features of end-component. The fluorescence color of mixed oil was obviously different. According to analysis of quantitative coefficient of fluorescence color(CIE-X, CIE-Y), the CIE chromaticity diagram shows nearly-linear changeof fluorescence color. With the increase of the amount of ES4 mature crude oil, the aromatic hydrocarbon content in the mixed oil decreased, and the fluorescence intensity also decreased, and the fluorescence color showed a significant blue shift. The fluorescence spectrum parameters(QF-535, fluorescence intensity at 567 nm, ratio of red to green, ratio of yellow to green) and the mixing proportion showed a good linear relationship, which could reflect the maturity of crude oil. As the maturity of mixed oil increases, the content of macromolecular hydrocarbons decreases, so the fluorescence spectrum parameters become small. The mathematical relation established by the mixing experiment can be used to identify the mixture ratio quantitatively, so as to determine the contribution of end member oil. This experiment can prove that these spectral parameters of fluorescence are effective to quantitatively characterize the contribution of end-member in mixed oil.

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants produced in case incomplete combustion of organic materials such as coal, petroleum, wood, tobacco, and other organic polymer compounds. More than 200 PAHs have been discovered to date, and many of them have carcinogenicity. PAHs are widely distributed in the environmentthat we live in. PAHs in water are mainly derived from domestic sewage, industrial drainage and atmospheric deposition. In this paper, three-dimensional fluorescence spectroscopy combined with BP (back propagation) neural network and alternating trilinear decomposition (ATLD) algorithm for qualitative and quantitative analysis of PAHs in water. In this paper, two PAHs, ANA and FLU, were used as analytes, and samples were prepared using methanol (spectral level). The samples were detected using a FS920 steady-state fluorescence spectrometer. The excitation wavelength was set at 200～370 nm, and data were recorded at intervals of 10 nm. The emission wavelength was 240~390 nm, and data were recorded at intervals of 2 nm. Setting the initial emission wavelength always lags the excitation wavelength by 40 nm to eliminate the interference of the first-order Rayleigh scattering. The sample data are then preprocessed using the BP neural network method. The BP neural network is used to compress the measured three-dimensional fluorescence data based on the principle of Error Back Propagation Training (BP). The method has flexible network structure and strong nonlinear mapping ability. The number of neurons in the input layer, the hidden layer, and the output layer can be set according to actual conditions, and the performance is also different when the structure of the network is different. Subsequently, the pre-processed three-dimensional fluorescence spectrum data were decomposed using the ATLD algorithm. Before the decomposition, the nuclear consistent diagnosis method is used to determine the number of components of the sample to be tested is 2. The results show that the excitation and emission spectra of ANA and FLU are very similar to the target spectrum, which can realize the rapid qualitative and quantitative analysis of PAHs (ANA and FLU) with severe spectral overlap. “Mathematical separation” replaces “chemical separation”. The predicted samples are imported into the trained BP neural network, and the network mean square error (MSE) of the sample data to be tested is less than 0.003, and the peak signal-to-noise ratio (PSNR) of the network is greater than 120 dB (typical peak signal in data compression). The noise ratio is between 30 and 40 dB, the higher the better. It can be seen that the BP neural network has better compression effect on the sample data. After BP neural network training, the fitting degree between the output value and the target value is high, and the fitting coefficient is 0.998, which has better data compression effect. Using the ATLD algorithm to decompose the samples to be tested, the average recoveries were 97.1% and 98.9%, and the predicted root mean square errors were 0.081 8 and 0.098 5 μg·L-1. Three-dimensional fluorescence spectroscopy combined with BP neural network and ATLD can achieve a rapid detection of trace amounts of PAHs.

The pollution of Hg(Ⅱ) in water environment is extremely harmful to the ecological environment and human health. The current methods for Hg(Ⅱ) detection mainly include atomic spectrometry, mass spectrometry, electrochemistry and so on. But the traditional detection methods need expensive equipments, complicated operation processes, and complex sample preparation, which limit the applications of these methods for Hg(Ⅱ) detection in real samples. It is still a great challenge to develop a sensitive, rapid, simple and cost-effective method for trace Hg (Ⅱ) detection in aqueous solutions. Test paper method is a rapid detection method which transfers the chemical reactions from the glass instruments to test paper. Based on the chemical reaction between the reagents and the targets, the test paper can qualitatively or semi-quantitatively detect the targets through color changes. Carbon dots (CDs), which are the carbon-based nanomaterials with particle sizes less than 10 nm, have many excellent fluorescence properties including low toxicity and high chemical stability. Inspired by the test paper method, a two-color fluorescent test strip for Hg(Ⅱ) detection in water was constructed based on the fact that the fluorescent of CDs could be effectively quenched by Hg(Ⅱ). The sensor, comprised of nitrogen doped carbon dots (NCDs) and rhodamine B (RhB), exhibited dual color emissions at 437 and 575 nm respectively under a single excitation wavelength of 350 nm. When the detection system is added with different concentrations of Hg(Ⅱ), the photoluminescence of the NCDs can be quenched by Hg(Ⅱ) due to synergetic strong electrostatic interaction and metal-ligand coordination between the surface functional group of NCDs and Hg(Ⅱ), while the fluorescence of RhB remains unchanged, and Hg(Ⅱ) can be quantitatively detected based on the ratios of the dual fluorescence emissions (F440/F580). Under the optimized detection conditions of 1 mmol·L-1 HAc-NaAc buffer solution at pH 7, the ratios of F440/F580 were linearly corresponded to Hg(Ⅱ) at the concentrations ranging from 0 to 3 μmol·L-1 with a linear equation of F440/F580=-0.785 2cHg (Ⅱ)+3.103 8 (r>0.99). The detection limit was 2. 7 nmol·L-1 (n=9) based on three standard deviations. The adding standard recoveries of Hg(Ⅱ) detection in lake water and tap water ranged from 91.9% to 117.9%. A visualized two-color fluorescent test strip was prepared by a simple soaking method of NCDs and RhB under optimal conditions. Upon the addition of different concentrations of Hg(Ⅱ), the color of test paper changed from light purple to orange accordingly under a UV lamp (365 nm), in which each of the detection time took only 3 minutes, and Hg(Ⅱ) could be detected as low as 10 nmol·L-1 by naked eyes. In addition, the detection method presented excellent specificity. Therefore, the established method has the advantages of high sensitivity and accuracy, easy operation and portability, and can be used to rapidly detect Hg(Ⅱ) on-site in water environment.

Silicon nitride ceramics have high temperature, corrosion and wear resistance; therefore, they are good candidates to be used in extreme working environments where metals and polymers are difficult to handle. Unfortunately, besides these excellent properties, these materials are difficult to process. The traditional grinding method is inefficient, and the mechanical damage of the material is serious. In this regard, laser-assisted machining is a new promising way for the efficient processing of silicon nitride ceramics. In this paper, we combined plasma spectroscopy and microscopic imaging methods to measure the damage threshold of pulsed laser irradiated silicon nitride ceramics to analyze the damage mechanism. For this experiment, we selected a hot-pressed sintered silicon nitride ceramic as the target material and built a test system with reference to the ISO21254 international damage threshold tests standard. Silicon nitride ceramics were irradiated bysolid-state Nd3+∶YAG pulsed laser at nanosecond and microsecond pulse duration using 1-on-1 method. The two pulse widths were respectively selected from 10 energy density gradients for laser irradiation, and with each fluence 10 points were irradiated. The spectral information was acquired using a fiber optic spectrometer, and the microscopic image information was acquired by using a metallographic microscope. Under the nanosecond pulse irradiation, damage will occuronce the plasma peak appearing on the spectrum. Analyzing the plasma peak on the spectrum, we could identify whether it contains the characteristic elements of the material to determine the damage. In order to distinguish air ionization breakdown, the interference was eliminated by comparing and analyzing the air plasma spectrum. Under the microsecond pulse irradiation, the microscopic imaging showed that at the beginning of the damage, there was a strong thermal radiation line of the spectrum but no plasma spectrum line. Further increasing the laser fluence, we observed a small amount of plasma peaks appearing on the spectrum. Therefore, the material damage threshold cannot be directly judged upon the plasma peaks. The damage morphology was observed with the metallographic microscope: and obvious ablation impact was visible inside the damage area after the nanosecond pulse irradiation. A large number of plasma lines appearing on the spectrum indicate that in case of the nanosecond pulse irradiation, the damage of the silicon nitride is mainly mechanical caused by plasma shock wave. The microsecond pulses, create hot ablation marks on the edge of the irradiated area with a large amount of molten material in this zone. The spectrum shows obvious thermal radiation features, which indicates that in this case the damage is mainly thermal, caused by the long pulse duration and the corresponding heat accumulation. As the energy density increases, a plasma peak is superimposed on the thermal radiation spectrum. The degree of damage after the appearance of the plasma peak on the spectrum is consistent with the peak intensity of the plasma. The results of plasma spectroscopy and microscopic imaging were compared and analyzed. The measured spectra were fitted with the zero probability damage threshold model. Thefit result showed that the plasma spectroscopy method is more suitable for the damage threshold measurement at nanosecond pulse width, and the corresponding damage threshold is of 0.256 J·cm-2. On the other hand, the microscopic imaging is more suitable for measuring the damage threshold at the microsecond pulse width; the corresponding damage threshold is of 6.84 J·cm-2.

