Hyperspectral data are characterized by high dimensionality and richer feature information. This high-dimensional data provides more opportunities to improve classification accuracy and precision in vegetation classification. Traditional feature wavelength modelling often results in poor classification accuracy due to too many input variables. To overcome this problem and improve the ability of the model to capture the subtle spectral differences of wetland vegetation, this paper explores the east coast of the Erhai Lake as the study area and hyperspectral data of three typical wetland vegetation (Mizuno, Ruscus, and Sophora japonica) are measured as the target samples.The sample spectral curves were SG smoothing as original spectra (OS), continuum removal transform (CR), and first-order differentiation (FD) and analyzed for spectral features; then, the original spectra were decomposed by Variational mode decomposition (VMD) into 8 scales. Next, the wavelengths selected by the Competitive adaptive reweighted sampling (CARS) algorithm were used as the characteristic wavelengths. Finally, the best combination of parameters found was used to put into the Bayesian algorithm optimized support vector machine (Bayes-SVM) for modeling. The results show that the number of feature wavelengths extracted by the CARS algorithm is reduced, and most of them are distributed in the absorption feature intervals of vegetation, and the effect of dimensionality reduction is significant; the model constructed by the 4th mode after decomposition (S4-CARS-Bayes-SVM) has the best classification effect, with a precision rate (PR) of 0.933 3, a recall rate (RR) of 0.888 9, an F1 score of 0.896 3, and AUC value of 0.928 6, i.e., this model has strong robustness as well as recognition performance.

Acoustic resonant cavity is an important element in photoacoustic absorption spectroscopy. The spherical acoustic resonant cavity is widely reported to have the advantages of small volume, high-quality factor, and easier improvement of gas absorption path compared with a traditional cylindrical acoustic resonator. To further reduce the cavity volume and realize the miniaturization of the cavity, this paper first reports a gas sensing system using photoacoustic spectroscopy based on a hemispherical acoustic resonant cavity with a radius of 15 mm and a volume of 7.07 mL. The frequency response of the cavity was characterized to optimize the operating frequency and microphone position through theoretical simulation and experimental verification. To further improve the performance of the photoacoustic spectroscopy gas sensing system, the gas absorption path is further enhanced by combining with a multi-pass cell by increasing the light inlet dimension of the acoustic resonant cavity. The multi-pass cell comprises two mirrors with a diameter of 25.4 mm, a radius of 100 mm, and a distance of 20 cm, which results in a light spot of single line mode on the mirror surface. The photoacoustic signal amplitude of methane and formaldehyde were enhanced by 6 and 4 times, respectively. Using a multi-microphone method, the photoacoustic signal was increased by four times. The developed photoacoustic spectrometer was coupled to two tunable lasers emitting at 1 653 and 3 640 nm, which were irradiated into the cavity in two opposite directions to realize dual-gas detection (methane and formaldehyde) with measurement sensitivity of 2.11×10-6(methane, 20 s) and 0.71×10-6(formaldehyde, 20 s), respectively. The photoacoustic spectroscopy system was calibrated by measuring different methane concentrations, which showed a good linear relationship between photoacoustic signal and methane concentration. Allans standard deviation method was used to evaluate the instrument stability of the photoacoustic spectroscopy system under long-term operation. The methane detection sensitivity was enhanced to be 0.4×10-6 with an optimal integration time of 660 s. The hemispherical acoustic resonant cavity, due to its good light source applicability and small volume et al., can be employed with good application prospects in the field of gas sensing using photoacoustic spectroscopy.

Cover glass is an important component of intelligent terminal products, with a smooth, transparent surface and complex and variable characteristics. Its appearance inspection is one of the important challenges in optical imaging detection. Currently, conventional detection methods are mainly based on visible light images. Still, due to texture similarity, stains are often misjudged as defects, making good products judged as defective, thereby increasing industrial costs. To overcome the above problems, this article proposes a method for detecting stains and defects on cover glass based on hyperspectral technology. This method selects the hyperspectral datas optimal feature spectrum and establishes a quantitative detection model to select key feature bands and accurately detect stains and defects. This article utilizes the optical properties of the ink and AA transparent areas and adopts a linear light source transmission imaging method. Through professional hyperspectral line scanning equipment, 50 hyperspectral images of mobile phone cover glass were effectively collected. A stain defect dataset was created, including 500 samples of clean and flawless cover plates and 100 samples of four types of stains and scratch defects, including glass fingerprints, adhesive substances, cleaning agents, and dust. Based on the above hyperspectral image dataset, this paper constructs a band selection method that comprehensively considers the spectral characteristics of stains and defects and the contribution and importance of each feature band. Eight feature bands (502, 526, 567, 689, 711, 789, 818, and 888 nm) that can effectively distinguish stains and defects are selected. Using machine learning algorithms for detection, experimental results show that 8 selected hyperspectral bands perform well in distinguishing stains and defects, with an accuracy rate of 95.4% and an error rate of only 4.7%. Hyperspectral imaging can capture the differences between defects and stains in the spectrum, achieving more accurate detection and providing a feasible new method for quality inspection of mobile phone cover glass. It can provide a reference for designing low-cost hyperspectral defect and stain detection cameras in practical applications.

