Frequency stabilized laser with narrow line width has an extensive application in the industrial production control. However, the Frequency variation of free working semiconductor laser limits the use of laser device. To stabilize the frequency of semiconductor laser, this paper presents a new way which is based on second harmonic absorption characteristics to achieve frequency stabilization of narrow line width diode laser. We have measured the second harmonic signal of water vapor with DFB diode laser of 1.396 μm, and the results show that laser output wavelength drift in 100 hours has been effectively controlled within ±0.16 pm. The absorption peak doesn’t vary with environmental temperature after frequency stabilization. This method is simpler and more reliable and has broad application prospects for frequency stabilization of diode laser.

Tunable diode laser absorption spectroscopy is a new trace gas detection technique provided with high precision in current. As the absorption of spectrum is popular for the advantages of high resolution, high analysis speed, non-contact detection and on-line monitoring, it has been widely applied in scientific research, industrial automation and other fields for gas monitoring techniques. In order to satisfy the requirements of accuracy and sensitivity with low concentration and the small signal noise ratio (SNR) of the trace gases for the instrument, it usually needs longer absorption optical path and complicated data processing algorithm which will surely increase the hardware and software costs of the analysis instrument. In this article, we propose an approach which utilizes the cooling and decompression effects of the high pressure gas when it is injected. With the detecting system hardware and software unchanged, we optimized the detecting system analysis capacity. For the installation of TDLAS detecting system where the sample gas inlet pressure is 0.3~0.5 MPa and exhaust pressure is 219.3 kPa, experimental results show that by using the jet vacuum enhanced method, we improved the signal SNR nearly 24 times, optimized the detection sensitivity almost to an order of magnitude and the precision increased 6.3 times.

The scattering cross section of Rayleigh-Brillouin is bigger than that of Raman scattering and it hence has an advantage in accurate tropospheric temperature profiling measurement. Moreover, accurate measurement of temperature under high pressure environment using Rayleigh Brillouin scattering is of great significance to monitoring of Space Shuttle Main Engine (SSME) Preburner and the scramjet engine. Both the deconvolution method and the convolution method are used to achieve the temperature retrieving of air under different pressures based on the spontaneous Rayleigh-Brillouin scattering. And the reasons induced the temperature retrival error are studied and a comparison of temperature measurement between the two methods is made. Before the deconvolution method based on Wiener filterbeing performed on the measured spectrum directly, the convolved spectra between the spontaneous Rayleigh-Brillouin scattering model and instrument transmission function are deconvolved to obtain the deconvolved spectra and the decovolved spectra are compared with the theoretical calculation spectra to retrieve temperatures. And the optimized singular value stacking number being 150, which is the key parameter of the deconvolution method, is obtained on account of temperature retriecal error being less than 1.0 K, the relatively unobvious fitting error and the short time consumption of retrieving temperature. And the spontaneous Rayleigh-Brillouin scattering spectra of air induced by the wavelength of 532nm of laser under the pressure of 1～7 bar at the temperature of 294.0 K are measured in experiment and the optimized scattering angle of 90.7° is obtained by the combination of theoretical spectrum and the principle of minimum value of χ２. After that, the deconvolution method and the convolution method are used to retrieve temperatures severally. Experiment results demonstrate that the spectral resolution is improved by using deconvolution method to some extent. Meanwhile, both the deconvolution method and the convolution method have good performance on temperature measurement under different pressures and the maximum errors between the retrieved temperature and the reference temperature are less than 2.0 K, temperature retrieving results of the deconvolution method are improved and time consumption of retrieving temperature is reduced with the pressure increasing, and temperature retrieving results using convolution method are better than those using the deconvolution method when the air pressure is low (≤2 bar), however, the results of both methods are close to each other and the absolute temperature errorsareless than 1. 0K when the air pressure is high (>2 bar). By analysis, it is found that the factors causing the temperature retrieval errors for both methods include the temperature fluctuations (±0.2 K), the effect of uncertainty of scattering angle and the known parameters on temperature retrieving and the spectral disturbancescaused by the nonlinear amplification of spectral noise of deconvolution method. The parameter measurement result can be improved in experiment by improving the signal-to-noise ratio of measured spectrum, the accuracy of optimized scattering angle and the deconvolution method.

In order to study the reflection characteristics of infrared low emissivity camouflage coating in Terahertz(THz) wave, the coating was prepared and its characteristic parameters such as visible image, infrared thermal image and infrared emissivity were tested. The complex refractive index of khaki-yellow infrared low emissivity coating in THz band was obtained by using the transmission THz time domain system. The characteristic matrix theory was analyzed, which was used to calculate the reflection spectrum of the coating in THz wave carried by the change of coating thickness(0.3~0.5mm) and incident angle(0°~60°) respectively. The results show that THz wave has multiple reflection peaks below 0.8 THz, and the maximum value is more than 90%. The result shows that the detection to metal targets that are coated by the infrared low emissivity camouflage coating by applying THz wave is meaningful. In addition, the thickness change of the coating has a great influence on the reflectivity of incident terahertz wave. The thicker the coating, the more reflection oscillation the THz wave has and the greater reflection peak value of it.The incident angle has a certain influence on the reflection characteristics of THz wave, but the overall effect is small, which is beneficial to the detection of multi-angle target by THz wave. Finally, the reflection characteristics of the metal plate coated with 0.42 mm thickness coating in 0.1~1.5 THz were measured, and the experimental results were compared with partial theoretical calculation results. The results demonstrate that the experimental results are in good agreement with the theoretical results, but there are also some deviations. The reason is mainly caused by sample thickness and sample parameter error, but the characteristic matrix theory still can be used to study the reflection spectrum of THz wave by infrared low emissivity stealth coating.

Spectral binary coding and multivalued coding technology can make objects spectra match, identify, and classify fast; but this kind of quantization coding methods will lose a lot of spectral details, and they cannot decode the reconstructed spectra similar to the original spectra. So they were only used for coarse horizontal applications in the past, such as rough classification. For resolving the above problems, a new spectral coding method, namely, HOBC (High-Order Binary Coding) was proposed based on high-order residual quantization. First, the original spectra were standardized by subtracting their own vector-mean, and thus the spectral sequences with a range of (-1, 1) were obtained. Second, the code with -1 and 1, its coding coefficient, and the residual (i. e., the first order residual) of a normalized spectrum were computed. Third, the binary codes with ±1 and their coding coefficients of the residuals from Two-Order to K-Order were computed order by order. At last, the K coding sequences and their corresponding coefficients were obtained. Using a typical spectral library dataset, spectral quantization encoding and decoding reconstruction experiments were carried out, compared with BC01 (spectral Binary Coding with 0 and 1), SPAM (SPectral Analysis Manager), a binary/quaternary hybrid coding, namely SDFC (Spectral Derivative Feature Coding), and a quaternary coding, namely DNA. During the experiments, first, the information entropy and memory storage of spectral shape feature and slope feature were calculated, respectively. Second, the spectral vector distance (SVD), spectral correlation coefficient (SCC), and spectral angle mapping (SAM) between the spectral shape feature and the original spectrum were calculated. The results of above experiments demonstrate that, on coding memory storage, HOBC 1—4 order encodings are equal to BC01, SPAM, SDFC, and DNA, respectively; on coding information entropy, HOBC 1—2 order encodings are equal to BC01 and SPAM, respectively, but HOBC 3—4 order encodings are higher than SDFC and DNA, respectively; on SCC, HOBC one order encoding is equal to BC01, but HOBC 2—4 order encodings are better than SPAM, SDFC, and DNA, respectively; on SAM, HOBC 1—4 order encodings are superior to the above four methods obviously, respectively; the four methods cannot be explicitly decoded and reconstructed, but it is easy to reconstruct the decoding sequence similar to the original spectrum for HOBC, and the SVDs of the reconstruct spectra are diminishing from a lower order to a higher order. Furthermore, the spectral coding and supervised classification experiments of 10 types of ground objects were carried out on the open spectral dataset of the Linze grassland foci experimental area. Results show that, on the three performance evaluation indices, i. e., Kappa value, overall classification accuracy, and average classification accuracy, HOBC is superior to the four coding methods. Especially, the classification performance of HOBC 4-order encoding is better than that of the original spectra. For the objects difficult to classify with small-sample and high similarity between classes, HOBC is also superior to other methods, and it is more robust. Therefore, first, HOBC can dramatically compress data. Meanwhile, its coding sequence can retain more information and have higher spectral separability, which can be used for fast identification and classification of spectra with high accuracy. At last, its decoding reconstruct data can also be used for the applications of target recognition and classification etc., theoretically, for the high similarity between the reconstruction spectra and original spectra.

With the increasing demands for medical diagnosis, more and more attention has been paid to the technology of biomolecular detection. As a high throughput and multiplexed molecular detection method, suspension array has developed rapidly in recent years. In this study, a Raman spectra-encoded suspension array with micro-quartz pieces as the carrier was prepared by the layer-by-layer self-assembly method, and a high sensitivity and high resolution optical system was built to realize the qualitative and quantitative analysis of the suspension array. The home-built optical system was obtained by coupling Raman spectroscope with a fluorescence microscope. For the Raman spectroscope, a 785 nm laser was converged on the sample through dichroic mirrors, reflector and object lens. Then the Raman scattering light produced by the sample passed through the objective lens, anti-reflection mirror, dichroic mirror and Raman filter, and focused on the slit of the spectrometer via the concave lens. And finally, Raman spectra can be obtained by the dispersion effect of the spectrometer. For the fluorescence microscope, which used the optical imaging principle, the excitation light could irradiate the sample uniformly through the objective lens by adjusting the distance between the concave lens and the excited light of 405 nm. Then, the emitted fluorescence passed through an objective lens, an anti-reflection mirror, dichroic mirror, a filter and a concave lens, and finally imaged on the matrix CCD. The coupling of the Raman spectroscope and the fluorescence microscope was completed by improving the optical path of the conventional portable Raman spectroscope and selecting the anti-reflecting mirror with the specific band and the objective lens with a focal length of 20×. In order to reduce the interaction between the Raman spectroscope and the fluorescence microscope, the appropriate dichroic mirror and filter were selected to improve the coupling system. The Raman spectra of the suspension array were detected by home-built system to accomplish the qualitative identification of each encoded micro-quartz pieces. At the same time, the fluorescence of the encoded micro-quartz pieces was excited and the fluorescence signal was collected to complete the quantitative analysis of the target analyst according to the fluorescence intensity value on each encoded micro-quartz pieces. Compared with traditional fluorescence-encoded suspension arrays, Raman spectra encoding method has the advantages of stronger stability and higher spectral resolution. This optical system integrates Raman spectroscope and fluorescence microscope, which solves the problem that there is no suspension array detection system based on Raman encoding method at present and can qualitatively and quantitatively analyze a variety of target molecules at the same time, improving the accuracy of the experimental results.

Formaldehyde (HCHO) is the most abundant carbonyl compound in the atmosphere. It is one of the most important intermediate products of non-methane volatile organic compounds. It is widely involved in photochemical reactions in the atmosphere and is also an important precursor of aerosols. Formaldehyde plays an important role in atmospheric chemistry. The emission of VOCs from the petrochemical industry is an important source of HCHO in the urban atmosphere. At present, the pollutants of NMVOCs such as HCHO in the chemical park are mainly near-ground concentration through point-type equipment and lack of stereoscopic monitoring data. The Differential Optical Absorption Spectroscopy technology has been successfully applied to the monitoring of pollutant gases such as SO2 and NO2. Due to the relatively weak optical absorption intensity of formaldehyde, the cross-interference of other gases in the inversion band is strong, and practical monitoring applications are relatively rare. The paper selects a petrochemical company and uses a passive DOAS method to accurately invert formaldehyde column concentrations. In this paper, the relationship between the absorption profile of formaldehyde and other two-dimensional correlation matrix that is used to fit the gas absorption cross-section is established, and the band with the smallest correlation between the absorption profile of formaldehyde and other gas absorption profiles is selected. The acquisition of the band that minimizes cross-interference from DOAS inversion of formaldehyde by other gases is achieved. At the same time, the spectrum actually collected in the field is selected, and different initial bands and cut-off bands are selected for iterative DOAS inversion. The residuals are used to evaluate the actual inversion effect of formaldehyde in different bands. In the region where the cross-interference between the cross-sections is small and the fitting residual is low, the widest band is selected as the best fitting band to achieve accurate DOAS inversion of formaldehyde. From the results of the two-dimensional correlation matrix of absorption profiles of formaldehyde and other gases, the correlation between formaldehyde and NO2, SO2, O3, and O4 is below 0.5 in most of the bands, and the cross-interference is small. The correlation between formaldehyde and BrO at the initial wavelength of 318～320 nm, cutoff wavelength of 340～346 nm and initial wavelength of 330～334 nm, and cutoff wavelength of 354～360 nm is less than 0.5 in these two wavebands, which is suitable as the inversion waveband of HCHO. Through the selection of different initial bands and cut-off bands for the iterative DOAS inversion of formaldehyde, combined with the correlation analysis results of fitting cross sections, 332.4～358.1 nm was finally used as the inversion band of HCHO, and the fitting residual was 10-4. In this paper, a passive vehicle-borne DOAS system is used to establish the HCHO best-fit band for the system by establishing a two-dimensional correlation matrix between absorption cross-sections and through iterative inversion of the measured spectrum. The fitting residual is reduced to 10-4. Based on the accurate inversion of formaldehyde and combined with GPS information of the system, the spatial distribution of formaldehyde concentration in a chemical company was acquired. During the entire field observation period, the error of HCHO inversion was less than 6%. The results show that the vehicle passive DOAS system can play an important role in quickly obtaining the spatial distribution information of formaldehyde in the chemical industry park, and provides an effective measurement method for the stereoscopic monitoring of formaldehyde in the urban atmosphere.

A 2D Pr-Cd heterometal-organic compound, ［Pr2Cd3(EDTA)3(H2O)11］·(H2O)14(1) (H4EDTA=ethylene diamine tetraacetic acid) has been successfully prepared by the H4EDTA ligand, Pr6O11 and CdCl2·2.5H2O. The structure of 1 was determined by X-ray single-crystal diffraction. Compound 1 crystallizes in the monoclinic space group C2, a=16.154(3) , b=14.863(3), c=14.875(3), β=115.855(3)°, V=3 214.2(9)3, Z=2. There are nanosized heart-like Pr6Cd6O12 wheel-clusters in the structure. The coordination geometries for the two seven-coordinated Cd2+ ions are both close to that of a monocapped trigonal prism. The Pr3+ ion is ten-coordinate and described as seriously distorted dicapped square antiprism. The completely deprotonated EDTA4- ligand link one Cd2+ and two Pr3+ ions. Four carboxylate O and two N atoms of the EDTA4- ligand are all coordinated to the Cd2+ cation and the remaining carboxylate groups connect one Pr3+ ion, respectively. The Pr3+ and Cd2+ cations are bridged by 2-O alternatively to form a Pr6Cd6O12 wheel-clusters. Each Pr6Cd6O12 is linked to six surrounding wheels by sharing Pr3+, forming a highly ordered layered network. The 2D layers are further packed in …AAA… stacking mode and the free water molecules are suspended between the layers. There are strong O—H…O hydrogen-bond interactions between water molecules and carboxylate groups, and the O…O distance ranges from 2.666 to 3.050 . The hydrogen-bond interactions play an important role in stabling the structure. At the same time, PXRD, TG/DSC, IR and 2D IR correlation spectroscopy, solid Luminescent spectrum and UV-Visible absorption spectrum are studied. Because there are strong O—H…O hydrogen bonds, the IR spectrum of compound 1 shows broad bands around 3 680～2 640 cm-1. The four carboxylic acid groups of EDTA4- ligand are completely deprotonated, The CO stretching vibrations peak of carboxylate groups of compound 1 shift lower wavenumber compared to H4EDTA ligand. Compound 1 shows emission peak at 360 nm that can be assigned to LMCT transition of between Cd2+and EDTA4- when it is excited at 325 nm. Compound 1 is a potential luminescent material. 2D IR correlation spectrum of 1 indicates that the stretching vibrations of O—H are sensitive with the thermal perturbation because of strong hydrogen-bond interactions between water molecules and carboxylate groups. The UV-Visible absorption spectrum of compound 1 shows the absorption bands of n→σ* and π→π* transitions of EDTA4- ligand and f→f transition of Pr3+.