This paper proposes a wavelength selection method based on stability and credibility partial least squares (SCPLS), to solve the problem that the ultraviolet visible (UV-Vis) spectra of multi-metal ion mixture solution were seriously overlapped and difficult to separate. In SCPLS, an exponentially decreasing function (EDF) is applied to select the variables in an iterative manner. In each iteration, a series of models are built with the sub-datasets sampled using the Monte Carlo strategy. Then, the stability and credibility of each variable are calculated, and the variables with high stability and credibility are selected by the EDF. Subsequently, the selected variables are used to construct a new variable subset for the next iteration. After the selection iterations are terminated, the root mean square error of cross validation (RMSECV) of each subset is calculated. The variable subset with the minimum RMSECV value is considered to be the optimal variable subset. The performance of SCPLS is evaluated with UV-Vis Spectral data set of Zn(Ⅱ), Cu(Ⅱ) and Co(Ⅱ) mixture solution and UV-Vis Spectral data set of Zn(Ⅱ) and Co(Ⅱ) mixture solution, and compared with that of full spectrum partial least squares (PLS) modeling and the moving window PLS (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) methods. The results show that SCPLS can not only reduce the complexity of the wavelength selection, but also ensure the stability of the wavelength selection process. And it can select the subset with the minimum RMSECV value. Thus, the RMSECV of Zn(Ⅱ), Cu(Ⅱ) and Co(Ⅱ) models obtained by SCPLS are 60.5%, 40.2% and 31.8% respectively lower than that of full spectrum PLS, and 29.8%, 26.1% and 0.8% respectively lower than that of SCARS. The average relative error of Zn(Ⅱ), Cu(Ⅱ) and Co(Ⅱ) is 2.14%, 1.25% and 0.74% respectively, of which the maximum relative error of Zn(Ⅱ) is 4.67%, the maximum relative error of Cu(Ⅱ) is 3.99%, and the maximum relative error of Co(Ⅱ) is 3.12%. And the RMSECV of Zn(Ⅱ) and Co(Ⅱ) models obtained by SCPLS are 39.4% and 24.9% respectively lower than that of full spectrum PLS, and 35.3% and 13.3% respectively lower than that of SCARS. The average relative error of Zn(Ⅱ) and Co(Ⅱ) are 1.23% and 1.10% respectively, of which the maximum relative error of Zn(Ⅱ) is 4.45% and the maximum relative error of Co(Ⅱ) is 4.57%. The proposed method can efficiently improve modeling accuracy.

The project was started in order to solve the problem of periodic outbreaks of cyanobacteria in Chaohu Lake, and to improve the state of long degradation cycle of low-density polyethylene materials. In this study, the low density polyethylene (LDPE) was taken as matric, and algae powder was taken as bio-material. Maleic anhydride grafted polyethylene (PE-g-MAH) was used as compatibilizer, and polyethylene wax and white oil were used as lubricant. The dosages of compatibilizer and algae powder were selected as the two factors in this paper. Mix the experimental materials in a certain proportion, then prepare the composite particles by double-screw extrusion method, obtain the samples by injection. By using ultraviolet-visible spectroscopy (UV-Vis) and fourier transform infrared spectrometer (FTIR Spectrometer), we can learn the spectral characteristics of materials, and analyze the structural changes of composite materials in the preparation, thus we can determine whether this method is feasible in preparing this composite in advance. Meanwhile, by using mechanical performance testing and scanning electron microscopy (SEM) as auxiliary means, comparing with the results of spectral analysis, we can adequately analyze the effects of algae powder and compatibilizer content on the structure and properties of the composite. Results showed that the absorption peaks appeared at 260nm and 620nm when using ultraviolet-visible absorption spectroscopy to analyze the separated liquid components obtained from the algae. It is proved that the algae contains phycobiliprotein and can be used as the bio-material in the preparation of composite materials. The characteristic peaks of amido bond has appeared respectively at 1 630, 1 540 and 1 440 cm-1 when using fourier transform infrared spectrometer to analyze the substances that participate in the reaction, which was in line with the peak law of amide bond; and the characteristic absorption peaks of O—H appeared near 3 300 cm-1, which can further verify the existence of the active site of algae powder. It can be seen in the infrared spectrum of maleic anhydride, the characteristic peaks of CO group appeared at 1 850 and 1 740 cm-1, and the characteristic peak of stretching vibration of C—O—C in cyclic anhydride appeared around 1 200 cm-1. However, the characteristic peaks of amide bonds, H—O group and maleic anhydride except the characteristic absorption peak of polyethylene were weakened or disappeared when using Fourier transform infrared spectrometer to analyze the composite material obtained by reaction, and it can be speculated that the maleic anhydride has an open-loop esterification reaction with —OH, and the maleic anhydride plays a role in connecting two different reaction systems in the preparation of biological composites. In addition, it can be investigated intuitively by using scanning electron microscopy that the increase of algae powder content will lead to the aggravation of clumping in the composite system, and the addition of compatibilizer enhanced the adhesion of the interface of the composite system. The mechanical property test showed that the mechanical properties decreased with the increase of algae powder content, especially the decrease of impact properties with 54.10%. Results showed that when 15.00% of algae powder was added in the composite, the tensile strength, flexural property and impact property of the composite first increase and then decrease with increasing of compatibilizer content. Therefore, the results of scanning electron microscopy and mechanical properties verify the foresight and correctness of spectral analysis results from the side, avoiding the waste of resources caused by blind experiments and other issue. The optimized conditions for preparation of the bio-composite were determined with 15.00% of algae powder, 3.00% of PE-g-MAH, 3.00% of polyethylene wax and 1.00% of white oil. The mechanical properties of the bio-composite prepared under this condition were 11.70 MPa of tensile strength, 20.00 kJ·m-2 of impact strength, 8.80 MPa of bending strength, and the bending modulus was 220.00 MPa.

Accurately and timely measuring water content of the crude oil is of great significance for water injection strategy adjustment, crude oil exploitation capacity assessment, and oil well development lift prediction. However, at present, most of China’s oil fields have entered the mid- or late- development stage with high water content. And the corresponding water content is difficult to measure accurately. Under this circumstance, this paper carried out research on the measurement of water content of the crude oil using near-infrared spectroscopy. Specifically, commonly employed methods for measuring water content of the crude oil were introduced, and advantages and disadvantages of these methods were analyzed. Theoretically, since the near-infrared absorption band of water is significantly different from the absorption of C—H bond in crude oil, according to Lambert-Beer’s law of absorption and linear law of absorbance, there is a strong response difference in the near-infrared spectrum of high water cut crude oil with different water content. Therefore, we proposed to use near-infrared spectroscopy to accurately measure the crude oil with high water content. And then, by analyzing the measured near-infrared spectrum, non-linear mapping between the water content of the testing crude oil and the near-infrared spectrum can be established. With the obtained non-linear mapping model, water content of the crude oil can be accurately calculated. In order to evaluate the performance of this method, we constructed a hardware platform for collecting near-infrared data. In this platform, Incandescent lamp was employed as a light source, and near-infrared spectrometer (Ocean Optics NIR512) was used to collect near-infrared in range 900～1 700 nm with 512 uniformly divided sub bands. The collected data were stored in the computer using the spectrometer supporting software. With the obtained near-infrared data, the raw data preprocessing was performed to reduce the influence of temperature and high frequency random noise, sample unevenness, baseline drift, light scattering, and et al. In this paper, S-G filtering, or first order derivative, or S-G filtering+first order derivative techniques were employed as the preprocessing method; Successive Projection Algorithm (SPA) was used to reduce the dimension of the raw data; Partial Least Square (PLS) and Multiple Linear regression (MLR) were employed to construct the corresponding non-linear mapping model; Root Mean Square Error (RMSE) and Correlation coefficient (R) were used to evaluate the quantitative measuring performance. Experimental results illustrated that: model constructed using S-G filtering+first order derivative as preprocessing method can achieve the best RMSE (RMSE=0.007 0, r=0.998 3); Model constructed with reduced dimensional data using SPA method is better than the one (RMSE=0.083 3, r=0.920 6) constructed by PLS with full band data and the one (RMSE=0.099 9, r=0.967 1) constructed by MLR with full band. Obviously, although the 31 dimensionality-reduced feature bands obtained by SPA method are only 6.05% of the full band data, the corresponding water content measuring accuracy of the crude oil is very promising. In general, we validate the feasibility of using spectroscopy technique to measure water content of the high water content crude oil, and satisfactory accuracy can be achieved. Therefore, it can be said that this paper provides a new method for water content measurement of high water content crude oil, and provides reference for accurately and timely measuring high water content crude oil using near-infrared spectroscopy.

Marine diesel engine oil in service will be contaminated by water, fuels, coolants, and other degradation products such as oxidation, nitrification and sulfonation, which can also be produced under the influence of high temperature or combustion chamber gas. In serious cases, these contaminants and products can lead to the failure of equipment. Analyzing the used oils can reveal its condition, therefore determine the optimum oil changed period and fault source, and avoid the abnormal wear and corrosion of diesel engine during operation. At present, the traditional methods are as follows that the coulometric Karl Fischer titration be used to measure the water content in oils and gas chromatography to determine the fuel dilution. Due to long time and high cost for oil analysis, these two methods mentioned above are not widely used in monitoring oils. For analyzing the lubricating oil at the molecular level, Fourier infrared (FT-IR) spectroscopy can be used to monitor oil more effectively. Unfortunately, it is not yet widely used at present because of the complexity of the spectrum. The oil samples sum up to 20, which are contaminated by water, fuel, ethylene glycol coolant, high temperature oxidation as well as new oil. Water concentrations in oil are 0.11%, 0.22%, 0.44% and 0.88%. The durations of high temperature for oil oxidation are 299, 323, 371 and 395 h. The fuel dilutions of oil are 1.5%, 3%, 6% and 12%. Ethylene glycol coolant concentrations in oil are 0.1%, 0.2%, 0.4% and 0.8%. The oil samples were analyzed by the Agilent Cary 630 FT-IR with a factory set pathlength of 100 microns and with the spectral range of 4 000～650 cm-1. FT-IR spectra of all oil samples were obtained with FT-IR spectrometer. It is determined that the corresponding characteristic band ranges of water, oxidizing products, fuel dilutions and ethylene glycol coolant respectively are 3 150~3 500, 1 670~1 800, 745~755, 1 030~1 100 cm-1. Monitoring parameters of FT-IR spectra include center point, left boundary, right boundary, left baseline and right baseline. A quantitative analysis model for contaminants in used oil was established. The fitting equations are about the concentrations of contaminants and the peak area of FT-IR spectra. Correlation coefficients between the contaminants of water, fuel dilutions and ethylene glycol coolant and the corresponding peak area of FT-IR spectra are 0.977 9, 1.000 0 and 0.989 5, respectively. The correlation coefficient between oxidation time and the corresponding peak area of FT-IR spectra is 0.999 6. The maximum relative errors between the predicted from the fitting equations and the actual value are 10% for the content of water and glycol more than 0.2%, and are 1% for oxidation time and fuel dilution. By new oil proportional dilution, 3 routine used oil samples were monitored with FT-IR spectrum analysis. The results showed that the water content of one oil sample was 0.38%, which exceeded the standard; the another oil sample was diluted to 19%, which exceeded the standard, and the other oil sample was normal. In the case of oil samples, which exceeded the threshold of the water, the relative error of the FT-IR measurement is 4.6% when compared with the Karl Fischer method. The fuel dilution of the oil sample, which exceeded the requirement of the standard, was verified by the change of viscosity. The FT-IR spectrum analysis and the change of viscosity are consistent in judging whether the used oil is or not to be changed. FT-IR spectroscopy was used to analyze the lubricating oil in use, the peak absorbances was selected, and the area of peak absorbances was calculated. By means of the established fitting formula, the contaminants types and degrees of oil can be monitored quickly and reliably. FT-IR spectroscopy method can meet the engineering requirements of used oil monitoring to a certain extent.

