Spectral imaging can record the spectral information of the scene to achieve high-fidelity color reproduction. The spectral image is usually mixed with the spectral information of the object and the light source information at the same time, so the existing color reproduction methods of spectral imaging need to obtain the light source information of the environment through the calibration target placed in advance or the object with known spectral characteristics, and then correct the color constancy of the image. However, it is usually difficult to meet the above conditions in the actual use of spectral cameras, which brings challenges for image high-fidelity color reproduction. A calculation method of image color constancy for spectral imaging was proposed. The spectral data obtained by the spectral imaging system were converted into the XYZ color space, and the image was divided into regions for statistics to get the statistical points of the image. According to the distribution rule of many common light sources, position weights and color temperature weights were applied to statistical points, and luminance weights were set to remove over-dark and over-saturated statistical points in the image. The chromaticity parameters of ambient light were obtained by weighted average. Moreover, the XYZ color space data of the image were converted to RGB color space according to the application requirements. The gains of different channels of the image were calculated according to the chromaticity parameters of ambient light to complete the spectral image’s color constancy calculation. In order to verify the effectiveness of the proposed algorithm, 140 spectral images were processed, and the reproduction angle error between the chromaticity of the ambient light obtained by the algorithm and the chromaticity of the real light source was calculated. The correction results showed that the proposed algorithm was better than the spectral images without processing and the Gray-world method. In order to further analyze the relationship between the algorithm correction results and human perception, a color psychophysics experiment was designed, 18 observers with normal vision participated in the experiment. The average score of all observers for the algorithm correction results was between good and excellent, which can meet the actual use needs. Along the direction of the average color temperature line, the chromaticity difference of the light source accepted by the observer was slightly larger than in other directions. When the image contained a large area of memory color, and the chromaticity of the light source shifted in a direction that increased the saturation of the memory color, the chromaticity difference accepted by the observer became larger. The results of objective and psychophysical experiments show that the proposed algorithm can deal with the image color constancy in spectral imaging well when the light source is unknown, and there is no calibration target, which lays a foundation for high-fidelity color reproduction of spectral images.

Nucleic Acid Testing (NAT) has become the “gold standard” for diagnosing SARS-Cov-2 infections in China. However, NATis affected by factors such as the disease course, specimen collection, the testing process, which are prone to false positives and missed diagnoses. The detection of SARS-Cov-2-specific IgM and IgG antibodies in serum has been used as an auxiliary method for SARS-Cov-2 detection, which can remedy the “false negative” shortcomings of NATand improve the positive rate of detection. A low-cost portable device is proposed in the paper for SARS-Cov-2 IgM and IgG antibody quantitative measurement. The device comprises a CPU signal processing module, a light source driver module, a multi-spectral detection module, a power supply module, and a display storage and communication module. Fluorescence immunochromatography technology combines the IgM and IgG antibodies in the sample to be tested with the quantum dots and trapsthem on the T line of the test strip. The light source driver module with a square-wave modulation is utilized to simulate the ultraviolet LED. Consequently the emitted ultraviolet light illuminates the T line and C line of the immunofluorescence chromatography test strip through a roof-shaped optical system. The quantum dot marker is excited by the light source to emit red fluorescence, and then the red fluorescence is captured by the multi-spectral detection module passing through the narrow-band filter. Then, the multi-spectral signal is subjected to the FFT transformation in the CPU to obtain its spectral feature. The ratio of the feature of the signal band and the reference band is applied for normalization processing to eliminate the background and environmental interference signal and to calculate the fluorescence intensity. The final detection results are displayed on the UI and uploaded to the server database, and data sharing and information management can be realized through a remote query on the computer. Therefore, the contents of IgM and IgG antibodies are determined according to the calibration coefficient of the feature ratio. Repeated experiments have been carried out using normal human serum samples as measurement reagents. The experimental results show that the CV value ranges from 0 to 8.30%, among which the CV value of the T line is only 3.45%. Two types of solution containing IgG and IgM antibodies are used to prepare a series of sample solutions with different mass concentrations using the gradient dilution method for the linearity experiment.The result is fitted by the least square method with a fitting coefficient of 0.997 5. Finally, the serum antibody test of the SARS-Cov-2 vaccine shows that the positive detection rate is up to 75%. The device has a tiny structure, low power consumption, simple operation and good performance. Therefore, it can be applied as an auxiliary means to improve the positive detection rate of SARS-Cov-2 infection effectively.

Absolute spectral responsivity is one of the important technical parameters of detectors. With the development of terahertz detection technology, it is becoming increasingly important to measure the absolute spectral responsivity of terahertz detectors accurately. Due to the lack of a continuously tunable terahertz light source and spectroscopic system in the terahertz band, the traditional method of measuring the spectral responsivity of ultraviolet, visible and infrared detectors can not be used to measure the spectral responsivity of terahertz detectors. In this paper, the relative spectral responsivity of 2~10 THz is measured based on the reflection method, and CO2 measures the absolute responsivity of 2.52 and 4.25 THz pumped gas laser as the light source, and the absolute spectral responsivity of 2~10 THz is obtained. The two frequency points of THz absolute responsivity and relative spectral responsivity are mutually verified. The ratio of absolute responsivity measurements at 2.52 and 4.25 THz is 0.753, and the ratio of relative spectral response measurements is 0.749. The difference between the two is only 0.004. Therefore, the reflection method used in the article to measure the relative spectral response of the terahertz detector is feasible. In addition, the test of water vapor in the terahertz band has a great influence. This article tests the attenuation characteristics of the atmosphere in the 1.5~10 THz band. The test shows that water vapor has a significant attenuation effect on the terahertz wave. When measuring different environmental humidity Will produce different results, so the humidity of the atmosphere needs to be strictly controlled during the measurement process of the terahertz detector. Especially before the 3.3 THz band, due to its weak signal, if the water vapor is too large or changes greatly during the test, the testing effect will be seriously affected. The system can satisfy terahertz detectors’ development, production, detection and application. It can guide material selection, process improvement, data compensation, optical system design and image processing, and promote the effectiveness of terahertz weapons and equipment. Therefore, the measurement of the absolute spectral response of the terahertz detector is very important for device designers, imaging equipment system designers and device users.

Tobacco is an important economic crop and source of tax revenue in our country. It makes a huge contribution to the country’s economic development. However, tobacco diseases affect the yield and quality of tobacco leaves seriously. Therefore, It is important that the use spectral analysis technology for early prevention and control of tobacco diseases. Objects of research are tobaccos inoculated with tobacco mosaic virus (TMV) and potato Y virus (PVY). The hyperspectral data of infected tobacco cultivated indoors and outdoors are collected respectively. In order to improve the detection accuracy of tobacco diseases, spectral data of two kinds of diseased tobacco are collected every two days, each disease data is divided into five severity levels in detail, and finally, 1 697 spectral data in the 350~2 500 nm band are obtained. In order to make effective use of hyperspectral tobacco data, this paper is based on a support vector machine (SVM), combined with a fast nearest neighbor band selection algorithm (FNGBS) and normalized matched filtering (NMFW), and proposes a combination of clustering and sorting Band selection algorithm (FNG-NMFW). Firstly, FNG-NMFW uses the FNGBS to group the tobacco spectrum finely and then sorts the groups of bands based on the NMFW algorithm to select the characteristic spectrum and realize the extraction and dimensionality of the tobacco spectrum. After completing the band selection, this paper uses SVM to classify tobacco characteristic spectra and achieves high-precision tobacco disease detection. The research results show that the model has stable performance and high accuracy. When the proportion of training samples is only 40%, an overall accuracy (OA) is better than 80%; when the number of feature bands is selected as 40, OA can be better than 85%. The algorithm can achieve higher accuracy for both TMV and PVY diseases, but the recognition accuracy of TMV is slightly lower than that of PVY. For the monitoring of TMV1 and TMV3, the algorithm can achieve monitoring with an accuracy better than 94%, and for the monitoring of PVY1 and PVY3, the accuracy of the algorithm is close to 90%, which shows that the algorithm can realize the early identification and prevention of two diseases. Compared with the model that uses full-band spectral data for disease detection, the FNG-NMFW model has obvious advantages. The accuracy of tobacco disease detection results is 94.46%, the accuracy is improved by more than 1.5%, and the modeling time is shortened from 12.9 seconds to 1.1 seconds.

Aims to overcome the shortcomings that the prediction accuracy of a single model is hard to improve further, A heterogeneous ensemble learning model based on the Stacking framework, combined with near-infrared spectroscopy analysis technology, was adopted to detect the oil content in oil shale in this study. A total of 230 oil shale core samples, collected from some block in Songliao Basin, were taken as the research object, whose oil content was measured by the low-temperature dry distillation method, and near-infrared spectral data corresponding to each sample was scanned simultaneously. The Monte Carlo algorithm was employed to eliminate outlier samples, and 213 samples, after removing outliers, were randomly divided into a training set and test set according to the ratio of 3∶1. The detrend coupled with the baseline correction method was used to eliminate the influence of noise and baseline drift in spectral data. After that, the random forest algorithm (RF) was used to extract the characteristic wavelength according to the importance of wavelength. In order to further reduce the data dimension, the CARS algorithm was used to extract the characteristic wavelength. Finally, PLS, SVM, RF and GBDT, whose parameters were optimized by grid search, were adopted as primary learners, and the PLS regression modelwas adopted as secondary learners to build the stacking ensemble learning model. The accuracy of the single and ensemble learning models for oil shale oil content prediction was compared under evaluation indicators of R2 and RMSE. The research results show that the RF-CASR method can effectively screen important wavelengths and improve the efficiency of the model, thereby improving the model efficiency. Heterogeneous integrated learning models based on Stacking have better predictive performance and greater stability than single models (SVM, PLS) and homogeneous integrated learning models (RF, GBDT). Based on multiple random divisions of the data set, the average R2 of the Stacking ensemble learning model is 0.894 2, an average increase of 0.062 3 compared with other models; the RMSEP of 0.5869 is an average of 0.147 4 lower than other models. The results of this study show that the heterogeneous integrated learning model based on stacking can combine the advantages of primary learners to predict the oil content of oil shale quickly and accurately, which provides a new fast and portable method for oil shale oil content detection.

