Due to the limitations of imaging mechanisms, the current technical conditions of remote sensing hardware are not yet able to acquire multi-band remote sensing images with high spatial and high spectral resolution simultaneously. Multi-band remote sensing image is a three-dimensional image collection that reflects the information of different narrow-band intervals. It contains two-dimensional spatial information and one-dimensional spectral information. The spatial information reflects the geometric characteristics of the scene, and the spectral information corresponds to the electromagnetic wave characteristics of the ground objects in different bands. To compensate for the deficiency of spatial information acquisition in multi-band remote sensing images, sharpening of the images, which enhances their spatial resolution by using auxiliary images, has been emphasized. The sharpening of multi-band remote sensing images can not only improve the visual effect of the images, but also lay the foundation for subsequent qualitative and quantitative remote sensing applications such as ground object classification, change detection and parameter inversion, and thus has been a very significant and continuously active research direction in the field of remote sensing image processing. This paper reviews the research progress of multi-band remote sensing image sharpening methods. Firstly, the connotation of multi-band remote sensing image sharpening is expressed. Secondly, from the perspective of panchromatic sharpening of multispectral (MS) images and in the context of algorithm implementation techniques, the research progress and problems of MS image sharpening methods based on Component Substitution (CS), Multi-resolution Analysis (MRA), Optimization Model (OM) and Deep Learning (DL) are investigated and discussed respectively. Thirdly, the sharpening characteristics of HS images are analyzed in light of the characteristics of Hyperspectral (HS) images that are different from those of MS, and some specific HS sharpening methods that are different from those of MS are discussed and summarized. Finally, the future development of multi-band remote sensing image sharpening methods is prospected. The reasons why the CS and MRA methods are currently more recognized by the mainstream and the future sharpening field will show a relevant convergence of multiple methods are discussed, respectively.

For instance, an increase in living and consumption level has significantly led to an increase in demand for food safety and quality of milk and its products. The quality of milk affects the production and consumption of dairy products. In order to ensure the quality of dairy products, methods and procedures have been developed to detect various milk adulterants in the collection, storage and production procedure. Most current analytical methods, such as chemical and instrumental analysis, are targeted detection methods, which require pre-treatment steps designed for adulterants and are cumbersome and time-consuming. In this paper, we proposed a non-targeted method based on mid-infrared spectroscopy developed for the identification of abnormal milk samples. The natural raw milk samples were collected from six pastures of the Mengniu company, and abnormal milk samples were prepared by adding multiple adulterants. Then the mid-infrared spectrum was measured and pre-processed with smoothing, multiple scattering correction, baseline correction and normalization. In order to improve the accuracy and robustness of models, Three different variable selection methods were implemented, such as uninformative variables elimination (MC-UVE), uninformative variables elimination-successive projections algorithm (UVE-SPA) and competitive adaptive reweighted sampling(CARS). Then, two classification algorithms, partial least squares discriminant analysis(PLS-DA) and support vector machine (SVM), were employed and compared in the discrimination models. The results indicated that SVM is the better classification algorithm achieving higher identifying accuracy, and CARS method screening performs better with PLS-DA and SVM classification models. The accuracy of the -SVM-CARS discrimination model achieved 97.84% and 94.55% for validation and prediction, respectively. The variables screened by the CARS method were mainly concentrated in the regions where the spectral features of the anomalous milk samples were more obvious. Further analysis of the misclassified sample showed that the model combination could more accurately identify the abnormal milk samples. These results demonstrate that abnormal milk can be identified successfully using mid-infrared spectroscopy with discriminant analysis, suggesting our techniques to provide an efficient and practical reference for milk adulteration and on-line detection of the production process.

This study aims to identify underwater benthic animals in situ, use random forest algorithm to achieve recognition classification detection, classify and identify target organisms for analysis, dig deeper into the data, and improve efficiency and reliability of decision making. The hyperspectral data of five common economic animals (scallop, ctenophore, veined red snail, wrinkled disc abalone, and imitation spiny ginseng) in different underwater environments were acquired, normalized and processed using random forest (Random Forest, RF) in machine learning, random forest based on principal component analysis method (Principal Component Analysis-Random Forest, PCA-RF), and random forest based on recursive feature elimination method (Recursive feature elimination- Random Forest, RFE-RF). Three random forest algorithms were used to classify five benthic species and for comparative analysis. By ranking the importance of the variables of RF, the reflection spectrum intensity data corresponding to the bands with higher ranking and higher contribution to the model were filtered. Then the top-ranked feature band data were input into the classifier, and the classification accuracy was obtained by optimizing the parameters. The classification results of the data were output to the confusion matrix, and the identification of the five samples could be seen. The lowest recognition accuracy of 64% was obtained for the veined red snail sample; the highest recognition accuracy of 100% was obtained for imitation spiny ginseng and ctenophore scallops; the recognition accuracies of 91% and 96% were obtained for the scallop and wrinkled disc abalone, respectively. The final classification accuracies of the three methods were 90.13% for RF, 95.20% for PCA-RF, and 98.74% for RFE-RF, which showed the feasibility of using the random forest algorithm in the classification of underwater hyperspectral data.

Because the space target is far away from the ground and the atmospheric medium strongly scatters the scattered light signal, it is difficult to get the accurate information of the target in ground-based measurement. In recent years, the rapid development of spectral observation technology has provided a new method for measuring space targets. However, it is difficult to distinguish the target directly from the spectral curve in the collected target spectral information because the target orbital height and material composition are mostly similar. Therefore, based on the bidirectional reflection distribution function (BRDF) scattering theory, the scattering spectral imaging model of space target is established. A group of geosynchronous(GEO) targets were experimentally measured by a 1.2 m aperture ground-based observation platform and spectral video imaging system. The spectral range is 400 to 720 nm, and the spectral resolution is 2 nm. A radial basis neural network algorithm is used to unmix the BRDF in spectral data. The BRDF of six typical materials for space targets is measured experimentally. Because the target is relatively far away, it has exceeded the diffraction limit of the detection system so that the target can be regarded as a point target. However, in ground-based measurements, the atmosphere is an important barrier between the detection system and the target. The target light signal will be strongly scattered by the atmospheric medium when passing through the atmosphere. This scattering greatly attenuates the light signal, but at simultaneously the light signal is amplified according to its original structure. According to the optical memory effect, the structure of the target optical signal remains unchanged after passing through the uniform atmospheric medium. Based on the above analysis, the target spot image in measurement should retain the information of the target projection structure. Therefore, a method of segmentation inversion for the texture region of the target light spot image is used to divide the target light spot into 10 texture regions and extract the corresponding spectral data. Through the transfer function calibration and noise reduction processing of the detection system, the spectral curve of the space geometry angle of the orbiting target in the observation period is obtained. Then the typical material spectral database is used for fitting inversion. The results show that the material types in texture areas No.2, No.5 and No.10 are different from other areas. At the same time, the material area ratio of each texture area is different. To further evaluate the fitting results, a non-singular matrix was used to evaluate the fitting effect, and the disturbance equation was analyzed. The highest fitting accuracy was 85.283 3, and the lowest was 76.982 7. It shows that the fitting results are relatively real. Target speckle image contains distinguishable target projection structure information. This study provides a new direction for detecting point target imaging and speckle image structure recognition.

As a new advanced composite material, glass fiber reinforced composite material has the unique advantages of light weight, high temperature resistance and impact resistance, which are widely used in aviation, military industry and other fields. However, during its production process, due to the influence of the manufacturing process, it is easy to produce micro defects such as delamination and inclusion inside material; on the other hand, in the life cycle of such composites material, due to the interference of external factors such as impact force and high temperature, there are defects such as burning marks and debonding on the surface and inside of the materials. The existence of defects reduces the safety factor of glass fiber-reinforced composites material as structural parts, so it is necessary to detect the defects in the materials. Terahertz time-domain spectroscopy benefits from the unique advantages of terahertz band as an effective supplement to the traditional nondestructive testing methods, terahertz time-domain spectroscopy has been widely used in the field of composite nondestructive testing in recent years because of its transient, low energy and fingerprint spectrum, which can effectively detect defects. The performance of glass fiber reinforced composites material is evaluated through the test results. However, when using terahertz time-domain spectroscopy to detect and analyze defects, it is found that some fringes diffuse with time in the tomography process of defects, the existence of fringes masks the shape of defects, affects the clear identification of defects, and further leads to missed and misjudgment of defects. There is little research on the analysis of fringe in defect tomography in theory. This paper proposes the finite-difference time-domain technology to model and analyze the interaction mechanism between terahertz wave and glass fiber reinforced composites material. A reflective numerical model is established in the frequency range of 0.2~1.5 THz. Through numerical simulation, the defects at the depths of 1, 3 and 5 mm in glass fiber reinforced composites material are imaged. It is also found that when the terahertz wave is vertically incident on the surface of the glass fiber reinforced composite material, the defects at each depth can be imaged, when the terahertz wave is incident on the surface of the glass fiber reinforced composite material at an inclination angle of 1°, and there is a 2° inclination on the upper and lower surfaces of the glass fiber composite material, alternating fringes appear in the defect imaging at each depth inside the material, it is verified that the fringes are caused by interference.

Trichel pulse discharge is a common unstable phenomenon in the corona discharge. In order to further reveal the discharge mechanism and discharge characteristics of the Trichel pulse, the optical characteristics of Trichel pulse discharge are studied atapressure of 600Pa by using the needle plate discharge structure. At an average current of 20～300 μA, the discharge is divided into Trichel pulse discharge mode and normal glow discharge mode. In the Trichel pulse discharge mode, the average interelectrode voltage decreases with the increase of the average current. Under normal glow discharge mode, the average interelectrode voltage remains unchanged with the increase of average current. The luminescence images at different average currents are obtained. The region from the cathode needle tip to the anode plate is divided into negative glow region, Faraday dark region, positive column region, and anode glow region. With the increase of the average current, the luminescence in the negative glow region, the positive column region and the anode surface is significantly enhanced, the volume of the negative glow region remains unchanged, the length of the Faraday dark region gradually increases, and the length of the positive column region gradually decreases. When the Trichel pulse disappears, the luminescence in the negative glow region shrinks to the cathode tip, the positive column region shifts close to the anode plate, and the luminescence in these two regions is significantly enhanced. The emission spectra at different average currents are measured by an emission spectrometer in 300~800 nm. The emission spectrum intensity in the wavelength range of 300~450 nm is higher, the second positive band system (C3Πu→B3Πg) of nitrogen molecules and the first negative band system (B2Σ+u→X2Σ+g) of nitrogen molecular ions. The emission spectrum is weak near 650~800 nm, the first positive band emission spectrum of nitrogen molecules (B3Πg→A3Σ+u). According to N2 (C3Πu→B3Πg) spectra, the vibrational and rotational temperatures of nitrogen molecules at different average currents are obtained by fitting the emission spectra of the second positive band system. The results show that the molecular vibrational temperature and rotational temperature increase with the increase of average current. The molecular vibrational temperature is 3 900~4 500 K, and the molecular rotation temperature is 430~450 K. This paper calculates the electric field intensity at different average currents using the intensity ratio of nitrogen molecule ion line 391.4 nm and nitrogen molecule second positive band spectral line 394.2 nm. The results show that the electric field intensity increases with the increase of the average discharge current, in the range of 145~200 kV·m-1, which indicates that the electron energy increases with the increase of the average discharge current. When the Trichel pulse disappears, the molecular vibrational temperature and electric field intensity increase significantly, indicating that the electron energy and electron density near the tip of the needle increase.