Tea is one of the three most popular drinks in the world. It can not only refresh the mind, but also help digestion and lower blood pressure. With the increasing advance of requirements of tea quality by people, it is necessary to achieve accurate identification of different varieties of tea to prevent the false tea brands and adulteration in the tea market from happening. In order to identify tea varieties quickly and accurately, a tea variety identification system was designed with a combination of Fourier transform near-infrared spectroscopy (FT-NIR) and a novel fuzzy maximum entropy clustering. When traditional fuzzy maximum entropy clustering (FEC) clusters the data with noise, clustering results are often prone to errors, that is to say, FEC is sensitive to noise. To solve this problem, a mixed fuzzy maximum entropy clustering (MFEC) was proposed by introducing possibilistic c-means (PCM)clustering into traditional FEC. MFEC has fuzzy membership and typicality values by iterative computing, and it can cluster FT-NIR data mixed with noise accurately. Firstly, three kinds of Anhui tea samples (i. e. Yuexi Cuilan, Lu’an Guapian and Shiji Maofeng) were prepared for FT-NIR data collection with Antaris Ⅱ spectrometer in the wave number range of 10 000～4 000 cm-1. Secondly, spectral data were preprocessed by multiple scattering correction (MSC), and then the dimensionality of the data was reduced to 10 by principal component analysis (PCA), and then the discriminant information of the data was extracted by linear discriminant analysis (LDA). Finally, MFEC and FEC were applied to perform clustering analysis on the data, respectively, and they were compared in the clustering accuracy and convergence speed. The results of this study indicated that in the condition of m=2, the clustering accuracy rate of MFEC was 100%, while that of FEC was 37.98%. MFEC achieved convergence after four iterations while FEC converged after 100 iterations. Therefore, MFEC could cluster spectral data more efficiently than FEC, and MFEC had the obvious superiority. Three types of Anhui tea samples could be classified correctly and efficiently by combining FT-NIR technology with PCA, LDA and MFEC. This method provided an innovative method and design idea for the identification analysis in the tea testing field, and it has certain theoretical value and good market application prospect.

The mental debris produced by the wear of power-shift steering transmission(PSST), which is uniformly mixed in lubrication oil, leads to the working environment degradation and the PSST failure afterwards. Therefore, it is essential to monitor the PSST degradation degree and formulate the condition-based maintenance(CBM) strategy, which can help improve the reliability and maintainability of the PSST. The oil spectral data contain wear position and wear state information, and its relationship with the PSST life reflects the distribution of the PSST degradation, which makes the oil spectral data-based degradation modeling and maintenance decision become possible. However, the current CBM studies of PSST are implemented by trend analysis of spectral oil data combined with predetermined threshold, without considering the maintenance costs and the equipment availability. In this paper, the CBM decision method of PSST is presented based on spectral oil data. First, considering the relationship between the PSST life and degradation variables and the contribution rate of each degradation variable to PSST degradation, the life model is established based on Weibull proportional hazards regression using the spectral oil data from historical faults. Then, the maintenance decision model of the PSST is further established with the minimum maintenance cost and maximum availability as the maintenance objectives for the training exercise and the execution task, respectively. Compared with the traditional PSST maintenance decision method, the proposed method takes into account the influence of maintenance cost and equipment availability, which provides an objective quantization scheme for CBM decision that can effectively determine the optimal maintenance time of the PSST according to the maintenance objectives. Finally, the effectiveness of the proposed method is verified by a case study using spectral oil datum from historical faults of several Ch series PSST, and the results indicate that the proposed method provides a reasonable formulation of the PSST maintenance strategy. The proposed method also provides a useful reference for other equipment’s maintenance decision.

Rice husk and sawdust are the focus of agricultural and forestry waste treatment and utilization. It has become a hot topic of research to make rice husk and sawdust into biochar and use it in environmental pollution and prevention, but there are little studies of dissolved organic matters in rice husk and sawdust biochar. Rice husk and sawdust biochars were prepared under different temperatures from 200 to 700 ℃. The characteristics of DOM from the biochars were analyzed by UV-Vis spectroscopy, three-dimensional fluorescence spectroscopy and infrared spectroscopy, in order to find the influence of different pyrolysis temperature on biochar DOM. The results showed that DOC concentration in rice husk and sawdust biochar decreased with the increase of pyrolysis temperature, and the DOC concentration in sawdust biochar was much higher than that in rice husk biochar at the same temperature. The UV-Vis spectrum curve of the biochar DOM of rice husk and sawdust gradually decreased with the increase of wavelength, and the absorbance of biochar DOM of rice husk first increased and then decreased with the increase of pyrolysis temperature, while the biochar DOM of sawdust continued to decrease. At the same time, the ultraviolet characteristic parameters (SUVA254 and SUVA260) of DOM from rice husk and sawdust biochar had the same changing trend with the increase of pyrolysis temperature, and the parameters of rice husk biochar DOM were higher than those of sawdust DOM at the same temperature. Three dimensional fluorescence spectra showed that the fluorescence peaks of rice husk and sawdust biochar DOM were mainly in the bands of λex/em=300~315/400~425 and λex/em=210~245/380~435, respectively representing humic and fulvic acid fluorescence peaks, which could be used to represent humic degree and hydrophobic component content of biochar DOM. With the increase of temperature, the humation degree and hydrophobic component content of the biochar DOM of rice husk first increased and then decreased, while the biochar DOM of sawdust gradually decreased. Moreover, the autochthonous index (BIX) of those DOM was not strong, indicating that the bioavailability and protein-like ratio of those DOM were low. The humification index (HIX) of DOM from rice husk biochar increased first and then decreased with the increase of temperature, while that of sawdust decreased gradually. In addition, the infrared spectrum results showed that, with the increase of pyrolysis temperature, the content of —OH in DOM of rice husk and sawdust biochar decreased gradually, the —CH2 and —CH3 did not change significantly, the aromatic ring CC, C—H was enhanced, and the degree of aromatization was enhanced.

Urban surface water is an important part of urban ecological environment. Hyperspectral remote sensing of surface water environment is an important application direction of hyperspectral remote sensing. Water extraction is the first step of hyperspectral remote sensing of surface water environment. Its main task is to obtain the contour of the surface water body from hyperspectral remote sensing data. The water body spectral index makes full use of the spectral information, and the calculation is simple, the implementation is easy, and the extraction effect is excellent. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), hyperspectral difference water index (HDWI) and index of water index (IWI) have been widely used in the extraction of open water bodies such as lakes and large rivers. In recent years, with the development of imaging spectroscopy technology, the acquisition capability of hyperspectral remote sensing data has also advanced rapidly, and spatial resolution and spectral resolution have been continuously improved. The rivers and lakes are basically distributed along the topography in the basin while the urban surface water is generally small, criss-crossed, forming a river network. When hyperspectral remote sensing data are used for urban surface water extraction, the spatial resolution of the image, the type of features and the complexity of the ground objects are very different from those of rivers and lakes. Therefore, the applicability of these commonly used spectral indices in urban surface water extraction needs to be evaluated. This article is based on this starting point and goal, taking the Jiaxing City, Zhejiang Province in China, which is in Jiangnan Water Town and has a dense river network as the research object, and using the high spatial resolution airborne hyperspectral remote sensing data acquired by airborne imaging spectrometer for applications (AISA) as data source. The optimal threshold is determined by Youden index. The overall accuracy, commission error, omission error and Kappa coefficient are used as the accuracy evaluation indicators. The suitability of NDVI, NDWI, HDWI and IWI in urban river network extraction was analyzed and evaluated. The results show that the trend of the shadow spectrum is similar to the water spectrum, and is the main factor causing high commission errors in the water body extraction. All four indices accurately suppress the shadows that fall in the vegetation, but do not effectively suppress the shadows that fall in the buildings. Although HDWI can suppress shadows cast in buildings to a certain extent, it cannot effectively suppress the bright buildings. Through further analysis of the spectrum of different types of water bodies and (the ground objects under) shadows, the water and shadow spectral curves are similar, and there are peaks around 560～600 nm, but the heights of water and shadow peaks are different. The water wave peaks are larger while the peak value of the shadow wave is lower. Therefore, by fully excavating the spectrum reflectance information at 560～600 nm in water bodies and shadows, it is expected to further suppress building shadows and improve the accuracy of water extraction in urban river networks.

Catecholamines (Cas) plays an important role in physiological function of human body as neurotransmitter and hormone. They are organic compounds that contain an amine group and a catechol group that is constituted by a benzene ring with two hydroxyl groups at 3- and 4-positions. In physiological conditions, catecholamine mainly refers to dopamine (DA), norepinephrine (NE) and adrenaline (E). Catecholamines are chemically unstable, prone to spontaneous oxidation and decompose easily when exposed to light or air. Lanthanide sensitized luminescence is a promising tool for clinical analysis and drug analysis. In lanthanide sensitized luminescence, lanthanide ions form complexes with organic compounds, these chelates display a well-defined luminescence characterized, mainly for the determination of organic analytes. Therefore, the key to determining catecholamine by the terbium sensitization luminescence is that the analyte forms an effective and stable complex with the central ion. It is a general understanding that catecholamines form rather stable chelate complexes with metal ions, the two oxygen of the phenolic groups acting as donor atoms. Therefore, the more alkaline the solution, the stronger the complex ability of catecholamine and metal ions. In order to prevent hydroxide formation ethylenediaminetetracetic acid (EDTA) is added into the alkaline solutions, EDTA as synergistic ligand serves to chelate Tb3+ with high affinity and keeps it soluble in water, terbium ion and synergistic ligands and catecholamines form stable ternary complex soluble in water and exhibit strong characteristic fluorescence of terbium. The system with cationic surfactant cetyltrimethylammonium chloride (CTAC) as sensitizer, can make the luminescence for catecholamine chelates increased by a factor of 4 to 6. UV absorption and fluorescent spectra were used to investigate the photophysical properties of the ternary complex and energy transfer mechanism. The study shows that the catecholamine is an effective absorber of ultraviolet radiation, and the possible mechanism of the ligand sensitized fluorescence may be explained based on intramolecular energy transfer. In the energy transfer process, the ligand catecholamine absorbs the radiation energy and transfers the energy to terbium ion through intramolecular energy transfer, thus generating characteristic emission of terbium. The main factors affecting the fluorescence intensity of ternary complex, such as solution acidity, reagent adding concentration and sequence, types of surfactants and interfering substances, etc., were discussed. Under the optimized condition, the luminescence intensity of the system is linearly related to the concentration of the catecholamines. Linearity is observed in the concentration ranges of 0.080～50.0×10-6 mol·L-1 for dopamine, 0.070～50.0×10-6 mol·L-1 for norepinephrine, and 0.070～50.0×10-6 mol·L-1 for epinephrine, with limits of detection as low as 2.4×10-8, 2.2×10-8 and 2.1×10-8 mol·L-1, respectively. The proposed method has been successfully applied to the quantitative determination of the three catecholamines in a pharmaceutical preparation. Due to the advantages of narrow emission bands, large stokes shift and long excited-state lifetimes, it will be possible to investigate this method further for automated analysis, clinical pharmacokinetics study, practical diagnostic for catecholamine-related pathologies, and it can be used in HPLC and CE detectors.