Nitrate nitrogen (NO3-N) is one of the “three nitrogen” (nitrate nitrogen, nitrite nitrogen, ammonia nitrogen) in water, can reflect the degree of pollution of the water environment, and is an important indicator of water quality assessment. High concentrations of nitrate nitrogen in the water body will not only lead to increased pollution of the water environment, but also pose a greater threat to humans, animals and aquatic products. The traditional nitrate nitrogen detection must be measured after the reaction, with a long time, complex operation, secondary pollution and other disadvantages. The spectrometry method has the significant advantages of rapid, non-destructive, and no reagent consumption. To address the problem that nitrate nitrogen is difficult to be detected quickly, a method for rapid quantitative analysis of nitrate nitrogen based on UV absorption spectroscopy was proposed. The UV absorption spectra of 42 samples of nitrate nitrogen standard solutions with concentrations ranging from 0 to 20 mg·L-1 were collected, and each sample was averaged 11 times to reduce the influence of instrument noise and environment. The SPXY algorithm was used to divide the training set and test set according to the ratio of 7∶3, and the UV absorption spectra data were preprocessed using the Savitzky-Golay (SG) filtering algorithm. The appropriate regularization parameter λ=0.203 6 was obtained by 10-fold cross-validation with Lasso regression, and then the Lasso regression was used to filter out the correlations with nitrate nitrogen in the full spectral range. The spectral features associated with nitrate nitrogen were selected in the full spectral range using Lasso regression. The absorbance at the feature wavelength was fitted with the sample concentration by partial least squares (PLS) to establish the regression model for nitrate nitrogen. The R2 and RMSE of the training set of the model built using the modeling method proposed in this paper were 0.999 91 and 0.060 15, respectively, and the R2 and RMSE of the test set were 0.999 72 and 0.046 91, respectively. In order to verify the effect of the SG-Lasso-PLS prediction model proposed in this paper, additional Lasso-PLS, SG-PCA-PLS and SG-PCA-PLS were built. PLS and SG-PCA-SVR prediction models were compared. The validation results show that the R2 and RMSE of the prediction models established by SG-Lasso-PLS are better than those of the other three. It indicates that SG filtering can eliminate the spectral signal’s random noise and improve the model’s prediction accuracy. Compared with the PCA data dimensionality reduction algorithm, Lasso can achieve spectral feature selection and data dimensionality reduction in the full spectral range, which can effectively eliminate the redundant information of spectral data and improve the model’s prediction accuracy. Therefore, the hybrid SG-Lasso-PLS model proposed in this paper can quickly and accurately predict the nitrate nitrogen in water bodies. As a basic study of nitrate nitrogen concentration detection, it can provide an algorithmic reference for fast and pollution-free water quality online monitoring scenarios.

In the rapidly establishing quantitative analysis model of near-infrared spectroscopy, feature wavelength selection is one of the more effective methods to improve prediction accuracy. Through selecting effective information, redundant data is reduced, and the effectiveness of the data set is improved. Random Forest (RF) is an integrated algorithm. The feature importance of spectroscopy wavelength can be calculated by using RF. And the mean square error average value is used as the feature importance result based on the mean decrease accuracy (MDA) method of Out-of-Bag data (OOB). The feature variables are selected to form the feature wave subset by setting the feature importance threshold. However, there is no theoretical basis for setting the threshold range. So it is necessary to explore the range of feature importance thresholds. On the other hand, due to the random characteristics of RF, invalid or even interfering variables may be included in the characteristic wavelength subset, and the selected effectiveness variables cannot be guaranteed. Therefore, the RF-iPLS feature wavelength selection algorithm is further proposed.The feature wavelength subset is divided into intervals by interval partial least squares (iPLS), which makes up for the problem of invalid variables caused by RF randomness and redundant information by iPLS. In order to illustrate the rationality of the RF-iPLS algorithm, the RF-MC-iPLS algorithm is constructed using by Monte Carlo (MC) method. The comparison feature subset is generated after 500 samples.Although the structure of RF-iPLS is similar to that of RF-MC-iPLS, its running time is shortened by 11.12%. The results show that the feature wavelength selection of the RF-iPLS algorithm is effective and has low time complexity in the prediction model. Furthermore, to verify the algorithm’s effectiveness, RF-iPLS was applied to grain protein near-infrared spectroscopy data sets and PLSR models were established. It is compared with the full spectrum PLSR and PLSR models based on different wavelength selection methods. The results show that compared with 117 wavelength points of the full spectrum, RF-iPLS selects 12 feature wavelength points. The RMSEC of the modeling set is reduced from 2.61 to 0.64. The prediction accuracy is improved by about 75.5%. The RMSEP of the prediction set is reduced from 2.63 to 0.69, and the prediction accuracy is improved by 73.8%. The prediction accuracy and optimal prediction results show that RF-iPLS is an effective feature wavelength selection method, and it can simplify the complexity of the near-infrared spectral quantitative analysis model and achieve efficient dimensionality reduction.

The near-infrared absorption features are easily affected by temperature changes, which reduce the accuracy of quantitative analysis. This paper presents the methods for analysing the source and amplitude of temperature disturbance based on the theory of two-trace two-dimensional correlation spectroscopy (2T2D-COS). The absorption spectra of glucose solution, with a concentration of 0～200 mg·dL-1, are measured in the temperature range of 34~38.5 ℃ with a stepwise of 0.5 ℃. After baseline correction and noise filtering, the concentration-dependent and temperature-dependent spectra are analyzed with the 2T2D-COS algorithm. In the asynchronous spectrum of temperature-dependent spectra, the cross-peaks are observed at 1 474 and 1 410 nm, corresponding to strong and weak hydrogen-bonded water, respectively. While in the asynchronous spectrum of concentration-dependent spectra, the cross peaks at 1 450 and 1 595 nm are related to the characteristic absorption of water and glucose. From the slice spectrum at 1 410 nm, the intensity of the peaks in the range of 1 410n~1 600 nm is increased with temperature, demonstrating good correlation between the cross peaks and temperature variance. The calibration model is established for the quantitative analysis of sample temperature with the cross peak intensity range of (1 475±4) nm. The root mean square error achieved 0.125 9 ℃ for sample temperature prediction with linear regression. The experimental results demonstrate that the temperature disturbances could be identified and qualified with distinctly separate cross peaks in the asynchronous spectrum, obtained using only a pair of spectra. The proposed approach also provides a reasonable way for researchers to simplify the in vivo measurement system and establish a more stable calibration model.

Recently, a high-quality gray Akoya pearl has been so popular in the market that many treated gray pearls appeared, resulting in a mess. Among them, the color of pearls changed by γ-ray irradiation treatment is the most difficult to identify. Therefore, distinguishing the origin of those gray pearls has become an urgent problem to solve. We chose a few white and light yellow Akoya pearls as samples to be treated by different doses of γ -ray irradiation. By observing the surface and drilled holes of these samples, the non-destructive spectroscopic test included photoluminescence spectra, UV-Vis spectra, and 3D fluorescence spectra to test and compare those unirradiated, irradiated, and naturally colored gray Akoya pearls. By comparing the amplification characteristics of samples, it is found that γ-ray irradiation samples have a light nacre and deep brown nucleus. In contrast, naturally colored ones show a brown interlayer of organic matter between a light nacre layer and white nucleus, and sometimes a “black spot” resulted from the concentrated area of organic matter. By comparing the spectroscopy characteristics of the samples, it is found that the fluorescence background of the photoluminescence spectra of the irradiated samples is higher than that of unirradiated and naturally colored ones. Their F/A value (1.34~1.98), the intensity ratio between the background peak and aragonite main peak, is slightly higher than that of naturally colored ones (0.52~1.12). The reflectance of UV-Vis spectra of irradiated samples is lower than unirradiated ones, absorbed at 360 nm in the UV region. In comparison, the absorption valley of the naturally colored samples located in the range of visible light at 430~530 nm, which changes with different overtone, sometimes there is a weak wide and weak absorption at 750~800 nm. 3D fluorescence spectra show that the luminescence center of irradiated and unirradiated samples is consistent, except fluorescence intensity reduces by half compared with unirradiated ones. However, the main luminescence center of the irradiated pearls is completely different from that of the naturally colored gray pearls. The strongest fluorescence center in 3D fluorescence spectra of γ-ray irradiated Akoya pearls is at Ex/Em of 374/449 nm and 463 min the form of double peaks, while that of the naturally colored gray Akoya pearl is at Ex/Em of 286 nm/340 nm. In a word, combined with the observation from the surface and the drill hole and 3D fluorescence spectra, it is well to distinguish between naturally colored gray Akoya pearls and γ -ray irradiated gray Akoya pearls. In contrast, the F/A value of photoluminescence spectra and UV-Vis spectra can be used as an auxiliary identification.

As a kind of special paper artifact, memorials are considered physical evidence that embody the emperor’s culture, of which the paper-making and dyeing process reflect the political culture and social development level of the Qing Dynasty. The composing materials of the memorial are fragile. Previous research on memorials have mainly focused on text content, style, political influence and restoration, while the analysis towards the original materials and crafts is relatively scarce. By employing various in situ non-destructive techniques, a systematical study has been conducted on the paper-making and dyeing process of a memorial from Cen Chunxuan, an important minister in the late Qing Dynasty. UV fluorescence photography and FTIR spectra showed that the yellow damask-decorated patterns on the folding page and envelope were woven with silk products. Comprehensive analysis results of ultra-depth-of-field microscopic observation, Raman spectroscopy and XRF demonstrated that the mineral pigment Orpiment with a pretty fine graininess was used to dye the memorial paper through the brush dyeing method. Orpiment is more expensive than organic dyes such as Phellodendron amurense, which not only raises the ability of moth and water resistance but also highlights the significance of memorials in the Qing Dynasty’s institutional culture. It is another discovery in the case of paper dyeing yellowing. In addition, this article also explored the application of optical fiber fluorescence spectroscopy to analyze the pigment of comments written in red on the memorial and found that the red handwriting was written in cinnabar, which was consistent with the results of Raman spectroscopy and XRF. The investigation of the materials and crafts of such paper artifacts as memorials promotes the understanding of the technical characteristics of the paper-making and dyeing process in the Qing Dynasty. It provides the scientific basis for the rational protection and restoration of such cultural relics. At the same time, this article also discussed the applicability of fiber optic fluorescence spectroscopy in identifying pigments used in paper artifacts, which further expanded the types and applications of non-destructive analysis techniques.

Stimulated Raman scattering is one of coherent Raman scattering. The signal generated by stimulated Raman scattering is significantly enhanced under the third-order nonlinear effect, and there is no interference from non-resonant background. Its spectrum is almost consistent with the spontaneous Raman spectrum. Therefore, the micro-imaging technology based on stimulated Raman scattering has the advantages of no labeling, high specificity and being non-invasive. It has been successfully used in biological cell imaging and has made many great achievements. Stimulated Raman signal has the same wavelength as excitation luminescence and is easily disturbed by excitation luminescence background noise. In order to solve this problem, the combination of optical modulation and phase-sensitive detection is often used to detect it. In the detection process, modulation depth influences the intensity and signal-to-noise ratio of the stimulated Raman signal. Because of this, this paper deeply analyzes the influence of modulation depth on stimulated Raman signal intensity and signal-to-noise ratio based on relevant theories. At the same time, considering the limitation of cell photodamage threshold on the sum of two excitation optical powers in applications such as bio-spectral imaging, the excitation optical power configuration method to obtain the maximum signal intensity and the best signal-to-noise ratio at different modulation depths is analyzed. By establishing a stimulated Raman experimental system, dimethyl sulfoxide is taken as the research object for experimental verification. The results show that when the stimulated Raman loss is detected under the limitation of photodamage threshold, at the same modulation depth, the signal intensity reaches the strongest when the optical power ratio of pump light to the one of stokes light is 1∶1,and the signal-to-noise ratio reaches the best when the ratio is 1∶2. When the optical power ratio of pump light to the one of stokes light is the same, the intensity and signal-to-noise ratio of stimulated Raman signal decrease with the decrease of modulation depth, and the correlation is approximately linear. The stimulated Raman spectrum of dimethyl sulfoxide obtained from the experiment also verified that the higher the modulation depth, the stronger the spectral signal and the better the signal-to-noise ratio and the better the spectral quality of the whole sample. The research results are the improvement of stimulated Raman microscopy in signal modulation and detection and can provide reference guidance for stimulated Raman spectroscopy detection and cell imaging experiments.