With the requirements of green chemistry, more researchers are committed to studying green eutectic solvents (DES). Choline chloride eutectic solvent (DES) has been used in many fields because of its non-toxic, pollution-free and easy preparation. DES formed by choline chloride (ChCl) and glycerol (Gly) was studied in this paper. Comparing the infrared spectra of ChCl, Gly and ChCl/Gly, it was found that NC4 and O—H had red-shift and the combination and disappearance of characteristic peaks, which all indicated that ChCl/Gly formed DES through N—H+Ch…Cl- and Cl-…H—OGly. In order to explore the changes of DES at different temperatures, the infrared spectral data of ChCl/Gly at 25～135 ℃ were tested with temperature as an external disturbance. It was found that the intensity, width and position of NC4 and O—H spectral peaks changed to a certain extent at different temperatures, but these characteristic peaks were all wide peaks with overlapping phenomenon. It is difficult to conduct one-dimensional spectral analysis, but two-dimensional infrared spectroscopy (2D-IR) can better analyze the complex solution system and explore its formation mechanism and intermolecular structure. 2D-IR analysis was performed on the data of NC4 and O—H characteristic peaks in ChCl/Gly as a function of temperature to obtain the dynamic change sequence and predict the different molecular clusters in the solution. The results Three automatic O—H peaks at 3 539, 3 380 and 3 177 cm-1 at 25~65 ℃. Different automatic peaks of NC4 also appear, which can be attributed to ChCl/Gly, ChCl/(Gly)2 and ChCl/(Gly)3. At 65~105 ℃, O—H has two automatic peaks at 3 539, 3 380 and 3 400 cm-1, and NC4 has corresponding changes, which can be attributed to ChCl/Gly and ChCl/(Gly)2. At 105~135 ℃, O—H only had a strong automatic peak at 3 380 cm-1, and NC4 only had an automatic peak, attributed to ChCl/Gly. Through 2D-IR analysis, it is concluded that molecular clusters in the solution dissociate into a more stable structure with the rise of temperature. In order to verify this conclusion and explore the specific connection mode of N—H+Ch…Cl- and Cl-…H—OGly formed by Cl- between molecules of different clusters as Bridges. The density functional theory (DFT) was used to optimize the configuration geometry at the B3LYP/6-311G++(2d,p) theoretical level, and the relevant thermodynamic data were calculated, which confirmed the possibility of the existence of different molecular clusters in ChCl/Gly. The study shows that the combination of 2D-IR and density functional theory calculation can be well used to analyze the law of different molecular clusters in DES with temperature change, which better solves the problem that it is difficult to analyze overlapping and wide peaks in a one-dimensional infrared spectrum under temperature disturbance.

Prochloraz and imazalil are commonly used preservative fungicides for citrus fruits. The mixture of the two pesticides can effectively reduce pathogenic bacteria drug resistance and achieve better preservative effects. However, the high concentration of pesticide residues on the surface of fruits and vegetables will affect consumers health. In this paper, a rapid and accurate method for detecting pesticide residues in the citrus epidermis was established based on the combination of surface-enhanced Raman spectroscopy and chemometrics methods by taking ugly orange as matrix, prochloraz and imazalil mixed pesticides as the research object. In order to compare the enhancement effects of the gold sol and silver sol, they were respectively used on prochloraz standard solution, imazalil standard solution and mixed pesticide solution of prochloraz and imazalil based on orange peel extract at the concentration of 10 mg·L-1 respectively. Then the Raman spectra of the prepared sample solutions were collected. The results showed that the enhancement effect of gold sol in prochloraz standard solution orimazalil standard solution was better, while silver sol in prochloraz and imazalil mixed pesticides was good. Additionally, to obtain the best reinforcing effect of gold sol substrate, a comparative test was carried out, which determined that the volume ratio of gold sol reinforced substrate to two standard pesticide solutions is 1∶1, and the concentration of agglomerating agent NaCl is 1 mol·L-1. According to the direction from high to low, the spectra of prochloraz standard solutions and imazalil standard solutions at different concentrations were collected. The detection limits were lower than 1 mg·L-1 and 0.5 mol·L-1 respectively, within the maximum pesticide residue limit of 5 mg·L-1 for citrus crops stipulated by the state. In the quantitative analysis experiment of mixed pesticides with prochloraz and imazalil, taking orange epidermis extract as matrix and silver sol with better enhancement effect as enhancement substrate, the surface enhanced Raman spectra of mixed pesticides including prochloraz and imazalil with gradient concentration (5~42 mg·L-1) were collected. Then, various pretreatment methods were used to optimize the spectral data, and four regression models containing support vector regression (SVR), support vector regression optimized by the Grey Wolf (GWO-SVR)algorithm, support vector regression optimized by particle swarm optimization (PSO-SVR)and support vector regression optimized by genetic (GA-SVR) algorithm were compared to establish anaccurate and reliable quantitative model. The results showed that the best prediction effect was achieved on the regression model which was established by support vector regression optimized by Grey Wolf (GWO-SVR) algorithm with the characteristic peak intensities of 829 and 1 168 cm-1 as input after the first-order difference preprocessing. The corrected correlation coefficient (RC), the root mean square error (RMSEC) of the correction set, the predicted correlation coefficient (RP), and the root mean square error (RMSEP) of the correction predicted were 0.978, 1.655 mg·L-1, 0.967 and 2.227 mg·L-1 respectively. In conclusion, the proposed method was proved to be effectively applied for the qualitative and quantitative detection of mixed pesticides with prochloraz and imazalil in the citrus epidermis. It could provide a new approach for detecting pesticide residues in citrus.

Soluble solids content (SSC) is an important physiological indicator of apple quality and maturation, and can be used for predicting the quality and maturity of apples. In this paper, 552 samples were collected at equal intervals of 3 d from the fruit swelling and setting stage to the complete mature stage, and the SSC was determined by collecting visible/near-infrared spectra from 380 to 1100 nm, and fused with fractional differential (FD) technique and replacement importance-random forest (Permutation Importance-Random Forest, PIMP-RF) algorithm to construct an ensemble learning model for SSC prediction in apple during maturing period. The results showed that the fractional differential orders of the PLS model were 0, 0.4, 1.1, and 1.6, and the results of feature importance and interpretability analysis by the PIMP-RF algorithm showed that the key wavelengths for predicting the SSC of maturity apples using visible/near-infrared spectroscopy were mainly in the visible band, which provided a theoretical basis for the future development of a rapid nondestructive detection device for Xinjiang Red Fuji apples. The ensemble learning model of apple ripening SSC constructed based on fractional differential technique and PIMP-RF algorithm has good prediction ability, with the correlation coefficient r equal to 0.989 2, mean absolute error MAE equal to 0.241 2, root mean square error RMSE equal to 0.309 1 and mean absolute percentage error equal to 0.018 3 in the training set. The correlation coefficient r of the test set is equal to 0.903 8, the mean absolute error MAE is equal to 0.549 9, the root mean square error RMSE is equal to 0.740 8, and the mean absolute percentage error is equal to 0.043 4, compared to the FD0-PIMP-RF, FD0.4-PIMP-RF, FD1.1-PIMP-RF, and FD1.6-PIMP-RF models, the ensemble learning model is optimal. Therefore, the integrated fractional order differentiation technique and PIMP-RF algorithm, combined with visible/near-infrared spectroscopy, can successfully and effectively predict the soluble solids content of apples during maturing period.

Due to the seriously overlapped infrared spectral peaks of each component in hydrocarbon gas mixtures, which is caused by the high similarity of molecular structures, it has always been a difficult problem in stoichiometry to precisely monitor the concentration. A rough and fine selection strategy binary gray wolf optimization (RSBGWO) algorithm is proposed to optimize infrared spectral features and establish a high-precision quantitative analysis model to address this challenge. It takes the mean value of root mean square error (RMSECV) of the spectral quantitative analysis model based on cross-validation as the fitness function. In the rough selection stage, the first global iteration is carried out to update the location information of the selected characteristic variables for α wolf, β wolf and δ wolf. In the fine selection stage, combining the characteristic variables for α wolf, the characteristic variables for β wolf and δ wolf after eliminating the corresponding characteristic variables in which position are not selected for α wolf, are used to update the location information of wolves, in order to reduce the RMSECV value gradually and make sure that the extracted characteristic wavelength is globally optimal. In addition, a nonlinear convergence factor is introduced to accelerate the convergence speed.The algorithm is tested on the infrared spectral data set of 359 mixed alkane gas samples, and the effect of the proposed algorithm is verified. Compared with bGWO and bPSO feature extraction algorithms, the MLR model based ontheRSBGWO algorithm proposed in this paper reduces the number of the selected feature by more than 96% and increases the relative prediction deviation (RPD) by more than 15. The root mean square error of prediction (RMSEP) is lower than the instrument error of gas distribution system used for data acquisition when analyzing the concentrations of methane, ethane, propane and carbon dioxide. Compared with the MLR model and PLS model of full spectrum modeling, the prediction accuracy of the MLR model and PLS model based on the RSBGWO algorithm proposed in this paper is significantly improved, and the dependence of prediction effect on the quantitative analysis model is reduced. The experimental results show that the method proposed in this paper can significantly improve the analysis effect of the quantitative analysis model of infrared spectroscopy. The method can promote the application of spectral detection technology in biopharmaceuticals, the food chemical industry, oil and gas exploration, etc., especially in the application occasions containing homologous organic compounds.

The study on the granularity of marine sediments is helpful in understanding the impact of human activities on the natural marine environment. The fusion of principal component analysis and successive projection algorithm combines the advantages of both spectral feature extraction methods. It can obtain richer feature wavelengths than a single feature extraction method, achieve rejection of irrelevant features and interference information, minimize the loss of feature information, and facilitate the analysis of sediment grain size. In this paper, 32 sediments from the surface layer of the intertidal zone of East Dayang Village in Qingdao City were divided into four sediment samples with different grain sizes of 0.3～0.2, 0.2～0.1, 0.1～0.075 and <0.075 mm. The visible-NIR reflectance spectra of 32 sediments with different grain sizes were measured separately, with 128 spectra samples. The 128 spectral samples were divided into modeling set and test set in the 2∶1, 1∶1 and 1∶2 ratio for analysis. An algorithm fused with principal component analysis and successive projection algorithm was used to extract the characteristic spectra of different grain-size sediments, and the support vector machine algorithm was used to build a grain-size classification model. The results show that the fusion algorithm test set correct rates of 83.33%, 82.81%, and 75.29% at 2∶1, 1∶1 and 1∶2, respectively. All the correct rates were significantly improved relative to the single feature extraction algorithm, except for the lower than 90.47% correct rate for the test set of the continuous projection algorithm at the 2∶1 ratio, indicating that the classification models were built by using the extracted feature spectra of the fusion algorithm. The classification model using the fused algorithm with the extracted feature spectra has an advantage over the model using two separate feature extraction algorithms under the condition of a small training set and clear particle size. Adopting a classification model a marine sediment particle size based on principal component analysis and continuous projection fusion algorithm can improve the correct classification rate results of marine sediment particle size, establish a particle size classification model with a higher correct rate, and provide a solution for fast particle size classification.