The NDVI (Normalized Difference Vegetation Index) calculated based on the spectral reflectance is proved as one of the important parameters to estimate the chlorophyll content of crops, which indicates the growth condition of crop quickly and nondestructively. Thus, the distribution of NDVI of crops can be studied by the binocular stereo vision system with visible RGB (Red, Green, Blue) and near infrared (NIR) images. And the NDVI distribution and dynamics of crops are monitored through the image analysis at different angles. After the spatial distribution maps of crop vegetation index were established based on the matching of RGB and NIR images, the spatial distribution characteristics and influencing factors were discussed by the visualization of NDVI. The RGB and NIR images of 51 maize plants were collected synchronously by the binocular stereo vision system at 90°, 54°, 35° respectively. The RGB-NIR images were pre-processed by Gauss filtering and Laplace operator enhancement. Firstly, three algorithms, namely, SURF (Speeded-Up Robust Features), SIFT (Scale-invariant Feature Transform) and ORB (Oriented Brief), were studied and discussed for RGB-NIR image matching and alignment. Four evaluation indices wereused to determine the optimal matching methodfor RGB-NIR image matching and alignment, including matching time, PSNR (Peak Signal to NoiseRatio), MI (Mutual Information) and SSIM (Structural Similarity Index). Secondly, the crop and background were segmented by using ExG (Extra Green) algorithm and Maximum Interclass Variance algorithm (OTSU). The R (Red), G (Green), B (Blue) and NIR components of the segmented RGB images were extracted. The influence of illumination was discussed and Spectral reflectance was corrected based on the I component of HSI (Hue-Saturation-Intensity) color model. Then, the NDVI of each pixel in the image was calculated, the spatial distribution map of crop vegetation index was drawn, and the distribution characteristics of NDVI under different shooting angles were compared and analyzed. The NDVI distribution was used to display the chlorophyll distribution of crop plants. The RGB-NIR image matching results showed that the matching time with SIFT (1.865 s)>SURF (1.412 s)>ORB (1.121 s), the matching accuracy with SURF≈SIFT>ORB, and the matching stability with SURF≈SIFT>ORB. According to discussion results, the SURF algorithm was selected as the optimal matching algorithm. In order to eliminate the influence of ambient light, the image reflectance was corrected by 4 gray level standard plates on the basis of discussing the I component and gray histogram of HSI model. The R2 of R, G, B and NIR component correction models were 0.78, 0.76, 0.74 and 0.77 respectively. The vegetation index distributions of leaves and stems of crops were presented from 90 and 35 angles, which could provide new technical support for analyzing and monitoring the nutritional status and distribution of crops.

Hyperspectral images contain a wealth of feature information, and they have been widely used in urban feature classification in recent years. In the process of hyperspectral image classification, the extraction of spatial spectral features directly affects the classification accuracy. Traditional hyperspectral image feature extraction methods only use 4 or 8 neighborhood pixels for simple convolution processing, thus losing a lot of complex and effective information. Convolution neural network (CNN) can automatically extract spatial spectral features and retain the same spatial information of the image, and the network model is simplified. However, with the increase of network depth, the network classification will degenerate, and the network lacks complementarity of relevant information, which will affect the classification accuracy. In this paper, a hyperspectral residual network for feature classification is designed for the degradation problem. Firstly, define the residual network module of the low, medium and high three-layer structure with convolution kernels of 16, 32, and 64. Then, convolve the 3-layer output features with 64 1×1 convolution kernels to complete the dimension matching and feature map. Next, the global average pooling (GAP) of the feature map is generated to generate the feature vector for classification. Finally, the Large-Margin Softmax objective function is introduced to achieve hyperspectral image classification. The experiments were performed using hyperspectral images from the Indian Pines, University of Pavia, and Salinas regions. The primary bands of the hyperspectral image were extracted by PCA. With the sample set of batch training being 100, the initial learning rate being 0.1, the momentum being 0.9, the weight delay being 0.000 1, and the maximum number of training iterations being 2×104, when the sample sizes of the three data sets are set to be 25×25, 23×23 and 27×27, the network depth is 28, 32 and 28, the classification accuracy of the three data sets is the highest, and the average overall accuracy OA is 98.75%, the average accuracy AA is 98.1% and the average Kappa coefficient is 0.98. The experimental results show that the classification method based on residual network can get more affective features. It can improve the classification accuracy with the increase of the number of residual network layers and the fusion of complementary information of different network layer outputs; Large-Margin Softmax achieves intra-class compactness. Separation between classes further improves classification accuracy.

The desertification of Gansu Hexi Corridor is serious, which seriously affects the environment of living and production activities of local residents. Hyperspectral remote sensing technology is an important research method for desertification land degradation degree, land type identification and remote sensing inversion. This paper takes desertification land in Hexi area as the study object. The object was analyzed for the relationship between its spectral characteristics and vegetation degradation degree, plant type, season and soil type, and the spectral characteristics of desertified land in Hexi region were discussed. The main results are as follows: (1) When the vegetation coverage is less than 20%, the vegetation degradation has little effect on the sand spectrum in different degradation stages of the same vegetation type, and the spectral reflectance of the sand is close to the bare land, especially when the vegetation coverage is less than 10%, the spectral curves of sand and bare land almost coincide, and it is difficult to reflect the degree of desertification of the land only from the vegetation landscape. (2) Different vegetation types have a certain influence on the reflectivity of sandy land spectrum. The spectral reflectance of sandy land with Nitraria tangutorum Bobr as constructive species is higher, followed by Haloxylon ammodendron sandy land, and the Tamarix ramosissim sandy land are relatively low. The sandy spectrum of the indicator plants under different succession stages of vegetation can reflect the desertification process of the land. (3) During the plant growing season, the spectral reflectance of sandy land is affected by soil, plant water content and plant phenology, which is higher than other months from August to October and lowest in July. The seasonal variation of the spectral curve of sandy land can reflect the changes in soil water content in sandy land. The research results provide a research basis for the determination of land desertification degree, seasonal information extraction and vegetation coverage estimation in remote sensing monitoring of desertification land.

The variation and the spatial-temporal distribution of soil moisture content have significant effects on heat balance, agricultural moisture, etc. Research on the inversion of soil moisture content using reflectance spectral information can provide a theoretical basis for realizing rapid test of soil moisture content and revealing the spatial-temporal variation of the soil moisture content. In this paper, a semi-empirical model of reflectance spectral of black soil with different moisture content was built to thoroughly explore the relationship between the soil moisture content and the reflectance spectral. First, 12 soil samples with different moisture contents were prepared. Secondly, reflectance spectral of the black soil with different moisture content gradients was measured by ASD Field Spec Pro 3 spectrometer. Then, the soil surface reflection model was built by using the Fresnel reflectivity; In previous studies, the diffuse reflectance in the Kubelka-Munk (KM) model was often considered as a constant for a given material and illumination wavelength or a parameter that needed to be inverted. It has been found through research that diffuse reflectance is related not only to material and wavelength, but also to soil water content. By using the absorption and scattering coefficients which related to the soil moisture content, this model described the relationship between the soil moisture content and the diffuse reflectance. Besides, a model of volume scattering component was built based on KM theory; Furthermore, a semi-empiricalmodel of reflectance spectral of black soil with different moisture content was built. Next, according to the measurement data, the least squares algorithm was used to invert the model parameters, and the model was simplified by analyzing the inversion parameters. Finally, the data of different moisture content gradients that were not used for modeling were substituted into the model to verify the validity of the model. The results showed that compared with the spectral simulation accuracy in the range of 400～2 400 nm under different moisture contents, the root mean square error (RMSE) of reflectance spectra of soil with moisture content of 200 g·kg-1 is 0.008 which is the largest, and the RMSE of reflectance spectra of soil with moisture content of 40 g·kg-1 is 0.000 6 which is the smallest, the mean value of the RMSE of reflectance spectraof soil under different moisture contents is 0.005 1. In the range of 400～2 400 nm, the root mean square error of prediction (RMSEP) of black soil reflectance spectra at different wavelengths is generally less than 0.008. The RMSEP at 1 920 nm band is 0.002 068 which is the smallest. The soil in Changchun was collected to test the reliability of the model, and reflectance spectra of 15 soil samples with different moisture content were measured. Six samples were selected for model validation, and the remaining samples were selected as a calibration dataset for model calibration. The results showed that in 400～2 400 nm band, the RMSEP of reflectance spectra at different wavelengths is generally less than 0.015. The RMSEP at 525 nm band is 0.000 922 5 which is the smallest. In conclusion, the established model has a high prediction accuracy and can be well applied to simulate the reflectance spectra of black soil with different moisture contents.