As an important means of material modification, single site regulation has developed rapidly in catalysis, energy, environment and other fields. The Surface charge, electronic structure and atomic space configuration can be effectively controlled by adjusting single sites, thus improving the overall properties of materials. In the field of Raman detection, surface charge and other key factors are widely recognized and currently being studied. However, there is no systematic study on the effect of single sites on surface charge regulation or Raman sensitivity. In this paper, the surface charge regulation of single sites (including single molecule, single atom and ligand complex in single atom center) is proposed and its effect on Raman detection sensitivity is studied. Through the classic specific response: the 6-mercapto 5-triazoline ［4,3-B］ -S-tetraazine (MTT) is synthesized with 4-amino-3-hydrazo-5-mercapto-1,2,4-triazole (AHMT) and formaldehyde, resulting in a very low detection limit of 10-12 mol·L-1 for the charge regulated Ag material by single molecule AHMT. It is much lower than the 10-9 mol·L-1 without single AHMT regulation, realizing the detection of formaldehyde molecule ultra-low concentration. We also studied the effects of single tungsten oxide regulation on the detection ability of standard molecules and pesticide residues in nonspecific reactions. Among them, the detection limit of Rhodamine 6G could be decreased from 10-12 to 10-14 mol·L-1, and the detection limit of thiramcould also be decreased from 10-9 to 10-11 mol·L-1. Therefore, the universality of the method of unit point control of surface charge was proposed. In addition, through the test of Zeta potential, it was found that the Ag surface potential regulated by single sites had a great change, which was more conducive to the capture of detection molecules, which was also the reason for the increased sensitivity of Raman detection, and also explained the universality of single sites regulation in the mechanism. Many speculations and research have been made on the deep mechanism: On the one hand, there is a chemical enhancement (CM) process due to the charge transfer between the single sites and the substrate. At the same time, there is a significant Electromagnetic field enhancement (EM) on the surface of Ag. The two enhancement mechanisms and the coordination with single sites system are necessary to further study the new physical mechanism of different single sites through experiments and theories. On the other hand, single sites may act as a new vibration mode, which synergistically interacts with surface plasmon resonance (SPR) and resonance of the molecule to be measured in the substrate to make Raman scattering resonance stronger, thus improving the sensitivity of Raman detection. The experiments and conclusions in this paper confirm the feasibility and necessity of single sites regulation in Raman detection, which makes it possible to further study in this field.

Laser-induced breakdown spectroscopy (LIBS) has the advantages of no sample pretreatment, simple operation, rapid detection, etc.,and it has been applied in many fields. In this experiment, femtosecond laser-induced breakdown spectroscopy (Fs-LIBS) was developed, using a femtosecond laser with a wavelength of 800 nm and a pulse width of 100 fs as the excitation light source and a gated ICCD as the detector. When LIBS detects bulk liquids, problems such as liquid fluctuations and splashes will occur, and the signal will be poor. In this experiment, NaCl standard solution jet flow was used to test and optimize the system, and Na(Ⅰ) 589.0 nm was chosen as the analysis line. The evolution of the LIBS emission spectrum after the femtosecond laser excitation was investigated. The maximum Na atom emission and the best signal-to-background ratio were obtained after 40 ns of laser excitation. It shows that femtosecond time-resolved LIBS can effectively eliminate the influence of broadband background emission and more efficiently detect the target. The effects of laser excitation power, ICCD gate width, the distance between the laser focus to sample surface on LIBS intensity and signal-to-noise ratio were studied and optimized. At the best experimental parameters: 40 ns delay time, 100 mW excitation power, 5 s gate width, and laser focus right on the front surface of the sample, the LIBS spectrum and Na content of a seawater sample were tested, and the detection limit of Na for NaCl standard solution was determined to be 0.98 mg·L-1. The experimental results show that the LIBS technology meets the requirements of rapid and real-time detection of elements, can be used to study the plasma dynamics evolution process and achieve a qualitative and quantitative elements analysis.

The turquoise deposits in China are mainly distributed in Hubei, Henan and Shaanxi provinces, among which Baihe county, Shaanxi is one of the most important turquoise deposits. The yellow and green associated minerals collected from Xiaodonggou turquoise deposit in Baihe County, Shaanxi Province, were studied by a microscope, X-ray powder diffraction, infrared, and Raman spectroscopy. The results showed that samples were composed of alunite, and a small amount of hotsonite and kaolinite. Different shades of alunite were observed via microscope, suggesting different mineralization periods. The backscattering images showed that alunite formed later were well-developed particles with the typical false cube-like rhombohedron shapes. The infrared spectra of alunite had the bands related to PO3-4/SO2-4 in the range of 3 697~3 488 and 1 638~433 cm-1, with additional bands at 3 697 and 3 620 cm-1 attributed to the hydroxyl group of kaolinite. The Raman spectra of green samples were characterized by SO2-4 of alunite. Moreover, the peaks of OH stretching vibration in kaolinite appeared at 3 700 and 3 626 cm-1. It was assumed that the alunite veins in this turquoise deposit are formed in low temperatures and a rich alkaline environment as the deposit was of weathering and leaching origin. When the essential components derived from ore-bearing solution or surrounding rock are insufficient to produce turquoise, alunite was preferred to be formed, occurring as veins and massive and associated with hotsonite and kaolinite. This paper provided valuable information on the origin of turquoise deposits.

Near-infrared spectroscopy can reflect the internal hydrogen-containing chemical bond stretching vibration and ensemble frequency absorption information of the sample, with the advantages of high-speed, economical, reproducible and environmentally friendly analysis, commonly used in food, pharmaceuticals and materials detection for analysis. Peas are one of the most important cultivated crops in the world, widely grown and distributed, with nutritional properties such as high starch, high protein as well as low lipid content, which consumers have long loved. In order to clarify the modeling differences in the NIR spectra of peas from different origins, modeling analysis was performed on peas from different origins. In this study, 42 pea samples collected from different regions of Nanyang city, Henan province, were investigated. Firstly, pea samoles’ nutritional components (total starch, protein, moisture, ash, and lipid) were determined. Then, with an emphasis on using the integrating sphere diffuse reflectance technology in Near-infrared spectroscopy, the spectra of different pea samples were collected in the wavelength range of 12 000~4 000 cm-1. By combining different pre-processing methods with discriminant analysis models (DA) to obtain the optimal pre-processed data and combining principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) along with orthogonal partial least squares discriminant analysis (OPLS-DA), the differences in the spectral characteristics were screened and analyzed, constructing and verifying the identification models of Nanyang peas.The results show that the overall difference in nutritional composition and content of Nanyang peas in different regions is slight (total starch 36.30%~46.93%, protein 16.37%~25.50%, moisture 6.78%~9.16%, ash 2.29%~3.38%, lipid 0.37%~1.43%), and the results of the discriminant model established based on Near-infrared spectroscopy showed that the accuracy of discriminant analysis for the DA model could reach 92.4%. The predictive abilities obtained from PCA, PLS-DA and OPLS-DA models were 96.7%, 85.1% and 83.6%, respectively, indicating that the above models can achieve accurate classification and identification of geographical origin for Nanyang peas. In addition, the results of the different bands among different geographical origins screened by the variable importance projection (VIP>1.0) method showed that 4 710~4 000, 5 320~5 200 and 7 200~6 220 cm-1 could be used as specific detection bands to identify geographical origins of Nanyang peas. Therefore, this study can provide a methodological basis to construct a database for the identification and traceability of pea’s origin in different regions.

To analyze the accumulation rule of pharmacodynamic substances of characteristic chemical constituents of Radix polygala in different growth years. FTIR was used to scan and analyze 12 batches of Radix polyphyllum with different growth years in Fenyang and Xinjiang of Shanxi Province. The infrared fingerprint of Radix polyphyllum was established, and the sequence of common peak rate and variation peak rate was analyzed. There were obvious common absorption peaks at 3 130, 2 920, 1 650, 1 540, 1 400, 1 260 and 1 050 cm-1. The characteristic absorption peaks of ethanol extract were in the range of 3 150～3 000, 2 950～2 920, 2 850, 1 740～1 710, 1 670～1 630, 1 540～1 520, 1 450～1 400, 1 100～1 050, 990, 530 cm-1. The number of absorption peaks increased with the increase of age. In the same year, there were differences in the number, shape and strength of absorption peaks near 3 380, 1 315, 1 060 and 990 cm-1 for P. polygonum, and the number, shape and strength of absorption peaks near 3 360 and 1 315 cm-1 for alcohol extract, and there was no characteristic absorption of P. polygonum in autumn. In terms of the accumulation of pharmacodynamic substances, the accumulation of pharmacodynamic substances in Fenyang and Xinjiang polygonum was larger in spring harvesting, and the accumulation of tenuifolin in fenyang polygonum, 3,6’-Disinapoylsucrose, and Polygalaxanthone Ⅲ increased with the growth of polygonum. The accumulation of 3,6’-Disinapoylsucrose increased firstly and then decreased, while the contents of Polygalaxanthone Ⅲ and some tenuifolin decreased with the increase of growth years. The common peak rates were 66.7%~100%, the variation peak rates were 0~63.6%, the common peak rates of alcohol extract were 66.7%~94.7%, and the variation peak rates were 0~30.0%. The common peak rate of the same period was high, and the peak rate between spring and autumn harvests was high. The common peak rate was high, and the accumulation of pharmacodynamic substances was similar to the increase in age, but there was no significant difference. The highest mutation peak rate was S-2-3 (picked in spring) and S-2-5 (picked in autumn), which was 63.6%. The two sequences were harvested in different seasons in different years, and the accumulation of pharmacodynamic substances differed greatly. The characteristic peaks of effective chemical components and the accumulation of pharmacodynamic substances of P. polygonum in different growth years can be analyzed and explored based on infrared spectroscopy and second derivative combined with double index sequence analysis, which can provide a reference for quality evaluation of Polygala tenuifolia.

The rapid recombination of carriers on plasmon metal nanoparticles significantly decreases the efficiency of traditional photocatalysts. The separation of hot electrons and holes can be achieved by recombining metal and semiconductors, improving the efficiency of photocatalysis. This paper combined Ag and TiO2 nanoparticles to improve the photocatalytic activity. The enhanced mechanism of the catalytic activity was explored. The effect of energy band bending in the space charge region between TiO2-Ag nanocomposites and the built-in electric field was studied, which provided a theoretical and experimental basis for designing high-performance SPR photocatalysts. Furthermore, the photocatalysis coupling reaction of PATP and PNTP was employed to study the catalytic performance of TiO2-Ag nanocomposites. The results reveal that the introduction of TiO2 improves the SPR catalytic activity of Ag. The main reason is that introducing TiO2 can improve the separation efficiency of electrons and holes between TiO2 and Ag.