Three-dimensional fluorescence technology is attracting attention in detecting emergency drinking water pollution events. However, some unsolved problems remain, such as being easily affected by water environment fluctuations, low detection rate facing low-concentration organic pollutants, etc. Therefore, in response to the demand for online monitoring, this study proposed a time series double thresholds method for anomaly detection in drinking water using three-dimensional fluorescence. This method applied principal component analysis (PCA) to extract the feature spectrum of the detected samples and trained the linear autoregressive (AR) model to predict the principal component of the water samples in the future. The eigenvalue difference was then obtained by comparing the predicted and measured ones. At the same time, combined with the change rate of the measured eigenvalues, the double threshold for time series was set to finally determine the start and end points of the pollution event to determine the entire pollution event. The research validated the proposed method by simulating high-concentration pollution events, low-concentration pollution events, and fluctuations in water background. The experimental results show that this method maintains the detection accuracy for high-concentration pollution events. Moreover, compared with conventional methods, the proposed method improved the detection performance in low-concentration pollution events and low-concentration pollution in high-interference environments. The detection accuracy is increased by 9.4% and 20.7%, respectively.

The farinograph characteristics of wheat flour determine the quality and the end use of wheat flour. The farinograph characteristics of wheat flour are influenced by wheat variety, origin, and milling process technology. There are four important farinograph parameters: water absorption, development time, stability time and degree of softening. Near-infrared spectroscopy (NIR) is widely used to determine wheat flour composition parameters, such as moisture, protein, ash and wet gluten content. Most of them directly use linear regression algorithms to establish models, which has low prediction accuracy, and there are few studies on detecting farinograph characteristics, and the results are also affected by the lack of sample richness. In this study, 968 samples of wheat flour from different countries and regions were collected, and an ensemble method of classification model and a regression model was proposed to improve the prediction accuracy of farinograph characteristics. Spectral preprocessing methods, including standard normal variation (SNV), linear detrending, multiplicative scatter correction (MSC) and Savitzky-Golay first-order derivative, were applied to the spectral data, and the best preprocessing method was selected with cross-validation. As for the modeling methods, the classical linear regression methods, i.e., partial least squares regression (PLSR) and principal component regression (PCR), were explored. The accuracies of the two methods are approximately equivalent. The root mean squared error of calibration (RMSEC) on farinograph parameters (i.e. water absorption, development time, stability time, and degree of softening) of the PCA model were 2.186, 1.838, 4.037, 21.693 and 2.039, 1.837, 3.968, 21.252 for PLSR correspondingly. The PLSR model requires fewer factors than PCR. Secondly, the two-stage regression model proposed in this paper was explored. Gaussian process regression (GPR) results were used as the classifier to cluster the samples, PLSR models were established in different clusters to predict the farinograph characteristics, and the sigmoid function was used to fuse the PLSR models. This modeling method can significantly improve the prediction accuracy of farinograph characteristics. The RMSEC on the predictions of farinograph parameters is 1.876, 1.160, 2.459 and 14.449 correspondingly.

The quality of Salvia miltiorrhiza in different origins varies greatly, and it is urgent to establish an accurate and rapid analytical method for discrimination. Laser-induced breakdown spectroscopy (LIBS) has the advantages of fast, real-time, high efficiency, which overcomes many problems of traditional analysis methods. Artificial neural network (ANN) has strong learning and generalization abilities, a fast and accurate analysis method. Therefore, a method for discriminating Salvia miltiorrhiza from different geographical origins was developed by using LIBS combined with ANN. In the experiment, the samples of Salvia miltiorrhiza from six different origins, such as Anhui and Gansu provinces were collected, and the spectra of Salvia miltiorrhiza samples were collected by LIBS spectrometer. Then, comparing the element characteristic peaks of LIBS, it was found that there are differences in the element emission intensity of Salvia miltiorrhiza from different origins, such as Fe (238.20, 373.71 nm) and Ca (315.89, 317.93 nm). A supervised classification model was established by the ANN method combined with 5 different spectral preprocessing methods: maximum and minimum normalization (MMN), mean centralization (MC), standard normal transformation (SNV), Savitzky-Golay smooth filtering (SG) and multiple scattering correction (MSC). The RAW-ANN model has achieved a test set classification accuracy of 94.54%; SNV and MC methods did not improve the classification ability of the ANN model; And the three preprocessing methods of MMN, SG, and MSC all have improved the classification performance of the ANN model. The SG-ANN model achieved the best classification effect, with a test set classification accuracy of 98.15%. At the same time, it has higher sensitivity, precision and specificity, of which Anhui and Henan provinces have the best discrimination results, with sensitivity, precision and specificity reaching 100.00%. The other four orgins sensitivity, precision and specificity are also above 95.00%. The results showed that selecting an appropriate spectral preprocessing method could significantly improve the classification ability of the ANN method and build a more relevant qualitative analysis model. The above results show that LIBS combined with ANN is a promising method for analysing and identifying Salvia miltiorrhiza, which provides a new idea for the quality supervision system of Chinese medicinal materials.

Circular dichroism (CD) spectroscopy is a well-established biophysical technique used to measure protein and its secondary structure and to detect changes in secondary and higher orders of structure for applications in research and the quality control of protein products such as biopharmaceuticals. However, objective comparison of spectra is challenging because of a limited quantitative understanding of the sources of error in the measurement. Statistical methods can be used for comparisons but do not provide a mechanism for dealing with systematic and random, errors. CD measurements in any two instruments may often present slight differences in spectral magnitude or wavelength, even for the same sample under comparable conditions. The small disparities between the polarization of the incident light from each instrument, light source, and final lamp output are examples of the variables that can produce such differences. On the other hand, the structural information acquired with the CD method can sometimes be hampered by the poor quality of the original CD data, and CD deconvolution analysis strongly depends on the spectral intensity. Here a helix predominate protein—cytochrome C was taken as the experimental object, and CD spectroscopy was used to measure the concentration of 0.05 mg·mL-1 cytochrome C aqueous solution after instruments were typically calibrated using standards. And then, a measurement model for CD spectroscopy of 0.05 mg·mL-1 Cytochrome C aqueous solution was presented, incorporating the principal sources of uncertainty to derive an uncertainty budget of spectral magnitude in wavelength 222 nm. The uncertainties of spectral magnitude were from measurement repeatability, concentration uncertainty of calibration solution and protein solution, the uncertainty of cell length of the cuvette, etc. After calibrating the instrument, these sources of uncertainty were comprehensively considered, and the magnitude uncertainty of 0.05 mg·mL-1 cytochrome C aqueous solution at the wavelength of 222 nm was (-4.53±0.54) mdeg, k=2. The uncertainty, evaluation found that the uncertainty of 1 mm cuvette cell length and the solution preparation process account for a significant part of the uncertainty component. Eliminating or reducing the impact of these factors can improve the measurement method to analyze the measurement process to achieve an objective comparison of CD spectra and improve the comparability and reliability of CD spectra. This work also provides an experimental reference for the interlaboratory comparison of circular dichroism measurement.

Based on gray Correlation degree and RSM Model, a multi-objective optimization Model for AAS analysis and determination of gold elements in gold ore is proposed. The foam pretreatment method, oscillation time, aqua regia concentration and thiourea concentration are selected as the optimization objectives, and the absolute value of the relative error of measurement results is determined as the quality index. The orthogonal design experiment based on SNR is established, the test results quality index and corresponding SNR are analyzed and tempered, and the grey correlation coefficient and correlation degree are calculated. The range of determined optimization targets is 0.026, 0.116, 0.176 and 0.375, respectively, and the target of foam treatment is the least significant in qualitative judgment. According to RSM Model, aqua regia concentration, oscillation time and thioureas concentration are determined as single factors in the box-Behnken method test. Three-factor three-level surface design is used to analyze the absolute value of the relative error of the measured results, a significance level table is made, and the response surface test is completed. The prediction model of the quadratic polynomial regression equation is established, and significance analysis is carried out. Its F=217.24, p<0.000 1 indicated that the model had high significance. The correlation coefficient of the model is 0.996 9, and the calibration determination coefficient is 0.992 4, indicating that the model could explain more than 99% of the response value changes. The response surface diagram and contour map are drawn for regression fitting of the test data. The response surfaces shape and the contour lines steepness are determined and analyzed. Finally, the optimal target parameters are found when aqua regia concentration, oscillation time and thiourea concentration are 11.33%, 27.39 min and 0.97% respectively. The relative error of sample measurement results is minimum. The model verification results show that the accuracy and precision of determination results are in line with DZ/T 0130.3—2006 (The Specification of Testing Quality Management for Geological Laboratories) by selecting gold ore national standard substances with different mass concentrations under the combination of optimal target parameters and conditions. The results show that the multi-objective optimization parameters of atomic absorption spectrometry for the analysis of gold elements in gold ore based on gray correlation degree-RSM Model are accurate and reliable, which verifies that the method is scientific and correct and can be applied to practical production and application. This method has unique advantages in the qualitative judgment of the primary and secondary relations among the condition parameters and quantitative calculation of the optimal combination level of the condition parameters, which is expected to play a role in the search for multi-objective optimization design parameter field platform and determine the optimal target combination more effectively.

Tellurium was a trace microalloying element of new advanced aluminum alloy, which could improve the strength, plasticity, heat resistance and corrosion resistance. The research, development, production and application of aluminum alloys put forward the demand for accurate and rapid determination of trace tellurium. However, there was still a lack of analysis standards and methods for tellurium in aluminum alloys at home and abroad. In order to solve the above problems, an analytical method for tellurium in aluminum alloy has been studied and established based on inductively coupled plasma atomic emission spectrometry to solve the above problems. The digestion method of aluminum alloy sample and the working conditions of the spectrometer were studied and optimized, such as analysis of spectral line, observation mode, radio frequency power, atomization gas flows, plasma gas flow and auxiliary gas flow. The optimized condition parameters were as below: radio frequency power of 1.20 kW; atomized gas flow of 0.75 L·min-1, plasma gas flow of 12.5 L·min-1 and auxiliary gas flow of 1.0 L·min-1. The observation mode was set to axial direction, and Te 214.282 nm was selected as the analysis line. 0.10 g of aluminum alloy sample was accurately weighed into the conical flask. Moreover, 5.0 mL of high-purity water, 5.0 mL of hydrochloric acid and 1.5 mL of nitric acid were added. Then heat until completely dissolved. After cooling, the test solution was diluted to 100.00 mL. The calibration curve solution of tellurium in aluminum alloy was established by the matrix matching method with 0.100 g of high-purity aluminum as the matrix. The calibration curve was a linear equation with a correlation coefficient of 0.999. The detection limit was 0.002%, and the limit of quantification was 0.005%. The optimized method was applied to the analysis of actual samples. The relative standard deviation of the determination result was no more than 5%, and the recovery rate of tellurium was 96%~109%. The determination results of the simulated sample were consistent with the theoretical value, and the bias was better than the reproducibility limit specified in GB/T 20975.25—2020. The proposed method has the advantages of sensitivity, accuracy, speed and greenness and can be used for the analysis of trace tellurium in aluminum alloys, filling the blank of the method for the analysis of tellurium in aluminum alloys at home and abroad, and providing technical support for the quality control of the development and application process of aluminum alloys containing tellurium.