Pigments are important constituents in tobacco, that relate to appearance and quality of tobacco, meanwhile, the degradation products of pigments have great effects on the quality of aroma of tobacco. According to their color and property, pigments aregenerally divided into three categories: green pigment, yellow pigment and melanin. In growth, chlorophyll is the main green pigment in tobacco, while xanthophyll and carotene are the main yellow pigments in mature tobacco. Melanin consists in the progress of modulation and fermentation when tobacco leaves are ripe. Analysis of pigments in tobacco is important for evaluations of raw tobacco and the tobacco product quality. Traditional analytical methods of pigments in tobacco based on liquid chromatography, normally need long time and complex sample preparation process, while Raman spectroscopic method is simple to operate and takes short determination time, so that it could be able to provide information about molecular functional groups. Accordingly a method of simultaneous detection of lutein and β-carotene in tobacco was developed with Raman spectroscopy in the present work. A solution of organic solvent extraction from a tobacco sample was enclosed in a glass vial and was detected to collect its Raman spectrum by focusing the laser on the solution inside. The excitation light wavelength was optimized and 455 nm was selected, under which high Raman signals were obtained. Other experiment conditions, including extracting solvent of pigments and the distance between the focal plane and the optical platform were optimized. Because the spectra were measured in different dates and the measurement conditions could change, normalization was used to correctphysical interference due to the changes with a selected internal standard peak from the solvent spectrum. To solve problem of spectroscopic interference due to severe overlap between Raman signals of lutein and β-carotene, partial least squares (PLS) was utilized to build multivariate calibration models between Raman intensities and concentrations of pigments. The results showed that the normalization process could help to correct the changes of the measurement conditions, derivative calculations cannot improve the models but the models can be improved significantly by selection of wavelength regions. When spectral regionswere selected between 798.2 and 1 752.8 cm-1 for lutein, regions of 798.2～1 752.8 and 2 254.2～2 784.5 cm-1 for β-carotene, the built PLS models showed the best performances with the prediction errors of RMSEP being 6.68 and 2.56 μg·g-1, respectively. The results indicated that Raman spectra combined with PLS could supply a new way to the quantitative analysis of lutein and β-carotene in tobacco with advantages of easy operation, short time and reliable prediction.

Spectral analysis has been widely used in wood physical feature parameter detection such as wood species, density, strength, surface roughness and humidity. However, the current wood detection is used to predict the single wood parameter. If the multiple wood parameter detections are required, the single wood detection needs to be performed some times. In order to improve the wood parameter detection’s efficacy, we propose a simultaneous prediction scheme for wood species and wood density parameters with only one prediction. First, the K/S algorithm is used to divide the training and prediction sets to make them representative. Then, two dimensionality-reduction methods of principal component analysis and wavelet transform are combined with BP neural network and least squares support vector machine to establish four prediction models that can predict both wood species and density. In experiments, a small fiber spectrometer of USA Ocean Optics USB2000-VIS-NIR is used to acquire the visible/near infrared spectral curves with a spectral interval of 350～1 100 nm. The results show that all four models can achieve simultaneous prediction of wood species and density, and the model established by wavelet transform dimensionality-reduction method combined with least squares support vector machine is relatively better. The correct recognition rate of wood species based on the combination of wavelet transform and partial least squares support vector machine is 100%, the density correlation coefficient of training set is 0.973 4, the density correlation coefficient of prediction set is 0.940 8, the density training root mean square error is 0.026 13, and the prediction root mean square error is 0.038 46. It lays a theoretical foundation for the development of portable real-time on-line detection instruments that can simultaneously predict several parameters of wood physical feature. Moreover, another spectrometer of FLAME-NIR with a spectral interval of 900～1 650 nm is also used to perform the same prediction experiments. By comparisons, we find that the prediction results with the FLAME-NIR model are slightly superior to those with the USB2000-VIS-NIR model. Therefore, our simultaneous prediction of wood species and wood density is practical with a definite stability, accuracy, and a low instrumentation cost.

Anti-acidification microbial consortium (AAMC) is effective in overcoming inhibition of microbial activity by acidification of composting material and avoiding the collapse of food waste composting under the synergistic action of acid-tolerant and acidophilic microorganisms. The degradation of organic substances could be accelerated by inoculation with AAMC. Complete degradation of organic matter and carbon refixation (the formation of humic substances) occur simultaneously during composting. Organic matter degradation has an interactive relationship with the formation of humic substances. The former provides raw materials for the latter. In order to explore the effect of inoculating anti-acidification microbial consortium (AAMC) on the quality of humic substances of food waste compost, the humic substances were grouped by resin column method and the influence of inoculating AAMC on molecular structure complexity and stability of the three fractions, such as fulvic acid, hydrophilic fraction and humic acid. In this study, Inoculation group (AAMC, Inoculation), alkaline compound treatment group (MgO and K2HPO4, AC) and natural composting group (C) were set. Three-dimensional fluorescence technology (EEM) was used to realize accurate and complete quantitative characterization of spectral properties of three humic fractions combined with two quantitative characterization methods FRI and PARAFAC. FRI results showed that the Pi, n values of those regions, which represented simple molecular structure components, such as carboxyl and protein source structure decreased in all three humic fractions after composting. The degree of reduction in the inoculation group was significantly greater than that in the control group and degree of reduction was in the order Inoculation>AC>C. The Pi, n values of those regions, which represented humic acid-like compounds with high aromaticity and polycondensation degree increased in all three humic fractions. The degree of increase in the inoculation group was significantly greater than that in the other two treatments and it was also in the order Inoculation>AC>C. PARAFAC results showed that the fulvic and humic acid fractions could be divided into short-wavelength, long-wavelength humic acids and protein-like substances such as tryptophan. The hydrophilic fraction could be further divided into short-wavelength humic acid, tryptophan and tyrosine components. At the end of composting, the Fmax of those components which were attributable to short-wavelength and long-wavelength humic acids increased, while the Fmax of those components which were attributable to protein-like substances such as tryptophan decreased. The increased (decreased) levels were the highest in the inoculation group and they were markedly higher in the inoculation group than those in AC and C groups. In summary, the results exhibited that inoculating of AAMC could significantly promote the complexity and stabilization of molecular structure, improve the aromaticity and polycondensation degree of humic fractions and the quality of humic substances in food waste compost, and facilitate conservation of water and fertilizer after compost was applied to the soil. AAMC has high ability of degrading and transforming small molecular organic acids, which could overcome low humification efficiency problem caused by the inhibition of acid accumulation on composting microbial activity. These might be closely related to high humification degree of inoculation group. Alkaline materials as additives could also promote the stabilization and structural complexity of humic fractions and increase the humification degree of compost to a certain extent. This may be related to the pH improvement of composting material, which enables the continuous degradation and transformation of small molecular organic acids and facilitates the humification process of compost.

Fusarium head blight (FHB) is a major disease of wheat, which can lead to wheat yield reduction or even crop failure, seriously affecting the quality of wheat seeds. Mycotoxins secreted by the diseased wheat are deposited in the food chain, and ultimately endanger human health. Therefore, recognition of wheat scab is very important. First of all, chromatography and enzyme-linked immunosorbent assay (ELISA) are widely used to detect scab. These methods were expensive, slow and have low accuracy. In recent years, hyperspectral imaging technology has been widely used in crop identification and detection, but in the application of wheat scab detection, sampling detection method is mostly used. After image acquisition, the region of interest (ROI) is manually selected through ENVI software. Preliminary preparations are complicated and easy to be missed detection. The undetected wheat grains quickly infect the surrounding grains during storage and transportation, which is difficult to ensure the safety and health of wheat. In view of this, this paper presents a fast visual recognition algorithm for wheat scab samples based on hyperspectral imagery and machine learning to reduce the rate of missed detection and improve the detection efficiency. The hyperspectral images of healthy wheat and infected wheat in 469~1 082 nm band were collected, and the mask image information of wheat samples was accurately obtained by histogram linear stretching combined with image segmentation. Savitzky-Golay smoothing denoising method and standard normal variable transformation (SNV) method are used for data preprocessing. Principal component analysis (PCA) and successive projections algorithm (SPA) were used to extract features, and 4 and 8 feature variables were selected respectively. There were 400 healthy wheat samples and 400 infected wheat samples collected in the mask image position, 75% of which were used for modeling set and 25% for testing set. Ten fold cross validation method combined with linear discriminant analysis (LDA), K-nearest neighbor algorithm (KNN) and support vector machine (SVM) was used to establish the classification model. The accuracy of the test set is over 90%, and the SPA dimension reduction model is better than the PCA dimensionality reduction model. Then, the effects of GRID, particle swarm optimization and GA three kernel parameter optimization methods on SVM model are compared. Among them, SG-SPA-SVM (PSO) model has the best classification effect. The accuracy of modeling set is 95.5%, REMS is 0. 2121, the accuracy of test set is 98%, and REMS is 0.141 4. Based on the prediction of sample points, the spectral curves of all wheat samples obtained by the mask were predicted and the recognition results were fed back to the mask and displayed in pseudo-color to realize the visual identification of infected grains. The results show that the classification model based on hyperspectral imaging technology combined with SG-SPA-SVM (PSO) algorithm can effectively, quickly, accurately, nondestructively and visually identify wheat scab, providing an algorithm basis for the development of automatic identification equipment for wheat scab.

In order to classify and set different prices on the basis of soluble solid content (SSC) of korla pears and promote the development of post-harvest processing healthily in standardization and industrialization, a fast, precise and nondestructive method to detect soluble solid content of korla pears was determined by applying hyperspectral reflectance imaging technology. 157 korla pears freshly and with no surface damage were collected as samples. Hyperspectral images with a spectral range of 400~1000 nm of pears were acquired by hyperspectral imaging system. Then the region of interest (ROI) function of ENVI 5.3 software was used to conduct spectral data extraction from each hyperspectral image of pear. Totally, 157 pear samples were divided into calibration set (105) and prediction set (52) based on the Kennard-Stone(KS)sample set partitioning method. The research compared the influence of accuracy of modeling in terms of the spectrum pretreatment methods of original spectrum, standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD) and second derivative (SD). The SNV was applied for smoothing and denoising of the original hyperspectral data. A variable selection method combining competitive adaptive reweighted sampling and mean impact value (CARS-MIV) was utilized to extract the characteristic variables from full spectrum (FS). The modeled samples of competitive adaptive reweighted sampling (CARS) are generated by random selection of Monte Carlo sampling, and the regression coefficients of variables will change accordingly. The absolute value of regression coefficients cannot fully reflect the importance of variables, and affect the accuracy of the model. To lower the impact, the mean impact value (MIV) algorithm is applied to select the independent variables for secondary screening, and the variables with bigger correlation are selected for modeling and analysis. In this paper, the variables selected by CARS, successive projection algorithm (SPA) and Monte-Carlo uninformative variable elimination (MCUVE) were used for comparison. Finally, the spectral information selected from full wavelength and the spectral information selected from four characteristic wavelength selection method were taken as input vector to build support vector regression(SVR)model to predict soluble solid content of korla pears. The performances of the models were evaluated by the root of mean square of calibration (RMSEC), the root of mean square of prediction (RMSEP), the correlation coefficient of calibration (Rc) and the correlation coefficient of prediction (Rp). By means of comparison, the CARS-MIV-SVR models achieved the optimal performance with the Rc reaching 0.985 94 and Rp up to 0.946 31. The RMSEC and RMSEP are 0.185 85 and 0.403 33 respectively. These experimental results demonstrated that CSRS-MIV method can efficiently improve the stability and accuracy of wavelength selection, and optimize the precision of prediction model. The hyperspectral technique combined with CARS-MIV-SVR model can meet the needs of determination of soluble solid content and be used to classify and set different prices on the basis of SSC of korla pears.