The vegetation indices based on fixed characteristic wavelengths cannot be applied to the diagnosis of chlorophyll content across multiple growth stages. To solve this issue, this study proposed a diagnostic parameter based on spectral coverage area, which can be applied in multiple growth stages. The canopy reflectance spectra of 325～1 075 nm and leaf samples were collected at jointing stage, booting stage and flowering stage. The spectral were pretreated by wavelet denoising and multiple scattering correction (MSC) method and the chlorophyll content was measured by spectrophotometry. The migration range of characteristic wavelengths across different growth stages was determined by correlation analysis and a spectral parameter, named Modified Normalized Area Over reflectance Curve (MNAOC), was proposed based on the migration range coverage area. Firstly, the orthogonal experiment of wavelet parameters was designed for selecting the optimal parameters combination of wavelet basis function, decomposition layer number, threshold selection rule and threshold adjustment scheme. By the comprehensive evaluation of the SNR and S, the best parameter set was (“sqtwolog”, “mln”, “3”, “db5”). Then, correlation analysis showed that the migration range was (700 nm, 723 nm) within the characteristic wavelengths across different growth stages. After the resolution analysis, linear regression models were established for chlorophyll content diagnosis by the MNAOC with the concentration of 0.5 mg·L-1. Among them, R2c of the models were 0.840 1, 0.865 5 and 0.833 8 for each stage respectively, and R2v of the models were 0.823 7, 0.817 4 and 0.807 6 for each stage respectively. Finally, compared with the dual-wavelength based vegetation indices, the applicability advantage of MNAOC across multiple growth stages was verified. The comparison showed that MNAOC calculated by 700 and 723 nm, which contained the spectral dynamic migration characteristics, was superior to other dual-wavelength based vegetation indices, such as Ratio Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI), in terms of model accuracy and universality in multiple growth periods. The results provided support for diagnosing chlorophyll content during the growth of winter wheat in field environment.

Raman spectroscopy and optimized geometry of the 2-Mercapto-5-nitrobenzimidazole (MNBMZ) molecule had been calculated at density functional B3LYP level using 6-31++g(d,p) basis set in this paper. Raman spectrum was obtained from the calculation results of the frequencies, and compared with the experimental Raman spectrum. The compared results showed that there is a blue shift in the range of 200～800 cm-1, however in the range of 800～8 800 cm-1, there is a red shift. A line of best fit of the experimental Raman frequences versus the calculated ones in the range of 200~1 800 cm-1, the correlation coefficient and the standard deviation were 0.998 and 14.98. The vibrational mode was assigned on the basis of potential energy distribution (PED) through the VEDA4. In addition, the Frontier HOMO-LUMO orbital and the compositions were discussed based on the calculated results, and the HOMO-LUMO gap was estimated to be 3.31 eV, which indicated that the electron will transfer from the HOMO to LUMO. The contribution of S to the HOMO orbital was 52.53%, and the contribution of N and O in nitryl was 23.03, 19.97, 19.36 to the HOMO orbital. The excited states were calculated by TDDFT, and the results showed that the adsorption wavelength is 213, 281 and 437 nm, but it is 213, 272 and 353 nm especially from the experimental spectrum in the methyl alcohol. This study provided a theoretical support for the analysis of MNBMZ.

The multi-target fiber spectroscopic telescope can obtain a large number of spectral data of different celestial bodies in one observation. The light detected from the celestial body passes through the slit of the spectrometer, and after passing through the optical fiber, it is transmitted to the CCD sensor to obtain a two-dimensional spectral image. After a series of processing by the fiber optic spectral data processing system, the available spectral data is finally output and stored. The one-dimensional spectrum is the main means by which we obtain information about the target celestial body. The LAMOST telescope is used to obtain the observed celestial information. Taking LAMOST as an example, before obtaining a one-dimensional spectrum, the telescope system first obtains a two-dimensional spectrum consisting of 250 optical fiber spectra after one observation, and then undergoes a series of processing to obtain a one-dimensional spectrum. However, due to the increased use time of the telescope, the components will wear and age, which will cause a certain degree of bending of the fiber trajectory in the two-dimensional spectrum. This bending is particularly evident on both sides of the two-dimensional spectrum. The ordinate direction of a two-dimensional spectrum represents the wavelength direction of the extracted one-dimensional spectrum, and the abscissa direction represents the flow direction of the extracted one-dimensional spectrum. The generation of such deformation affects the subsequent wavelength calibration and flow. The calibration makes the extracted one-dimensional spectrum information inaccurate. The current initial solution is to minimize the impact by comparing with the calibration lamp spectrum. This not only causes a waste of time and manpower, but also has low accuracy and efficiency. In this paper, we propose a method of straightening the curved two-dimensional line based on the curve distance method. Firstly, the gray center of gravity method is used to locate the 250 fiber center trajectories in a two-dimensional spectrum, and the abnormal point is set. The robust local regression method is used to eliminate the curve, and then the center trajectory is curve-fitted to obtain the equation of the fiber trajectory. By simulating the inverse process of the curve bending, that is, keeping the curve distance between the two points on the trajectory unchanged, and then bending the spectrum Map to the vertical normal line to complete the straightening process. At the same time, the gray value of each corresponding point is kept unchanged, and the sparse problem of generating pixel points is solved by edge processing and interpolation operation. Finally, the one-dimensional spectrum extraction is performed by the accumulation method, and the one-dimensional spectrum extracted after straightening is compared with the undimensionally extracted one-dimensional spectrum. The difference between the two ends of the one-dimensional spectrum before and after the straightening is large, and the difference is passed. The value line also illustrates this. The method realizes the automatic alignment of the two-dimensional spectrum, which greatly improves the efficiency and accuracy of extracting the one-dimensional spectrum. The two-dimensional spectral pre-processing and alignment method proposed in this paper is validated on the LAMOST data at first. Considering the similarity of the principle of the multi-target Optical Fiber Spectral Telescope system, this method can also be applied to other multi-target Optical Fiber Spectral Telescope systems, and has good reference and application value.

Quartz-enhanced photoacoustic spectroscopy (QEPAS) based spectrophone has developed rapidly in recent years. With the benefit of a quartz tuning fork (QTF), the QEPAS technique offerscompact structure and low sensitivity to surrounding noise. However, it’s difficult to use the standard QTF for slowly relaxing gas molecule detection as the high resonance frequencyof QTF. And it is a big challenge touse the laser with low beam quality as the excitation light sources of the QEPAS system because the gap size between the prongs of the standard QTF is too narrow. Non-standard QTFs (f0≠32.768 kHz) have been installed in QEPAS system as acoustic transducer in recent years. Therefore, the influence of the resonance frequencies of QTFs on the QEPAS system performance must be studied in detail. In this paper, the water vapor was used as the target gas and was detected via different QEPAS-based gas sensors in which the four QTFs with different resonance frequencies were installed as the acoustic transducer. The experimental results show that the resonant frequency of the QEPAS acoustic transducer has a significant effect on the signal-to-noise ratio of the QEPAS system. The reported results are extremely useful in the design of the QEPAS spectrophone.

It’s of great significance toconduct researches of plasmonic enhanced random lasing based on metal nanoparticles which have special property and potential applications. Plasmonic enhanced random lasing has been used in surface fluorescence enhancement, optical switching device, surface plasmon laser and so on. In this paper, we propose a convenient and high-efficiency way to fabricate Au nanoparticles and study the random lasing property based on a dye-doped film covering on these particles. By changing sputtering time with 40, 80, and 120 s, different sizegold nanoparticles are prepared by sputtering and the rmal annealing on quartz substrate. The particle size increases with sputtering time enlarging. After being covered by DCJTB-doped PMMA film, low-threshold random lasing phenomenon is obtained by a 532 nm pulse beam pumping. In this study, themean particle size of Au nanoparticles, obtained at 40, 80 and 120 s sputtering time, is 230, 250 and 390 nm, respectively, and the threshold for generating random lasing under 532nm pump beam excitation is 20.5, 17.5 and 12.5 μJ·pulse-1, respectively. The larger the size and the smaller the particle spacing of Au nanoparticles, the shorter average free path of photon scattering. In that case, the light can be effectively scattered among metal particles for many times, so the scattering efficiency can be significantly improved, resulting in low threshold laser emission. When the absorption peak of Au nanoparticles is exactly matched with fluorescence peak of the dye, the fluorescence effect can be significantly enhanced. As a result, more dye molecules can be excited to generate energy level transition, and the density of photonic state is increased. During limit of damaging dye molecules by pump light, the laser can be obtained by stimulating dye molecules in multiple cycles with a slightly decrease of lasing intensity at the same level pump beam power, which is helpful for the research and development of random laser devices. The experimental results meet well with lasing theoretical analysis, which plays a significant role in clarifying random lasing emission mechanism of Au nanoparticles on photon scattering and plasmon resonance on optical absorption enhancement. Our study could provide a convenient technical method for high-efficiency and low-threshold random laser research which is expected to promote the development and application of random laser devices.

Iron is an essential trace element, which plays an important role in the life process, but excessive intake of ferric iron will reduce the oxygen carrying capacity of the body, causing unstable hemoglobin disease and methemoglobinosis. Whether from the point of view of human health or environmental protection, it is of great significance to explore a simple, rapid, sensitive and selective method for the determination of Fe(Ⅲ). Fluorescence analysis is an excellent method of molecular spectroscopy. It has the characteristics of high sensitivity, good selectivity and simple operation. It has also made good progress in the detection of heavy metal ions. At present, the determination of Fe3+ by fluorescence method has been reported, but some of them have low sensitivity, poor selectivity and toxicity of organic reagent. In this article, a simple, rapid and sensitive fluorescence method for the determination of Fe(Ⅲ) has been developed, using tetramethylbenzidine (TMB) fluorescence reagent. In pH 4.5 Tris-HCl buffer solution at 35 ℃, the reaction of H2O2-TMB was slow. When Fe (Ⅲ)was added, it catalyzed strongly H2O2 oxidization of TMB to form strong oxidized product TMBox with strong fluorescence. Using excited wavelength of 280 nm, TMBox exhibited a strong fluorescence peak at 405 nm, and the fluorescence intensity increased linearly with the increase of Fe(Ⅲ) concentration in a certain range. The fluorescence analysis conditions were optimized by univariate transformation. The pH of Tris-HCl buffer solution was 4.5, its concentration was 3.3×10-4 mol·L-1, the concentration of TMB was 3.0×10-5 mol·L-1, the concentration of H2O2 was 6.0×10-6 mol·L-1, and the reaction time was 35 min at 35 ℃. Under the selected conditions, the fluorescence signal of the system increased linearly at 405 nm with the increase of Fe3+ concentration in the range of 0.027～400 nmol·L-1. The linear equation is F405 nm=2.31c+50.0, the linear correlation coefficient R2 is 0.985, and the detection limit is 0.008 nmol·L-1. According to the procedure, the influence of coexistent substances on the determination of 200 nmol·L-1 Fe3+ was tested, with a relative error of ±10%. Results indicated that 100 times HCO-3, K+, SO2-4, NH+4, Mn2+, Na+, Cu2+, Al3+, Zn2+, F-, Mg2+, Ba2+, Ca2+, Co2+, NO3-, NO2-, 50 times CO2-3, Cr6+, 10 times Hg2+, BSA did not interfere with the determination. It showed that this Fluorescence method had good selectivity. Thus, a simple, rapid, sensitive and highly selective fluorescence method for the determination of Fe (Ⅲ) was developed. Sample solution of dairy products was prepared by the following steps: accurately absorbd 1.4 mL dairy products with 600 L acetic acid (V/V=3%), centrifugated for 3 min at 10 000 r·min-1, then took the centrifugal supernatant 1 mL with 48 L 2.5 mol·L-1 NaOH, mixed well, centrifugated for 3 min at 10 000 r·min-1, and finally piped 1 mL the supernatant, and diluted to 5 mL to get the sample solution. Then the new catalytic fluorescence method was used to determine the content of Fe(Ⅲ) in milk samples, with satisfactory results. The relative standard deviation was 0.29%～0.41%, and the recovery was 94.6%～108.0%.

The proton binding heterogeneity of forest soilfulvic acid (FA) sub-fractions (FA3-FA13) was investigated by use of synchronous fluorescence spectra (SFS), combined with two-dimensional correlation spectroscopy (2D COS) and modified Stern-Volmer model. XAD-8 adsorption techniques coupled with stepwise elution using Na4P2O7 buffers were successfully developed to separate the soil FA into FA sub-fractions with low heterogeneity (FA3-FA13). FA3-FA13 contained protein-like (i. e., tyrosine-like and tryptophan-like), fulvic-like and humic-like materials, and FA7-FA13 contained more contents of protein-like materials than both FA3 and FA5. The intensities of fluorescence peaks for different fluorescent materials in FA3-FA13 were affected significantly by the change of alkaline pH. The complex distributions of the auto-peaks and cross-peaks in synchronous and asynchronous maps derived from 2D COS were related to the proton binding heterogeneity of fluorescent materials in FA3-FA13. The protein-like, fulvic-like and humic-like materials in FA5-FA13 occurred in the same spectral directionby the pH perturbations. At alkaline pH conditions, the dissociation constants (pKa) of fluorescent materialsin FA3-FA13were quantified by the SFS-2D COS combined with modified Stern-Volmer model. The pKa values of tyrosine-like, tryptophan-like, fulvic-like and humic-like materials were ranged 6.11, 8.93～10.12, 9.32～10.65, and 9.70～10.63 (R>0.854), respectively. The lower pKa value (6.11) of tyrosine-like materials of FA3 indicated that the tyrosine-like material scontained more aromatic structures and adjacent phenolic functional groups. Similar pKa values were presented for the tryptophan-like, fulvic-like and humic-like materials of FA3-FA13(8.93～10.65), and were similar to the pKa values (8.0～12.02) of hydroxyl-benzene and amino acid. This result suggested that the tryptophan-like, fulvic-like and humic-like materials of FA3-FA13 had similar proton affinities, and the phenolic functional groups and amino acid components played a major role in the proton bonding process. The sequential changesof fluorescent materials with specific wavelengths in FA3-FA13 were consistent with their increasing trends of pKa values. Additionally, the heterogeneous distributions of proton binding sites were both presented in different fluorescent materials and same fluorescent materials of FA sub-fractions. The pKa values (6.11～9.16) of tyrosine-like and tryptophan-like materials with specific wavelengths of FA3 were smaller than those of humic-like materials (9.70～9.97), and their increasing trends were consistent with the sequential orders (250 nm→275 nm→425～490 nm). The increasing trends of pKa values of tryptophan-like, fulvic-like and humic-like materials of FA5-FA13 were consistent with the sequential variations: 275 nm (8.93～9.70)→375～495 nm (9.88～10.16)→350 nm (10.65) for both FA5 and FA7, 275 nm (10.11)→290～400 nm (10.35) for FA9, and 265～345 nm (9.32～9.80)→360～450 nm (10.06～10.13) for FA13. In addition, the sequential wavelength changes and pKa values of same fulvic-like materials in FA13 showed the orders of 325 nm (9.32)→375～425 nm (10.06～10.13). With the advantages of reducing theoverlaps of spectra and capturing the sequential wavelength changes, the SFS-2D COS combined with modified Stern-Volmer model will provide supports for exploring complex interactions between dissolved organic matter and contaminants in future studies.