The rapid and accurate determination of calcium, magnesium, aluminium and silicon content in iron ore plays an important role in iron ore quality assessment. The accurate determination of calcium (CaO), magnesium (MgO), aluminium (Al2O3) and silicon (SiO2) in iron ore using laser-induced breakdown spectroscopy (LIBS) remains a challenge due to the overfitting of multivariate analysis methods and matrix effects between different types of samples. In this paper, variable importance-back propagation artificial neural network (VI-BP-ANN) assisted LIBS was used for the first time to quantify the content of SiO2, Al2O3, CaO and MgO in iron ore. In this study, LIBS spectra of 12 representative samples of 244 batches of iron ore were collected, spectral pre-processing methods were optimised, the importance of LIBS spectral features was measured using random forest (RF), RF model parameters were optimised using out-of-bag (OOB) errors, and variable importance thresholds were used to optimise the input variables for the BP-ANN calibration model. The variable importance thresholds and the number of neurons were optimised by five-fold cross-validation (5-CV) of the coefficient of determination (R2) and root mean square error (RMSE). The results showed root mean square error of prediction (RMSEP) for the SiO2, Al2O3, CaO, MgO content of the test samples were 0.372 3 wt%, 0.129 8 wt%, 0.052 4 wt% and 0.149 0 wt% respectively, with R2 of 0.977 1, 0.950 4, 0.987 8 and 0.997 7, respectively. Compared to using the same preprocessing method as input to the three PLS, SVM and RF models, the VI-BP- ANN model showed excellent performance in both the calibration dataset and prediction dataset. The results indicate that the combination of LIBS and VI-BP-ANN has the potential to achieve fast and accurate prediction of calcium, magnesium, aluminium and silicon content of iron ore in practical application.

Detecting the ash content of biomass raw materials was the basis for efficient energy conversion. However, the traditional high-temperature calcination method was time-consuming and costly, while the near-infrared spectroscopy analysis technology could achieve non-destructive, rapid and low-cost qualitative, and quantitative analysis of unknown samples. This study used 1465 biomass raw material samples of 5 locations and 10 types as the research object. The sample set was divided into 9 sample sets by the “screening classification set method” to construct the ash content model of biomass samples by near-infrared spectroscopy. The main results were as follows: the best principal components of corn straw (M), wheat straw + corn straw + cotton straw (WCM), and wheat straw+weeds+garden leaves (WWL) were 5, 6, and 6, respectively. The R2cv of corn straw (M) was 0.975, the R2p of WCM was 0.983, and the model fitting degree was the highest. The RMSE of the set of Changbai+cotton straw (WC) was 0.588 7 and 0.486 4, respectively. The highest ratio of prediction to deviation (RPDcv) of M was 6.3, and the highest ratio of prediction to deviation (RPDp) of WCM was 7.8. The minimum average relative deviation (ARDcv) of maize straw (M) collection was 6%, the minimum average relative deviation (ARDp) of maize straw and WCM collection was 8%, and the RMSECV/RMSEP of wood (W) collection was 1.01. The R2 range of the set model of ash content of 9 biomass samples was 0.753 8～0.979 4, and there was a good linear relationship between the predicted value and the measured value. Among them, H set (R2=0.942 5), M set (R2=0.979 4) and the WCM set (R2=0.978 7) had the best fitting degree and linear relationship. The R2 of the L set (wood scrap) was the lowest, and its value was 0.753 8. The main factor in judge the influence was that the sample contained impurities such as sediment, adhesive, and paint. In order to solve the problem of raw material detection and evaluation of common biomass power plants, 9 biomass ash collection models were used to predict and evaluate the average relative deviation (ARD) of 11 biomass samples. The grass sample model had a good prediction effect (ARD range was 3.7%～16.5%). The “screening classification set method” was used to divide the sample set to establish the near-infrared spectrum biomass ash content model, and its fitting degree, robustness, and accuracy were higher than those of the full sample set model.

The spread of high-risk opioid heroin has caused severe harm to national stability, social economy, and peoples life and property safety. Efficient and accurate detection/identification methods for heroin and its metabolites are significant in combating drug crimes, dealing with drug-related cases and anti-drug campaigns. Surface-enhanced Raman spectroscopy (SERS) has the merits of fast detection speed, simple operation, high sensitivity, fingerprint identification and non-destructivity, which can realize efficient and portable detection of drugs. If combined with pattern recognition, it can improve the efficiency of data processing and avoid the occurrence of human misjudgment. Thereby the purpose of automatic and accurate classification and identification can be achieved. In this work, to achieve sensitive detection and efficient identification of trace heroin and its metabolites in solution, a method combining SERS measurement based on Au-coated SiO2 composite nanosphere array (Au/SiO2 NSA) and pattern recognition is proposed. Firstly, gas-liquid interface self-assembly and magnetron sputtering deposition prepare Au/SiO2 NSA with good SERS activity and signal reproducibility. Employing such an array as SERS substrate (chip), combined with a portable Raman spectrometer, the high-efficiency detection of heroin and its main active metabolites (6-monoacetylmorphine (6-MAM) and morphine) in water solution is successfully achieved with the detection limit of 10-4 mg·mL-1. Next, to perform qualitative/quantitative identification of heroin and its metabolites, SERS spectral data are processed via hierarchical cluster analysis (HCA), principal component analysis (PCA) and support vector machine (SVM). When classifying heroin, 6-MAM and morphine, on the foundation of the accurate classification of them by both HCA and PCA, the PCA-SVM models based on radial basis function, linear kernel function, polynomial kernel function or sigmoid kernel function all can 100% qualitatively identify them. When adopting the PCA-SVM model to analyze heroin. 6-MAN and morphine quantitatively, the accuracy of quantitatively distinguishing different concentrations of heroin can reach 90.1% using the SVM model based on radial basis function. Via the SVM model of the linear kernel function, the accuracies in discriminating different concentrations of 6-MAM and morphine are 84.8% and 70.2%, respectively. This work provides a high-quality substrate (chip) with practical value for sensitive detection and accurate identification based on SERS but also puts forward a feasible approach for efficient classification and identification of heroin and its metabolites.

Rutin, an important kind of flavonoid, has some physiological functions, including antitumor activity, anti-diabetic, anti-oxidation, etc. Thus, it is important to develop a novel method for the simple and sensitive determination of rutin. A simple, highly sensitive and highly selective fluorescence sensor has been established to detect rutin based on glutathione-stabilized copper nanoclusters (GSH-CuNCs). GSH-CuNCs with excellent luminescent properties were prepared using glutathione (GSH) as a stabilizer and ascorbic acid (AA) as a reductant. Ultraviolet absorption spectra, fluorescence excitation and emission spectra studied the optical properties of GSH-CuNCs. The results showed that the GSH-CuNCs have strong fluorescence emission at 420 nm with an excitation wavelength of 365 nm. The absorption spectra showed that the GSH-CuNCs have an obvious absorption peak at 293 nm (UV region), but no absorption was observed above 400 nm (visible region). It was indicated that the GSH-CuNCs had molecular-like properties and did not have larger copper nanoparticles, suggesting the high purity of GSH-CuNCs. Moreover, the fluorescence intensity (at 420 nm) of GSH-CuNCs remained at about 96% after being stored at 4 ℃ for 3 months. When rutin was present, the fluorescence intensity of GSH-CuNCs was significantly quenched. It was ascribed to the absorption spectra of Rutin overlapped largely with the fluorescence excitation spectra of GSH-CuNCs, which led to the inter-filter effect (IFE). The effect of pH and reaction time for the detection of Rutin was optimized. It was found that the quenching effect was the best when pH was 7.5 and the reaction time was 10 minutes. Under optimum experimental conditions, the fluorescence emission spectrum of GSH-CuNCs was recorded with different concentrations of Rutin. The results confirmed that the sensor had a good fluorescence response to Rutin, with a linear range of 1.00～200 nmol·L-1 and a limit of detection of 0.300 nmol·L-1. Different interfering substances with the same concentration were introduced into the sensing system. It was observed that the fluorescence intensity of GSH-CuNCs could be quenched only in the presence of Rutin, which implied that this method has a good selectivity for determining Rutin. The method has been applied to detect rutin content in buckwheat tea samples. This assay was no modification, simple and convenient, and with less sample consumption has good sensitivity.

Yu grain soil has been used in Mongolian medicine for a long time, but due to the lack of quality indicators, the quality cannot be guaranteed, which significantly affects its application. In this study, the material composition, structure and element content of 9 batches of Yu grain soil samples were determined by FTIR, XRD and ICP-MS, and the quality control method of Yu grain soil was explored. The results showed that the FTIR wavelengths of 9 batches of raw Yu grain soil were 3 696, 3 620, 1 621, 1 164, 913, 797, 778, 695, 537 and 469 cm-1 have common peaks, of which 797 cm-1 is Fe—O—Fe stretching absorption peak, 695 cm-1 is Fe—O—Fe symmetrical stretching absorption peak, 469 cm-1 is the characteristic peak of Si—O—Si. The main phases of XRD of Yu grain soil are Fe2O3 and SiO2, and are accompanied by other minerals such as Al4(OH)8(Si4O10), K(Al4Si2O9)(OH)3, CaCO3 and some phosphates. The diffraction angles 24.870, 33.116, 38.436 in the XRD diffraction spectrum of raw product Yu grain soil are the X-ray diffraction peaks of Fe2O3, and the diffraction angles 20.837, 26.608, 36.512, 39.437, 40.235, 42.423, 45.759, 50.102, 54.827 are SiO2 X-ray diffraction peaks. ICP-MS determined the elements in Yu grain soil. The results showed that the element composition of Yu grain soil was very rich, and the element content of different production areas and batches were quite different. The content of Fe in Yu grain soil was the highest, and its average value was 56.9 mg·g-1. The limit standard is that the total iron content of raw Yu grain soil should not be less than 4.55%, and the limit of Pb, As, Hg, Cu and Cd elements in raw Yu grain soil should not exceed 50 μg·g-1. The element clustering results show that the samples S3, S5, S6, and S7 are classified into one class at the Euclidean distance of 10—15, and the samples S1, S2, S4, S8, and S9 are classified into one class, the results of cluster analysis showed that there were differences in the element composition and content of Yu grain soil in different production areas. The Yu grain soil from Henan and Inner Mongolia can be divided into one category, and the Yu grain soil from Shandong and Qinghai can be divided into one category. Four principal components were identified, and the cumulative contribution rate was 90.462%. K, Sr, Be, and As were screened as characteristic elements of Yu grain soil samples.