Lodging stress is one of the main disasters in maize production, which seriously affects the yield and quality of maize and mechanical harvesting ability. It is the basis of remote sensing monitoring of maize large-scale lodging disasters to analyze the changes of maize canopy structure and spectral response characteristics under different lodging stress. Stem lodging, stem fold and root lodging were set up in the tasseling stage and the middle filling period. Based on the field continuous observation experiments, the effects of growth stages and lodging types on dynamic changes of canopy structure and self-recovery ability of maize were analyzed. Hyperspectral data of lodging maize canopy were processed by the traditional spectral transformation and continuous wavelet transform. Leaf area density (LAD) was taken as the index of the lodging maize canopy structure characteristics. The best sensitive bands and wavelet coefficients of leaf area density were selected. The hyperspectral response model of leaf area density was constructed based on random forest method, andthe accuracy of the model was verified by the measured samples which were not involvedin modeling. Focus on the influence of wavelet decomposition scale and spectral resolution on LAD spectral response ability. The results showed that leaf area density, as a canopy structure indicator of total leaf area per unit volume, had a good response relationship with lodging stress intensity. The LAD of lodging maize in the filling stage was generally higher than that in the tasseling stage. The LAD of the tasseling stage was as follows: stem fold>root lodging>stem lodging>no lodging. The LAD of the filling stage was as follows: root lodging>stem fold>stem lodging>no lodging. After continuous wavelet transform, the response ability of maize lodging canopy spectrum to leaf area density is generally better than that of traditional spectral transform. The response ability of lodging maize canopy spectrum to leaf area density after continuous wavelet transform is generally better than that of traditional spectral transform. As the wavelet decomposition scale increases, the correlation between LAD and sensitive bands is stronger, and the correlation coefficient of 10 scale is the highest, reaching 0.74. The accuracy of the model constructed by continuous wavelet transform is generally better than that of traditional spectral transform. The model constructed by the original spectral wavelet transform has the highest precision, the R2 of test sample is 0.811, and the RMSE is 1.763. It showed that continuous wavelet transform technology can highlight and utilize the subtle information in the canopy spectra. Therefore, leaf area density can effectively quantify the variation characteristics ofmaize canopy structure under different lodging stress. Continuous wavelet transform can effectively improve the response of the canopy spectrum to the structural parameters of the lodging maize. The model of lodging maize leaf area density based on random forest method has high accuracy and stability, which can provide prior knowledge for remote sensing monitoring of summer maize lodging disaster at regional scale.

To find a fast, accurate, and effective method for the identification of honey adulteration, near-infrared spectroscopy combined with chemometrics was used to analyze natural honey and adulterated honey in this paper. First, 224 samples were collected for the study, including 112 natural pure honey samples from 20 common honeys in China, and 112 adulterated honey sample were prepared with 6 different syrup samples according to different syrup contents(10%, 20%, 30%, 40%, 50%, or 60%). Near infrared spectral data (wavelength range of 400～2 500 nm) of all samples were obtained by near infrared light instrument scanning. Then, first derivative (FD), second derivative (SD), multiple scattering correction (MSC), and standard normal variation (SNVT) pre-processing of the original spectra combined with PLS-DA (linear algorithm) and SVM (non-linear algorithm) modeling, respectively, were adopted to establish a differential model of natural honey and syrup-adulterated honey and compare the effects of different pretreatment methods on the honey adulteration identification model established by the two different modeling algorithms. The penalty parameter c and the kernel function parameter g of the SVM algorithm were optimized by three optimization algorithms: grid search, genetic algorithm, and particle swarm optimization. The analysis results showed that the PLS-DA model established by the FD preprocessing had the best effect, and the accuracy of the best PLS-DA model was 87.50%. After MSC pre-processing, the SVM model with the penalty parameter c of 3.031 4 and the kernel function parameter g of 0.329 8 was the best. The accuracy of the best SVM model was 94.64%. It can be seen that the non-linear SVM algorithm combined with the NIR spectral data natural honey and syrup-adulterated honey identification model is better than the PLS-DA model.

With the further development of terahertz technology, terahertz has shown its unique advantages in food safety detection. Flour (wheat flour) is the staple food in most areas of northern China. Besides, benzoic acid(BA), as the important preservative of acid food, is often added to extend the preservation time of food. However, the excessive use of food additives would cause serious damage to human health. This paper explores the feasibility of detecting food additives through terahertz technology and conducts empirical study on benzoic acid in flour by terahertz time-domain spectroscopy (THz-TDS) technology. The terahertz time-domain and frequency domain spectrum of the mixed samples (flour and benzoic acid) were obtained. As shown by absorption coefficients, benzoic acid presented obvious absorption peak at 1.94 THz. Meanwhile, the absorption coefficient of flour increased at a certain slope, which indicated that the characteristic identification of benzoic acid in flour could be carried out by terahertz technology. In order to establish the quantitative detection model of benzoic acid additive in flour, terahertz time-domain spectra of benzoic acid doped with different percentages (mass fraction) in flour were collected, and the absorption coefficient spectrum was obtained through calculation. It was found that the absorption peak amplitude enjoys positive correlation with benzoic acid content. As for the detection method, firstly, explore the effects of different spectral pretreatment methods on THz spectroscopy, and then adopt methods like Smoothing, Multiple Scatter Correction (MSC), Baseline and Normalization to carry out correct processing. After correction, PLS model was established to select the optimal pretreatment method. Secondly, establish PLS and LS-SVM regression models for the determination of benzoic acid content in flour. The experimental results verify that PLS model established after normalization was more optimal, with correlation coefficient of prediction (rp) of 0.979 and root mean square error of prediction (RMSEP) of 1.30%. By comparison, it was proved that the most optimal quantitative determination model of benzoic acid content in flour is LS-SVM model with correlation coefficient of prediction (rp) of 0.987 and root mean square error of prediction (RMSEP) of 1.10% after the normalization of terahertz absorption coefficient. MLR model was established by only two bands of 1.946 and 1.869 THz with correlation coefficient of prediction (rp) of 0.955 and root mean square error of prediction (RMSEP) of 1.90%. It is concluded that a new solution for the nondestructive detection of benzoic acid additives in flour was developed, and method guidance was provided for the detection of other types of additives, all of which have an important significance for the healthy development of flour industry.

The hyperspectral imaging technology was used to detect the frozen and mechanical damaged potatoes. The Zolix’s Image~λ “spectrum” series of hyperspectral imaging device was employed to obtain the intact, frozen and mechanical damaged potato hyperspectral data within band range of 387～1 035 nm; Secondly, the 60×60 pixel sizes of region of interest in the intact, frozen and mechanically damaged potato hyperspectral image was cropped to calculate the average reflectance values; The reflectance spectral curves of frozen potato samples had the obvious absorption peaks near the visible wavelengths of 440, 560 and 680 nm; The reflectance spectral curves ofmechanical damaged potato samples had the obvious absorption peaks near the visible wavelengths of 560 and 680 nm, and the absorption peaks and valleys near the visible wavelength of 680 nm were significantly lower than the frozen potato samples; The reflectance spectral curves of intact potato samples were relatively smooth, and there were no obvious absorption peaks appearing near the visible wavelengths of 560 and 680 nm; There were three absorption peaks near the visible wavelengths of 440, 560 and 680 nm in the bruised samples, and there was a significant reflectance peak near the visible wavelength of 410 nm. Four categories of potato samples demonstrated the different fingerprint characteristics in the reflectance spectral curves, which could be further used for the aim of potato quality discrimination. The instrument, detection environment, illumination intensity and other factors would add the noise variables to the obtained raw spectral data, so thirdly, the chemometric pretreatment methods were employed to eliminate the influence of noise in the raw spectral curves. There were 70 percent of the four kinds of potato samples randomly selected as the training dataset and the remaining 30 percent as test dataset; Fourthly, the method of local outlier factor (LOF) was used to identify the neighborhood point density of the spatial region of the collected potato spectral curves in order to find the abnormal non-nearest neighbor sample distribution to eliminate the abnormal samples; Fifthly, three types of boosting algorithms of extreme gradient boosting (XGBoost), categorical boost (CatBoost) andlight gradient boosting machine (LightGBM) were used to extract the effective characteristic spectral bands from the potato hyperspectral curves, so that the dimensions of massive hyperspectral data for the subsequent classification modeling were reduced; Finally, the characteristic wavelengths of extracted effective spectral data were used to construct the discriminant model of potato quality. The established classification model by using the LightGBM+Logistic regression reached the highest discriminant accuracy of 98.86%. Our study provided the theoretical basis and technical support for effectively monitoring potato quality in the process of modern agricultural production.

Effects of adding lignite on the spectral properties of dissolved organic matter (DOM) in sheep manure organic fertilizer were obtained, which provided a basis for the evaluation of the maturity of high humic acid organic fertilizer. Inner Mongolia lignite and sheep manure were used as raw materials, 10% lignite was added to ferment organic fertilizer of sheep manure, and the DOM extracted from organic fertilizer samples at different stages was analyzed by UV-visible spectrum analysis, fluorescence spectrum analysis and Fourier Transform infrared spectroscopy. Ultraviolet-visible absorption spectroscopy analysis showed that the E465/E665 value of organic fertilizer samples decreased first and then increased with the fermentation of organic fertilizer. From 9.110 4 at the beginning of organic fertilizer to a minimum of 4.647 7, and then to 5.390 1 at the end of organic fertilizer, A1 showed a decrease and then increased, and both A2 and A3 showed a trend of increasing first and then decreasing, and the peak appeared at 12 d. Synchronous fluorescence spectroscopy showed that the ratio of fluorescence peak intensity (I470/I435) increased from 0.452 8 starting from organic fertilizer to 0.655 2 at the end of organic fertilizer, and AHLR/AFLR showed an upward trend, rising from 0.673 9 for organic fertilizer to 1.040 8 for organic fertilizer. After 18 d of organic fertilizer fermentation, the fluorescence intensity of the organic fertilizer after fermentation was lower than that of the organic fertilizer at the beginning, and the relative fluorescence intensity of the 10% lignite-added sheep manure organic fertilizer was significantly higher than that of the un-added lignite. Fourier transform infrared spectroscopy showed that carbohydrates and proteins in DOM were gradually decomposed during the fermentation of organic fertilizer for 18 d, while the content of carboxyl groups and benzene ring-containing substances increased significantly. With the progress of fermentation, the non-humus substances in DOM are converted into humus-like substances, the degree of polymerization or aggregation of unsaturated structures becomes larger, and the stability increases. The addition of lignite can effectively promote the decomposition of lignin substances, improve the degree of aromatization of DOM and accelerate the maturity of organic fertilizer.