H2O and CH4 play key roles in the process of climate change, then real-time online measurement of H2O and CH4 concentrationshas always been one of the hot issues of domestic and foreign scholars. In this paper, an off-axis cavity enhanced absorption spectroscopy device was establishedwith two high reflectivity mirrors of 99.997 6% combining a tunable semiconductor laser operating around 1.653 μm as the light source, andthe high sensitivity measurement of H2O and CH4 was carried out. The effective absorption optical path of the system was calibrated by the absorption area-concentration relationship. The feasibility of the absorption area-concentration relationship was first verified by an optical absorption cell with a known optical path, and it was used to calibrate effective optical path of the cavity enhancement system. The results showed that the effective absorption path of the cavity enhancement system with the base length of 21 cm reached 8 626.3 m. The linear response calibration test was carried out with 7 groups of CH4 standard gases of different concentrations (0.2~1.4 μmol·mol-1) when the pressure was 5.06 kPa, and the fitting relationship curve between the integrated area of CH4 absorption and the concentration was obtained. The stability of the system and the minimum detectable sensitivity were analyzed by Allan variance. The results showed that the optimal average time for detecting CH4 was 100 s, and the minimum detectable concentration limit was 7.5 nmol·mol-1. The optimal average time for detecting H2O was 200 s, and the minimum detectable concentration limit was 55 μmol·mol-1. The data processing method for improving the measurement precision of the system was also analyzed. The results showed that Kalman filtering could greatly improve the measurement precisionand reduce the response time of the systemcompared to the multiple averaging method. Finally, the experimental system of off-axis cavity enhanced absorption spectroscopy device combining with Kalman filtering technology was used to measure the CH4 and H2O concentration in real atmosphere for two days. The average daily concentration of CH4 was 2.1 and 2.08 μmol·mol-1, respectively. The average daily concentration of H2O was 11 515.6 and 11 628.6 μmol·mol-1, respectively. It can be seen that the experimental device of off-axis integration cavity can be used for atmospheric CH4 and H2O detection, andthe established system can also be used for high-sensitivity CH4 and H2O monitoring in relevant industrial fields.

In the process of production, the addition of benzethonium chloride to the wipes is achieved by impregnation of the pharmaceutical solution, that is, the adsorption of benzethonium chloride molecules in the pharmaceutical solution by nonwovens. Because the process of benzethonium chloride adsorption is a very short time, the study about the adsorption rate of benzethonium chloride by nonwovens has not been reported. In our work, the adsorption of benzethonium chloride by nonwoven was as research subject, and according to the fact that the characteristic absorption peak of benzethonium chloride was at 269 nm, by building an on-line kinetic ultraviolet-visible spectroscopy monitoring system and using a peristaltic pump to transport the solution to the flow cuvette of the spectrophotometer for cyclic detection, the absorbance value of benzethonium chloride solution in the adsorption process can be determined online. Based on the relationship between the absorbance value and benzethonium chloride, the adsorption of benzethonium chloride was deduced and calculated. Therefore, a method for the accurate and rapid determination of benzethonium chloride adsorption by nonwoven was established, which is suitable for on-line monitoring of the concentration of benzathonium chloride solution. The adsorption process of benzethonium chloride was theoretically analyzed by Weber and Morris diffusion model, which provides guidance for further study on the adsorption of benzethonium chloride by nonwovens. The results showed that the adsorption of benzethonium chloride is a continuous dispersion process; The first stage of linear adsorption is related to the surface diffusion, the second stage is the intergranular diffusion process and the third stage is the equilibrium dynamic process of adsorption and desorption. The diffusivity constant of the first stage (41.60) is much higher than that of the second stage (15.63), indicating that surface diffusion plays an important role in the whole adsorption process. This paper can provide good guidance for rational selection of benzethonium chloride concentration and optimization of process in production of wet wipes.

The visual and real-time monitoring of temperature has always been an attractive research direction. Fluorescent sensing is a semi-invasive temperature detecting method with the advantages of high-sensitivity, rapid-response and real-time visualization, which has been widely applied in biomedicine. However, conventional fluorescent detecting results can be easily effected by the fluctuation of external conditions which can cause deviation. Therefore, ratiometric fluorescent probes have been developed to solve this problem, because the two fluorescent signals can achieve intercalibration, improving the accuracy of the method. Traditional ratiometric fluorescent temperature probes are always based on down-conversion emission (fluorescence). This type of probes requires excitation at short wavelength like ultraviolet, which has poor penetrability and potential damage to biological tissues. Besides, the auto fluorescence from tissues become strong interference to the probes. Frequency upconversion is a photoluminescence phenomenon excited at long wavelength and emitting at short wavelength. Fluorescent probes based on upconversion can overcome the drawbacks of conventional ones. Triplet-triplet annihilation (TTA) upconversion system requires two kinds of molecules, sensitizer and emitter, and has both up/down-conversion itself, which perfectly meets the requirement of ratiomatric fluorescent probes. However, ratiomatric fluorescent temperature probes based on TTA upconversion are barely reported, and even in the reported work, additional reference probe is still needed. Ratiomatric fluorescent temperature probes only based on the up/down-conversion luminescence of TTA system itself is still a great challenge. Herein, a traditional TTA system (PdOEP/DPA) is encapsulated into micelles assembled by amphipathic polymer, Pluronic-F127, yielding a TTA upconversion nanoscale micelle temperature probe. As the temperature rises, the hydrophilicity of PEO segment in Pluronic-F127 decreased, yielding the micelles shrink inward and become smaller. The confined space inside the micelles results in greater collision probability of the TTA molecules, causing higher TTA upconversion efficiency and intensity. Meanwhile, the phosphorescence intensity of sensitizer slightly declines. The ratiometric fluorescence composed of up/down-conversion fluorescence signals of the TTA system can achieve linear detection of temperature from 25 to 60 ℃, which can be observed by naked eye due to the color change of the emitting light from magenta to violet. The detecting results also have good repeatability. Encapsulated by thermo-sensitive polymer, the TTA system can be applied both in aqueous solution and in air atmosphere, solving the problems of poor water solubility and quenching by oxygen. Besides, the thermos-sensitive polymer brings the TTA system remarkable temperature response capability. This novel type of ratiometric fluorescent probe based on TTA upconversion micelles shows advantages of simple preparation, great biocompatibility, high sensitivity and human eye recognition. No extra reference probe is needed. This method will open an efficient avenue for vivo temperature detection.

The tailing ponds are widely distributed in China. Once the surface tailings are under low moisture content, the tailing dust would cause severely environmental pollution driven by the wind action. Because of the large area and rapidly variation of moisture content of tailing ponds, the traditional method with the limitation on low efficiency, safety and high cost cannot meet the requirement of quick and dynamic moisture content monitoring. Currently, although the remote sensing technology based on spectral model can provide accurate prediction of soil moisture content, this model is not fit to tailing moisture content prediction because of the different characteristics and components between soil and tails. Therefore, the Fengshuigou tailing pond in Liaoning Province was selected as the study area. First, the samples of tailings at different moisture content were collected and configured. Then, the visible-near infrared spectra of the samples were measured and analyzed. Furthermore, the relationship between moisture contents and spectral characteristics was established. Finally, the remote sensing inversion model for moisture contents prediction was built and applied for mapping the moisture content in this study area. This study yielded the following results: (1) The moisture content has a significant effect on the spectral characteristics of tailings, and the reflectance decreased obviously as the moisture content increased. The longer the wavelength is, the more significant the effect of water content on the spectrum is. (2) The remote sensing model based on the spectral characteristic for tailings moisture content prediction was established. In terms of the band 6 and band 7 from Landsat8-OLI imagery, the ratio index (RTI), normalized difference index (NDTI) and difference index (DTI) of tailings were proposed and selected as the input data for the random forest model. By comparing the random forest model and Log reflectance model, the random forest model can generate more accurate predicting results. (3) The tailings moisture content map was generated by applying the random forest predicting model with spectral index based on the Landsat-OLI imagery. From the field verification, the coefficie of determination (R2), RMSE, RPD, and ARE is 0.798, 0.077, 1.970 and 20.1% respectively between the predicted and field measured moisture content. The results could provide an effective and real-time method for large scale moisture content predicting of the tailing ponds from the metamorphic iron ore area.

In recent years, more and more functional activities have been discovered with the in-depth study of the monascus pigment, but some toxic effects of the monascus pigment have raised questions about its safety. Therefore, it is important to elucidate the interaction between monascus pigments and macromolecules in human body for further study of their transport, metabolism and toxic side effects. Spectroscopy is an effective method to study the interaction of small molecules with proteins in solution. It has been widely used in research for its high sensitivity, strong selectivity, low sample size, and simple method. In this study, rubropunctamine(Rub) was taken as the typical representative of monascus pigment to research the interaction of Monascus pigments with macromolecules bovine serum albumin(BSA). The fluorescence quenching effect of different concentrations of Rub on BSA was investigated by endogenous fluorescence spectroscopy and synchronous fluorescence spectroscopy. Then Stern-Volmer equation, the Lineweaver-Burk function and the Van’t-Hoff equation were used to determine the type of action, the number of binding sites and the interaction mechanism of BSA and Rub. The effect of Rub on the BSA secondary structure was quantitatively determined by circular dichroism spectrum. Finally, using the computer to perform the molecular docking simul- ation on the interaction of Rub and BSA. The results show: (1) Rub has a strong fluorescence quenching effect on BSA, and endogenous fluorescence spectrum shows that endogenous fluorescence decreases by 306.1 and emission wavelength shifts by 6.8 nm. Synchronous fluorescence shows that fluorescence quenching mainly occurs on tryptophan residues. (2) The dynamic quenching rate constant Kq calculated by the Stern-Volmer equation is 2.335×1012 L·(mol·S)-1, which is much larger than the maximum diffusion collision constant allowed: 2.0×1010 L·(mol·S)-1, and the annihilation is a pure static quenching process. (3)The binding constants reach above 103 L·mol-1 which is calculated by the equation lg［(F0-F)/F］=lgK0+nlgcQ, and the number of binding sites is approximately 1. The apparent binding constant becomes smaller with increasing temperature. (4) Under different temperature, ΔH, ΔS and ΔG are less than zero, so the interaction can occur spontaneously and hydrogen bonding and van der Waals force are the main interaction forces. (5) the α-helical content in the secondary structure of BSA combined with Rub decreased from 29.4% to 20.2%; The β-fold increased from 39.9% to 50.7%; β-rotation decreased from 6.5% to 3.5%; The random coil increased from 24.2% to 25.6%. (6) Rub is located in the pocket formed by Arg458, Asp108, Glu424, Ser428 and other amino acids in BSA, and it has Van Der Waals force with Arg458 and hydrogen bond interaction with Arg144 which affects Trp213 microenvironment.

The plasma cavity formed by metal ion droplets and liquid polydimethylsiloxane (PDMS) as a novel surface-enhanced Raman (SERS) substrate integrates plasmonic nanoparticles into optical devices, improving SERS detection, practicality and reliability, however, there are few studies on their optimal growth conditions compared to other substrates. Here, we used the banned veterinary drug Malachite Green (MG) as a probe molecule to examine the characteristics of the plasma chamber under different growth conditions, including growth temperature and metal ion concentration, to study the optimal growth conditions of the plasma chamber. When the aqueous metal ion solution is dropped onto the mutually incompatible liquid PDMS, a spherical cavity with an opening is spontaneously formed by the combination of surface tension and gravity. At the same time, the metal ions diffuse into the uncured PDMS and react with the residual Si-H groups. The metal ions are gradually reduced to metal nanoparticles, and gradually accumulate on the surface of the cavity as the PDMS solidifies, eventually forming a plasma chamber. It can not only be used as an angle reflector to confine the incident light in the cavity, but also can be used as a nano-scale photon source to scatter the absorbed light into the cavity. These two functions work together to further enhance the Raman enhancement of MG based on the original enhancement of the substrate. The higher growth temperature accelerates the growth of the PDMS while accelerating the growth of the metal ions, so that the growth process of the metal nanoparticles is terminated prematurely. The higher the ion concentration, the larger the metal ion particles formed. However, the particle diameter is too large, the number of hot spots on the surface of the plasma chamber will decrease, and the Raman enhancement of MG will be weakened. Therefore, there must be optimized plasma chamber preparation conditions to maximize the enhancement of the substrate to MG. We set the growth temperature of 15, 20, 25, 30 ℃ and the ion concentration of 0.05, 0.5, 5, 50 μg·mL-1. The results show that the plasma chamber achieves the best Raman enhancement of MG at a temperature of 25 ℃ and 0.5 μg·mL-1 growth conditions. The optimization of plasma chamber growth conditions can lay a foundation for improving the SERS enhancement effect of this type of substrate and repeatable preparation.

Ozone (O3) has been widely used for reducing bacteria in fresh meat. However, O3 treatment has a negative impact on the red meat color, and the action mechanism of O3 on red meat color is still lack of research. The existence of myoglobin (Mb) is the basis for determining the key factors of red meat color. Therefore, the spectroscopic characteristics of myoglobin (Mb) under O3 were analyzed by UV-Vis absorption spectroscopy, fluorescence spectroscopy and circular dichroism (CD) spectroscopy. Moreover, the protein oxidation characteristics and molecular dynamics simulation were used to explore the effect and mechanism of O3 on Mb molecule. The results of spectroscopic studies show that the O3 treatment can decrease the intensity peak of the iron porphyrin ring at about 412 nm and the characteristic peak of oxygenated myoglobin (OMb) near 540 and 580 nm in the ultraviolet-visible spectrum of Mb. The characteristic peak of the iron porphyrin ring blue-shifted. It also caused changes in the endogenous fluorescence and synchronous fluorescence spectra of Mb measured at a fixed excitation wavelength of 280 nm, indicating that the fluorescence intensity of Mb was reduced by O3 and the fluorescence peak intensity contributed by the iron porphyrin group was increased and it also caused a blue shift in the characteristic peak of the fluorescence spectrum of the tyrosine residue. The characteristic peak intensity of the three-dimensional fluorescence spectrum decreased and the light scattering intensity increased. It was concluded that O3 would cause the protein oxidation of Mb, the exposure of hydrophobic group of the amino acid residues in Mb and the conformation change of the protein. The CD spectroscopy results show that the longer the contact time between O3 and myoglobin, the more obvious the change of protein secondary structure, resulting in a decrease in the content of α-helix and an increase in random curl. Combined with the chemical detection on the content and characteristics of Mb, it shows that O3 caused the decrease of OMb content, and the increase of MMb, carbonyl and sulfhydryl content, indicating that O3 treatments could lead to the protein oxidation. Moreover, O3 treatments increased the hydrophobicity of protein surface, indicating it resulted in the polarity change of the microenvironment of the protein system. Molecular dynamics simulation results show that O3 can increase the RMSD value of Mb peptide chain, affect the stability of Mb peptide chain, and weaken the interaction between porphyrin ring and Mb peptide chain. The change in RMSF value Mb peptide chain discovered that amino acid residues of Mb near the active pocket changed obviously; Molecular dynamics simulations of protein structural changes were consistent with the results of spectroscopic experiments, namely, the alpha-helix in Mb decreased and the irregular curl increased after O3 treatment. In conclusion, O3 treatment could interact with the residues of Mb, led to the changes in the secondary structure and the hydrophobicity of Mb, and brought on the oxidation of protein and the exposure of iron porphyrin ring, therefore resulting in the change of red meat color. This study can provide theoretical basis for the color protection of red meats.