The spectral reflection curve characteristics of vegetation are different from other substances, such as soil and water, and are closely related to the performance of their physiological traits. The actual angle causes errors in the coverage information obtained by the sensor due to the inconsistency of the incidence effect when looking down and sideways at the canopy vegetation. In this paper, we conducted a real-time multi-angle observation experiment of grassland vegetation in the desert grassland of Siziwang Banner, Inner Mongolia, by using a field online multi-angle spectrometer designed and assembled by ourselves. The four indices, namely the Normalized Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Optimized Soil Adjustment Vegetation Index (OSAVI) and Photochemical Vegetation Index (PRI), were used for multi-dimensional data comparison. We analyzed the correlation characteristics between the spectral diversity of grass physiological traits at different sensor observation angles (SVA) and solar altitude angles (SEA). It was found that the greater the sensor observation angle, the weaker the intra-day variability of canopy reflectance at different wavelengths, showing obvious variability in observation angles and differences in angular sensitivity, and the standard deviation of daily variability of vegetation reflectance was smaller when the sensor observation angle (SVA) was near 75° or observed vertically downward. The reflectance of vegetation was positively correlated with the solar altitude angle for a fixed sensor observation angle. Similarly, the angular effects of different vegetation indices also differed, with the OSAVI index being most sensitive at a sensor observation angle (SVA) of 45°, with the lowest value occurring in different months and the maximum value of the PRI index occurring below a sensor observation angle (SVA) of 60°. It was also found that selecting a certain sensor observation angle for different vegetation growth stages or for different solar altitude angles (SEA) is more beneficial to obtain valid and accurate data. The analysis results of data collected from the field online multi-angle spectrometer in this paper provide scientific data support for the correction of satellite image products, accurate monitoring of vegetation remote sensing, and accurate estimation of grassland biomass.

Fushun jet is a lignite variety with a low degree of coalification and mainly organic residue of plants. Its macerals mainly include textinite, corpohuminite, gelinite, resinite and cellulose. The quality of east-pit jet, mid-pit and west-pit jet decreased successively, as well as the content of the resinite in them, which are 30%, 25% and 10%, but their density increase in turn, which are 1.193, 1.196 and 1.289 g·cm-3 respectively. Under the fluorescence microscope, it is observed that the fluorescent substances in the Fushun jet include resinite and textinite, and there are two different types of resinite, namely α- resinite and β- resinite, while the content of textinite is little. The shape of α-resinite is mostly spindle shape, with uneven surface and contour, β-resinites surface is uniform with a clear outline, and its shape is mostly circular, oval or spindle. In general, The content of α-resinite is lower, and the size of α-resinite is small. Moreover, the fluorescence intensity of α-resinite is significantly higher than β-resinite. Of east-pit jet α-resinites content is about 10%, β-resinites content is about 20%; Of mid-pit jet α-resinites content is about 5%, β-resinite is about 20%; Of west-pit jet α-resinites content is about 3%, β-resinite is about 7%. The results of photoluminescence spectra of two different types of resinite in the Fushun jet show that the spectrograms of α-resinite and β-resinite have multiple peaks, and their positions are relatively similar, which are 411~412, 524~528, 551~553, 583, 600 and 625 nm respectively, which only reflect a little difference in intensity. λmax of α-resinite is around 525 nm, FWHM is about 120 nm, the range of Q is 0.459~0.899, and its oxidation degree is low. λmax of β-resinite is around 553 nm, FWHM is about 180 nm, the range of Q is 0.919~1.30, and its oxidation degree is higher than α-resinite. Compared with Fushun ambers fluorescence spectrum, it was found that the λmax of Fushun Amber is around 434 nm, more significant than the two types of resinite in the Fushun jet but close to the weak fluorescence peak of resinite at 432 nm. It shows that the oxidation degree of resinite in the Fushun jet is higher than amber, so the jet should precede amber in the formation order.

The emeralds from Zambia, with high economic value, occupy an important position in the domestic jewelry market. In order to enrich the traceability information of emerald origin, a comprehensive test of emeralds from the Kagem mine in Zambia was conducted using conventional gemological identification instruments, combined with test and analysis methods such as laser Raman spectroscopy, laser exfoliation inductively coupled plasma mass spectroscopy, Fourier transforms infrared spectroscopy and UV-Vis-NIR spectroscopy, to study the gemological, chemical composition and spectroscopic characteristics of the Kagem emeralds and providing practical and effective methods for identifying the characteristics and origin tracing of the emeralds from the origin. The results show that the emerald samples from the Kagem mine ranged from green to blueish green. The refractive index was higher than other origins, and varied from 1.580 to 1.595. The emeralds from the Kagem mine were typically inert to long- and short-wave UV radiation. The emeralds showed no reaction under the Chelsea filter. Dichroism was medium yellowish green and bluish green. Magnified observation shows that the emeralds contained abundant solid-phase inclusions inside. The gas-liquid two-phase inclusions are mostly elliptical or flat strips, and the gas volume accounts for about one-third of the inclusions. Laser Raman spectroscopy shows that the tubular inclusions were actinolite, the black-brown metallic minerals were magnetite, the black irregular inclusions were carbonaceous, and the columnar inclusions were albite. The chemical composition of emeralds distinguishes Kagem from other areas of origin. Compared to other origins, Kagem emeralds exhibit chromogenic elements rich in Cr and poor in V. There are high Fe, high Mg and high alkali metal elements in Kagem emeralds. The infrared spectra show that the characteristic absorption peaks of type Ⅰ water in emeralds of this origin were mainly at 7 268 and 7 140 cm-1, and the characteristic absorption peaks of type Ⅱ water were mainly 7 075, 6 840, 5 340, 5 205, and 1 619 cm-1. The IR absorption peaks of type Ⅱ water were stronger than those of type Ⅰ water, indicating that the relative proportion of type Ⅱ water was greater than that of type Ⅰ water. This feature can be distinguished from the emerald with poor alkali. The UV-Vis-NIR absorption spectra of emeralds were related to Cr3+, Fe2+ and Fe3+, and the positions and intensities of the absorption peaks were different in different directions.

Tissue Oxygenation is an important indicator of blood perfusion and oxygenation in tissues and blood. It is of great significance in clinical diagnosis and daily monitoring. Hyperspectral imaging is a new method to evaluate StO2 because of its non-contact and abundant spectral information. However, hyperspectral imaging equipment is expensive and complex to operate. These disadvantages limit its use environment and development. Traditional industrial cameras have a high spatial resolution of RGB images of skin tissue, but their spectral resolution is low. If the spectral resolution can be improved, it is possible to measure physiological parameters with high precision. This paper proposes a new method for estimating StO2 based on hyperspectral reconstruction of RGB images. Based on the depth learning method, the reconstruction model from RGB image to the hyperspectral image of skin tissue is constructed, and the hyperspectral image of skin tissue with high reliability is obtained. Then, the spatial two-dimensional distribution of StO2 is obtained using the improved Beer Lambert law formulations. This paper collected RGB images and hyperspectral images of hands of 49 subjects under different blood perfusion conditions using a common visible light camera and a hyperspectral camera as data sets. Based on the dimensionality reduction and denoising of hyperspectral images, the 450~600 nm (including 31 spectral channels) bands were selected as the reconstructed spectral bands according to the characteristic spectra of oxyhemoglobin and deoxyhemoglobin. The convolutional neural networks for spectral reconstruction of skin tissue based on depth learning is constructed. The experimental results show that the skin reflectance spectra obtained by the reconstructed model are in good agreement with those obtained by the hyperspectral camera, and the mean absolute error (MAE) between the two in the test set is 0.009 38, the root mean square error (RMSE) was 0.014 81. Then the similarity between the StO2 measurements from the reconstructed model and those from the hyperspectral camera was quantitatively evaluated. We used the samples in the test set to generate the StO2 spatial distribution maps by two methods respectively, and the two-dimensional correlation coefficients between them were in the reliable range (greater than 94%). These results show that the proposed method based on hyperspectral reconstruction of visible light images is reliable. This study provided a simple and low-cost method of StO2 monitoring for clinical diagnosis and monitoring of various diseases.

The classic formula Yiguanjian consists of 6 herbs: Rehmannia glutinosa Libosch, Angelica sinensis (Oliv. ) Diels, Glehnia littoralis Fr. Schmindt ex Miq, Lyciumbarbarum L, Ophiopogon japonicus （L. f） Ker-Gawl, Melia toosendan Sieb. et Zucc are effective in nourishing the liver and kidneys and de-stressing the liver and Qi. Infrared spectroscopy has the advantage of being fast and non-destructive. Infrared spectroscopy provides complete information from different batches of Yiguanjian benchmark samples. The infrared spectra of the samples were collected using a Fourier transform infrared spectrometer. The raw spectra were pre-processed to obtain relative peak heights and to attribute shared peaks. The infrared spectral data were evaluated using HCA, PCA and OPLS-DA. The results showed that the sugar skeleton stretching vibration absorption peaks in the 868, 822 and 779 cm-1 bands of the 15 batches of Yiguanjian benchmark samples were mostly contributed by Lyciumbarbarum L and a few at the 815 cm-1 band were contributed by Ophiopogon japonicus（L. f） Ker-Gawl. The single decoction of Rehmannia glutinosa Libosch at 1 148 cm-1 band, the single decoction of Glehnia littoralis Fr. Schmindt ex Miq at 1 158, 1 082, 1 019 cm-1 band and the single decoction of Angelica sinensis (Oliv. ) Diels at 993 cm-1 band all contributed to the glycosidic composition. The absorption peak of soluble lipid glycosides in the 1 746 cm-1 band of the single decoction of Melia toosendan Sieb. et Zucc is obvious, but the absorption peak is not obvious in Yiguanjian benchmark samples. It may have changed chemically during decoction. The HCA results showed that S1, S2, S15 clustered into one group, S9, S11, S12, S13, S14 clustered into one group, S3, S4, S5, S6, S7, S8, S10 clustered into one group when the distance between groups=10. Indicating that there was some variation in the internal quality of different batches of consistent decoctions. It indicates some variation in the internal quality of the 15 batches of Yiguanjian benchmark samples. The PCA classification results were in general agreement with the HCA results, and the combined principal component scores were calculated for different batches, with batch No.3 Yiguanjian being the best quality decoction and batch No.1 being the worst. Analysis of the load scatter plots yielded 1 104, 1 142, 1 412, 1 260, and 868 cm-1 band peaks contributing more to principal component 1; 777, 2936, 923, 1 721, 818, and 637 cm-1 band peaks contributing more to principal component 2. OPLS-DA results are consistent with HCA and PCA results. Using VIP>1 as a criterion, seven bands that led to differences between samples were screened, 777, 637, 923, 2 936, 1 260, 1 412 and 1 630 cm-1, respectively, and the results were generally consistent with the importance weighting variables looked for in the PCA loading diagram. The established method of infrared fingerprinting of Yiguanjian is simple and accurate, which can be used for the rapid identification and analysis of the classical formulae and provide a reference for the quality control and evaluation of the classical formulae of Yiguanjian.