The paper reported the comparison of recognition for tomato surface color and internal color by spatially resolved and conventional single point visible and near infrared (SP Vis/NIR) spectroscopy. Spatially resolved (SR) spectra and SP Vis/NIR spectra were acquired using the newly spatially resolved spectroscopy system (wavelength: 550~1 650 nm), the portable Vis/NIR spectrometer (wavelength: 400~1 100 nm) and the portable NIR spectrometer (wavelength: 900~1 700 nm), for 600 “Sun Bright” tomatoes with six color stages (green, breaker, turning, pink, light red and red), based on their surface and internal color distribution, respectively. Partial least squares discriminant analysis (PLSDA) models for SR spectra and SP Vis/NIR spectra were developed and compared. The results showed combination of the SR spectra could further improve the classification of tomato color based on optimal single SR spectra, with classification accuracy for surface and internal color of 98.8% and 84.6%, respectively. The SR spectra with short source-detector distance were useful for recognition of tomato surface color, while SR spectra with large source-detector distance could better assess tomato internal color. The NIR spectra were comparable with SR spectra for tomato surface recognition with classification accuracy of 95%, however, SP Vis/NIR spectra could not evaluate tomato internal color accurately, and the classification accuracy was much lower than that of SR spectra, which indicated that SR spectra have great potential for the recognition of tomato color.

Powdery mildew, as a kind of cucumber disease with high outbreak frequency, spreads very fast, often leads to yield reduction and can’t achieve the expected economic benefits. Especially in serious years of disease outbreak, the reduction of cucumber in some areas was as high as 20%. This paper proposed a subinterval interval partial least squares regression (SI-PLSR) based on visible spectrum image for cucumber powdery mildew non-destructive detection. We usedCanon EOS 800D and Ocean Optics USB2000+ optical fiber spectrometer to collect visible spectral images and reflectivity curves of 200 cucumber powdery mildew leaves. Firstly, we used wavelet transform and watershed algorithm to extract the target leaves from the real-timevisible spectral images of cucumber powdery mildew leaves. Secondly, The Otsu algorithm optimized by Gauss fitting was used to segment the powdery mildew lesion. Thirdly, we established the PLSR in 350～1 100 nm band and calculated the cross validation root-mean-square error (RMSECV). At the other hand, 350~1 100 nm was divided into 20 sub-intervals, and established the PLSRindependently. The sub-intervals of RMSECV smaller than the full band were selected to form the joint interval. Finally, the SI-PLSR model was established based on powdery mildew lesions images and joint interval. Results show that 188 target leaves were extracted from 200 susceptible leaves visible spectral images successfully of which 157 were more than 95% and 31 were between 90% and 95%. The success rate was 94.00%. The average misclassification rate of powdery mildew was 5.81%. The average false negative was 1.55% and the average false positive was 4.26%. PLSR was established for 20 sub-intervals, and the results showed that the RMSECV values of the 5, 6, 7, 11, 12, 13 and 19 sub-intervals were lower than those of the full-band modeling, indicating that the spectral information of these seven sub-intervals contributed greatly to the identification of powdery mildew, which was relative to the wavebands of 470～520, 530～580 and 700～780 nm showing peaks. Therefore, these 7 sub intervals should be selected to establish the joint interval. The principal component number of SI-PLSR model was 7. RC, RV and RMSEC, RMSEV were 0.975 2, 0.907 3 and 0.919 5, 1.091. Compared with the full band PLSR model, the RC and RV of SI-PLSR was closer to 1, and the RMSEC and RMSEV were smaller. The above results showed that the SI-PLSR model proposed in this paper which effectively removed redundant information in visible spectral data and enhanced the stability of the model can be used to identify cucumber powdery mildew quickly and accurately, providing a method and reference for the diagnosis of cucumber diseases.

Nitrogen is an important element affecting the growth of winterwheat. The real-time application of nitrogen fertilizer based on the demand of winterwheat is one of the key problems to be solved in modern agricultural precision fertilization. Unmanned Aerial Vehicles (UAV) remote sensing technology has the advantages of high resolution, high timeliness and low cost in the monitoring of winterwheat growth, which provides an important data source for solving the problem ofwinter wheat fertilizer demand monitoring. Therefore, studying the multi-spectral image data of UAV and constructing its relationship model with winter wheat yield and fertilization is very important for precision fertilization research. This study carried out field trials with four different kinds of nitrogen levels in a typical production area of winter wheat in Huantai, Shandong. The multispectral images of winter wheat at the returning green stage were collected from experimental area with different nitrogen fertilization levels using Sequoia multispectral sensor equipped with UAV. Meanwhile, winter wheat canopySoil and Plant Analyzer Development (SPAD) and yield were measured. Six vegetation index such as NDVI, SAVI and MCARI2 were obtained after calculation, and established UAV multispectral images vegetation indexes and the winter wheat canopy SPAD of linear function, quadratic polynomial function, logarithm function, exponential function and power function, to screen out the sensitivity index of winter wheat canopy reflecting different nitrogen levels. Further, according to the relationships of different nitrogen fertilization levels with sensitive vegetation indexes and winter wheat yield, a variable nitrogen fertilization model based on vegetation indexes was constructed and applied to simultaneous images. The results are as follows: (1) SPAD could reflect the nitrogen fertilization level and growth of winter wheat, and the canopy reflectance of winter wheat with different nitrogen fertilization levels varied greatly. (2)The structural vegetationindex and SPAD fit better than other types of index. and the optimal vegetation index of the estimation model established based on SPAD was MCARI2 (R2=0.790, RMSE=0.22), which was considered as the sensitive vegetation index of nitrogen fertilizer. (3) Based on the yield-nitrogen fertilizer model and yield-sensitive vegetation index model, the variable rate fertilization model of nitrogen fertilizer was Nr=10 707.63×MCARI22-5 992.36×MCARI2+715.27. Based on the model, a variable nitrogen fertilization map for winter wheat was produced in the experimental area, which was highly consistent with actual fertilization. In this study, the model and method of nitrogen fertilization for winter wheat based on UAV multispectral data was proposed, which provides areference for the precise fertilization of winter wheat.

As near infrared spectroscopy (NIR) has many advantages, such as high efficiency, being non-destructive and environment-friendly and on-site detection, it is especially suitable for rapid modeling and analysis of drugs. However, there are some shortcomings such as weak absorption intensity and overlapping bands. It is necessary to establish a robust and reliable chemometrics model to analyze NIR. Deep convolution neural network (DCNN) is an important branch of deep learning method, which extracts data features layer by layer, combines and transforms them to form higher-level semantic features. It is widely used in computer vision, speech recognition and other fields, and has achieved great success, but has not been reported in drug NIR analysis yet. Based on the deep convolution network model, this paper studies the multi-class modeling of drug NIR. According to the characteristics of drug NIR data, several one-dimensional deep convolution network models for multi-class and multi-manufacturer drug NIR classification are designed. The overlapping arrangement of convolution layer and pool layer in the model is employed to extract NIR data features layer by layer, and the output layer is connected with the softmax classifier to predict the classification probability of NIR data. Before the output layer, the global maximum pooling layer is used to solve the problem of restricting the size of input dimension and too many parameters in the full connection layer. At the same time, batch normalization and dropout are introduced in the network model to prevent the gradient vanishing and reduce the risk of network overfitting. The impact on the modeling effect with different convolutional network layers and different convolution kernel sizes is analyzed. At the same time, the influence of five classical data preprocessing methods is explored. Taking NIR samples of cefixime and phenytoin tablets as experimental datasets, a multi-class and multi-manufacturer classification model of drugs is established. The model achieved good classification results in the experiments of binary-classification and multi-classification. In eighteen classification experiments, when the ratio between training set and test set was 7∶3, the classification accuracy was 99.37±0.45, which achieved better classification performance than SVM, BP, AE and ELM. At the same time, inference speed of deep convolution neural network was faster than SVM and ELM, but training speed was slower than both. A large number of experimental results showed that the deep convolutional neural network can accurately and reliably distinguish the NIR data of multi-class and multi-manufacturer drugs, with good robustness and scalability. The proposed method can also be extended to the application of NIR data classification in tobacco, petrochemical and other fields.

The binding of BDE47 to lysozyme was investigated by molecular modeling combined with three-dimensional (3D) fluorescence, circular dichroism (CD) techniques, fluorescence spectroscopy, and time-resolved fluorescence decay under simulative physiological conditions. The results indicated that the quenching reaction of BDE47 to lysozyme was observed, and the quenching mechanism was suggested as static quenching. Molecular docking showed that the amino acid residues, TRP62, TRP63, ARG61, ASN59, ALA107, ILE98 in lysozyme have interactions with BDE47. The hydrogen bonds were formed between the O atom of BDE47 and TRP62 with the distances of 2.2 . The 3D fluorescence experiments showed that the fluorescence intensity of lysozyme gradually decreased in the presence of BDE47 and a red shifted was observed, suggesting that the microenvironment around the TRP-residues of lysozyme has changed during the binding process. Furthermore, CD spectra implied that the interaction of BDE47 with lysozyme induced conformational change of lysozyme, and the content of α-helix structures in lysozyme decreased. The binding distance r between the donor (lysozyme) and acceptor (BDE47) calculated using Frster’s nonradiative energy transfer theory was 3.31 nm, indicating a high probability of energy transfer from lysozyme to BDE47. The thermodynamic parameters at different temperatures indicated that the hydrogen bonds and van der Waals forces played a predominant role in the spontaneous binding process. The results were consistent with the molecular docking and binding free energy analysis.