In this study, the natural antioxidants myricetin, morin, capsaicin and betaine were selected as the research objects, and the interaction between several antioxidants, DPPH free radical and human albumin was studied by synchronous fluorescence spectroscopy and three-dimensional fluorescence spectroscopy. Results showed that capsaicin, betaine, VC do not have quench effects with human serum albumin, and myricetin, morin and DPPH have quench effects with human albumin. The reactions are static quenching caused by stable complexes, combining hydrophobic interaction with HSA.,The binding sites are 1, the main binding sites are near the tryptophan groups, and DPPH and human serum albumin’s quenching process changes the structure of human serum protein hydrophobicity, causes protein conformation changes, and the interaction between myricetin, morin and human albumin does notcause its conformation changes. Fluorescence spectroscopy were used to study the inhibitory effect of several antioxidants on DPPH-induced direct damage to human serum albumin. The inhibition rate of myricetin, morin, capsaicin, betaine and VC to DPPH damage HSA was 25%, 18.30%, 85.38%, 4.02% and 84.58%. Capsaicin inhibits the damage of human serum albumin by inhibiting the action of DPPH. The binary system reaction results showed that myricetin reacted with Morin ternary system to form competitive binding sites with DPPH, and Myricetin and Morin inhibit DPPH damage to human serum albumin by occupying binding sites, while Betaine can neither occupy the binding sites nor scavenge free radicals, so the inhibition ability is the weakest. The results showed that the inhibitory ability of several natural antioxidants is closely related to the main functional groups in the molecular structure.

As an important energy and industrial raw material, petroleum brings benefit to human beings and the environment pollution is increasingly serious. Therefore, rapid and accurate detection of mixed oil becomes an important content of identification of its source and protect ecological environment. Petroleum substances are generally composed of aromatic hydrocarbon and its derivatives with strong fluorescence characteristics, and fluorescence spectroscopy is an important means of detecting mixed oil with the advantages of high sensitivity, fast analysis and small weathering effects. And it has obtained good results for components identificationand concentration prediction of oil spill by various algorithms of second-order calibration algorithm and third-order calibration algorithm. Second-order calibration has the shortcomings of weak tolerance to noise, sensitivity to number of components, and limited real application of mixed oil detection. Aiming at these problems, a novel method is proposed to detect mixed oil in this paper based on the combination of three-dimensional fluorescence spectroscopy and alternating weighted residue constraint quadrilinear decomposition (AWRCQLD) algorithm. Firstly, using ethanol as a solvent, 7 calibration samples, 4 prediction samples and 3 blank samples of jet fuel and lube with different volume ratios were prepared. Secondly, the fluorescence spectra of 42 samples of the mixed oil at different experimental temperatures were obtained by FLS920 fluorescence spectrometer, and the effect of scattering was removed by using blank subtraction. Then, the optimum number of components was estimated by core consistency diagnosis and residual analysis. Finally, using AWRCQLD algorithm, 4-PARAFAC algorithm and second-order calibration algorithm to analyze the fluorescence spectra, and the qualitative identification and quantitative prediction of mixed oil samples were made. The research results show that the interval of the obtained recovery rate of jet fuel prediction samples is 96.7%~102.7%, and the root mean square error of prediction is 0.015 mg·mL-1; the interval of the obtained recovery rate of lube prediction samples is 96.9%~101.7%, and the root mean square error of prediction is 0.009 mg·mL-1. The four-dimensional response matrix constructed can more accurately determine the concentration of jet fuel and lube at different experimental temperatures, and the recovery rate is higher, the root mean square error is smaller, and can meet the requirements of accurate quantitative analysis. Compared with the second-order calibration algorithm and 4-PARAFAC algorithm, AWRCQLD algorithm can better reflect the superiority of the third-order calibration algorithm and the comprehensive prediction ability is stronger under seriously overlapped fluorescence spectra of jet fuel and lube. The purpose of rapid detection of mixed oil can be achieved by AWRCQLD algorithm. The study provides a rapid and accurate “mathematical separation” method to detect mixed oil not based on “physical and chemical separation”, and also provides a necessary technological support for detection of petroleum mixed oil.

With the advantages of low cost, good quality, strong plasticity, plastics are widely used in industrial production and daily life. However, waste plastics are prone to environmental pollution and secondary hazards without being handled properly. Recycling is expected to be a silver bullet to solve the problem of waste plastics, with the premise of accurate classification. Traditional sorting methods of waste plastics are time consuming, inefficient, and difficult to classify rapidly and effectively. Laser-induced fluorescence technique is usually used for rapid identification and quantitative analysis of organic pollutants such as oil and polycyclic aromatic hydrocarbons in water and soil with simple operation, high detection efficiency and little sample usage. It can be used to quickly collect the fluorescence spectra of different plastics, combined with the corresponding pattern recognition algorithm, the rapid and accurate identification of plastic materials can be realized. In this study, 358 sets of fluorescence spectra from eight kinds of plastics (ABS, HDPE, PA66, PLA, PP, PET, PS, PVC) were collected. A spectral matrix of 358×10 was constructed based on the characteristic peak of the spectra. and then it was processed by the method of principal component analysis, after that the linear correlation in the original spectral matrix was reduced and the accuracy of the data was improved. The results show that the cumulative variance contribution of the first three principal components was 98.085%, which was enough to characterize the main information of the original spectral matrix. Spectral classification was performed using the principal components PC1, PC2, and PC3 as inputs. Among them, the spectral polymerization degree of the same kind of plastic was high, and plastics composed with different elements such as PA66, PLA, HDPE, and PVC have better spectral resolution, while plastics containing the same elements such as PET and PLA have poor spectral resolution. The PCA algorithm is not accurate enough to identify unknown plastics. BP-Neural network was widely used in pattern recognition and classification research. The simplified feature matrix obtained by the PCA algorithm was used as the input set of the BP-neural network algorithm. Among them, 256 sets of data were randomly selected as the training set of the BP-neural network model, and the remaining 102 sets of data were used as detection sets. The value of the hidden layer of the BP neural network was set to 1, while the bipolar Sigmoid function was selected as activation function. Eight plastics were set as the output layer. The results showed that only one set of HDPE spectra in the 102 sets of spectra was misidentified as PS, and the remaining 101 sets of data were all correctly identified. The total recognition accuracy of the fluorescence spectra of eight plastics was 99%. So the laser-induced fluorescence technology combined with BP-neural network algorithm can be used to quickly and accurately identify different plastics. This study provided a new reference for automated intelligent sorting of waste plastics, reducing recycling costs and lowering the risk of waste plastics.

The spontaneous emission spectra of flame are closely related to flame characteristics such as flame structure and temperature distribution. And the combustion state of flame can be reflected clearly by radiation intensity and distributions of excited radicals without being destabilized. Based on a bench-scale opposed multi-burner (OMB) gasification platform, a fiber spectrometer and a high-temperature endoscope coupled with a CCD camera were applied to investigate the two-dimensional distributions of CH* of diesel diffusion flames. The effects of equivalence ratio and impingement on emission spectraand CH* distributionsof flame were further compared. The results show that there exists OH* (306.47, 309.12 nm), CH*(431.42 nm), Na*(589.45 nm) as well as K*(766.91, 770.06 nm) radicalsin diesel flames. In addition, due to incomplete combustionofdiesel fuel, a lot of black carbon is emitted, which leads to strong continuous black-body radiation in visible wavelengths. The black-body radiation interferes with the detection of the CH* characteristic peak, and the lower the equivalence ratio, the stronger the background radiation, and the greater the interference to the detection. According to Planck’s law and interpolation method, background radiation can be subtracted from total radiation in the band around 430 nm. The peak intensity of CH* decreases monotonically with the increase of equivalence ratio. Meanwhile, the contours of CH* radiation appear in the form of three-peak, double-peak, and single-peak along the direction of flame development, and eventually shrink into a circular nucleus centered on the reaction zone. As the equivalence ratio increases, the thresholds of each form continuously decrease, andthe reaction zone gradually shrinks and moves downstream. When the equivalence ratio increases to 1.0, the diesel fuel burns completely, CH* radiation intensity decreases significantly, and the intensity and distribution of CH* chemiluminescenceof fuel-lean flame remain stable. The flame lift-off length can be evaluated by CH* radiation. For one-burnerjet flame, the flame lift-off length increases significantly and then decreases slightly with the increase of the equivalence ratio. The peak intensity of CH* of impinging flame is always higher than that of jet flame. The lift-off length of impinging flame increases slightly with the increase of the equivalence ratio. More obviously, the confining effect of impingement makes the lift-off length of impinging flame not easy to fluctuatewith the change of the equivalence ratio, which enables the combustion process to be stabler. This provides an intuitive and effective method for quantitatively judging the flame combustion state, as well as an experimental basis for the study of the chemical kinetics of diesel combustion.

In this research, the coke layer on the surface of ascension pipe is investigated, and X-ray fluorescence spectrometer (XRF), X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FTIR) and Laser confocal Raman spectrometer (Raman) are applied to investigate mineral composition of the coke, component structure and molecular structure of different coke layer. The research focuses on the differences of coke layer from outer surface to inner surface, and further reveals coking mechanism of ascension peipe heat exchanger. The research displays that the elements of ferrous, sulfur and chromium in dust can catalyst polycyclic aromatic hydrocarbons (anthracene, naphthalene et. al) in raw gas to form carbon particles and deposite on the surface of ascension peipe, providing carrier for tar condensation when the temperature decreases to coking temperature. All of the coke layers contain aromatic structure, and from outer surface to inner surface, the aromatic lamellas spacing (d002) gradually decreases, the value of diameter (La) firstly decreases then increases, and the stck high (Lc) and layer number (N) are stable first then increase. The graphitizing process of the coking layer is from inner layer to outer layer, and —COOH and C—O structures on the edge of the aromatic layers degrade and peel out to form highly regular conjugate structure. The C element in the coke layer is in the form of mixture of crystalline carbon and amorphous carbon. The above research provides experimental and theoretical basis for solving problems of coke and corrosion of ascension pipe, improving heat exchange efficiency, effectively recovering sensible heat of raw gas and decreasing energy consumption of coking enterprises.

The bronze-iron bimetallic objects unearthed from the Guo State Cemetery in Sanmenxia City are evidences of early use of iron in China’s Central Plains areas. This paper carries out scientific analyses of 3 bimetallic objects from tomb No. 2009 of this cemetery using metallography microscopy, Scanning Electronic Microscope-Energy Dispersive X-ray Spectroscopy (SEM-EDS) and Electron Microprobe (EMPA). It is revealed that the iron blade of spear head (STG001) was made of bloomery iron. Multiple-phased inclusions of silicate and iron oxide were elongated during the manufacturing process and arrayed along the deformation directions. Residue iron pills in the knife (SGT002) and the Ge Blade (SGT003) were found to be content notable Ni and a few of Co, and the result of line scanning analyses by the SEM-EDS shows interval distribution of nickel among different phase, indicating the meteoritic origin of the iron metal used to make the blades. The original Ni content of the meteoritic iron would fall in the ranges of IIIC and IIID types which will give the meteorites a microstructure of Finest (Off) or Ataxite. Metallography of bronze part of samples SGT001 and SGT 002 were observed to be typical as-casting microstructure of tin bronze with corroded α solid solution matrix and dispersed particles of (α+δ) eutectoid. The fact that there is no sign of deformation and recrystallization at the interface of iron and bronze parts indicates no further mechanical processing had been applied in the joint region. It is then inferred that the pre-made iron blades by were embedded into the casing mould and connected with bronze part by a cast-on process. On the basis of analytical results, this paper also briefly reviews the early use of iron metal in China and points out that the co-occurrence of meteoritic and manmade iron in the Guo State Cemetery demonstrated that this period had been a crucial stage in the development of iron metallurgy in China. The Northwest Region of China had played an important role during the transmission of early bloomery iron metallurgy while the establishment of cast iron technology in China’s Central Plains area would have a close relationship with the pre-existed technological tradition of bronze casting during the Shang and Zhou Periods.

In-situ analysis or on-line detection is a major advantage of laser-induced breakdown spectroscopy (LIBS) technology. However, in the outdoor environment, people cannot uniformly pre-process samples, then it is difficult to ensure the detection accuracy when facing the various types of samples. In this paper, a multi-line calibration method is proposed to solve the above problem, that is, the calibration curve is established by calculating the intensity ratio of multiple analytical lines and the internal standard element lines, which can reduce the error caused by spectral signal fluctuation and improve linear correlation and detection accuracy. In this experiment, the lead brass alloy samples were taken as an example. The quantitative detection of Pb elements in lead brass samples with different thicknesses (the maximum variation is 2 mm) was carried out by LIBS, and the traditional calibration method and multi-line calibration method were used respectively to establish the calibration curves. The experiment found that for irregular samples, the detection accuracy of the traditional calibration method is very poor, which has no obvious linear relationship from the calibration curve. When the internal calibration method of a single line is adopted, the linear correlation of the calibration curve is greatly improved, and the adjusted determination coefficient reaches 0.724 89. While using the multi-line calibration method (considering the sum of the intensities of multiple analytical lines), it is found that the adjusted determination coefficient of the calibration curve reached 0.984 6 when five Pb lines (Pb 261.42 nm, Pb 280.20 nm, Pb 368.35 nm, Pb 405.78 nm and Pb 520.14 nm) were selected. It can be seen that this method eliminates the spectral intensity fluctuation error caused by sample irregularity and significantly improves the measurement accuracy. While increasing the number of analytical lines can further increase linear correlation, but it also increases the computational complexity, so it is important to choose the appropriate analytical lines. In addition, the multi-line calibration method can also eliminate the matrix effects and spectral interference to a certain extent, which is a simple, effective and universal data processing method. Of course, this method also has limitations (such as extremely heterogeneous sample, extremely irregular sample surface which results in the laser energy below the breakdown threshold, etc.), but by adjusting and optimizing the detection device scheme (for example, increasing laser energy, increasing the diameter of focus spot, using a long-focus lens, etc.), we can improve the advantages of this method. This research content of this paper can provide a new idea for the application of LIBS in-situ analysis and on-line detection.