Olive oil has become the main category with increasing daily consumption of vegetable oils due to its high nutritional characteristics. According to the processing technology, olive oil is divided into different quality grades such as virgin, refining and blending. Because the prices of different grades of olive oil are quite different, the olive oil market has a certain degree of real attribute problems, such as substandard quality. At the same time, there are complex indicators related to grade identification, and most of the corresponding physical and chemical testing methods involve large-scale laboratory equipment with high testing costs, low efficiency and heavy workload. Our country is a major importer of olive oil, and adopting the model of product standard confirmation and determination of indicators one by one, it cannot meet the rapidly increasing requirements for rapid customs clearance of imported products. This research focuses on the rapid quality assessment requirements of imported olive oil at the port supervision site. It develops a method of simultaneous collection of multi-spectral information and dimensionality reduction fusion imaging, which combines the characteristic data of the visible-ultraviolet spectrum and the Raman spectrum to construct the Raman-Ultraviolet, visible 2D spectrum. Then the fingerprint feature is judged by two-dimensional imaging. The standard 2D fusion imaging source map of extra virgin olive oil, refined olive oil and pomace oil is constructed, which is used as the grade discrimination standard to compare the two-dimensional spectrum to determine the olive oil grade visually. Finally, combined with the spatial angle value conversion algorithm, the olive oil grade is qualitatively judged. Through the angle value calculation, the angle between extra virgin olive oil and refined olive oil is between 0.794 7 and 1.094 7, and that of olive-pomace oil is between 1.157 0 and 1.319 8. The angle values between the extra virgin olive oils are all less than 0.1, which can be used to determine the grades of different olive oils, and the angle decision model is used for quantitative analysis of olive oil adulterated samples. Preparing mixed samples of different olive oil grades and calculating the model correlation coefficients of extra virgin mixed refined olive oil and pomace oil to be 0.994 2 and 0.991 0, respectively. Different samples are taken for verification, and the relative error is between -4.48%% and 2.58%. This study uses Raman-UV-Vis fusion spectroscopy combined with chemometrics to establish a standard two-dimensional spectrum to visually determine the grade of olive oil and establish a virgin olive oil adulteration detection model for quantitative analysis of olive oil content to achieve food quality and safety at the port Multi-dimensional, high-precision and high-accuracy visual display of risk information. Adopting the rapid screening method of imported olive oil quality grade can effectively improve the monitoring efficiency of the port’s attention to risks, improve the accuracy of imported food supervision, and provide technical support for the intelligent transformation of the port food risk monitoring mode.

In forensic identification, cases involving electrocution death are frequent and the identification of electric shock before and after death remains one of the difficult problems in forensic pathology identification. The experiments were carried out to classify and identify electrocution death and post-mortem electrocution behavior from the viewpoint of heart tissue through Fourier transform infrared spectroscopy fused with machine learning model. 30 rats were subjected to electrocution death, post-mortem electrocution and control treatment, and their heart tissue spectra were scanned by a spectrometer, and a total of 70 spectral feature wavelength points were extracted using a competitive adaptive re-weighting algorithm, and a random forest model was established to identify the feature wavelength The results showed that the accuracy of model classification recognition before and after feature wavelength extraction was 34.9% and 73.7% respectively, which verified the effectiveness and necessity of the feature wavelength extraction method, and the partial least squares model, traditional support vector machine and support vector machine model optimized by particle swarm algorithm and grey wolf algorithm were established for classification recognition. The results showed that the accuracy of the models was 61.07%, 34.48%, 100% and 98.46% respectively, and the particle swarm optimized support vector machine model with feature extraction was found to be the most effective. In order to exclude the interference of the “biological death period”, 60 rats were treated in the same way, and each group was divided into two subgroups: 0.5 h and 1 h post-mortem, and the spectral data were scanned again by Fourier transform infrared spectrometer-SVM model analysis, the results showed that the method could achieve an accuracy of 80.85% for classification and identification. It provides a new research idea and method for forensic identification in electrocution death. It shows that FTIR combined with machine learning models can be an essential research significance as a complementary tool to provide relatively objective judgements.

The Shan-Shan Guildhall in Liaocheng, Shandong, was built in the eighth year of the Qianlong period. After several extensions and reconstructions, it has a compact layout and gorgeous decoration. It is a well-preserved excellentarchitectural complex in ancient guild halls. The guild hall has the functions of a temple and business hall. It is a witness to the commercial prosperity of the Grand Canal and has high artistic and cultural value. So far, the research on Shan-Shan Guildhall in Liaocheng mainly focuses on its architectural style, art aesthetics, etc. No scientific analysis of the manufacture techniques and materials used in its architectural color paintings exists. Here, 27 red pigments samples from the Shan-Shan Guildhall in Liaocheng were analyzed by microscopic observation, SEM-EDS, laser Raman, and infrared spectroscopy. It was found that the red pigments are Iron red (Fe2O3), vermilion (HgS), red lead (Pb3O4) and madder dyes (mainly C14H8O4 and C14H8O5). Multi-layer repainting techniques are commonly used, including applying new paint after remaking the ground battle, and the construction technique of ancient architectural color paintings that directly draws two layers of paint, such as “red lead base and vermilion cover”. Theresult is consistent with the documentary record. In addition, the technique of adding calcium and barium sulfates as modifiers to red pigments was also revealed. It is the first time to find madder dyes as architectural color painting pigments in our research, which strongly support the literature record of using organic dyes for architectural color paintings in the Qing Dynasty. Previous studies on the analysis of madder dyes in ancient cultural relics mainly focused on archaeological textiles, mostly using mass spectrometry, which requires expensiveequipment and complex sample pretreatment. In our work, the laser Raman spectroscopy was used to perform convenient and non-destructive analysis for madder dyes in ancient architectural color paintings, and good identification results were obtained. It will provide an important technical reference for the scientific analysis of organic dyes in ancient architectural color paintings.

The provenance of raw materials of bronzes and the cultural interactions between regions embodied in them are the key issues that archaeologists focus on. Huili County, Sichuan Province, is located at the “crossroads” between the Eurasian Steppes, the Central Plains of China, South Asia and Southeast Asia, and is on the famous “Tibet-Yi Corridor”. The bronzes unearthed from Huili also contains a variety of cultural factors. However, previous studies on bronze culture in this area have mostly focused on the cultural interactions embodied in the shape of tombs and the types and decorations of burial objects, with little discussion on the provenance of minerals of multi-style bronzes and the specific modes of cultural interaction between regions. In this work, 8 multi-style bronzes collected by the Cultural Relic Administration Center of Huili County were analyzed using a portable X-ray fluorescence spectrometer (pXRF), and multi-receiver inductively coupled plasma mass spectrometer (MC-ICP-MS). On this basis, the lead isotope data of copper and lead mines and related bronzes were collected and compared to archaeological typology to grasp the interaction between different regions and bronze cultures. The composition results obtained by pXRF show that these samples have various alloy types, including pure copper, tin bronze, lead-tin bronze and tin-lead bronze. The lead isotope ratios results obtained by MC-ICP-MS indicate that the two Shizhaishan style drums and Yanyuan culture style tree-shape bronzes should be importation which used the lead material from south central Yunnan province. The results of previous studies on several Shizhaishan style bronze drums show that the mineral materials are all from south central Yunnan, indicating that the raw mineral and production of bronze drums, which symbolize power and status, are relatively unique and may be monopolized by the ruling class. While the Shizhaishan style bell is, an imitation used the copper material from Huili County. The results indicate complex culture interaction modes, including spiritual level and material objects among Huili, Shizhaishan, Yanyuan and other surrounding bronze cultures. This study also shows that using pXRF and MC-ICP-MS to clarify the composition and the source of mineral materials of bronzes combined with documentary records and archaeological typology can provide positive evidence support for in-depth exploration of specific modes of cultural interactions between regions.

Spectroscopy is the use of light on the interaction of substances, providing the microstructure of substances and different chemical analysis methods to achieve the quantitative and qualitative substances. A large number of spectral analysis techniques are used in the process of making murals. In this study, taking Huapen Guandi Temple of Yanqing in Beijing as an example, the techniques and materials formaking murals are analyzed by spectral analysis and other techniques. Huapen Guandi Temple is located in Huapen village, Yanqing District, Beijing. It was built in the fourth year of the Yongzheng reign (1726) of the Qing dynasty. Many spectral analysis techniques are used in the process analysis of mural making. This study takes Beijing Yanqing Huapen Guandi Temple as an example to analyze the mural making process and materials. Using a range of spectroscopic techniques, including X-ray diffraction, Raman spectroscopy and laser particle size analyzer, the study found that the mural of Guandi Temple is supported by an adobe wall. The main components of the ground layer are quartz, calcite and albite, and the main components of the white powder layer are gypsum. The red pigments in the pigment layer are iron red, lead and cinnabar. Blue is azurite, yellow is chrome yellow, black is carbon black, white is gypsum, green is Paris green, green earth and chrome green. The Binder of gold coating with pitch powder is cooked tung oil and rosin resin, and the gold foil is red gold with a gold content of 86.1%.The Raman spectrum can not only distinguish the pigment of the fresco but also prove it through the research of the pigment history and enrich the historical information of the fresco. It is of great significance to study and protect the ancient frescoes by combining the spectral analysis data of cultural relics with the literature data and fully excavating the information behind the cultural relics. Through the difference in the making technology of the wall of the same wall in Guandi Temple, this paper shows that the material and the craft of the mural are affected by the position. It is expected that the researchers will find the typical cases of murals by the regulations by studying the differences of murals making materials and techniques and summarizing the regulations of murals making in past dynasties.

Princess Temple, located in the Wutai Mountain area of Shanxi Province, is an architectural relic of the Ming and Qing Dynasties that integrated a religious site with a place of folk entertainment. The wall paintings of Daxiong Hall were a relatively well-preserved Water-and-Land-Mural of the Ming dynasty. The research not only reveals the materials and artistry applied to wall paintings but also supplies first-hand data for selecting restoration materials and protecting the wall paintings in the future. The integration of analytical techniques applied includes optical microscopy (OM), Raman spectroscopy (RAM), Fourier Transform infrared spectroscopy (FTIR), laser particles size analyzer (LPSA), X-ray diffraction (XRD), X-ray fluorescence spectrometry (XRF), scanning electron microscopy with energy dispersive spectrometry (SEM-EDS) and pyrolysis-gas chromatography/mass spectrometry (PY-GC/MS). The results showed that the mural chose an adobe brick wall as the support structure, and the ground plaster layer was divided into a coarse mud layer and a fine mud layer, then coated with a white powder layer, and finally decorated with pigments on the surface. The wall-supported components and the base plaster layer are similar to those of the local soil. The overall particle size composition is powder in the majority, and the sand content of the fine mud layer is higher than that of the coarse mud layer. Furthermore, the coarse mud layer was mixed with wheat straw, and the fine mud layer was reinforced with fibrilla. In particular, infrared spectrum analysis determined Kaolin was determined as a white powder layer. The Raman spectroscopy analysis showed that the pigments used azurite, lead white, cinnabar, iron red, ferrite yellow, red lead, synthetic atacamite and botallackite. In addition, The black pigment was further determined as pine wood soot ink, according to the relative concentration of the main polycyclic aromatic hydrocarbons and the biomarkers of pine wood. At the same time, PY-GC/MS was applied to identify animal glue as an adhesive of pigments. The results could provide more original information for the study of the art history of Princess Temple and enrich the scientific understanding of the materials and workmanship of Water-and-Land-Murals in Shanxi in the Ming Dynasty.