Escherichia coli (E. coli) is an important indicator bacteria in food and environmental monitoring, therefore, monitoring the number and sterilization effect of E. coli has attracted extensive attention. Based on many advantages of fluorescence spectroscopy, such as high sensitivity, high speed, strong stability and so on, the relationship between the concentration of E. coli and the intensity of E. coli emission peak is studied, and a method for monitoring the concentration of E. coli conveniently, and rapidly at low concentration is given. Namely, the emission spectrum of E. coli can be got by irradiating E. coli solution with 289 nm excitation light, then the fluorescence emission characteristics of E. coli solution with different concentrations are given, and the relationship between the intensity of characteristic peak of E. coli and the concentration of E. coli is analyzed. Besides, the effect of silver nanoparticles on the fluorescence emission of E. coli is studied by fluorescence spectroscopy, and the sterilization effect of silver nanoparticles on E. coli is analyzed. The results show that: (1) E. coli has obvious fluorescence characteristic peaks at 332 and 425 nm respectively, when the excitation light at 289nm irradiates the aqueous solution of E. coli. The intensity of fluorescence peak decreases with the decreasing of E. coli concentration. And when the concentration of E. coli is less than 20%, there is a linear relationship between the concentration of E. coli and the intensity of characteristic peak at 332 and 425 nm. (2) When silver nanoparticles are added to the solution of E. coli, within 4 hours, the longer the existence time of silver nanoparticles is, the weaker the fluorescence characteristic peak of E. coli is, which means that sterilizing rate increases with the increasing of the time. Increasing the amount of silver nanoparticles or increasing the ambient temperature, the sterilizing rate of E. coli can be improved. The results of this paper are useful for the enumeration and sterilization study of E. coli in food and environment.

Combined with chemometrics methods, rapid quantitative analysis of the quality control components of Trollius ledebouri was developed by modern infrared spectroscopy. The reference data of the representative components, orientin and vitexin, were obtained by HPLC method, and the infrared spectra were obtained by Fourier transform infrared spectroscopy. On this basis, the index components were correlated with the infrared spectra data by chemometrics methods, and the fast prediction models of the index components were established. Thirty-six extracts of Trollius ledebouri were obtained by room temperature extraction, heating reflux and ultrasound-assisted extraction with different ratios of methanol-water as solvent. The contents of orientin and vitexin in the extracts of Trollius ledebouri were determined by HPLC method, and the infrared spectra of the samples were measured by Fourier transform infrared spectroscopy (FTIR) aided by horizontal attenuated total reflectance (HATR) accessory. TQ Analyst EZ Edition software was used to establish the models, in which 29 extract samples were as test set and the rest as calibration set. Cross validation correlation coefficient (R2) and cross validation error mean square root (RMSEC) were used as indexes to select spectral pretreatment method, quantitative analysis method and modeling wave range, and root mean square error of prediction (RMSEP) was used to evaluate the prediction effect of the model. The optimized spectral pretreatment methods were standard normal distribution verification (SNV) correction and second derivative (13-point smoothing) of standard normal distribution. The quantitative analysis method was PLS. And the optimal wave range of orientin and vitexin were 2 050~650 and 1 900~650 cm-1, respectively. The correlation coefficients of orientin and vitexin models constructed by PLS method were 0.919 8 and 0.970 8, respectively. The relative deviations of the predicted results were -2.0%~3.2% and -3.4%~4.7%, respectively. Since the possessing has unique advantages, such as rapid measurement, fingerprint characteristics, qualitative and quantitative analysis, being environment friendly, the infrared spectroscopy can be used to indicate the representative ingredients of Chinese herbal extracts quickly, accurately, environment friendly and efficiently, thus providing a new idea and a feasible solution for the quality control of traditional Chinese herbal medicines.

Tongling and Nanling region is abundant in copper ore resources. From the 1980s, a large number of the sites of mining and smelting have been found in this region, the earliest of which could date back to the Erlitou period. Slags, furnace walls and other smelting remains from the sites of mining and smelting provided a good deal of archaeological materials for the study of the smelting technology of copper ore in ancient China. Slag samples collected from the smelting sites in Tongling and Nanling region, Anhui Province were analyzed by means of X-ray diffractometer (XRD), X-ray fluorescence spectrometer (XRF) and scanning electron microscope with energy-dispersive X-ray spectrometer (SEM-EDS) to understand the smelting process of copper ore. The results of XRD showed that the main phase compositions in the slag samples included fayalite, augite and hedenbergite, accompanied with quartz, cristobalite and magnetite, et al., which corresponded with the phase characteristics of copper smelting slag. From the contents of SiO2, CaO and Fe2O3, all slag samples were divided into three groups: Type Ⅰ, Type Ⅱ and Type Ⅲ. The Type Ⅰ was Iron-Silicon-Calcium slag, which was high in the contents of calcium, iron and silicon. The calcium contents of the Type Ⅰ slag were much higher than those of the TypeⅡ and the Type Ⅲ slag. The Type Ⅱ was iron-rich slag, and its Fe2O3 contents were higher than those of the Type Ⅰand the Type Ⅲ slags. The Type Ⅲ was silicon-rich slag which was higher in SiO2 contents and lower in the calcium and iron contents. The Fe2O3 contentsin all slags were higher than the common melting slags. Combined with the results of XRD, it was concluded that all slag samples are from the smelting process of copper ores. Calcium and iron levels in the Type Ⅰ and Type Ⅱ slags vary obviously and show the significant negative correlation, which indicated that calcium and iron in the slags was not controlled artificially and it is very possible that the calcium and iron in the slags were from the copper ores. Based on the contents of calcium and iron, it could be inferred that the early craftsmen in the Tongling and Nanling region didn’t seem to understand the effects of calcium-bearing flux and iron-bearing flux and master the technology of matching ore of different kinds of copper ore. According to the results of SEM-EDS, the metal prills in the slags mainly included matte, copper and arsenic bronze, which showed that the smelting activities of copper and arsenic bronze coexisted in Tongling and Nanling area. The matte prills in the slags were mainly from different sites of mining and smelting, and the matte prills with the increasing copper content in turn were not extensively found in the same sites of mining and smelting. Therefore it was difficult to certify the existence of the matte smelting process, and it was not verified whether the smelting process of “copper sulfide ore-matte-copper” has been widely used in this area in the Pre-Qin Period. The matte prills in this work were possibly produced by the dead roasting process of copper sulfide ores or cosmelting process of the copper sulfide-oxide ore. On the basis of the arsenic bronze prills in the slag of Xiajiadun site, we could draw a conclusion perhaps that the ancient craftsman in this area mastered the cosmelting technology of arsenic copper in the Western Zhou period. The research results have an important significance for the study on the origin, the development and production mode of early smelting process in ancient China.

The Chinese Jade culture has a long history. The processing techniques of jade have been continuously developed and improved during various periods, and to some extent, it can reflect the condition of productive forces, cultural, trade, technical communications and other information of ancient societies. In this paper, X-ray fluorescence spectroscopy (XRF), Laser Raman spectroscopy (LRS), Optical microscopy (OM), combined with silicone resin molding method are used to analyze some jade artifacts unearthed from cemeteries dated to Eastern Zhou Dynasty in Xiyasi, Xinzheng, Henan province. Mineral property is determined by XRF and LRS, then OM technology is used to characterize the micro-marks on surfaces, in holes of the jade artifacts, and also on the surfaces of silicone molds. Based on the features of micro-marks, the processing techniques have been identified, especially for the processing techniques of incising decorations and perforations. The relationships between mineral properties and processing techniques are discussed finally. The results of XRF and LRS show that the main mineral phases of jade artifacts include tremolite, talc, mica and crystal. According to the features of micro-marks obtained, there are two kinds of tools used to incise the decorations. One is the rotary wheels, and the other is hand-held hard tools. The micro-mark characteristics of drilling process show that solid drills and tubular drills are used to drill holes from single-side or double-sides/multi-sides. Although solid drilling technology is adopted in some jades, there are some differences in drills’ shape and abrasive sand used or not. Jades of different material properties adopt different processing techniques. Talc jade, whose mohs hardness is 1, is mainly plaques and uses pointed hand-held tools to incise surface decoration and solid tool-heads which are probably cone-shaped to drill holes without adding abrasive sand. Mica jade, whose mohs hardness is 2~3, is mainly slotted rings called Jue and uses rotary wheels to incise the surface decorations. It adopts single-side drilling method and its tools for drilling are tubular drills. Tremolite jade, which has a mohs hardness of 5~6, is also mainly plaque shaped, surface decorations are incised by rotary wheels, and the holes are mainly carried out by solid tools to drill from both sides with abrasive sand. The shape of the drills is cone-shaped, very similar to that of talc jade. For crystal beads, its mohs hardness is 7, the drilling technology mainly adopts solid tools to drill from both or multiple sides and the drills might be cylindrical. This indicates that connections between the processing technology of jade and the material properties and shape of jade do exist.

Quantitative detection of oil slick thickness in the ocean is an essential basis to achieve an accurate estimate of oil spills and provides primary data for the development of oil pollution emergency response. In this paper, we use diesel(0# diesel), motor oil (Mobil motor oil 20w-40), Lubricants (Shell Helix 15w-40, Shell Helix 10w-40, Shell Helix 5w-40) as the research objects, using laser-induced fluorescence (LIF) obtains the spectra of materials. The oil film thickness-fluorescence intensity calibration curves are established, and the detection limits of five kinds of oils are calculated. The accuracy of the quantitative detection of different oil film thicknesses in different water is analyzed. The results show that the fluorescence spectra of 0# diesel and Mobil motor oil 20w-40 are significantly different from those lubricants. The fluorescence peak of diesel is at 326 nm, and its FWHM is 60 nm. Mobil motor oil 20w-40 has three fluorescence peaks at 360 nm/375 nm/390 nm, and the FWHM is about 100 nm. The fluorescence spectra of the three lubricants (such as Shell Helix 15w-40, Shell Helix 10w-40, Shell Helix 5w-40) overlap significantly, and the fluorescence peaks are located at 334, 344, and 343 nm, respectively. With the increase of oil slick thickness, the fluorescence intensity of the five kinds of oil films is rising. The calibration curves of oil slicks have good correlation, and the correlation coefficients(r) are 0.997 8, 0.997 9, 0.996 4, 0.997 8, and 0.996 0, respectively. The detection limits are 0.03, 0.02, 0.02, 0.03 and 0.05 μm. It can be seen that the average relative errors of quantitative detection of five kinds of oil films in different water are less than 14%, and the average relative standard deviations are not greater than 10%. The results can be used to measure thin oil films and provide a technical means for on-line monitoring of oil film thickness at sea.