The content of silicon and iron in primary aluminum were detected by self-built LIBS device. The micro-morphology analysis of the primary aluminum sample was carried out before the experiment, It was found the distribution of silicon element is relatively uniform in the primary aluminum except little silicon has agglomerated in partial areas, the iron elements mostly appeared in agglomerated form, and there was no obvious distribution rule. The effects of laser energy on plasma spectrum characteristics were investigated in the paper. It was found that the signal-to-noise ratio of silicon and iron analytical lines increased firstly and then decreased with the increasing of laser energy, when the laser energy reached 160mJ, the signal-to-noise ratio was maximized, so the laser energy 160 mJ is the more reasonable experimental condition. The calibration model was established based on the CC method using two standard samples (pure aluminum standard samples and self-selected standard samples) under the above reasonable experimental conditions. The results showed that the calibration curve established by the self-selected sample was not ideal compared with the calibration curve established by standard samples, and there were large errors in the results, The fit goodness of the iron element calibration model is only 0.821 3, and the relative standard deviation is also large. When the pure aluminum standard samples were used, under the condition of fixed sample, the fit goodness of the calibration curves for silicon and iron elements was 0.961 1 and 0.974 1, respectively, and the relative standard deviations were 8.85% and 9.43%, respectively. Errors expressed by error bars increased with the increasing of silicon and iron contents in the pure aluminum standard samples. Under the condition of rotating sample pool, the fit goodness of the calibration curves for silicon and iron was 0.978 5 and 0.988, respectively, and the relative standard deviations were 3.78% and 3.4%, respectively. The calibration results showed that the fit goodness was significantly improved and the relative standard deviation was also reduced compared with fixed sample pool condition. The calibration model was significantly better than the model established by the self-selected samples. The 25 samples were detected using two different calibration models by LIBS, the relative errors for the results obtained from different models were compared, and the content of pure aluminum samples has a larger concentration gradient and a wider distribution, so the models obtained from pure aluminum samples have relatively poor adaptability to low-iron primary aluminum samples, while the calibration model established by the self-selected samples is not ideal, but the measurement adaptability for low-iron primary aluminum samples is relatively good. The plasma generated by laser-induced primary aluminum was diagnosed. The plasma temperature was calculated to be approximately 34 100.14 K from the Boltzmann diagram of several magnesium ion lines, The plasma electron density was estimated to be 1.69×1017 cm-3 by the Stark broadening of a line of magnesium, which confirms that the assumption that the plasma obtained from laser induced raw aluminium is in a local thermodynamic equilibrium state is valid.

Spectrum scanning was conducted to characterize xanthate solution by Ultraviolet spectrophotometry. Two strong absorption peaks at the wavelength of 226.5 and 300 nm could be observed, respectively. And the absorption peak at 300 nm was stronger than that at 226.5 nm. Then, the standard curve method was used to measure concentration of the standard samples with different concentrations, and the data set was fitted linearly. It was shown that linear correlation was good at both wavelengths of 226.5 and 300 nm, and better correlation could be found at 300 nm. Therefore, high concentration xanthate solution could be measured at 226.5 nm, whereas low concentration xanthate solution could be measured at 300 nm. Afterwards, quantitative analysis of xanthate solution at different concentrations was carried out at 300 nm. The results showed that either absorbance was at maximum of 1.672 or minimum of 0.032, the linear correlation of standard curve of xanthate solution still remained good. Correlation coefficient decreased as absorbance increased continuously. It should be noted that concentration of xanthate needed to be limited less than 20 mg·L-1 while conducting quantitative analysis. In addition, concentration of xanthate solution was measured at 300 nm under different PH of xanthate solution. It was found that at pH 3, absorbance decreased and xanthate began to decompose. When pH reached 2, absorbance became 0 and xanthate completely finished decomposition. High adsorption performance of xanthate by chalcopyrite could be explored at pH range of 5~10, and highest performance occurred at pH 9. Furthermore, adsorption capacity of xanthate by chalcopyrite surface was also measured at 300 nm. The experimental data were respectively fitted by different equation models, i. e., Freundlich and Langmuir isothermal adsorption equation model, pseudo-first-order and pseudo-second-order kinetic equation model. Sequentially, adsorption kinetics and thermodynamics of xanthate by chalcopyrite surface were studied. The results indicated that in the range of 288 to 303 K, temperature change exerted little effect on the adsorption capacity. The adsorption isotherm of xanthate by chalcopyrite surface was more consistent with Langmuir isothermal model. The actual equilibrium adsorption capacity of xanthate on chalcopyrite Qe was less than or close to theoretical monolayer saturated adsorption capacity, and Qm values were very close to the experimental values, indicating that adsorption of xanthate by chalcopyrite surface was dominated by monolayer chemical adsorption. With the increase of temperature, the adsorption capacity increased, meaning that temperature increment was beneficial to promote adsorption. The adsorption of xanthate on chalcopyrite was predicted to be exothermic but only small increasing extent of adsorption capacity could be observed. Thus, it would be reflected that the adsorption of xanthate on chalcopyrite is less affected by temperature. The adsorption process was spontaneous, with entropy increase and heat adsorption. The thermodynamic parameters could be calculated by Van’t Hoff equation, namely, adsorption enthalpy change ΔH=48.703 41 kJ·mol-1, entropy change ΔS=219.403 88 J·(mol·K)-1, and the adsorption free energy change ΔG=-16.054 93 kJ·mol-1. Therefore, the adsorption process could be defined as chemical adsorption. Adsorption of xanthate on chalcopyrite was more consistent with pseudo-second-order kinetic equation model. Qt value increased with temperature elevation, and the change range was very small. Consequently, it revealed that adsorption process of xanthate by chalcopyrite surface was endothermic, however, it was affected by temperature to a small extent. This was in agreement with the conclusion of thermodynamic analysis, and the value of Qe obtained by fitting was very close to experimental value.

The wood-adhesive joints played an important role in transferring stress, which was an important parameter for bearing capacity of wood members. So, the bonding performance in high temperature determined the fire resistance of wood members. Larch wood, structural resorcinol-phenol-formaldehyde (PRF) and melamine-urea-formaldehyde adhesive (MUF) were selected as objects, and wood moisture content, density, parallel-to-grain tangential shear strength of solid wood, and bonding properties of joints with different adhesives of a total of 216 specimens exposed to elevated temperature ranging from 20 to 280 ℃ were tested. Fourier transform infrared spectroscopy (FTIR) was used to reveal the influences of high temperature on wood-adhesive joints. The results showed that physical reactions occurred to larch wood, and wood color change was not obvious, because there was only density reduction caused by water release at a temperatures ranging from 20 to 150 ℃. When the temperature increased until 200 ℃, thermal degradation of larch wood started, the density decreased slowly and the color gradually deepened. When temperature continued to increase, the wood specimens sharply darkened, and thermal degradation was intensified. So the density loss increased. At 280 ℃, larch wood was charred, and its color was completely converted to black. The density was 72.49% of that at room temperature. The relationship between parallel-to-grain tangential shear strength of larch wood and high temperature was negatively correlated. The shear strength of larch wood was 9.654 MPa at 20 ℃. The shear strength decreased with the increase of high temperature ramped from 20 to 110 ℃. At 150 ℃, wood shear strength decreased to 60.68% of that at room temperature. The shear strength of larch wood decreased obviously when exposed to elevated temperature ranging from 150 to 280 ℃. At 280 ℃, wood shear strength decreased to 1.054 MPa. The bonding properties of joints exposed to high temperature attributed to the thermal stability of adhesives. At room temperature, larch wood has good bonding properties with PRF and MUF. The shear strengths of joints of larch wood with PRF and MUF were 9.071 and 9.619 MPa, respectively, and the wood failure percentage was above 80% at room temperature. With the increase of high temperature, the shear strength of wood-adhesive joints decreased obviously, and the joints with PRF exhibited better than MUF when exposed to a higher temperature. The shear strengths of joints with PRF and MUF both decreased at 20～150 ℃, and was similar to that of larch wood. At 150 ℃, shear strength of joints with PRF and MUF were 60.61% and 60.92% of that at room temperature, respectively, and the wood failure percentage was more than 70%. The shear strength of wood-PRF joints decreased rapidly at the temperatures ranging from 150 to 280 ℃, which was similar to that of larch wood, and was 0.774 MPa at 280 ℃. The bonding properties of wood-MUF joints were more affected by high temperature. Wood failure percentages of joints with MUF were 10% at 220 ℃, and the shear strength was 0 MPa at 280 ℃. FTIR analysis showed that there was no obvious change in the chemical structure of PRF at 20～150 ℃. The further chemical crosslinking and the breaking of ether bond and methylene bridge occurred to PRF as the temperature was higher than 150 ℃. A slight pyrolysis occurred to PRF, but the chemical structure still remained entire. There was no obvious change in chemical structure of MUF exposed to high temperature ranging from 20 to 150 ℃ as same as that of PRF. When the temperature was higher than 200 ℃, the characteristic peak of hydroxymethylwas weakened, the isocyanate group appeared, and the thermal degradation grew severe. So, PRF had a higher heatresistance than MUF. The study can provide data support for the selection of raw materials, and provide a basis for improving the theory and method of fire resistance design for timber structure.

During crop growth, it is often infected by external factors such as pests and diseases. If effective monitoring, diagnosis and scientific control can’t be carried out, it will easily leds to improper or excessive spraying of pesticides. It will not only affect the yield of crops and the economic benefits of farmers, but also cause serious environmental pollution. In recent years, a serious muskmelon leaf spot caused by Cercospora citrullina occurred in Guangxi, which leads to yield reduction and economic losses. In this study, hyperspectral imaging technology was used to detect muskmelon Cercospora leaf spot. Hyperspectral images of healthy leaves and diseased leaves with varying degrees of lesionwere collected at 380~1 000 and 900~1 700 nm. Regions of interest were selected and the corresponding average reflectances spectra was obtained. It was found that the mean reflectance of healthy leaves and the diseased leaves were significantly different and changed regularly according to the degree of lesion. Near 540 nm, the spectra of healthy leaf and leaf with slight lesion had a peak, which disappeared gradually with the increase of lesion degree. In 700~750 nm, the leaf reflectance curve increased sharply, and there was a significant “red edge effect” of green plant spectral curve. In the range of 750~900 nm, the reflectance spectra of healthy leaves and leaves with mild lesions changed steadily. The reflectance of healthy leaves was higher than that of the lesion area. The reflectance decreased with the increase of lesion degree. And this change regularity lasted until 900~1350 nm in the near infrared region. Principal component analysis (PCA) and minimal noise fraction (MNF) were used to observe the characteristics of early leaf lesions. After pretreated with PCA and MNF, the area of infection was more obvious, especially for early lesions. Three-dimensional scatter plot was drawn based on the scores of the first three principal components extracted from hyperspectral images. Although some samples with different degrees of lesion overlap, the distribution of lesion samples and healthy samples is distinct. K-nearest neighbor (KNN) method and support vector machine (SVM) were used to establish the discriminant models. The correctness of KNN model for healthy sample discrimination in the test set was 98.7%. And the discriminant rate of lesion samples increases with the severity of lesion. For the lighter lesion samples, SVM model has higher discriminant accuracy and better classification effect than KNN model. Generally, hyperspectral images had a high discriminant rate (>97%) for healthy samples and lesion samples, however, the discrimination of different lesion degrees is not good enough. It can be concluded that hyperspectral imaging technology can be used to detect muskmelon Cercospora leaf spot disease, but the discrimination of different lesion degrees still needs to be improved in the future.

In order to effectively cope with the feed safety risk caused by illegal additions, improve the detection methods of feeding fat and oils and meet the supervision requirements of feed quality and safety, reliable animal fat and oils were collected, and experimental sample set was obtained by adulterating different proportion (1%, 5%, 10%, 20%, 30% and 40% W/W) of ruminant fats in terrestrial animal fat and oils. Fourier transform infrared spectroscopy (FTIR) combined with stoichiometric analysis was used for discriminant analysis of ruminant constituent in terrestrial fat and oils. Results showed, for the sample set of 1%～40% adulteration proportion, the correct discriminant rate of partial least squares discriminant analysis model was 100%, and no false positive and false negative was found. For the sample set of 0.1%～40%, 0.2%～40%, 0.4%～40%, 0.6%～40% and 0.8%～40% adulteration proportion, the correct discriminant rates were all lower than 100%. With the decrease of the lowest adulteration proportion, the number of false positive and false negative obviously increase, the correct discriminant rate decreases gradually, and the detection limit of FTIR discriminant analysis is proved to be about 1%. The discriminant analysis mechanism was further discussed by comparative analysis of fatty acids, infrared spectral band and chemical bond. It was proved that the absorption peaks at 3 006 cm-1 (representing the tensile vibration of C—H (cis-)) and 914 cm-1 (representing the flexural vibration of HCCH(cis-)) of non-ruminant samples were higher than those of ruminant samples. These mainly reflected the significant difference of cis and unsaturated fatty acids. The absorption peaks at 965 cm-1 (representing the flexural vibration of HCCH(trans-)) of non-ruminant samples were lower than those of ruminant samples, reflecting the significant difference of trans and saturated fatty acids. The content of trans-CC bond for 1% adulteration proportion was significantly higher than the other samples of lower proportions. There was no significant difference for the content of cis-CC bond and C—H (—CH2—) bond between the samples with different adulteration proportions. Therefore, the discrimination of ruminant constituent in terrestrial animal fat and oils by FTIR was mainly based on the characterization of trans-CC bond structure. In summary, infrared spectroscopy can be used as a technique to discriminant ruminant constituent in terrestrial fat and oils with both high efficiency and accuracy.

Rhododendron anthopogonoides Maxim., a traditional Tibetan ethnodrug, has been used for antitussive, expectorant, antiasthmatic, heat-clearing and detoxicating, stomachic and swelling agent for a long time, besides, it is also commonly used for treating rheumatoid arthritis. And most of them are wild. Thus, in this study, R. anthopogonoides from 13 different regions were identified in the range of 4 000～400 cm-1 by adopting infrared fingerprint (IR) in order to identify the adulterants, regions and quality of this herbal medicine effectively. Results showed that the infrared spectrum of the samples are similar. And the main IR absorption peaks of the samples were identified and assigned. Then, the fingerprint of R. anthopogonoides was established and the characteristic peaks were at 3 404, 2 921, 2 852, 1 734, 1 625, 1 449, 1 374, 1 266, 1 060, 534 cm-1. However, there were still some differences in the number, position and intensity of the characteristic peaks at 1 517, 1 316, 1 161, 825, 779, 594 cm-1. Moreover, the common peak ratio and variant peak ratio dual-indexes sequential were also calculated and established, respectively. In addition, the cluster analysis was used to analyze the fingerprint data by using SPSS software. What’s more, the grouping results of sequential analysis of dual-indexes and cluster analysis were nearly the same although the analysis principle and aspect of the two methods were different. And it was shown that the two methods are reliable and can be used to analyze the differences in regions and quality of the samples. Results also showed that the common peak ratios of the samples are ≥68.75, and the variant peak ratios are ≤27.27. The common peak ratios are higher when the samples grow in the closer regions with the similar climatic conditions and growing environments, while variant peak ratios are higher when the samples grow in the farther regions with the different climatic conditions and growing environments. The results of cluster analysis showed that when the Euclidean distance is 15, the samples can be clustered into three categories, where R2, R3 and R4 are one class, R7, R8, R10, R11 and R12 are another class, and the rest are classified into the last class. When the Euclidean distance is 20, the samplesare divided into two categories, where R2, R3 and R4 are one class, and the rest are classified into the other class. When the Euclidean distance is 25, the samples from 13 regions are grouped together. So, the relationships between the quality of R. anthopogonoides and their origins can be summarized intuitively by combining the results of cluster analysis with the figure of sampling plots’ location made with ArcGIS. To sum up, fingerprint combined with sequential analysis of dual-indexes and cluster analysis provides a new method which is effective and rapid for the identification of R. anthopogonoides with the adulterants, regions and quality.