The surface temperature of asteroids is a key parameter in studying the thermophysical properties of planets. The study of orbital dynamics of small and medium-sized celestial bodies in the solar system has a variety of practical applications, including predicting the orbit of planetary bodies and the cratering rate caused by impact and selecting appropriate detection and sampling targets for spacecraft. For NEAs, it is significant to analyze their orbital structure, orbital evolution, and future offset trajectory of Neos［1］. China is about to launch “tian wen-2” to detect the thermal radiation and sample the weathering layer of the near-Earth Asteroid 2016HO3. “Tianwen-2” is expected to reach orbit around 2016HO3 shortly. this paper studies a temperature-specific emissivity separation algorithm, which is used to extract the radiance data from the “Osiris Rex” thermal radiation spectrometer (OTES), Calculate the surface temperature of the planet "Bennu" with similar thermophysical properties to the asteroid 2016HO3［2］. The temperature emissivity separation algorithm combines the emissivity normalization method (NEM), ratio method (rat), and emissivity maximum-minimum difference method (MMD). It is a surface temperature inversion algorithm with high accuracy at present. To verify the algorithm’s accuracy, this paper uses the spectral data of the CRISM spectral library to study algorithm’s sensitivity to the target temperature and the band. Among them, due to the limitation of the instrument and other reasons, there is no error derivation of the length of the wave band. The results show that: (1) set the temperature range to 115~ 415 K and the step size to 5 K. With the increase in temperature, the root means square inversion error temperature increases and the error of emissivity is roughly unchanged. (2) The sample temperature is 295 K, and the starting point of the band used by the algorithm is 7.5 μ m. Sampling interval 0.04 μm. The endpoint is 10~13.8 μm. Step 0.2 μm. It is found that the wavelength range is 7.5~13.8 μm. The MMD module of the algorithm has the highest accuracy. The algorithm is used to solve the surface temperature of the planet “Bennu”. The results show that the average error of the temperature calculated by the algorithm is -0.366 0 K, and the standard deviation is 1.039 3 K except for pixels with large solar altitude angles and in high latitudes.

Astronomical three-dimensional spectral imaging technology is a real-time spectral acquisition technology for all source targets in the observation field of view. It can simultaneously obtain the spectral domain and two-dimensional spatial domain information of the target by single sampling. Optical fiber integral field unit (IFU) is the key component of astronomical three-dimensional spectral imaging technology. The image plane information is subdivided into several units and transmitted to the spectrometer through the segmentation of the received image plane. In this process, the two-dimensional spread source target is reorganized into a non-interference linear array for sampling and extraction by the spectrometer, which can effectively improve the temporal resolution of astronomical observation. This paper introduces an IFU with 242 fiber units, currently applied to the fiber array solar optical telescope (FASOT-1B) system of Yunnan Observatory of the Chinese Academy of Sciences. In order to meet the index requirements of FASOT-1B and obtain the observation effect of high transmission efficiency, high spectral resolution and high time resolution, the IFU adopts the structure of a microlens array and fiber array. The microlens is a regular hexagon spherical mirror, and the spatial filling rate is nearly 100%. Considering the design parameters of the front telescope system and the back-end spectrometer system of the fiber integral field-of-view unit, a pair of 11×11 microlens arrays is an optimized design. The distance between adjacent microlens is 300 μm, and each microlens corresponds to the sky area of 1.5″. The received light is incorporated into the corresponding fiber core with the focal ratio F/8.2. The relationship between fiber core diameter and spectral resolution of the spectrometer is analyzed systematically. The design specifications of the fiber are 35/105/125 μm. This parameter can not only meet the requirements of the optical fiber to receive all the optical information transmitted by the microlens but also can obtain the spectral resolution and relatively short slit width to meet the system’s requirements. The influence of fiber diameter and micro-hole depth on the actual incidence focal ratio of the IFU array is quantitatively analyzed, and the micro-hole size is selected as 130 μm in diameter and 3 mm in depth. A two-dimensional arrayed optical fiber is reorganized at the pseudoslit end, and the optical information is imported into the spectrometer in a linear arrangement. The distance between adjacent fibers is 130 μm. The problem of IFU fiber fixation and polishing is solved. The average energy transmission efficiency of IFU is 77.7%, and the RMS is 1.6%. All fiber output focal ratio EE90 is slower than F/7. The RMS value of the lateral (alignment) offset of the IFU pseudo-slit end fiber is less than 2.7 μm, and the RMS value of the longitudinal (perpendicular to alignment) offset is less than 1.8 μm. After the installation and debugging of IFU and FASOT-1B systems, the confirmatory observation was carried out, and the Stokes spectrum of the MgI chromosphere in the solar NOAA12738 active region was successfully obtained. This IFU has also become the first fiber plus convex lens IFU independently developed and applied to scientific observation in China.

The signal intensity of the earth at night is one-millionth of the reflected visible light intensity during the day. Small changes in the pixel response characteristics of the spaceborne low-light CCD imaging payload will significantly affect the imaging quality. The analysis of the on-orbit response characteristics of the CCD push-broom load shows that the abnormal response results in multiple bright lines along the track with different intensities in the low-light image, which have the characteristics of time-varying quantity, random position and nonlinear response. A correction method is proposed in which the spatial domain is loosely matched and the radiation domain is strictly mapped. By calculating the relative deviation of the mean radiation value of the pixels along the track, the bright line detection threshold is determined by histogram analysis and automatic detection is realized. On this basis, for each bright line, the method of establishing reference radiation value first and then sorting mapping is adopted to achieve bright line correction. In order to verify the effect of the algorithm, the low-light observation data of five typical uniform scenes including sea surface, desert, lake ice, fog and glacier, are selected for testing. The test results show that after correction, bright lines in the images disappear. The overall non-uniformity is improved by 44%, the non-uniformity of strong bright lines is relatively improved by 60%, and the signal-to-noise ratio of the typical dark background image is improved from 2 to 4.2. The method has the characteristics of real-time detection and correction pixel by pixel and is suitable for the operational radiometric correction of push-broom CCD on optical remote sensing satellites without on-board calibration devices.

The automated analysis technique has gradually replaced the traditional optical observation method in the geoscience fields. However, mineral phase maps obtained from these newer approaches are overly dependent on transforming from elemental to mineralogical compositions. And then, the important pieces of information are destroyed with a complete description of the origin and history of mineral materials. Therefore, using appropriate detection means to analyze the mineralization and genesis which have important implications for mineral mineralization. Here, through comparison with the Raman Mapping, optical microscope, and TMSCAN integrated mineral analyzer for SEM (TIMA), the distribution characteristics, chemical composition, mineral structure, and crystal orientation are studied of typical aegirine rare earth minerals. The results show monazite grains are mostly euhedral-semieuhedral unequal grain structures and fine particle sizes of 100～500 μm. Some particles were metasomatized by phosphorus solution indicating that the deep monazite minerals formed multi-stage mineralization. Bastnaesite minerals are fine-grained and particles are distributed and wrapped irregularly. Aeschynite minerals aggregate clumps or radial forms in irregular. It has obvious directivity that the formation time is consistent with calcite. The ratio of peak area of P—O symmetric stretching mode (ν1) and P—O symmetric bending vibration mode (ν2) is obtained by Raman Mapping data analysis, in which the different growth orientations phase diagrams were obtained of ［PO4］3- monazite minerals in micro areas. Combined with the light microscopic characteristics of monazite. It is proved that monazite mineral has at least three stages of mineralization. The discovery also provides strong evidence for the multi-stage mineralization of the Bayan Obo deposit. In addition, as a fast image method to judge different growth orientations of phosphate, carbonate, and sulfate minerals. It can provide important evidence for judging the fact of multi-stage mineralization for various mineral types. The application of Raman Mapping technology in mineralogy lays the foundation for mineral occurrence characteristics.

In recent years, a new kind of “porcelain-added” turquoise has appeared in the market, which is very similar to the raw ore of turquoise. This kind of “porcelain-added” Turquoise uses inorganic binder phosphate or silicate as additives to fill the turquoise, to improve the porcelain degree of turquoise. Therefore, it is named after “porcelain-added” turquoise. The appearance of this kind of “porcelain-added” turquoise is very similar to that of natural turquoise. At present, the research on this type of turquoise is relatively weak. In this paper, the chemical composition characteristics and vibration spectrum characteristics of turquoise treated with phosphate “porcelain” were systematically studied and analyzed using conventional gemological instruments, infrared spectrometer, X-ray fluorescence spectrometer, UV-Vis spectrophotometer and fluorescence spectrometer. The results show that the turquoise treated with phosphate “porcelain” is an aphanitic structure, and its surface is mostly waxy glassy luster, with black or white lumpy color spots and iron wires. The average relative density (2.38) of turquoise treated with “porcelain” is less than that of natural turquoise with a similar appearance (2.60); The fluorescence is inert as a whole, but some Turquoise treated with “porcelain” will have blue and white fluorescence distributed along the micro cracks on the surface of the sample under the UV fluorescent lamp; X-ray fluorescence (XRF) spectrometer was used to test and analyze the composition of the sample. The main chemical composition of turquoise treated with phosphate “porcelain” deviated from the theoretical chemical composition value of natural turquoise, ω(Al2O3) is between 20.91% and 39.45%, ω(P2O5) is between 42.32% and 53.46%, ω(CuO) is between 6.54% and 11.38%, ω(feot) between 0.43% and 22.2%, ω(SiO2) is between 0.28% and 4.52%, ω(K2O) between 0.05%～0.36%; The P/Al ratio of turquoise treated with phosphate and porcelain is 1.47～2.10, which is generally higher than that of natural turquoise; The infrared absorption spectra of turquoise treated with phosphate “porcelain” mainly show the vibration spectra of crystal water, hydroxyl water and phosphate groups, and the vibration frequency is basically the same as that of natural turquoise; The UV-Vis spectra showed that the spectral peak position of phosphate treated Turquoise was slightly offset from that of natural turquoise, but the overall trend was the same; The fluorescence of the three-dimensional fluorescence spectrometer is weak, and the intensity of the fluorescence center varies widely.

“Trapiche” gemstone has a special phenomenon of hexagonal symmetry similar to “six starlight” caused by the uneven distribution of inclusions or chromogenic elements. Gemstones with this special phenomenon can be divided into two different types, one kind is the “trapiche” gemstone, which is formed by separating the main crystal into different regions by inclusions. The other is a “trapiche-like” gemstone, which looks like the “trapiche” gemstone because of its internal reasons. UV-VIS-NIR spectroscopy shows that the main absorption peaks are dominated by 375 and 450 nm, which induced by Fe3+ and its crystal field spectrum. The wide absorption band near 560 nm should be the charge movement spectrum due to the charge transfer between Fe2+ and Ti4+ ions, which can be found in most colors of the sapphire except the yellow one. The mineral phases from the Raman spectrum in all parts of Shandong sapphire with a “trapiche-like” pattern are corundum phases. All Raman shift peaks are concentrated at 378, 417 and 576 cm-1, confirming that sapphire crystal is not separated into different growth regions by mineral inclusions and it should belong to the “trapiche-like” type. EDXRF results confirm that the aluminum in sapphire is much higher than other elements and reaches the theoretical value of corundum mineral, more than 97%, which is consistent with the Raman results. LA-ICP-MS results show that the amount of Fe and Ti elements contained in the “core” and “arm” were significantly higher than that of another area, which can make these regions darken. Shandong Sapphire’s “trapiche-like” pattern is different distribution of coloring elements, the inclusions cannot divide main crystals into the different regions, and this ChangLe sapphire still belongs to the “trapiche-like” type.