Many cephalosporins are widely used, and the structures have specific shared characteristics. However, there is still a lack of systematic comparative research on the property differences between different structures. With the continuous development of research technology, the combination of spectroscopic technology and quantum chemical calculation has become one of the most effective methods to explore structural behavior and observe the electronic structure of drug molecules. This work aimed to systematically and comprehensively analyze the relationship between the structure and properties of non-prodrug-type third-generation cephalosporins.In this study, the density functional theory method X3LYP/6-311+G(d, p) was applied to conduct in-depth research on its geometric structure, infrared spectrum, ultraviolet spectrum, frontier molecular orbital, molecular electrostatic potential, reactivity descriptors, and further molecular docking. The theoretical values including ceftizoxime (CZX), were consistent with the crystal structures. The basic structure of cephalosporin (BSC) is similar in dihedral angle and bond angle, but atoms such as N and O greatly influence the bond length. The IR spectra of ceftazidime (CZX) and cefotaxime (CTX) calculated in theory agree well with the measured values in the experiment. The CO stretching vibration of the non-prodrug-type third-generation cephalosporin carbonyl group is mainly concentrated between 1 766 and 1 644 cm-1. The strong peaks in the range of 1 650~1 550 cm-1 are caused primarily by N—H in-plane bending vibration, and the primary amines will produce strong peaks in the range of 1 340~1 020 cm-1. The most substantial absorption peaks in the UV-Vis spectrum are mainly concentrated in the vicinity of 200~250 and 300~350 nm, and electron orbital transitions, including HOMO, contribute their main components to LUMO+1, HOMO-2 to LUMO, and HOMO-1 to LUMO+2. Molecular surface electrostatic potential and local reactivity descriptors analysis predict that the maxima and minima are mainly distributed near the sites where hydrogen atoms, carbonyl O atoms, and N atoms of amino groups are concentrated, respectively. Frontier molecular orbitals gap and global reactivity descriptors showed that ceftazidime(CAZ) is the most active molecule, but cefoperazone(CFP) is the opposite. A molecular docking study showed that the interaction with HSA was dominated by hydrophobic interaction, hydrogen bond, and π-cation interaction, and the results of the electrostatic potential analysis were verified. The spectral information and structural properties of non-prodrug-type third-generation cephalosporins obtained in this study based on density functional theory can provide vital information for quality control in preparing imitation products, new drug screening and development of derivatives, and understanding and analyzing mechanisms of action.

Soil total nitrogen is an important nutrient index. Hyperspectral technology is used to study and build a hyperspectral estimation model of total nitrogen content in Shajiang black soil, which provides a reference for crop fertilization and the development of precision agriculture. This paper attempts to study the feasibility of discrete wavelets to estimate soil total nitrogen content. Taking different wheat nitrogen fertilizer treatments in Shangshui County, Henan Province, as the experimental area, 100 samples of Shajiang black soil with a depth of 0~20 cm were collected. After the soil samples were air-dried in the dark and processed by grinding and screening, the spectra were collected in the darkroom of the laboratory. The total samples (100 sand ginger black soil) were divided into 75 modeling sets and 25 validation sets. The original spectrum was transformed by the first derivative, and the first derivative spectrum was analyzed by correlation analysis and discrete wavelet transform respectively. At the same time, the hyperspectral estimation model of soil total nitrogen content was constructed by combining the support vector machine and the k-nearest neighbor algorithm. The correlation between the single band of the original spectrum and the first derivative spectrum and soil total nitrogen were systematically analyzed. The results showed that after the first derivative transformation, the spectrum had a better correlation with soil total nitrogen, and the correlation coefficient reached 0.84 at 1 373 nm. The discrete wavelet algorithm selects the best mother wavelet and decomposition level of the first derivative spectrum. The results show that the wavelet coefficients decomposed by the Sym8 function can better reconstruct the spectral information of soil total nitrogen. Further, based on the low-frequency coefficients of decomposition layer L1—L11, the support vector regression and k-nearest neighbor regression estimation models of soil total nitrogen content were established respectively, and all the estimation models were compared. The model constructed by combining the low-frequency coefficients of decomposition layer L5 with k-nearest neighbor is the best. The determination coefficient of modeling is 0.90, the root mean square deviation is 0.09 g·kg-1, and the relative analysis error is 3.78. The validation determination coefficient is 0.97, the root mean square deviation is 0.05 g·kg-1, and the relative analysis deviation is 4.30. At the same time, compared with the model constructed with the full band and the sensitive band selected after correlation analysis as input, the accuracy of the K-neighbor model is improved by 3.2% and 9%, and the accuracy of the support vector machine model is improved by 6.7% and 11.6%. The results show that the first derivative transform and discrete wavelet technology can effectively suppress the impact of noise, improve the estimation accuracy of soil total nitrogen content, reduce the dimension of spectral data, simplify the complexity of the model, and provide a reference for the accurate estimation of the total nitrogen content of Shajiang black soil.

Cotton foreign matter (FM) harms fiber quality as it may damage cotton fiber during ginning processing or cause flaws in finished textiles. Therefore, detecting and classifying foreign matter are important in the cotton production process and quality assessment. The mulching film is a unique impurity in machine-harvested seed cotton in China. Since the mulching film is commonly used to grow cotton in Xinjiang, the remaining fragments are mixed into cotton during mechanical harvesting. This study placed 12 types of common cotton foreign matter, including mulching film fragments, between two lint layers. A push-broom-based hyperspectral imaging system was used to acquire images of the mixed foreign matter and lint samples in transmittance mode at the spectral range of 400~1 000 nm. The hyperspectral transmittance images were first corrected using flat-field correction and cropped due to noise at the edges. The images at 500 nm were chosen for manual region-of-interest (ROI) selection. Mean transmittance spectra were extracted from the ROIs and normalized across all samples. Canonical discriminant analysis (CDA) and the first three canonical variables were used to group foreign matter and lint, and multivariate analysis of variance (MANOVA) was employed to evaluate the differences between each combination of two types of foreign matter using the first three canonical variables. Then, the interval Random Frog (iRF) method was used to extract 12 feature wavelengths. A support vector machine (SVM) classifier was used to classify the transmittance spectra of full and selected wavelengths respectively, and the accuracies were compared and analyzed. The results show that the average classification accuracy of all types of foreign matter and lint at the full wavelength was 84.4%. The method in this paper was feasible for classifying foreign matter in the inner layer of cotton, including plastic packaging, paper, and mulching film. After extracting the feature wavelengths, the classification accuracy of 4 types of foreign matter with similar appearance and similar chemical composition (broken stem, hull, bark, brown leaf) was lower, but all exceeded 73%. The classification accuracy of seed meat, green leaf, paper, plastic package, mulching film, and lint was over 90%. The average classification accuracy of all foreign matter and lint types was 86.2%. Compared with the classification results of the full-wavelength, the average classification accuracy of the selected wavelength was improved by 1.8%.The results of this study can provide a theoretical basis for the research on the detection of foreign matter in the inner layer of cotton and have a guiding role for the application of hyperspectral transmittance imaging technology.

The nitrogen content of crops affects the growth status of crops. A suitable nitrogen content can greatly improve the growth and yield of crops. Therefore, it is very important to monitor nitrogen content quickly. This study aimed to explore the potential of combining vegetation indices and spectral feature parameters acquired by UAV imaging hyperspectral to improve the accuracy of nitrogen content estimation during key growth stages of winter wheat. Firstly, the UAV was used as a remote sensing platform with hyperspectral sensors to acquire hyperspectral remote sensing images of four major growth stages of winter wheat: plucking, flag picking, flowering, and filling stages, and the nitrogen content data of each growth stage were measured. Secondly, based on pre-processed hyperspectral images, we extracted the canopy reflectance data of winter wheat at each growth stage. As a result, we constructed 12 vegetation indices and 12 spectral feature parameters that can better reflect the nitrogen nutrient status of the crop. Then, the correlation between the spectral parameters and the nitrogen content of winter wheat was calculated, and vegetation indices and spectral feature parameters with a strong correlation with the nitrogen content in each growth period were screened out. Finally, a nitrogen content estimation model based on vegetation indices and vegetation indices combined with spectral feature parameters was constructed using Stepwise Regression (SWR) analysis. The results showed that (1) most of the selected vegetation indices and spectral feature parameters were highly correlated with the N content of winter wheat. Among them, the correlation of vegetation indices was higher than that of spectral feature parameters; (2) although it is feasible to estimate winter wheat based on individual vegetation indices or spectral feature parameters, the accuracy needs to be further improved. (3) compared with a single vegetation index or spectral feature parameter, the accuracy and stability of the nitrogen content estimation model constructed by vegetation index combined with spectral feature variables using the SWR method were higher (at the plucking stage: modeling R2=0.64, RMSE=24.68%, NRMSE=7.96%, validation R2=0.77, RMSE=23.13%, NRMSE=7.81%; flag picking phase: modeling R2=0.81, RMSE=15.79%, NRMSE=7.41%, validation R2=0.84, RMSE=15.10%, NRMSE=7.08%; flowering phase: modeling R2=0.78, RMSE=9.88%, NRMSE=5.66%, validation R2=0.85, RMSE=9.12%, NRMSE=4.76%; filling stage: modeling R2=0.49, RMSE=13.68%, NRMSE=9.85%, validation R2=0.40, RMSE=18.29%, NRMSE=14.73%). The results showed high accuracy and stability of the winter wheat N content estimation model constructed by combining vegetation indices and spectral feature parameters obtained by UAV imaging hyperspectral. The research results can provide a reference for the spatial distribution and precise management of winter wheat N content.

The remarkable and mysterious tonic effect of Ophiocordyceps sinensis has always concerned people. In particular, its pharmaceutical value and total arsenic exceeded the standard, which caused many doubts about Ophiocordyceps sinensis. Therefore, it is necessary to analyze the level of essential trace elements in Ophiocordyceps sinensis and conduct safety evaluations on potentially toxic trace elements. Based on the atomic emission spectrum, atomic absorption spectrum, and atomic fluorescence spectrum, a better analysis scheme is selected according to factors such as applicability, sensitivity, precision, linear range, and the ability to resist the forced interference of major elements with the target analytes. Select a microwave-assisted digestion program to minimize matrix interference. The content of five elements Fe, Zn, Mn, Cu were analyzed by ICP-OES spectrometry, the content of Pb was analyzed by graphite furnace atomic absorption spectrometry, and the content of Se and As was analyzed by tomic fluorescence spectrometry. The average contents of eight trace elements in Ophiocordyceps sinensis from 15 producing areas were analyzed. They are Fe, Zn, Mn, Se, Cu, Sr, As, Pb. The results showed that the average content of Mn, Fe, Zn and Se in Ophiocordyceps sinensis is at a high level, and the average content of Cu and Sr in Ophiocordyceps sinensis is at a general level, the average content of Pb is within the limits of the Pharmacopoeia. The average content of As is beyond the limits of the Pharmacopoeia. The average content of Fe was 1 770.5 μg·g-1, that of Zn was 106.2 μg·g-1, that of Mn was 60.5 μg·g-1, that of Se was 0.055 μg·g-1, that of Cu was 17.4 μg·g-1, that of Sr was 4.4 μg·g-1, that of As was 11.42 μg·g-1, and that of Pb was 2.33 μg·g-1. The average content of Mn and Fe in Ophiocordyceps sinensis from Maqin County, Qinghai province, reached the highest levels of 84.1 and 3 089.8 μg·g-1 , respectively. The average content of Zn in Ophiocordyceps sinensis from Qilian County, Qinghai province, reached the highest level of 163. 0 μg·g-1. The average content of Se in Ophiocordyceps sinensis from Zhiduo County, Qinghai province, reached 0.083 μg·g-1. There were regional differences in the average contents of Fe, Mn, As, Zn in Ophiocordyceps sinensis.