Portland cement (PC) and calcium aluminate cement (CAC) are sorts of inorganic materials applied widely. Gel materials, with short setting time and high strengths, can be prepared by blending PC and CAC. Under rich-water conditions (water-cement ratio>1), the PC-CAC-based rich-water materials can be obtained by adding appropriate amount of gypsum into Portland cement-calcium aluminate cement binary system. However, the long-term strength of the rich-water materials tended to decrease. To improve the strength properties of the PC-CAC-based rich-water materials, certain amount of sodium silicate was blended into the PC-CAC-gypsum ternary system. Herein, RMT-150 mechanical experimental system was applied to test the strengths of the PC-CAC-based water-rich materials with different additions of sodium silicate, thus the strength evolution properties and the impact of sodium silicate on the strength can be illuminated. Then, scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) were adopted to characterize the micro-structures of the corresponding materials, aiming to analyze the development of micro-morphologies and hydrated phases, further to illuminate the strength evolution mechanism of the PC-CAC-based rich-water materials. Strength test results show that the early strength of the rich-water material was low, and its long-term strength would be reduced; however, by adding the sodium silicate, the early strength of the PC-CAC-based rich-water materials can be improved, and the long-term strength retrogression can be reduced partly. When the addition of the sodium silicate was more than 3%, the long-term strength retrogression of the rich-water material could be controlled effectively. The results of SEM, XRD and FT-IR indicate that without addition of sodium silicate and hydrated for 14 days, the CAH10 and C2AH8 with hexagonal structures changed to be C3AH6 with cubic structures, and this crystal transformation caused the long-term strength attenuation of the PC-CAC based water-rich material. When the sodium silicate addition was 1%, on the 3th day for hydration, more calcium silicate hydrate (C-S-H) gel formed compared with the rich-water material without sodium silicate, which brought benefits to the increase of the early strength of the PC-CAC-based rich-water material. After 14 days of hydration, XRD presented the diffraction peaks of C2ASH8 at d=11.75 and 6.24 . And the diffraction intensity of C3AH6 was detected on the 28th day, and was lower than that in the material without sodium silicate, which was confirmed by the vibration bond caused by C3AH6 and appeared at 3 643 cm-1 in FT-IR. This indicates that the addition of sodium silicate can inhibit the formation of C3AH6 by promoting transformation of CAH10 and C2AH8 to C2ASH8. However, the crystal conversion could not be inhibited completely by the sodium silicate addition of 1%, thus the long-term strength still decreased. When the sodium silicate addition rose to 4%, the formation of C2ASH8 had an obvious increase, besides, C3AH6 could not be detected on the 28th day, which indicates that the crystal transformation has been inhibited completely. Therefore, the long-term strength retrogression of the rich-water material was controlled effectively.

Food safety has been the focus problem concerned by the society and the general public whose current state is serious, so it is of great practical significance to realize the rapid detection of harmful substances in food. Synthetic pigment is a common food additive whose excessive and illegal addition is still one of the important problems in food safety, which greatly endangers the health of the people and the healthy development of food industry. Common synthetic pigment detection methods have the disadvantages of long time consumption and high cost, which are not suitable for real-time monitoring and rapid screening of synthetic pigments. In order to overcome the shortcomings of the traditional methods, this paper proposes the use of surface-enhanced Raman spectroscopy to detect synthetic pigments. This method has the advantages of fast detection speed and high detection sensitivity, and can achieve real-time detection in the field. In addition, since Raman detection methods often rely on complex sample preparation operations, and common solid phase extraction techniques rely on manual operations, which are complicated and time consuming, and seriously restrict the rapid detection efficiency of food. Therefore, in this paper, the automatic solid phase extraction device, including mechanical, electrical and software modules, was designed, and by precise control of peristaltic pump flow rate and multi-way valve switch in four steps of activation, loading, rinsing and elution, the device realized the automatic fast solid phase extraction of food samples. In the experimental part, the juice drink with different patent blue V concentrations was prepared, and then the device was used to pre-treat the patent blue V in the juice, in which the extraction column packing and the time and parameters of each step were rationally selected, and then the Raman spectra of patented blue V were successfully detected. The experimental results showed that, compared with traditional manual extraction, the automatic solid phase extraction device has saved nearly half of the extraction time (10 minutes down to 5 minutes) and can process five samples at the same time. The extraction time was not easy to be affected by human factors which significantly improved the efficiency and stability of the sample pretreatment. Meanwhile, because it was relatively less interfered by the external environment, a stronger Raman spectral signal (about 50% enhancement) could be obtained, so the extraction effect was also satisfactory. The results of different concentrations of patented blue V samples show that the method can achieve a detection concentration of 0.5 mg·L-1, which can effectively meet the needs of on-site monitoring, and is characterized by fast, convenient and high sensitivity.

Understanding the forms of phosphorus present in soil is important for elucidating its accumulation, migration, transformation, and bioavailability in the environment. At present, however, studies on the spatial variability of different forms of phosphorus in soil across large-scale transects are rare. In the present study, seven zonal forest soils from sites across different climatic zones were collected along a latitudinal transect in eastern China. The soils used included brown coniferous forest soil from the cold temperate zone, dark brown soil from the middle temperate zone, brown soil from the warm temperate zone, yellow brown soil from the northern subtropical zone, yellow soil from the middle subtropical zone, lateritic red soil from the southern subtropical zone, and latosol from the tropical zone. A chemical extraction method was combined with solution phosphorus-31 nuclear magnetic resonance (31P NMR) spectroscopy to analyze the phosphorus forms present in the soils and determine their relationships with other soil properties. The concentrations of total phosphorus, available phosphorus, inorganic phosphorus, and organic phosphorus in the tested soils ranged from 179.8 to 825.2, 2.41 to 15.3, 92.6 to 351.2, and 14.7 to 474.4 mg·kg-1, respectively. The concentrations of four organic phosphorus components (i. e., active, moderately active, moderately stable, and highly stable organic phosphorus) obtained through continuous chemical extraction were 1.38～30.9, 8.63～213.7, 3.01～32.2, and 1.73～199.2 mg·kg-1, respectively. According to solution 31P NMR spectra, both inorganic (i. e., orthophosphate and pyrophosphate) and organic (i. e., phosphomonoester, phosphodiester, and phosphonate) forms of phosphorus were identified in the test soils. Moreover, neo-inositol hexakisphosphate, D-chiro-inositol hexakisphosphate, RNA mononucleotides, α-glycerophosphate, myo-inositol hexakisphosphate, β-glycerophosphate and scyllo-inositol hexakisphosphate in phosphomonoesters and deoxyribonucleic acid in phosphodiesters were also identified. In all the tested soils, polyphosphate was not detected. Phosphonate was not detected in the soils except in brown coniferous forest soil and dark brown soil, whereas deoxyribonucleic acid was not detected in lateritic red soil. Inorganic phosphorus was dominated by orthophosphate, while organic phosphorus was dominated by phosphomonoester. In general, regardless of whether chemical extraction or solution 31P NMR spectroscopy, the concentrations of total phosphorus, available phosphorus, inorganic phosphorus and organic phosphorus and its fractions tended to decrease from brown coniferous forest soil in the cold temperate zone to latosol in the tropical zone. There was a correlation between phosphorus forms identified using solution 31P NMR spectroscopy and those identified using chemical extraction method. Orthophosphate was most closely related to active organic phosphorus; phosphomonoester and phosphonate were most closely related to moderately active organic phosphorus; and pyrophosphate and phosphodiester were most closely related to moderately stable organic phosphorus. 31P NMR spectroscopy is a more effective method than chemical extraction to understand the spatial variability in soil phosphorus at a detailed molecular level.

There is no silver-bullet solution of eliminating noise during the acquisition process of soil hyperspectral. As the noise interference, the observations of soil spectra are in low signal-to-noise ratio, which affects the estimation accuracy of soil organic matter content. This paper attempts to adopt the wavelet energy features method to reduce the noise in soil hyperspectral and improve the estimation accuracy of soil organic matter content. The Yunlianghu Farm of Qianjiang City, Hubei Province, located in the hinterland of Jianghan Plain, was selected as the experimental area, and 80 samples of paddy soil with a depth of 0~20 cm were collectedin September 2016. After pretreatment (air drying, milling, sieving), soil sample spectral reflectance and determine soil organic matter contentwere collected in the laboratory. The concentration gradient method was employed to divide the whole sample set (80 samples) into a calibration set (54 samples) and a validation set (26 samples). Continuous wavelet transformation was performed using mexh as a wavelet basis function, transforming the soil hyperspectral into sensitive wavelet coefficients of 10 decomposition scales. Then the root mean square of the wavelet coefficients was calculated scale by scaleto define wavelet energy features, and the wavelet energy features vector was determined by the wavelet energy features. The correlation coefficients between the wavelet coefficients and the organic matter content were calculated scale by scale and wavelength by wavelength, and the wavelet coefficient which reaches the extremely significant level (p<0.01) was defined as the sensitive wavelet coefficients. Principal component analysis was conducted to calculate the principal component loads of soil hyperspectral and wavelet energy features vector, respectively. The trend of principal component information of modeled independent variables before and after wavelet energy features transformation would be judged from the difference between the first principal component contribution rate and the spatial dispersion of the first three principal component scores degree. Moreover, regression models were established based on wavelet energy features vector and sensitive wavelet coefficients, respectively, to estimate soil organic matter content. The results showed that with the increase of soil organic matter content, the full-band reflectance decreased, but the spectral reflectance curves of different soil samples were similar, and the reflectance in the near-infrared bands (780~2 400 nm) was higher than that in the visible bands (400~780 nm). The sensitive wavelet coefficients corresponded to wavelengths of 494, 508, 672, 752, 1 838, and 2 302 nm. The first principal component contribution rates of soil hyperspectral and wavelet energy features vector were 96.28% and 99.13%, respectively. The first three principal component scatter points of wavelet energy features vector were more spatially aggregated than those of soil hyperspectral, which demonstrated that the wavelet energy features method effectively reduces the influence of noise. Comparing the estimation models of soil organic matter content, the multivariate linear regression model adopting wavelet energy features vector as the independent variable had the highest estimation accuracy, whose determination coefficients (R2), relative estimate deviation (RPD), and the root mean squared error (RMSE) of validation set were 0.77, 1.82, and 0.82, respectively. Therefore, the wavelet energy features method which is proved to raise the signal-to-noise ratio of the data without adding to the complexity, could improve the estimation accuracy of soil organic matter and realize the dimensional reduction of soil hyperspectral data. This method can be applied to studies like on-the-go soil properties measurement and soil quality monitoring.

Theoretically, hyperspectral remote sensing aided content estimation of soilheavy metal can greatly reduce the cost of conventional chemistries. As a result, hyperspectral remote sensing is gradually becoming a key technology to effectively explore the spatial distribution of soil heavy metal and consequently guide theprevention and remediation of heavy metal polluted soil. However, currently reported hyperspectral retrieval models for soil heavy metal estimation are mostly with laboratory spectra under specifically controlled conditions. Due to the impacts of environmental factors, such as illumination, soil moisture content, and roughness onin-situ field spectra, the wide implementation of in-situ field spectra based remote sensing detection of soil heavy metal is still experiencing the difficulty of reliability. For this, 46 soil samples were firstly collected from a mining area in Hengyang of Hunan Province, China. Then the spectra (ranged 350~2 500 nm) and Cd content of these soil samples were measured using ASD field spectrometer and ICP-atomic emission spectrometry under in-situ field and laboratory conditions, respectively. Then, considering the prior knowledge of laboratory spectra, the principal stepwise regression method was used to develop the Cd content estimation model based on combined laboratory and direct standardization (DS) algorithm transformed in situ field DS spectra with the model robust test by cross-validation. In order to further prove the effectiveness of the model with combined laboratory and DS transformed in-situ field DS spectra, the performance of this model was then compared with four types of hyperspectral remote sensing models including those with spectra from the laboratory, in-situ field, DS transformed in-situ field only, as well as with combined laboratory and in-situ field, one by one. The result shows that while the precision of the hyperspectral remote sensing model with in-situ field spectra (R2=0.56) is lower than the one with laboratory spectra (R2=0.64), the precision of the model with DS transformed in-situ field spectra is improved (R2=0.66). The model with combined laboratory and DS transformed in-situ field spectra is the one with the highest accuracy (R2, 0.72). Meanwhile, this highest robust model discloses that the wavebands located at 482, 565, 979, and 2 206 nm have significantly strong correlations with the soil Cd content. And this result is physically consistent with the model with laboratory spectra. In summary, results in this study confirm the role of the prior knowledge of laboratory spectra and DS algorithm in enhancing the reliability of in-situ field spectra based hyperspectral remote sensing model for soil Cd content estimation. It could provide new theoretical and methodological evidence for the development of soil Cd content estimation by using hyperspectral remote sensing.

The Qingtian stone is one the Four famous Seal Stones in China, which is from Qingtian County, Zhejiang Province. All of the Qingtian Stones are primary ore, belonging to mined Seal Stone. The digged Qingtian Stone has various colors. The red Qingtian stone is not an abundant species among Qingtian stones, which shows unique dark reddish brown. Four kinds of red Qingtian Stones have been studied in this paper, which are Jelly Flower-red Stone, Flower-red Stone, Pomegranate-red stone and Orange-red stone. Their mineral composition and color causingare studied by means of optical microscopy, X-ray powder diffraction (XRD), and Raman spectrum (LRM). Under optical microscopy, the red color distribution had been carefully observed in the slices of four kinds of red Qingtian Stones. The red parts of Jelly Flower-red Stone and Orange-red stone are composed of granulose or massive disseminated in the matrix. But red parts of Pomegranate-red Stone and Flower-red Stone are composed of dots or nervation disseminated in the matrix. In XRD tests, the main and minor mineral compositions had been studied, and the type of the stone had been decided. The main miner composition is pyrophyllite in Jelly Flower-red Stone, Flower-red Stone and Orange-red Stone. So they belong to Pyrophyllite-type Qingtian Stone. Pyrophyllite has two polymorphic types, which are 1Tc and 2M. According to the form and site of the diffraction peaks of XRD pattern at 19°~ 22° (2θ) and 28~31°(2θ), Jelly Flower-red Stone is mainly composed of 2M pyrophyllite, and a minor of 1Tc pyrophyllite. Flower-red Stone and Pomegranate-red Stone are mainly composed of 2M pyrophyllite. The minor compositions are dickite in Jelly Flower-red Stone, quartz in Flower-red Stone, muscovite in Pomegranate-red Stone, respectively. However, the main composition is dickite in Orange-red Stone, which belongs to Dickite-type Qingtian Stone. Dicket has order→disorder structure, which can be decided by XRD diffraction peaks at (020), (110), (112). The minor composition of Orange-red Stone is quartz. Minor and trace minerals in the red Qingtian Stones were tested by LRM. And LRM was mainly used to detect minerals at red parts, and to decide the color causation of red Qingtian Stones. The results indicate that all of the four red Qingtian Stones contain hematite. On the other hand, Jelly Flower-red Stone also contains hematite and pyrophyllite. Flower-red Stone also contains a lot of quartz and minor pyrophyllite and rutile. Pomegranate-red Stone also contains pyrophyllite and rutile. Orange-red Stone also contains quartz. Red color is caused by hematite in Jelly Flower-red Stone, Flower-red Stone and Orange-red Stone, but is caused by rutile in Pomegranate-red Stone. Therefore, all of the four studied Qingtian Stones are colored by impurity minerals.