As a common carbonate mineral, calcite is formed in various geological environments and is widely distributed on the earth. It can change into different phase states with the temperature and pressure change in the deep earth. Its physicochemical properties under different conditions can be of certain guiding significance for understanding the deep carbon cycle of the earth. Due to the decomposition of calcite near 1 164 K, there are relatively few studies on the phase transition of calcite at high temperatures, especially the phase transition from CaCO3-Ⅰ to CaCO3-Ⅳ, which needs more experimental data. In this study, the behavior of calcite at different temperatures was studied by high-temperature Raman (298～1 323 K) and XRD (298～1 223 K) under the CO2 gas stream. High-temperature Raman experiment shows that, with the increase in temperature, the Raman peak obviously moves to the low frequency, the half-height width of the peak gradually increases, and the peak intensity decreases. The Raman shift of 276 cm-1 gradually moves to the lower frequency with increasing temperature. When the temperature rises to 973 K, the Raman shift appears abnormal behavior and moves to 265 cm-1. The frequency remains unchanged in the range of 1 023~1 223 K. When the temperature rises to 1 248 K, it moves to 266 cm-1 and remains unchanged between 1 248 and 1 298 K. Th Raman shift of 706 and 1 430 cm-1 also behavior abnormally near 1 223 K. The abnormal temperature of the Raman shift is speculated to be related to the phase transition of CaCO3-Ⅳ to CaCO3-Ⅴ, which is very close to the phase transition temperature reported by previous research. High-temperature XRD experiments indicate that, with the increase in temperature, some diffraction peaks move to a lower angle, some diffraction peaks move to a high angle, and some adjacent diffraction peaks merge and gradually separate. The peak (211) disappears near 1 123~1 173 K, which is speculated to be related to the phase transition from CaCO3-Ⅰ to CaCO3-Ⅴ. The a-axis of calcite shows negative thermal expansion, and the c-axis shows positive thermal expansion. The thermal expansion equation fitted the cell parameters at different temperatures, and the thermal expansion coefficientα0(a) is -0.60(2)×10-5/K, α0(c) is 2.42(4)×10-5/K, and α0(V) is 1.21(2)×10-5/K. However, there was no obvious change in the XRD pattern near 1 000 K. That is, no CaCO3-Ⅳ was found.

The conversion of lignocellulosic biomass with high content of hydroxyl groups to liquid substances with high reactivity through liquefaction was considered a promising route for realizing their high-value utilization. The competitive reaction of degradation and polycondensation in lignocellulosic biomass’s liquefaction process directly affects the liquefaction product’s characteristics. The liquefaction of Chinese Sweet Gum’s (Liquidambar formosana) fruit was carried out at various times (30, 60, 90, 120, and 150 min) using polyethylene glycol and glycerin (4∶1 V/V) as liquefaction reagents to investigate the degradation and polycondensation reaction process in liquefaction. Fourier infrared spectroscopy (FTIR) combined with principal component analysis (PCA) and X-ray diffraction (XRD) were used to characterize the liquefied residues and liquefied products. The results showed that the liquefaction efficiency gradually increased with the extension of the reaction time, and the highest liquefaction efficiency was 88.79%. The optimal liquefaction time was 120 min when the liquefaction efficiency was 87.91%, and the hydroxyl value of the liquefied product was 280 mg KOH·g-1. FTIR and XRD analysis showed that lignin and hemicellulose were priority degraded at the initial stage of the liquefaction reaction. The crystalline cellulose began degrading at a later stage, accompanied by a polycondensation reaction. Principal component analysis results suggested that the distribution of functional groups of the liquefied residues obtained at different liquefaction times was relatively independent, which could be used as the basis for judging the degradation time of each component in the liquefaction process. Moreover, the polycondensation reaction gradually became dominant after 90 min of liquefaction. In addition, to explore the feasibility of liquefaction products as biomass polyols in the polyurethane foam field, polyurethane foams were successfully prepared by adding different contents of liquefaction products (10%, 20%, and 50%). FTIR showed that liquefaction products could replace polyols in the preparation of polyurethane foam, and the addition of liquefaction products did not change the chemical structure of polyurethane foam. The study would provide a theoretical basis for further exploring the liquefaction reaction of lignocellulosic resources and the liquefaction utilization of L. formosana fruit.

Tomato is a kind of nutritious vegetable in the world. It is deeply loved by people and is widely grown worldwide, especially in China. Tomatoes not only play an important role in people’s lives but also play a pivotal role in our industrial production. The export of tomatoes in our country is also increasing. The sugar, acidity, vitamin C and solid soluble content of tomato are important evaluation indicators to reflect the internal quality of tomato, and the content of solid soluble content is the sum of these internal qualities, which can better characterize the internal quality of tomato, so the solid soluble content of tomato can be achieved. The rapid detection of solid content is of great help to our industrial production and daily life, and the traditional method will do an irreversible destructive analysis of tomato samples, which is time-consuming and labor-intensive. It is not easy to meet the needs of modern industrial production in my country. Therefore, the development of rapid non-destructive testing technology for internal tomato quality has become a problem to be solved. In recent years, near-infrared spectroscopy has been widely used in many fields with the advantages of being fast and non-destructive. In this paper, based on the near-infrared spectroscopy detection method, the correlation modeling and prediction of soluble solids content reflecting the sweetness of tomato were studied. A near-infrared spectroscopy detection platform was built in the experiment, and a total of 255 tomato samples of different maturity and varieties were selected, and spectral data and soluble solid value were collected for each sample. The research compares spectral data preprocessing methods such as SNV, MSC, NOR and SG and uses the K-S algorithm to divide the modeling calibration and validation sets. At the same time, to improve the detection reliability and modeling efficiency, the research and comparison of band selection algorithms such as CARS, RF, SPA and UVE are carried out for spectral data dimensionality reduction. In the experimental results, the preprocessing of SNV plus second-order 15-point SG smoothing combination combined with the selection of CARS bands, the model established by using the selected 54 bands has a better prediction effect, and the correlation coefficient R2 of correction, verification and cross-validation is up to 0.90, 0.89 and 0.91, the root mean square error RMSE was 0.14, 0.15 and 0.14°Brix, respectively. The results show that the self-built near-infrared spectroscopy detection platform can better realize the rapid detection of tomato sugar.

The tailings dams, as a high-potential manufactured debris flow danger source, has the risk of dam failure when the moisture content is too high, and the generation of dust in the state of low moisture content will endanger the surrounding environment. Monitoring tailings’ moisture content is of great significance to the safety of tailings dams and the environmental protection of mining areas. Compared with traditional sampling and testing methods, hyperspectral remote sensing has the characteristics of a large observation area, easy acquisition of real-time data, and rich spectral information, which provides a means for rapid and high-precision monitoring of tailings moisture. The high-silicon type iron tailing in the Anshan-Benxi iron ore group was selected as the research area, and 77 tailings samples were collected on the spot.The spectral data was obtained by Vis-NIR (350~2 500 nm) spectrometer, and the competitive adaptive reweighted resampling method (CARS) was introduced to screen out the optimal bands and establish three-band spectral indices (TBI), combining random forest (RF), particle swarm optimization extreme learning machine algorithm (PSO-ELM) and convolutional neural network (CNN) model, a tailings moisture inversion model was established to obtain the spatiotemporal distribution characteristics of surface moisture in the tailings dam. Using the domestic Gaofen-5 hyperspectral satellite as the data source, the model was applied to obtain the temporal and spatial distribution characteristics of the surface moisturecontent in the tailings dam. The results showed that: (1) The spectral reflectance of tailings decreased significantly with the increase of moisture content, the spectra characteristic appeared in the O—H absorption bands at 1 455 and 1 930 nm, and the absorption depth gradually decreased with the decrease of moisture content; (2) Based on the CARS method, 18 moisture sensitive bands were screened out, and further use the sensitive bands to construct different forms of three-dimensional tailings moisture content characteristic spectral indices. It is proposed that TBI5=(R1 097.47-R1 990.67)-(R1 990.67-R437.39), which has the highest correlation with moisture content, reaching 0.844 4; (3) Based on the three-dimensional spectral indices combined reflectance data set and the CNN method, the measured spectral model achieves the verification accuracy R2=0.92, the residual predictive deviation (RPD) =3.43. Based on this model, the spatial distribution results of tailings moisture content in the study area were obtained by inversion using the Gaofen-5 satellite data. The moisture content field verification model prediction result in R2 reached 0.79.The result is relatively effective. This study can provide a reference for large-scale real-time, and rapid monitoring of iron tailings moisture content based on hyperspectral technology.

K9 glass is widely used in of high-power lasers because of its high hardness, good thermal stability, low expansion coefficient and high transmittance. However, the problem of contaminant-induced damage to optical components has become one of the bottlenecks restricting the development of high-power lasers. The in-depth study of the damage mechanism of optical components is important to control the damage formation. In order to investigate the damage mechanism, the spectral detection analysis method is proposed, and the mechanism of Al2O3-induced laser damage in K9 glass is studied by this method. In this method, the EDS spectroscopy techniques were used to investigate the damage morphology and the corresponding changes in the atomic percentages of elements before and after the damage. The physical and ablation chemical changes that occurred during the damage process can be explored. In addition, the ionization process during the damage is diagnosed and discussed combined with LIBS technology. The investigation of the damage principle of optical elements and the real-time monitoring of the safety of optical elements are realized. The results show that during the laser-induced contaminant damage, the morphology of Al2O3 particle changes and micro damage crater also appeared in the K9 glass. In addition, the atomic percentage content of Al2O3 particles changes due to the deformation of the particles, the Na2O contained in the K9 substrate combines with oxygen, causing an increase of the atomic percentage content of O elements, and SiO2 changes into ultrafine particles through the vaporization-condensation process, which leads to a decrease in the atomic percentage of Si elements. These changes directly reflect the high-temperature melting phenomenon during the damage process. The ionization breakdown process can be detected using LIBS, and the characteristics of a plasma flash in the damage process are obtained. Furthermore, the physical processesmentioned above were modeled and simulated, and the heat conduction during the damage process and the plasma shock wave propagation characteristics within the substrate were analyzed using COMSOL simulations. It is shown that during the damage process, the particle temperature reaches 2 800 K, which is higher than its melting point (2 313 K) and similarly, the substrate temperature (2 500 K) is also higher than its melting point (1 673 K), which directly causes a phase transition and generates a plasma under subsequent laser irradiation. The high-pressure impact of the plasma causes the appearance of micro melt damage craters on the substrate. The simulation analysis verifies the feasibility and accuracy of the LIBS technology and EDS spectral analysis to investigate the damage mechanism of optical components, which can be used not only for the analysis of the damage mechanism but also for the real-time monitoring of the stable operation of high-power laser systems.