Adding metal fuel can improve the energy density of the propellant and alleviate the instability phenomenon of high frequency combustion of the ramjet. Boron has been of considerable interest as fuel for propellants and explosives due to its high gravimetric and volumetric calorific values. However, its combustion is inhibited by the high melting point, the high boiling point and the oxide layer that covers the particles. Aluminum and iron have high combustion heat, fast energy release rate and high theoretical combustion heat utilization. Aluminum and iron are introduced to improve borons combustion efficiency and actual combustion heat value. Aluminum and iron increases the exothermic heat of surface reaction and promotes the ignition and combustion of boron. Boron is mixed with aluminum and iron to make composite metal fuel to solve the problems of difficult ignition and poor combustion performance. Solid fuel with great ignition performance and high energy density can be obtained. The effects of ignition and combustion characteristics of boron-based composite fuel were explored using a dispersion combustion system. The ignition phenomenon of boron-based composite metal fuel was recorded by the high-speed camera, and the temperature distribution was calculated by using the two-color pyrometry method. The combustion mechanism of boron-based composite metal fuel was analyzed using characterization methods. The results showed that adding aluminum and iron reduced the ignition delay time and combustion time. The number of boron particles ignited increased at the same time.The combustion process of boron was intense. The addition of nano-aluminum increased the combustion temperature, while the addition of nano-iron decreased the combustion temperature. The obvious green light was observed during the temperature measurement of boron-based composite metal particles in dispersion combustion.The emission spectrum showed that the green light come from the intermediate product BO2 generated by boron combustion. After dispersion combustion, the agglomerates of boron-based composite metal particles were mainly oxidation products, which also contained a small amount of nitrogen. The product agglomeration phenomenon of the boron-based composite metal particles after dispersion combustion was obvious, and the fracture of the irregular block boron was aggravated. After the boron-based composite metal particles entered the drop tube furnace, the temperature of the additive nanoparticles rose rapidly in a short time by thermal radiation. The aluminum and iron particles started to burn and release heat energy. The heat released by combustion accumulates inside the particles. Then the heat was absorbed by the boron particles, which broke the oxide layer on the boron surface. The internal boron contacted the air. The temperature continued to the ignition point of boron.The mixed metal started to burn and release heat energy. The heat was absorbed by the boron particles, which promoted the combustion of boron.

To analyze the effects of the real spectral response of microwave remote sensors on microwave radiance simulation in the assimilation modules of numerical weather prediction systems, the real and ideal spectral responses of the fourth channel, which reflects the atmospheric and terrestrial radiance simultaneously, of FengYun-3D Microwave Temperature Sounders-2(FY-3D MWTS-2) were applied. Based on line-by-line atmospheric absorption models and predictors of Radiative Transfer for TOVS(RTTOV), different optical depth fitting parameters by real and ideal spectral responses of FY-3D MWTS-2 were generated by applying the least square method. Radiance background fields by real and ideal spectral responses can be simulated from four dimension variation assimilation module of the China Meteorological Administration-Global Forecast System (CMA-GFS) by using the Final (FNL) global operational analysis dataset from the National Center of Environmental Prediction(NCEP) as the meteorological background fields. Observation Minus Background (OMB) under different sea and land types could be calculated using the observations of FY-3D/MWTS-2 and simulated radiance backgrounds. The results show that microwave radiance backgrounds simulated by real spectral response were higher than ideal spectral response under low radiance observation situations. Simulated radiance backgrounds were closer to observations when the radiance observation increased. Radiance backgrounds by real spectral response were closer to radiance observations than by ideal spectral response under global region except for a small part of the Antarctic region. Compared with using ideal spectral response, when the real spectral response was applied, the Root Mean Square Error (RMSE) of OMB on land region decreased by 1.8%; the RMSE of OMB in sea region decreased by 2.6%; the RMSE of OMB in land sea border region decreased by 4.72%, the total RMSE of OMB decreased by 2.17%. Standard Deviation (STDV) and Mean Absolute Error (MAE) also decreased by 0.75% and 4.6%.

Ultra-thin fiberboard with the thickness of 0.8mm is an innovative experimental product in the fiberboard category. The bark content greatly influences the setting of its production equipment parameters and the quality indicators such as static curvature strength and water resistance. It is important to determine the bark content in ultra-thin fiberboard wood fiber accurately. At present, the accurate determination of bark content is difficult. A fiber bark content detection model was established by hyperspectral near-infrared imaging system combined with relevant algorithms, and the fiber bark content detection method was innovated. In this experiment, spectroscopic sample images of poplar fibers containing poplar bark of 0%, 3%, 5%, 7%, 10%, 12%, 15%, 20%, 25%, 30%, and 100% were determined by the hyperspectral imager. The results of pretreatment of mean centralization (MC), multiple scattering corrections (MSC), standard normal variable transformation (SNV) and first-order (1-Der) derivative were analyzed, then the MSC was selected as the best pretreatment method for this test model. The spectral data pretreated by MSC were extracted by SPA and CARS, and the band combination with the highest correlation with the bark content was obtained, and the full-band model was compared and analyzed to establish a partial least squares regression (PLSR) model. From the experimental data, we can see differences in the predictive performance of the model of partial minimum secondary return (PLSR) established by pretreatment of MC, MSC, SNV and 1-Der. Among them, the performance of the MSC-PLSR model is the best. The correction determinant R2C is 0.994, the prediction determinant R2P is 0.985, the correction square root error RMSEC is 0.831%, and the prediction square root error RMSEP is 1.336%. 37 and 49 characteristic bands were extracted by SPA and CARS respectively, among which the CARS model was better, R2C was 0.991, R2P was 0.979, RMSEC was 0.885%, and RMSEP was 1.335%. The experimental results show that the hyperspectral imaging system combined with the corresponding algorithm can realize the detection of the bark content of the fiber. The studys results provide technical support and theoretical reference for the detection of the bark content in the production of ultra-thin fiberboard, which can effectively realize the quantitative detection of the bark content in the fiber, and innovate a model method that can determine the bark content of the fiberboard.

Disruption and salt efflorescence are mainlycaused by the soluble salts found in the mural paintings, which are irreversible and hurt the frescoes health. Quantifying the inversion of soluble salts contained in murals using non-contact hyperspectral techniques is significant. A mural salt content inversion model based on a hyperspectral salt index is proposed to address the problems of high cost, low timeliness, and the need for field sampling for mural salt detection. Indoors, mock mural samples with varying salt content gradients (salt soil ratio: 0～1%) were made with yellow sandy soil and wheat straw fine hemp mixed with anhydrous Na2SO4. The ASD-FieldSpec4HI-RES spectrometer was used for spectral acquisition. The sample spectral set was created after break pointcorrection and averaging, which was randomly divided into a calibration subset and validation subset in the ratio of 7∶3. The original reflectance (R) was subjected to four enhancement processes: first-order differentiation (R+1D), first-order differentiation after Continuum Removal (CR+1D), first-order differentiation after Logarithm Reciprocal (LR+1D), and first-order differentiation after Savitzky-Golay smoothing (SG+1D). The original reflectance and enhanced spectral data were correlated with the salt concentration, and the top three strong correlation bands with high contribution were extracted. Single-band regression models were established by linear and parabolic fitting with the strongest correlation bands, respectively. The first three correlation bands were used to create a hyperspectral mural salinity index (MSI) which was then compared to the normalized salinity index (NDSI), three salinity indices (SI1 SI2 SI3), and the brightness index (BI) for accuracy evaluation. The evaluation metrics are the coefficient of determination (R2), root mean square error (RMSE), and slope of the fit scatter line with intercept. The results show: (1) with the increase of salt content, the overall reflectance spectral curve first decreases and then increases. The reflectance is lowest in the range of salt content of 0.3%～0.6% for the mural samples. (2) The sensitive bands of Na2SO4 in the murals are 1 420, 1 940 and 2 210 nm, and there are also some sensitive bands in the visible range. (3) The first-order derivative transformed spectra are strongly correlated to salt concentration, with the highest R2 enhancement of 0.646. (4) The R-1D-MSI inversion model has the highest accuracy, with R2C and RMSEC of 0.857 and 0.116, respectively. This study can provide a new technical means for the rapid and nondestructive detection of salt content in murals.

Pine wilt disease has severely damaged pine forest resources in China, emphasizing the need for early and accurate diagnostics to prevent, control, and ensure national forest ecological security. Current diagnostic techniques include forest symptom diagnosis, pathogenic nematode identification, and the flow glue method, but these techniques have limitations in diagnosing needles before or at the stage of very few discolorations. Hence, a needle resistivity detection method based on spectral analysis of pine wilt disease is proposed. The study collected coniferous reflection spectral data of Pinus massoniana (8～9 years old) inoculated with Bursaphelenchus xylophilus in the wild, measured at different times using the Ocean Optics USB2000+. The average spectral reflectance at the canopys upper, middle, and lower positions was taken as the spectral reflectance of the plant. The needle cross-section was approximated to an ellipse, and a 4 cm section was cut from the middle of the needle to measuring its width, thickness, and resistance value using a M4070 LCR tester to calculate the resistivity. The original spectrum (OR) underwent spectral transformations using the first derivative (FD), second derivative (SD), logarithmic transformation (LOG), reciprocal transformation (1/R), and continuum removal (CR) methods. Characteristic bands were extracted from the original spectrum and each transformed spectral data using the random forest algorithm to invert the needle resistivity. The least squares support vector machine (LSSVM) algorithm analyzed the modeling effect of selected feature bands and the needle resistivity, identifying the best prediction model of needle resistivity. The study found that the needle resistivity of P. massoniana inoculated with B. xylophilus and the control reached a significant difference (p<0.01) in the early stages after a very small number of coniferous discolorations. The comprehensive performance of the spectral data shows that the secondary derivative transformation was found to be the best, with the characteristic bands being 594.986, 646.107, 646.451, 782.896, 784.841, 839.164, 863.890, 902.021, 947.901, and 962.315 nm. The study established that the prediction model established by SD-RF-LSSVM showed the highest accuracy, with an average R2 of 0.848 and an MAE of 32.331 and 7.067 for the modeling set and verification set, respectively. Compared to the model established using raw data (OR), this models R2 increased by 4%, and MAE decreased by 2.5% and 18.9%, demonstrating the feasibility of inverting the needle resistivity using the needle reflectance spectrum. Overall, this study provides a rapid estimation method for needle resistivity and offers ideas and methods for early diagnosis and monitoring of pine wilt disease based on remote sensing.