At present, the research on remote sensing inversion of heavy metal in water is still relatively weak, therefore the study on the spectral characteristic of heavy metal polluted water in nature is an important basic work, which is an important theoretical basis for the band selection when realizing the remote sensing inversion, and the measuring results are also the important parameters necessary for the remote sensing inversion model in the future. Firstly, using Analytical Spectral Devices (ASD) spectrometer, measuring the water-leaving reflectance spectrum curve of mine drainage of Dabaoshan Mountain as an example of typical heavy metal polluted water under two different water depths and light conditions, we found that there was a stable reflection peak in 600~700 nm (red light). Then further comparing the reflection peak position of mine drainage of Dabaoshan Mountain with two types of water (turbid water and eutrophication of water) which are common in nature, we found that the reflection peak position of Changhu Reservoir near the Quartz Factory as an example of turbid water was in 550~700 nm (green and red band) and that of Beijiang River near the Shaoguan Smelter as an example of eutrophication of water was in 550~600 nm (green band), and the position of the reflection peak of these three kinds of water was different, which means that the reflection spectrum of this heavy metal polluted water has a good separability with these two common types of water. On this basis, through a combination of water quality remote sensing model and the indoor water extinction coefficient measurements, we obtained the scattering coefficient and absorption coefficient spectrum of the mine drainage of Dabaoshan Mountain, and further eliminated water molecules absorb effect, finally got the absorption spectrum curve of compositions in this heavy metal polluted water, with the results showing that: it absorbed the strongest in purple band, while the weakest in red band; Starting from 400nm, the absorption coefficient decreased rapidly, then slowdown in the blue and green light band; arrived at the yellow light band, it decreased rapidly again until 676 nm, which reached the minimum; then it increased rapidly to 750nm, and then the change slowed down. Finally, combined with the water quality test results of water samples, the causes of the spectrum of the heavy metal polluted water were analyzed, and we found that the water color in-situ and its absorption coefficient spectrum characteristics were consistent with the color and absorption coefficient of ferric sulfate solution measured in our previous study, therefore we considered that the spectral characteristic of this water sample was caused by the ferric sulfate and its hydrolysate. The results above showed that the absorption spectrum of the mine drainage of Dabaoshan Mountain had obvious characteristics, and the position of the reflection peak and strongest absorption wavelength was clear, which were the important characteristic bands for future extraction of heavy metal concentration in water using satellite remote sensing technique. In this paper, the reflectance spectrum, extinction coefficient spectrum, scattering coefficient spectrum and absorption coefficient spectrum of the mine drainage of Dabaoshan Mountain as an example of typical heavy metal polluted water were obtained for the first time, which provides a method basis for the optical parameters inversion of drainage in other heavy metal mines and also lays a good theoretical foundation for the quantitative extraction of heavy metal concentration in water using remote sensing technology in the future.

Crude protein (CP) is the key parameter for evaluating nutritive value and quality of pasture. It has a great significance for evaluating crude protein content of pasture quickly and accurately in animal husbandry. For confirming the hyperspectral characteristic bands and optimal detection model of crude protein content in pasture, we randomly selected thirty-five sample plots each month from May to September, 2017 in Dorbet, Heilongjiang Province, one hundred and seventy-five samples for all. A 1 m×1 m quadrangle was placed at the sample point during sampling, and all the aboveground pastures in the quadrangle were collected, weighed and stored in cold storage. After carrying the samples the laboratory, we collected the hyperspectral information immediately and determined the chemical values of crude protein by Kjeldahl determination, establishing the hyperspectral dataset of crude protein content. We used five pre-processing methods including SG, MSC, SNV, 1-Der, DOSC to process the hyperspectral data and then, built the PLSR models for confirming the optimal pre-processing method. Based on the optimal pre-processing result, the characteristic bands of crude protein were selected by successive projections algorithm and random frog algorithm, then the PLSR models were built for confirming the optimal selection method of characteristic variables and the optimal hyperspectral detection model. The results showed that the hyperspectral detection model based on SNV was the best in the five pre-processing methods. Thirty bands were selected by SPA and distributed in 530 to 700 nm and 940 to 1 000 nm. Six bands were selected by RF, and respectively were 826.544, 827.285, 828.766, 971.012, 972.494 and 973.235 nm. Therefore, the optimal hyperspectral detection model was SNV-RF-PLSR in this research, and the accuracy of model was good. The results of this research provided an optimal model and theoretical basis for hyperspectral detection of crude protein in pastures and in addition, developed new technique solutions for guiding the production of grassland industry.

This paper aims to explore the response of Fourier transform mid-infrared (FT-MIR) spectra to the changes of the main components in wheat scab with infected different grades and to realize a non-destructive detection of grades of wheat scab based on FT-MIR spectroscopy combined with Sparse Representation based Classification algorithms. The FT-MIR spectra of 95 wheat samples infected with different grades of wheat scab samples were collected in 4 000~400 cm-1. The sensitive wavelengths in the FT-MIR spectra of wheat samples were selected by X-loading Weights and Random Forest algorithms, and Sparse Representation based Classification algorithms were used to build models to predict grades of wheat scab. The results showed that the characteristic wavelengths selected by XLW algorithm and RF algorithm achieved an accuracy of more than 90% for each qualitative analysis model, thus, the characteristic wavelength extraction algorithms could effectively simplify the model and improve efficiency. RF-SRC model had the best results, because the accuracy of the modeling set was 97% and the accuracy of the test data set was 96%. Being infected different grade wheat scab could cause the change of the content of water, starch, cellulose, soluble nitrogen , protein and fat in wheat samples. The characteristic wavelength selected by the RF algorithm could reflect the difference of the spectral characteristics of the FT-MIR spectra of these materials, so the grades discrimination of wheat scab by the RF-SRC model can achieve the best effect. Therefore, it is feasible to distinguish the grades of FHB in Wheat by using FT-MIR spectroscopy and pattern recognition method. This paper explained the mechanism of measuring the grades of FHB in Wheat by FT-MIR.

Chlorophyll content is an important indicator for evaluation of plant nutrition and the occurrence of the pests. The traditional spectrophotometry causes damage to plant leaves and can not be used to obtain the chlorophyll content in a real time, fast and non-destructive way. The chlorophyll meter is recently developed to measure the relative content of chlorophyll (referred to as SPAD value). But this method cannot be used to quantitatively obtain the actual content. The optical radiation transmission model PROSPECT can quantitatively describe the effects of leaf pigment, moisture and structural parameters on the reflection spectrum of the leaves with careful consideration of the biophysics, chemistry and the process of energy transfer in the leaves. In the paper, therefore, this model is used to simultaneously inverse the chlorophyll content and SPAD value of vegetable leaves, and obtain the chlorophyll content of plant leaves real-timely, quickly, non-destructively and quantitatively. First, the reflection spectra of the leaves for three vegetables were measured several times, and the SPAD values of these leaves were measured with a chlorophyll meter. Then, the spectral data were preprocessed to obtain the average reflectance spectrum. Second, the averaged reflectance spectra were fitted by the PROSPECT model with the Euclidean distance as the evaluation function. The maximum distance of the three vegetables in the fitting process was 0.008 9, the minimum was 0.006 4, and the average was 0.007 5. Such a low Euclidean distance indicated that the model could well fit the reflectance spectrum of vegetable leaves. Thirdly, according to the fitting results, the chlorophyll content and the transmittance spectrum were inversed, and the inversed SPAD value of the leaves was calculated with the light transmittance of the leaves at 940 and 650 nm as input parameters. Fourth, this paper established a relationship model between the inversed chlorophyll content, the inversed SPAD value and the measured SPAD value. Two main results were obtained: (1) the chlorophyll content obtained by the model has a good linear relationship with the measured SPAD value and the relationship model is y=1.463 3x+16.374 3. The correlation coefficient between them is 0.927 1. The coefficient of determination of the model is 0.862 and the root mean square error is 2.11. (2) A good linear relationship of y=0.986 9x-0.668 3 is obtained for the inversed SPAD value and the measured SPAD value. The correlation coefficient between them is 0.845 1.The coefficient of determination of the model is 0.714 3 and the root mean square error is 3.380 2. The research shows that the PROSPECT model can be used to obtain the chlorophyll content and SPAD value of vegetable leaves nondestructively and quantitatively with the measured reflectance spectrum of plant leaves as input parameters. This method can be extended to other plants for chlorophyll measurement and real-time monitoring and can provide reliable data support for variable rate fertilization and precision planting. The results presented in this paper can be applied to monitor the growth of the vegetables nondestructively.

Hyperspectral imaging technique which is a non-destructive method combines image and spectral techniques to obtain image and spectral information of target objects’ and qualitative and quantitative analysis using spectral data has been widely used in the field of agricultural product testing. This paper uses visible/near-infrared spectroscopic imaging technique combined with chemometrics methods to achieve the non-destructive detection of fructose content of Lingwu long jujube during storage. The chemical value of jujube fructose was determined by High performance liquid chromatography (HPLC), and the hyperspectral images of long jujubes were collected using near-infrared hyperspectral system, and the average spectral data for each sample area of interest were extracted. Support Vector Machine With RBF Nucleus (RBF-SVM) Model for establishing storage time of long jujube. Orthogonal Signal Correction (OSC), Multiple Scatter Correction (MSC), Median Filter (MF), Savitzky-Golay (SG), Normalize (Nor), Gaussian filter (GF) and Standard Normalized Variate (SNV) were used to preprocess the original spectral data. To reduce the amount and dimension of data, the characteristic wavelengths were extracted by Backward interval Partial Least Squares (BiPLS), Interval Random Frog(IRF) and Competitive Adaptive Reweighted Sampling (CARS); the partial least squares regression( PLSR) model and principle component regression (PCR) were established based on full spectra and characteristic wavelengths for predicting fructose of Lingwu long jujube. The results indicated that the accuracy of the RBF-SVM model calibration set was 98.04%, and the accuracy of the prediction set was 97.14%, which could well predict the storage time of the jujube; The BiPLS, IRF and CARS methods were used to select characteristic wavelengths with 100, 63 and 23 from 125 wavelengths, respectively. In order to simplify the model and improve the accuracy of prediction of the model, the CARS algorithm was used to perform secondary extracted characteristic wavelengths of BiPLS and IRF and select characteristic wavelengths with 18 and 15, respectively, which significantly reduced the number of characteristic wavelengths. Comparing models of the full band spectrum with the models of extracted characteristic wavelengths of PLSR and PCR, PLSR model based on the characteristic variables selected by CARS was the best, and correlation coefficient of Calibration set (Rc) and root-mean-square error of Calibration set (RMSEC) of the model were 0.854 4 and 0.005 3, and correlation coefficient of prediction (Rp) and root-mean-square error of prediction set (RMSEP) of the model were 0.830 3 and 0.005 7, respectively, which indicated that CARS effectively reduced the dimension of the spectrum and simplified the data processing. The results showed that visible/near-infrared hyperspectral imaging technique combined with chemometrics methods and computer programming can effectively detect fructose content of Lingwu long jujube rapidly and non-destructively, providing a theoretical basis for the detection of internal quality of Lingwu long jujube.

Heilongjiang Province is the largest japonica rice producing area and commodity grain base in China. In the process of rice planting, selecting suitable rice varieties is the key to achieving high yield. In agricultural production, the selection of rice varieties is influenced by factors in many aspects. Generally speaking, different rice varieties planted in the same temperate zone have little difference in appearance, or even no difference. It is difficult to make an accurate distinction by visual observation. In order to accurately distinguish different varieties of japonica rice seeds that are difficult to distinguish by naked eyes, a rapid non-destructive discrimination method for japonica rice based on near-infrared spectroscopy (NIRS) was proposed. 3 varieties of japonica rice seeds (seeds 5th, seeds 6th and Sui japonica 4th) planted in Heilongjiang reclamation area were selected as the research object. For each variety, 40 samples were selected, 30 of which were used as modeling set and 10 as prediction set. The NIRS data of all 120 samples were obtained by scanning. The noise at both ends of the original spectral data (11 520～4 000 cm-1) were clipped, the spectral data in the range of 8 250～5 779 cm-1 with strong absorbance were selected as the research band. Firstly, a reference model was established, that is, BP model 1 was established directly from raw spectral data, and BP model 2 was established from the spectral data preprocessed by first derivative (FD) and Savitzky-Golay (SG). The classification accuracy of model 1 was 93.3% with RMSEP=0.232 8, and the iteration time was t=3 882.9 s. The classification accuracy of model 2 was 100% with RMSEP=0.070 6, and the iteration time was t=954.5 s. Comparing the evaluation parameter RMSEP of the two models, it was found that FD+SG preprocessing can improve the prediction ability of the model. However, because the two models do not reduce the dimension, the amount of data is too large, the input nodes of the model are too many and the iteration time is too long, which is not conducive to the practical application. Therefore, the wavelet transform with multi-resolution characteristic was used to reduce the dimension of the data. The residual sum of squares of the prediction set (Press value) were used as the evaluation index. Sym2(symlet2) wavelet with decomposition scale 5 was selected to compress and reduce the dimension of the spectral data from 601 dimension to 21 dimension. The results of wavelet transform were used as the input of BP model 3, which was compared with model 1. The classification accuracy of the model 3 was 93.3% with RMSEP=0.225 0, and the iteration time was shortened to 198.9 s. The comparison results showed that dimensionality reduction based on wavelet transformation can reduce the input of the neural network, thus simplifying the structure of the neural network and improving the iterative speed, but the effect of improving the prediction ability of the model is not obvious. The comparison results of the three models showed that FD+SG preprocessing can improve the prediction ability of the model, and the wavelet transform can improve the iteration speed of the model. Based on above analysis results, a neural network discrimination model 4 with 21 inputs, 15 hidden layers and 3 outputs of FD+SG+wavelet transform was established. Moreover, its recognition rate of classification was 100% with RMSEP=0.029 3 and the iteration time was t=98.8 s, which could identify three different japonica rice varieties quickly, accurately and non-destructively. Therefore, the method of wavelet reduction and back propagation artificial neural network (BP) discrimination model based on near infrared spectroscopy can be used for rapid and nondestructive discrimination of japonica rice seeds, providing a reference method for other crop seeds recognition.