Thermometry based on nitrogen-vacancy (NV) center in diamond realizes temperature sensing by measuring the zero-field splitting parameter (D) between the sublevels of its ground state. Since diamond has high stability, the resistance to interference, and can be processed into different sizes, thermometry is regarded as a feasible solution to high-precision temperature measurement on the micro-nano scale, which has the potential to be developed into practical application. In the measurement of D, the laser is applied to excite spin polarization and a microwave is then used to manipulate the electron spin. The optically detected magnetic resonance (ODMR) spectrum is obtained by detecting fluorescence released in the transition of the electrons, and the spectral lines are fitted to determine the values of D. Fluctuation of laser power is one of the important sources of experimental noise. To realize a high probability of spin polarization applying laser with high power is necessary. However, the fluctuation at a relatively high laser power will influence the probability of spin polarization, decrease the stability of the fluorescence intensity detected, and increase the error of data. The traditional method, which outputs the photon count rate directly without any reference for normalization, will reflect the fluctuation of laser power in the spectrum. This paper proposes a method to reduce the laser-induced error, in which a reference value of photon count without effective manipulation of microwave can be measured at each count using a specifically-encoded pulse sequence, and normalized fluorescence intensity will be obtained in the spectrum after normalizing the photon count under the manipulation of microwave to the reference. In this way, the effect of the laser power fluctuation on the spectrum will be weakened. The comparative experiments were carried out at 300 K on the ODMR measurement system built in the laboratory. A count time of 0.8 s and a preparation time of 0.001 s were determined considering the optimization of the two time-related parameters. Under different laser powers, three groups of D values were measured by the direct fitting, mathematical, and pulse code normalization methods. The experimental results demonstrate that using pulse code to obtain reference for normalization can effectively suppress laser-induced error to 62.5% of that without any reference, and improve the data accuracy from 179 kHz with no reference and 165 kHz with mathematical reference to 56.9 kHz by comparison. The method can effectively suppress the laser-induced error, improve the resolution of the determination of D, and lay a foundation for the practical development of thermometry based on NV centers.

Diseases affect crop quality seriously and cause economic losses. Disease spot segmentation is an important process of identification and disease severity estimation, whose segmentation results can provide an effective basis for subsequent identification and severity estimation. Due to the irregularity and complexity of lesions, and the visible spectrum image of lesions in the natural environment is susceptible to be change in illumination, traditional image processing methods have low accuracy, low universality and robustness for image segmentation of lesions. In this regard, this article proposed a method for the segmentation of crop leaf diseases based on semantic segmentation and visible spectrum images. Firstly, taking peanut brown spot and tobacco brown spot as the research objects, 165 visible spectrum images were collected using a Nikon D300s SLR camera. The visible spectrum images of the diseases were pixel-labeled through the Matlab Image Labeler APP, and the brown spot and background area were respectively marked. Secondly, the labeled dataset adopted image enhancement methods such as horizontal flipping, vertical flipping, changing brightness, etc., to obtain 1 850 enhanced sample data sets and randomly divided them into the training set, validation set and test set according to the ratio of 8∶1∶1. At the same time, in order to save computational cost, the pixel resolution of the data set was adjusted to 300×300. Finally, four types of disease spot segmentation models were constructed based on the three semantic segmentation networks of FCN, SegNet and U-Net. The effects of data enhancement and disease types on the lesion segmentation model were explored. Four segmentation indicators were used to evaluate the model’s segmentation effect. The test results showed that only for lesion segmentation, image enhancement could improve the segmentation accuracy of the model. The model’s Mean Precision (MP) and Mean Intersection over Union (MIoU) were 95.71% and 93.36%, respectively. The 4 semantic segmentation models were significantly better than the Support Vector Machine (SVM). Compared with the U-Net, SegNet-2 and SegNet-4 segmentation models, FCN can effectively avoid the influence of light changes. The Precision (P) of lesion segmentation and the Intersection over Union(IoU) reached 99.25% and 97.55%, respectively. For the lesion classification and segmentation experiment, the Precision (P) of FCN for the two diseases reached 90.41% and 97.54%, and the Intersection over Union(IoU) of the two diseases reached 95.61% and 70.30%, respectively, which were better than the other three segmentation models. FCN can distinguish disease types well while segmenting disease spots, which has good generalization and robustness and realize the identification and segmentation of disease spots in natural scenes and provide a technical reference for the severity estimation of mixed diseases.

This paper proposes a polarization spectral multidimensional information fusion method based on nonsubsampled contourlet transform to address the shortcomings of traditional optical methods that make it difficult to identify camouflaged spectral targets in complex backgrounds and the common fusion methods that tend to lead to image information loss. The multidimensional information reconstruction algorithm was designed based on the acquired multidimensional information such as target space, spectrum and polarization, and the basic data of polarization state including Stokes parameters as well as the degree of polarization and angle of polarization were extracted. NSCT is used to fuse the basic polarization parameters to improve the image’s information content and improve the camouflage’s recognition accuracy. The images Q and U with the same edge information are first decomposed using NSCT. Regional energy-weighted fusion is performed for the decomposed low-pass sub-bands; for the high-pass sub-bands, LBP features are used for weighted fusion according to the characteristics of polarization features, such as small gray value and high influence by illumination. At the same time, the proposed method is compared with four types of fusion methods, and the fusion results are evaluated objectively according to five indicators: information entropy, standard deviation, mean gradient, contrast and peak signal-to-noise ratio, and the target recognition accuracy is compared with plain images, polarized fused images and polarized hyper-spectral images. The information entropy of the fused image is 6.998 6, the standard deviation is 45.599 8, and the average gradient is 19.808 6. Compared with the original intensity, the improvements are 5.1%, 14.04%, and 7.3%, respectively, ranking first among the four types of fusion methods. It is shown that the method proposed in this paper effectively achieves polarization-based feature fusion and enhances the difference between the artificial target and the natural background. At the same time, the recognition accuracy of the fused polarized hyperspectral image for the target reaches 0.986 2, which is 21% higher than the target recognition accuracy of the single-intensity image. The experimental results show that the proposed method can effectively fuse the intensity and polarization information to improve image contrast and readability. The fused image also significantly improves target recognition accuracy, overcoming the problem of high false alarm rate of traditional spectral means for camouflage target recognition, and providing a new and effective means for new concept spectral camouflage disclosure, which has great application value.

Pleurotus ostreatus is one of the wide varieties of edible fungus, ranking third for its yield in China. Except for its delicious taste, appreciated by consumers, it is known to be rich in high-quality protein and polysaccharides with various biological activities. However, there are different kinds of P. ostreatus following their quality, and the existing nutrient composition analysis methods are time-consuming and high in composition. It is difficult to meet the requirements of the detection of their nutrient composition, as well as for other edible fungi. Fourier Translation Infrared Spectroscopy (FTIR) technology, characterized by high-speed detection, convenient technique, simultaneous analysis of multiple compounds, and safe and environmental protection, was thus used combined with stoichiometry to develop mathematical models, to assess those nutrient compounds. Therefore,the infrared spectra of 85 samples from P. ostreatusas fruiting bodies collected nationwide were determined. 5 kinds of spectral data pretreatment methods, multiple scatter correction (MSC), standard normal transformation (SNV), orthogonal signal correction (OSC), smooth plus first derivative (F-GD), and smooth plus second derivative (S-GD) were used. Following the model of the validation set regression coefficients, OSC combined with S-GD, and OSC combined with F-GD were the best pretreatment methods for the fruiting body protein and polysaccharide models. Under the optimal spectral pretreatment, 7 458 spectral bands were extracted by the LASSO algorithm, and 93 characteristic wavenumbers of protein and 92 for polysaccharides were obtained, with a compression rate of 98%. PLS model was established by fitting the characteristic wavenumbers with the protein and polysaccharide contents of P. ostreatus fruiting bodies detected by chemical method. The results showed that, for the protein model, the R2 regression coefficient of the calibration set was 0.999 8, RMSECV was 0.047 7, the R2 regression coefficientof the validation set was 0.987 2, RMSEP was 0.506 8, and RPD was 8.840 6 greater than 3, while for polysaccharides model, The R2 regression coefficient of calibration set was 0.999 9, RMSECV was 0.020 1, the R2 regression coefficient of validation set was 0.980 3, RMSEP was 0.292 9, and RPD was 7.119 8 greater than 3. The models thus had good predictive ability and robustness. This research provides a practical reference to determine a high-speed detection method for the nutrient content ofedible fungi by FTIR, a foundation to establish a nutritional quality evaluation for P.ostreatus and the promotion of their high-quality development, even for other edible fungi.

Vegetation heavy metal pollution monitoring is important for hyperspectral remote sensing monitoring. In order to qualitatively use hyperspectral remote sensing technology in monitoring heavy metal pollution of vegetation, the reflectance spectrum data collected from potted experiment collection was studied. The potted maize experiment in the laboratory stressed by different Cu2+ and Pb2+ stress concentrations was set up. The reflectance spectra of maize leaves and the contents of Cu2+ and Pb2+ under different concentrations of Cu2+ and Pb2+ were measured by the basic data on copper and lead-contaminated maize. A complete set of data sets for the heavy metal copper and lead-contaminated maize plant was formed. This study proposed a copper lead detection index CLDI, which realized the monitoring of heavy metals copper and led stress in two varieties of maize with different cultivation periods. Experiments of copper and lead pollution with different concentrations were designed, and the measured spectral reflectance interval of 450~850 nm of maize leaves was processed by the first order differential (D) and continuum removal (CR), and the DCR (Differential Continuum Removal) spectral curve was obtained. The Pearson correlation coefficient (r) was used to analyze the DCR and the biochemical data, and select characteristic bands sensitive to heavy metal Cu (Copper). The calculated Pearson correlation coefficients suggested that the DCR value at 490~520 and 680~700 nm presented a positive linear correlation close to 1 with the Cu2+ (Copper ion) contents in soil and leaves and a negative linear correlation that was close to -1 was present in the range of 630~650 and 710~750 nm. We selected the DCR value of wavelengths 505, 640, 690 and 730 nm to establish CLDI and compared it with conventional vegetation indices (VIs) by calculating the Pearson correlation coefficient between them and Cu contents in soil and leaves. We used the spectral data of different varieties of maize leaves obtained in 2017 to compare CLDI with the conventional vegetation index (VIs). CLDI was applied to monitor the pollution degree of maize leaves under lead stress. The results suggested that CLDI showed a significant correlation with Cu2+ and Pb2+ (Lead ion) stress concentration, and the correlation of CLDI was much stronger than that of other vegetation indices. The proposed CLDI detects the pollution degree of maize with different varieties and in different periods under copper and leads stress with the advantages of straightforward calculation, robustness, high effectiveness, and universality. This study focused on the laboratory leaf scale; it can provide the theoretical basis for monitoring heavy metal stress on the canopy scale.

Rare-dispersed element mineral resources are the key mineral resource in the national economy. The information extraction of content is the basis for potential evaluation and target optimization of mineral resources, but the existing analysis technology of rare-dispersed elements cannot meet the needs of rapid detection and potential evaluation. The analysis technology of rare-dispersed element based on Hyperspectral is a way to solve this problem. Therefore, the Sinongduo-Zexue ore concentration area in Tibet is the study area, and the hyperspectral inversion method and inversion model about the content of rare-dispersed element cadmium (Cd) in lead zinc ore are studied. ASD FieldSpec 3 spectrometer and supporting software are used for spectral data acquisition and pretreatment. Based on spectral feature analysis, multi-type spectral transformations such as first derivative (FD), second derivative (SD), logarithm of the reciprocal (AT), first derivative of logarithm of the reciprocal (AFD), second derivative of logarithm of the reciprocal (ASD) are carried out, the characteristic bands selected by Pearson correlation coefficient (r) are used for the modeling and inversion of random forest (RF), artificial neural network (ANN), support vector machine (SVM), the effect and prediction accuracy of content inversion models are evaluated by the coefficient of determination (R2), and root mean square error (RMSE). The results show that the spectral reflectance of the sample is concentrated in the range of 40%~60%, and the absorption peaks are formed at 1 420, 1 920 and 2 200 nm. The characteristic bands cover the visible and near-infrared bands, and 771~2 051 nm is the optimal range of the characteristic band. SD is the best spectral data dimensionality reduction method, which has selected 15 characteristic bands. ASD has selected 8 characteristic bands, and AFD has selected 2 characteristic bands. FD and AT did not select the characteristic band. In the characteristic band inversion of SD selection, the SD-ANN model (R2=0.884, RMSE=2.679) has the best prediction effect of cadmium content, followed by the SD-SVM model (R2=0.830>0.8, RMSE=1.382), SD-RF model has the worst prediction effect (R2=0.505ASD-SVM>ASD-ANN>SD-SVM>ASD-RF>SD-RF. The study summarizes the hyperspectral characteristics of cadmium, establishes the hyperspectral inversion method and model of cadmium content, provides a reference for hyperspectral inversion, nondestructive testing and rapid analysis of rare-dispersed elements such as cadmium, and provides theoretical support for the potential evaluation and target optimization of rare-dispersed element mineral resources in the high-altitude exploration area.