Hyperspectral imaging (HSI) is an image data technology based on narrow bands. It combines imaging with spectral technology to obtain continuous and narrow-band image data with high spectral resolution. Hyperspectral imaging technology is widely used in rapidly identifying food, agricultural products, Chinese medicinal materials and other samples because of its rapid and non-destructive characteristics. Xinhui Citri Reticulatae Pericarpium has a high market value, and the market price of the sample is higher because of the longer storage age. At the same time, the accuracy of manual identification of the tangerine peel market is difficult. Based on hyperspectral imaging and chemometric, this study established a rapid and nondestructive identification method for Xinhui Citri Reticulatae Pericarpium of different aging years. Hyperspectral information of 5 aged samples in the vision-near-infrared band (400~1 000 nm) is collected. The average spectral value of the Region of interest (ROI) of the hyperspectral image was extracted as the original sample spectrum. Standard data were obtained after black-and-white correction. After denoising the data by 5 pretreatment methods, including Multiplicative scatter correction (MSC), first Derivative (D1) and Second Derivative (D2), SG smoothing (SG) and Standard Normal Variate Transformation (SNV), Partial least square-discriminant Analysis (PLS-DA), Random Forests (RF), and Support Vector Machine (SVM) and other methods are used to establish the classification model. The accuracy of prediction results is used as the evaluation index to select the best model. A confusion matrix was used to evaluate model classification performance.The results showed that the Multiplicative scatter correction (MSC) combined with the PLS-DA method was the optimal identification model for outer epidermis data, and the identification accuracy of the prediction set reached 97.59%. For inner epidermis data, the raw data combined with the PLS-DA method was used as the optimal identification model, and the identification accuracy of the prediction set reached 97.59%.Using the inner epidermis data and the 19 characteristic wavelength modeling based on the Successive projections algorithm (SPA), the accuracy rate of the whole simulation is still above 90%.The characteristic wavelength modeling extracted by SPA can achieve a similar recognition effect as the full-wavelength model. Removing redundant variables can greatly reduce the complexity of the model and reduce the operation time of the model. Hyperspectral imaging combined with the chemometric method can realize the rapid, nondestructive identification of Xinhui Citri Reticulatae Pericarpium samples of different aging years, providing a theoretical basis for developing exclusive miniaturized equipment.

CO2 and CH4 are the main greenhouse gases emitted by cities. Mobile observation methods with high precision and high spatio-temporal resolution are beneficial to understand the details of their distribution in cities, and the dynamic changes of sources. This work analyzes the existing mobile monitoring methods for atmospheric greenhouse gases. On this basis, a mobile observation system is built based on self-developed equipment off-axis integrated cavity spectrometer, a three-dimensional anemometer and vehicle differential GPS, and a matching greenhouse gas monitoring system is developed. The aerial observation data analysis software system has observed the CO2 and CH4 of typical roads in Hefei City, analyzed the hot spots of typical CH4 concentration, and observed CH4 in the landfills. The results show that the distribution of CO2 concentration in the first ring road of Hefei has a good correlation with the impact of urban non-point source emissions, while the distribution of CH4 has a poor correlation with it, and it is greatly affected by point sources. CO2 and CH4 concentration distribution in the second ring road is closely related to the distribution of surrounding forests, water sources and business circles. Generally, the average (median) concentrations (median) of CO2 and CH4 in the first and second rings in the morning and evening peak periods are higher than those in the idle time, and the concentrations in the first ring are higher than those in the second ring. Three-dimensional ultrasonic anemometers and GPS are used to calculate real-time natural wind speed and wind direction. The result showsthat the hot spots of CH4 concentration on the road is mainly from natural gas filling stations, biochemical pools, natural gas vehicles, etc. Among them, the correlation coefficient between CH4 and CO2 emitted by natural gas vehicles is about 70%. The emissions are large during idling, starting and slow driving. The high CH4 concentration captured in Feidong and Feixi, domestic waste landfills, is related to the incomplete sealing layer of the landfill and the unorganized release of the surrounding waste incineration power plant workshop. The Gaussian diffusion model estimated that the CH4 emission rate when the workshop door of the Feixi landfill was opened was an order of magnitude higher than when it closed. The fugitive emission of CH4 from Beicheng and Lujiang landfills was smaller than the first two. This study proves that the urban mobile observation system can provide a reference for establishing a comprehensive urban carbon emission monitoring system on the one hand and provide basic data for the study of urban greenhouse gas concentration characteristics on the other hand.

Due to the large amount of redundant information in hyperspectral data, it greatly impacts the accuracy of hyperspectral estimation. The purpose of this study is to find the best algorithm for the screening of feature bands to realize the accurate monitoring of the lead content of heavy metals in soil and to provide a reference for soil pollution prevention and control. The lead contents and spectral data in the oasis soils of the Weigan-Kuqa river delta in Xinjiang were used as data sources, and 92 valid soil samples were identified using the Monte Carlo cross-validation algorithm (MCCV), and the spectral data processed by the first-order differential transformation of the reciprocal logarithm are selected through correlation analysis. The random frog (RF) algorithm is combined with the competitive adaptive reweighted sampling (CARS) algorithm. The iteratively retains informative variables (IRIV) algorithm and the successive projections algorithm (SPA). The RF-CARS, RF-IRIV and RF-SPA algorithms are constructed to screen the bands. Taking the reflectivity of feature bands as the independent variable and the content of heavy metal lead in the soil as the dependent variable, the extreme gradient boosting (XG Boost) and geographically weighted regression (GWR) methods were used to construct the estimation model of the lead content in the soil. The results show that: (1) The spectral transformation treatment can effectively enhance the sensitivity of the spectrum and lead content. The spectral characteristics after the first-order differential transformation of the reciprocal logarithm are obvious, and the correlation coefficient can reach 0.620 (p＜0.001). (2) RF-CARS, RF-IRIV and RF-SPA algorithms extract 6, 9 and 7 feature bands from hyperspectral data, all located in the near-infrared spectral region. The three algorithms have strong feature extraction ability, greatly reducing redundant information in spectral data. (3) The accuracy and stability of the soil lead content estimation model constructed based on that the RF-IRIV algorithm are higher than those constructed by RF-CARS and RF-SPA, showing the RF-IRIV algorithm can more accurately retain the bands related to soil lead content. In addition, the performance of the GWR model is better than that of the XGBoost model, and the constructed RF-IRIV-GWR model has the good predictive ability, which can be used as the optimal estimation model for soil lead content in the study area. The R2, RMSE and RPD of the validation set of the RF-IRIV-GWR model are 0.892, 0.825 mg·kg-1 and 3.09 respectively. Based on the random frog (RF) and iteratively retains informative variables (IRIV) algorithm combined with geographically weighted regression (GWR) modeling method, it has certain advantages in quickly and accurately estimating soil lead content, which can be used for dynamic monitoring of soil heavy metal pollution.

The detection of the exhaust gas components of a fuel engine has important reference values for engine condition determination and environmental pollution monitoring. For this study, a weedkiller engine powered by No.95 gasoline was chosen as the experimental prototype, and the exhaust gas from the engine was blown directly into the signal acquisition focus of the Raman integrating sphere spectrometer. The high gas detection limit and the ability of the Raman integrating sphere spectrometer to probe all molecular gas qualitatively and quantitatively have been used to probe the molecular component of the gas in the tails. The gas component detected in the trailing gas is dominated by N2, O2, CO2, CO, and unburned gas. The relative Raman characteristic peak intensities of O2 (1 553 cm-1), CO2 (1 285 cm-1 and 1 388 cm-1), CO (2 144 cm-1), and unburned gasoline (2 894 cm-1) were obtained by normalizing the Raman spectral intensities using the Raman characteristic peak intensity of nitrogen vibrations as a standard. It can be found that the characteristic peak of CO does not appear in the spectrum of volatile matter of air and gasoline, and the content of O2 and CO2 in volatile matter of gasoline does not change significantly compared with that of air, while the relative intensity ratio of Raman characteristic peak of CO2 Fermi formant 1 388 and 1 285 cm-1 change. The working state of a lawnmower is divided into idler, first and second gear. When operating, the O2 content in the exhaust components is all below that in the air, allowing quantitative analysis of the amount of O2 consumed during engine operation. The O2 content in the exhaust increases relatively when the fuel engine is increased from idle to first and second gear. It is because as the engine gear increases, so does the air intake, and the proportion of oxygen involved in engine combustion is relatively reduced. At the same time, the amount of CO2 in the tailpipe gas increases dramatically compared to the amount in the air, suggesting that the working process of the fuel engine produces large amounts of CO2. The proportion of CO2 in the tailpipe gas gradually increases as the gear is raised and the engine power is increased. One of the main sources of CO2, the main cause of the greenhouse effect, is the use of fossil fuels. The data shows a positive correlation between the amount of CO in the tail gas and the amount of gasoline in the tail gas, suggesting that when combustion is insufficient, there is more gasoline left, and the amount of CO as a product of insufficient combustion also increases. With increasing engine gear, the absolute intensity of the characteristic peak of N2 decreases due to the decrease of the Stokes scattering strength of nitrogen with increasing engine exhaust temperature. In this paper, the Raman integrating sphere spectrometer is used to analyze the changes engine exhaust composition under different conditions. Moreover, the relationship between engine state and gas concentration is preliminarily established. We explore the application of Raman integrating sphere technology to fuel engine exhaust detection and verify its feasibility.

Eutrophication of sea-going rivers seriously threatens the regional ecological environment and human safety as they transport pollutants from land-based sources to the sea. With the deep implementation of Xi Jinpings ideology of ecological civilization, the widespread application of management systems such as the “river chiefs” and the “bay chiefs”, as well as the comprehensive fight against pollution, the water quality of rivers and near-shore waters entering the sea has steadily improved. Despite this, water quality fluctuates, and pollution prevention remains vital. There is an urgent need to strengthen real-time and effective large-scale remote sensing monitoring to track and consolidate treatment effectiveness. Recently, as high-resolution satellite and UAV remote sensing technology has rapidly developed, a research hot topic in the field has been the application of quantitative remote sensing monitoring of water column components in river water bodies to promote further improvement of pollution prevention and control. In this paper, a quantitative remote sensing inversion study of chlorophyll a and trophic level index (TLI(Σ)) of the main seagoing rivers in Lianyungang was conducted utilizing field measurements of chlorophyll a, total phosphorus, and total nitrogen content in the Qiangwei River, Linhong River, Gubo Shanhou River, and Guan River during June 2022, as well as Sentinel-2A MSI L2A satellite images. A significant correlation was found between chlorophyll concentration, TLI(Σ), and visible band reflectance. In particular, this was evident in the three bands of 490, 560, and 665 wavelengths, which can be used as sensitive bands for modeling. Analysis showed that the correlation coefficients of R(λ) and Chl a were -0.697, -0.681 and -0.693, respectively, and the correlation coefficients of R(λ) and TLI(Σ) were -0.728, -0.744, and -0.706. The accuracy comparison of the inversion models revealed that the multiplicative power model with R(665) as the independent variable in logarithmic coordinates of chlorophyll concentration was the optimal model for its remote sensing quantitative inversion (R2=0.67, MAPE=47.34%, RMSE=12.89 μg·L-1), while the multiplicative power model with R(560) as the independent variable model was the optimal model for the quantitative remote sensing inversion of TLI(Σ) (R2=0.61, MAPE=4.36%, RMSE=3.45). Using Sentinel-2A MSI L2A images acquired on 25 June 2022, models were applied to calculate the spatial distribution of chlorophyll concentration and TLI(Σ) of the main seagoing rivers in Lianyungang. As a result, the Qiangwei/Linghong River had the highest chlorophyll concentration and the highest TLI(Σ), followed by the Gubo Shanhou River and the Guan River, with the lowest. The inversion results were generally higher in the upper reaches of the rivers than in the lower reaches.