Many different varieties of rice look very similar, but their chemical composition and final product quality vary greatly, which causes huge economic losses each year as a result of variety confusion. Identification of rice varieties is the practical requirement for developing high quality grain engineering. In this paper, a fast and non-destructive method for rice variety identification using hyperspectral imaging technology was proposed. The main research contents and results were as follows: (1) Average spectrawere extracted from the region of total 150 samples with wavelength from 388～1 000 nm. In the full band, the reflectance was most obvious at 600～800 nm, which was calculated by Stacked stacking and curve-smoothing for increasing its differences. (2) Principal component analysis (PCA) was used to analyze the reflectance data smoothed. It was found that the wavelength with the largest weight coefficient was located at 680 nm and used as the characteristic wavelength. Loading the texture image of the characteristic wavelengths, the texture characteristic parameters of each rice sample were calculated as follows: Mean, Variance, Entropy and Skewness. Meanwhile, the thresholding method was used to separate the target from the background, and the morphological parameters of each grain werecalculated as follows: areas/pixels2, perimeter/pixels, length of long axis/pixels, length of short axis/pixels. Based on the texture characteristics and morphological characteristics, the Fisher discriminant analysis model, partial least squares regression (PLSR) mode and Artificial neural network model (ANN) were established respectively for rice variety identification. (3) The results showed that the cumulative variance contribution rate of function 1 and function 2 established by Fisher discriminant analysis reached 93%, which could better explain the rice variety information. Comparing the function value of the sample with the square Mahalanobis distance of the group centroid, the individuals with similar values were taken as the same category. The overall recognition accuracy of the five rice varieties could reach 95.3%. The PLSR model: Yvarieties=0.03Xmeans-0.36Xvarious-0.24Xentropy+0.37Xskewness+0.31Xarea-0.32Xperimeter-0.39Xlength of long axis+0.45Xlength of short axis, with correlation coefficient (r)=0.98, corrected root mean square (RMESS)=0.29, cross validation root mean square (RMESSCV)=0.32, the accuracy of rice varieties identification could reach 95%. The neural network model is a two-layer feedforward network with sigmoid hidden and soft max output neurons, which randomly divides 150 samples into training samples, validation sets and test sets according to the ratio of 70%∶15%∶15%. With training algorithm of conjugate gradient method and evaluation index of Cross-Entropy method, the accuracy of rice variety identification can reach 98%. The overall results show that the neural network model of rice variety identification is superior to Fisher discriminant and PLSR in classification accuracy, which has an important guiding significance for rapid and non-destructive identification of rice varieties.

In order to explore a better hyperspectral inversion model for monitoring nitrogen content in rice canopy leaves by remote sensing, based on rice plot experiments, the canopy height spectral data of rice at different growth stages were obtained. Based on the comprehensive comparison of the first derivative (1-Der), standard normal variable transformation (SNV) and SG smoothing method, a spectral processing method (SNV-FDSGF) combining standard normal variable transformation with SG filtering method of first derivative was proposed. The sensitive bands of different growth stages were screened out by non-information variable - competitive adaptive reweighted sampling method (UVE-CARS). Two sensitive bands of each growth period were randomly combined to construct a difference spectrum index DSI (difference spectral index), a ratio spectral index RSI (ratio vegetation index) and a normalized spectrum index NDSI (normalized defference spectral index) with high correlation with nitrogen content in rice leaves. Among them, the optimal vegetation index and determination coefficient R2 at the tillering, jointing and heading stages were: DSI(R857, R623), 0.704; DSI(R670, R578), 0.786; DSI(R995, R508), 0.754. Using the superior three planting indices in each growth period as inputs, the adaptive differential optimization extreme learning machine (SaDE-ELM), radial basis function (RBF-NN) and particle swarm optimization BP neural network (PSO-BPNN) inversion models were constructed respectively. The results showed that SaDE-ELM had the best modeling effect. Compared with RBF-NN and PSO-BPNN, the stability and prediction ability of the model were significantly improved. The determination coefficient R2 of training set and verification set of each growth phase inversion model was above 0.810 and RMSE was below 0.400, which could provide certain theoretical basis for quantitative prediction of nitrogen content in rice canopy leaves.

In this paper, we mainly study a new method for estimating the Mg abundance of stars from the medium and low resolution spectra based on ELM algorithm. LAMOST provides us with a massive low-resolution spectrum, and determining the abundance of Mg elements in these spectra will help us understand the history and evolution of the Milky Way. At present, the traditional method of determining the abundance of Mg element from medium and low resolution spectra is the template matching method. However, this method is difficult to optimize parameters and is sensitive to noise. Therefore, it is necessary to study new methods to estimate the Mg abundance. The experiment show that ELM algorithm is agood alternative to traditional method. The accuracy of ELM algorithm on MILES spectra is 0.009 9 (0.15) dex, while on the LAMOST spectra with signal-to-noise ratios larger than 50 it is 0.002 7 (0.11) dex. A comparison of ELM with other four algorithms shows that ELM algorithm can accurately estimate the abundance of Mg elements from low resolution spectra and can be applied to the LAMOST spectra.

Star spectral classification is a foundational work of stellar research. The Morgan-Keenan (MK) classification system which was developed in 1970s is the most widely used classical classification system. However, MK based interactive decision classification system has some difficulties when dealing with massive quantity of astronomical spectral data. Nowadays the most widely used method of automatically classification is template match which neglects measuring the spectral line. As a result, one of the most popular topics is how to extract features from massive data objectively and precisely and to apply the features for making classification decisions. In this paper, we processed the spectral data of LAMOST DR4 stars to obtain the line index as input data and used the official released labels of the spectrum as outcome. The XGBoost algorithm was applied to automatically classify the stellar spectra and rank the features. In this way, the identified and potential line indices which are sensitive to classification were revealed. Firstly, we labeled and selected the spectral data of stars with B, A, F and M by LAMOST high signal-to-noise ratio (S/N>30) with the sample size amounting to around 41. 4 million. Then, the line indices of spectral data was calculated to reduce the dimension and to filter out the redundant information. Secondly, the processed star spectral data were randomly divided to a training set and a test set. By modifying the parameters, the required classification decision tree model was fitted by training set using XGBoost algorithm and the stability and availability of the model were validated by test set to avoid over-fitting. In the meantime, the classification features were extracted by the algorithm’s own function. Finally, the branch with the highest probabilities was selected as the final decision tree model. Through experiments, it is shown that the XGBoost model has a better performance in self-adaptability under fixed parameters with less affection in data sets and the overall accuracy rate as high as 88.5%. Moreover, the output classification decision tree is more consistent with identified features and the numerical characteristics of spectrum and its corresponding range are obtainable through the model. This would shed light on providing quantitative rules for evaluating classification decision trees with numerical spectral features.

Classification of stellar spectral data is one of the most basic tasks in automatic recognition of celestial spectra. The study of spectral classification can provide clues to the evolution of stars. With the development of science and technology, astronomical data are also moving towards the era of big data. The number of stars that need to be processed is increasing. How to classify them automatically and accurately has become one of the difficult problems that astronomers have to solve. At present, there are few methods to solve the problem of Star automatic classification. In this paper, a convolution neural network based method is used to classify star spectral MK system. The network is composed of data input layer, four convolution layers, four pooling layers, full connection layer and output layer. Compared with traditional network, it has the advantages of local perception and parameter sharing. In this paper, a simple and efficient convolution neural network with four convolution layers is constructed by Tensorflow in Python 3.5 environment. Dropout is applied to the full connection layer to prevent over fitting. Dropout’s basic idea: When the network model is trained, some neural network nodes are discarded in a certain proportion, so that they do not play a role temporarily. Dropout can be understood as a very efficient neural network model averaging method, because it does not depend on some local features, it can make the network model more robust. The one-dimensional star spectrogram used in the experiment was downloaded from the LAMOST DR3 database. First, the spectrum was intercepted by pretreatment. After uniform sampling, it was initialized by min-max standardization method. The experiment consists of two parts. The first part classifies the spectrum according to the star spectrum MK system. Each training sample contains 1 000 spectral data and 400 spectral data. First, the CNN network is trained by training samples, and then 3 000 iterations are carried out. Then, the test samples are divided into several parts by the trained network. The second part is the classification of adjacent two types of star spectra, in which the O-type star data set sample is 250 spectra, and the rest are 4 000 spectra. The data are divided into five parts, one of which is selected as test set each time, the rest as training set, using 5 fold crossover. The accuracy of the model was calculated by the verification method, and the BP neural network was used for comparative experiments. The indicators to evaluate the network model include accuracy rate P, recall rate R, F-score and accuracy rate A. The experimental results show that the classification accuracy of the six types of stars is more than 95%. When classifying the adjacent types of stars, the classification results are not ideal because of the small sample size of O type stars. The classification accuracy of the other types of stars is higher than 98%. All the above results prove that CNN algorithm can classify the stars. The classification of stellar spectra is well solved.

Temperature is a key parameter of the state of the atmosphere. Temperature data play an important role in such fields as atmospheric dynamics, climatology, meteorology, and chemistry. It is also an indispensable input parameter for remote sensing inversion of other parameters. As a remote sensing instrument, lidar has been used in the detection of meteorological elements (wind, temperature, Aerosol Optical Depth, etc). And lidar techniques for the remote sensing of atmospheric temperature profiles have reached the maturity stage for routine observations. Currently, there are some types of temperature lidars, such as Raman lidar (vibration and rotation), resonant fluorescence lidar and Rayleigh scattering lidar. However, a high power laser and a complicated background filter are required for Raman lidarto ensure the accuracy of the temperature, resonance fluorescent lidar cannot detect the temperature in the stratosphere, and most of lidar based on Rayleigh scattering can only measure the relative temperature of the atmosphere. That is to say, the definition of response functions and calibration procedures is necessary for temperature retrieval. The time resolution of the method of atmospheric temperature measurement based on solid cavity scanning F-P interferometer is low. In the lower atmosphere, Rayleigh scattering spectrum of molecule is influenced by the Brillouin scattering spectrum, the superposition of two signals to form Rayleigh-Brillouin scattering spectrum, so there is a large error in temperature obtained by measuring the full width at half maximum of echo spectrum, and the particles scattering has a great influence on the retrieval results when the temperature is inverted by the integral technique. In this paper, Fizeau interferometer and PMT array are proposed to measure the molecular Rayleigh-Brillouin scattering spectrum, and the parameter optimization design of free spectral range, solid cavity length, cavity reflectivity of Fizeau interferometer and the scanning interval were carried out. And the method of reducing particle scattering effect is proposed. The information of discrete points on the RB spectral was obtained by Fizeau interferometer that the parameters was optimized, and the square method was used to get the fitting line. The temperature retrieval was achieved by comparing the theoretical spectra obtained with the 1976 U. S. standard atmospheric model and the Tenti’s S6 model. The simulation results prove that the proposed method is feasible to reduce the influence of particle scattering, and the error of atmospheric temperature between the top of the boundary layer to the top of the tropopause is less than 1K without considering the influence of cloud and wind. This temperature retrieval method can detect the absolute temperature profile with high precision and temporal resolution. There is a reference significance for the investigation of filter system of similar lidar, providing a set of feasible spectroscopic system solutions and temperature retrieval methods for our country’s ground-based and spaceborne hyperspectral thermometry lidar.

Terahertz time-domain spectroscopy is a widely used spectrum measurement technology in terahertz frequency domain, which can be applied to the spectrum analysis of many substances, and has an incomparable role in the research of chemistry, semiconductor and biomolecule. However, the consumption of the sample is more, and the occupied space of the whole system is larger in the terahertz time-domain spectroscopy system. These limitations hinder the further development of this system. In order to overcome the limitations of the system, a terahertz on-chip system, which integrates THz generating device, detecting device and waveguide transmission device on a silicon wafer, is designed. Due to the high integration of this system and the limitation of low-temperature gallium arsenide (LT-GaAs) of photoconductive antenna growth conditions, how to fabricate the LT-GaAs semiconductor film substrate and transfer and bond it is a key step in the THz on-chip system. The epitaxial wafer consists of semi-insulating gallium arsenide, GaAs buffer layer, AlAs sacrificial layer and LT-GaAs layer. In order to obtain LT-GaAs thin film with a thickness of 2 μm more efficiently, the selective corrosion rate of HCl solution at different temperatures and different concentrations with AlAs sacrificial layer is studied. The optimum volume ratio, concentration and optimum temperature of HCl during the preparation of LT-GaAs thin film are 13.57% and 73 ℃. Compared with the existing processes, this method has higher safety performance and lower equipment requirements. Finally, the single-layer LT-GaAs thin film is transferred and bonded to a silicon wafer. Terahertz signals generated by excitation of the photoconductive antenna structure using a femtosecond laser pulse are detected, and the experiment shows that the LT-GaAs film acquisition and transfer bonding process satisfies the production requirements of the THz on-chip system and has laid a solid foundation for the development of the THz on-chip system.

In order to solve the problem that the composition of oil pollutants is complex and the spectrum overlap is difficult to identify, qualitative and quantitative analysis of oil pollutants was carried out by three-dimensional fluorescence spectroscopy combined with Algorithm Combination Methodology (ACM). Rayleigh scattering in fluorescence spectra has a great influence on the detection of three-dimensional fluorescence spectrum. In this paper, the missing data recoveryr-principal component analysis (MDR-PCA) method was proposed. The principle is that the single fluorescence spectrum excitation emission matrix conforms to bilinearity and can be analyzed by principal component analysis (PCA). The scattering interference data were first deducted completely, and then the deducted part was repaired by using the remaining effective signal data in the iteration process. This method not only eliminates the scattering interference, but also makes full use of the effective information in the fluorescence spectrum matrix. The three-dimensional data were constructed by using the excitation-emission fluorescence spectra of different concentration mineral oil. The sample data were obtained from carbon tetrachloride solutions of 0# diesel, 95# gasoline and ordinary kerosene solutes. The trilinear decomposition algorithms commonly used for three-dimensional fluorescence spectral data analysis include parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), and self-weighted alternating trilinear decomposition algorithm (SWATLD). PARAFAC is based on the strict principle of least squares and has strong anti-noise ability. Its model is the stablest and the error is expected to be the smallest. It can provide the best fit of 3D data array, but the convergence speed of PARAFAC algorithm is slow and correct. The estimated number of components is more sensitive. The ATLD algorithm is based on the Moore-penrose generalized inverse of singular value decomposition to realize the trilinear model decomposition. By using the inverse diagonal element and the tangent singular value to solve the generalized inverse, the convergence speed of the method is greatly improved, and the sensitivity of the algorithm to the component number is reduced, but the operation of the diagonal element makes the ATLD method more sensitive to noise. SWATLD inherits the advantages of ATLD, which is insensitive to the number of components and fast convergence, and has the characteristics of being insensitive to noise levels. However, the SWATLD algorithm has a slightly lower ability to resist collinearity than ATLD. This paper divides the iteration process according to the change of loss function in the iteration process of trilinear decomposition algorithm, and proposes the algorithm combination method (ACM)-combining ATLD, SWATLD and PARAFAC, giving full play to the advantages of each algorithm, and realizing the complementary advantages of the second-order correction algorithm. The three-dimensional fluorescence spectra of two-component and three-component mineral oil samples were analyzed by ACM algorithm, and the recovery rates of three mineral oil samples were calculated. The recovery rate of diesel was 97.08%, the recovery rate of gasoline was 97.34%. and the recovery rate of kerosene was 97.25%. The analytical spectrum and the recovery rate show that the ACM algorithm can realize species identification and concentration measurement of oil pollutants.