Biodiversity is the basis of human survival. Affected by the environment and climate change, the decrease in global biodiversity is becoming increasingly serious. Therefore, the study of regional biodiversity has great significance in protecting endangered species’ habitat, planning and utilising the regional resources reasonably. Three dynamic habitat indices (DHI) of cumulative, minimum and difference were constructed by datasets of NDVI, EVI, FPAR, LAI and GPP of multispectral remote sensing vegetation products from 2002 to 2018. The multiple regression analysis was used to study ① the applicability of DHIs constructed by different multispectral remote sensing indices (NDVI, EVI, FPAR, LAI and GPP); ②the complementarity of species diversity expressed by cumulative, minimum and difference DHI models; ③the impact of climate change on biodiversity in China; ④the ability of cumulative, minimum and difference DHIs to express species richness. The research shows that ① there is a strong correlation between corresponding DHIs based on the same MODIS multispectral vegetation indices (correlation coefficient from 0.77 to 0.98), so they can substitute for each other; There is a certain correlation among these three DHIs, while they cannot replace each other. ②Compared with DHIs constructed by NDVI, EVI, FPAR and LAI product data, GPP-DHIs have the strongest ability to monitor biodiversity in China and have a good correlation with species richness (correlation coefficient from 0.32 to 0.84). ③Continuous climate change will affect the total productivity of vegetation significantly in the large region, and extreme climate has little impact on the large region; Evapotranspiration has a more significant impact on the total productivity of vegetation in large-scale regions than temperature, and precipitation. ④Environmental change has the greatest impact on amphibian species richness, followed by birds and mammals. ⑤The cumulative DHI and minimum DHI in China gradually increase from the northwest inland to the southeast coastal area. The minimum DHI in the northwest, north China, high altitude area, high latitudes, and northwest desert areas are very small, which indicate the ecological environment in the southeast coastal area is more suitable for biological survival, and the harsh environment affects biodiversity seriously. The difference in DHI shows a higher spatial pattern in Northeast and North China, and a lower spatial pattern in Central and South China, which indicates that the living environment of species in Northeast and North China changed greatly, and the living environment of species in Central and South China was relatively stable.

Glycation reaction can induce the structural change of protein in food stuff; Ara h2 is one of the main proteins in peanuts, and it can be used as a model protein to study the structural change of the glycation products of peanut protein. However, the effects of different reducing sugars on Ara h2 glycation have not been reported. Therefore, this article took Ara h2 as the research object to study the changes in the molecular weight, the secondary and tertiary structure and the functional groups of Ara h2 before and after glycation by SDS-PAGE, endogenous fluorescence, synchronous fluorescence, ultraviolet spectrum, circular dichroism, Fourier transform infrared spectroscopy and other spectroscopic techniques. The effects of six reducing sugars (ribose, xylose, galactose, glucose, fructose and lactose) on Ara h2 were analyzed to clarify the structural change of different Ara h2 glycation products. The results of SDS-PAGE showed that these electrophoretic bands of Ara h2 modified by xylose and ribose moved up significantly, and their glycation degree was the largest, compared with other reducing sugars. Ultraviolet spectrum analysis showed that glycation reaction would change the absorption peak intensity of Ara h2, and modification with pentoses had the strongest absorption intensity (absorption peak intensity of xylose was the largest). The results of endogenous fluorescence, synchronous fluorescence and three-dimensional spectral scanning showed that glycation reduced the fluorescence intensity of Ara h2 and pentose modified Ara h2 had the lowest fluorescence intensity. It might be due to the structural unfolding of Ara h2 caused by glycation, which exposes aromatic amino acids to the water environment and leads to fluorescence quenching. Circular dichroism analysis showed that the content of α-helix increased after Ara h2 was modified by different reducing sugars, among which modified by xylose showed the highest helix content (15.6%). Fourier transform infrared spectroscopy showed that the absorption peaks of Ara h2 (modified by xylose and ribose) shifted from 3 327.41 to 3 318.43 and 3 321.09 cm-1, respectively; At 1 700~1 600 cm-1, the absorption peak intensity of Ara h2 modified by xylose and ribose was slightly higher than that modified by other reducing sugars. Therefore, different reducing sugars have different effects on the structure of Ara h2 glycation products; The shorter carbon chain and the less steric hindrance of reducing sugars led to a higher glycation degree and a greater impact on the structure of Ara h2.

The plasticizer is a kind of polymer material additive widely used in various fields of people's life and work, long-term or high concentration of exposure to human health has huge potential harm. Now plasticizer harm events occur frequently, and the detection of plasticizers has become a top priority. Currently, the main detection methods of plasticizers are gas chromatography, gas chromatography-mass spectrometry and high-performance liquid chromatography. However, the above methods have disadvantages such as cumbersome pre-treatment, high technical requirements, low sensitivity and long detection time, which is not conducive to the rapid detection of plasticizer. Raman spectrum analysis technology has no sample preparation, fast detection and molecular fingerprint information and other characteristics, can respond to qualitative-quantitative analysis to determine the material, the surface-enhanced Raman spectroscopy analysis technology asa branch of Raman spectrum analysis technology, has a high sensitivity, high selectivity and the advantages of the non-invasive, It breaks through the limitation of the inherent low sensitivity of ordinary Raman spectroscopy. It can obtain the structure information which is difficult to be obtained by ordinary Raman spectroscopy, which gradually plays an advantage in plasticizer detection. This paper briefly describes the principle of Raman spectrum analysis technology, summarizes the common Raman spectroscopy analysis technology in the plasticizer characteristic peak identification and quantitative test of the high content of plasticizer, and the application of surface-enhanced Raman spectroscopy in the detection of low-content plasticizers. Nowadays, the substrates used for plasticizer detection using surface-enhanced Raman spectroscopy are mainly gold and silver nanoparticles. In this paper, the research progress of surface-enhanced Raman spectroscopy in trace and trace plasticizers is reviewed according to the types of substrates used in surface-enhanced Raman spectroscopy (Au nanoparticles, Ag nanoparticles, Au@Ag nanoparticles). Finally, the existing problems in the detection of plasticizers by Raman spectroscopy are pointed out, and the development trend in the future has been prospected to provide reference and solutions for the study of plasticizer detection in the future.

Building energy consumption occupies more than 30% of total energy consumption in China. Building energy conservation is an important part of China’s policy on energy conservation and emissions reduction. It is important to realize building energy conservation by improving its thermal and humidity regulation performance through the passive regulation performance of the building itself. Decylic acid and palmitic acid were used to prepare decylic acid-palmitic acid composite phase change material, which phase changes temperature within the comfort range of the human body. The decylic acid-palmitic acid composite phase change material, tetraethyl silicate and tetrabutyl titanate were used as raw materials to prepare decylic acid-palmitic acid /SiO2@TiO2 photocatalytic phase change microcapsules (D-T microcapsules) which have heat, humidity adjustment and air purification function. It is conducive to building energy saving and improving indoor air quality. In this study, the dosage of deionized water (the molar ratio of deionized water to tetraethyl silicate), pH value, the dosage of clecylic acid-palmitic acid composite phase change material (the molar ratio of decylic acid-palmitic acid composite phase change material to tetraethyl silicate), the dosage of tetrabutyl titanate (molar ratio of tetrabutyl titanate to tetraethyl silicate) and the dropping acceleration of tetrabutyl titanate, these five effects were analyzed to study the effects on the particle size, composition, morphology, air purification function, thermal and humidity regulation performance of D-T microcapsules. The laser particle size analysis results showed that the amount of deionized water and tetrabutyl titanate had important effects on the particle size distribution of D-T microcapsules. The excess water system can effectively disperse T-D microcapsules and prevent agglomeration. TiO2 generated by the hydrolysis of tetrabutyl titanate was wrapped on the surface of decylic acid-palmitic acid@SiO2. Thus the dosage of tetrabutyl titanate affected the particle size of D-T microcapsules. Scanning electron microscopy showed that excessive decylic acid-palmitic acid composite phase change material would cause leakage of phase change material. The rapid drop acceleration of tetrabutyl titanate affected the hydrolysis reaction rate and would led to TiO2 agglomeration. XRD analysis showed that pH value was the key factor for preparing anatase phase TiO2 with photocatalytic performance. Therefore, when the dosage of deionized water is 90∶1, the pH value is 2, the dosage of decylic acid-palmitic acid composite phase change material is 0.5, the dosage of tetrabutyl titanate is 0.8, and the dropping acceleration of tetrabutyl titanate is 20 min, the prepared D-T microcapsules have complete morphology, uniform particle size and anatase structure. After 6 hours of the formaldehyde degradation test, the degradation rate of formaldehyde can reach 67.87 %. There is an obvious phase transition temperature platform between 18~23 ℃, with a duration of 300 s. When the relative humidity is 84.34%, the equilibrium moisture content is 0.181 9 g·g-1, and the moisture capacity between 32.78%～84.34% is 0.161 3 g·g-1.

Aiming at the problems of oil monitoring data of wet clutch, such as scattered sources, a large amount of data and unstable time axis, the multi-data obtained from the spectral analysis are fused. Using the advantages of real-time prediction and accurate prediction of the Wiener process, a model is established to predict the remaining life of the clutch. Firstly, the indicator elements obtained from the clutch life test are fused by permutation entropy weighted evidence fusion method to construct the health index; Secondly, combined with the Wiener process, the degradation model is established, and the parameters in the model are estimated by the maximum likelihood method; Thirdly, the residual life prediction model of the clutch is obtained by updating the parameters according to the historical degradation data; Finally, by comparing the prediction model with an example, it is found that the prediction accuracy of the Wiener process prediction model based on the health index of multi-element fusion is greatly improved compared with the single indicator element prediction. Its prediction point is closer to the experimental value. Through observation, it is also found that when the wet clutch operates for about 50 to 60 h, the prediction point has an obvious change, while the prediction point has an obvious deviation from 220 to 230 h, and it is close to the test value again at about 240 h. The mutation point corresponds to the three stages of clutch wear: initial wear, normal wear and severe wear. The research results show that the prediction model based on oil spectrum data and the Wiener process has the advantages of strong real-time prediction and high prediction accuracy for the remaining life of the wet clutch. The comparison between the prediction results and the test values shows that the different wear states of wet clutch also have a certain impact on the prediction results, especially the wear state transition point has a greater impact on the prediction results.