Ergosterol Peroxide is a steroid derivative with various biological properties such as anti-cancer, anti-inflammatory, etc. It also has certain antibacterial activity in marine ecosystems. Therefore, analyzing the structure of ergosterol peroxide is crucial for exploring its activity mechanism. As an important quantum chemical calculation method, density functional theory has been increasingly applied in predicting the structure, energy, frontier molecular orbitals, and organic structure spectroscopic analysis of molecules. In this work, the spatial structure of ergosterol peroxide molecular was constructed using GaussView 6.0 software. Based on the density functional theory DFT-B3LYP method, the initial structure of ergosterol peroxide was initially optimized using the 3-21G basis set in Gaussian 09W software. Based on the coarse optimized structure, the structure was further optimized using the 6-311++G (d, p) basis set to obtain the molecules most stable configuration, energy, and frontier orbital distribution. Then, based on optimizing the structure, the theoretical infrared (IR) and Raman spectra of ergosterol peroxide were calculated using the 6-311G basis set. The error frequency correction factor of the theoretical calculation results was selected as 0.961 3 for correction. The experimental IR and Raman spectra of ergosterol peroxide solid powder were measured using experimental methods. From theoretical calculations and experimental results, it can be seen that in the theoretical infrared spectrum, ergosterol peroxide moleculesmainly exhibit significant vibrations in the range of 3 700～2 800 and 1 500～600 cm-1. The former mainly exhibits stretching vibrations, and the latter contains multiple vibrations. The characteristic peak frequency wavenumber error of both theoretical and experimental infrared spectra isless than 30 wavenumbers, indicating that the theoretical calculation results are relatively reliable. The corresponding bands in the theoretical Raman spectrum from 2 966 to 2 879 cm-1 and the experimental spectrum from 2 978 to 2 856 cm-1 are assigned to C—H stretching vibration characteristic peaks. The peak positions in the theoretical Raman spectrum are slightly blue-shifted compared to the experimental spectrum, but overall, they agree well. This study analyzed the optimal structure, frontier molecular orbitals, and vibration spectra of ergosterol peroxide, providing a theoretical basis for vibration spectrum detection and structure identification. Fundamental structural and spectral data were provided to further explore the application of ergosterol peroxide in marine ecosystems and the pharmaceutical field.

Quick and accurate acquisition of information, such as emulsified oil spillstypes and oil content, is of great significance for monitoring offshore oil spill pollution. Mid-infrared spectroscopy is a simple and efficient detection method that can characterize the structure information of substance molecules by the position and intensity of spectral characteristic peaks. However, applying infrared spectroscopy technology to detect emulsified oil spills has not yet yielded mature research results. Based on this, this paper selected three representative gasoline samples, 92#, 95#, and 98#, to establish an emulsified gasoline system and three representative white oil samples, 5#, 7#, and 10#, to establish an emulsified white oil system. The spectral data of emulsified oil spill samples were obtained by mid-infrared spectroscopy, and pretreatment was carried out. Then, a Linear Discriminant Analysis (LDA) algorithm was used to identify oil species from emulsified oil spills. Based on this, the Competitive Adaptive Reweighted Sampling (CARS) and Random Forest (RF) methods were used to select the feature wavelengths with linear and non-linear relationships with oil content, respectively. This reduces data dimensionality and enriches the diversity of feature data. Then, use eXtreme Gradient Boosting (XGBoost), 1D Convolutional Neural Network (1D-CNN), Support Vector Regression (SVR) as the base learners, and Partial Least Squares Regression (PLSR) as the meta-learner to build a two-layer Stacking integrated learning model to predict the oil content in emulsified oil spills. The test set determination coefficients of emulsified gasoline and emulsified white oil obtained in the Stacking integrated learning model were 0.982 4 and 0.987 3, respectively, and the root mean square errors were 0.041 0 and 0.034 0, respectively. Compared to XGBoost, 1D-CNN, SVR, and PLSR, the Stacking integrated learning model has better stability and accuracy. The above research results indicate that the detection method based on mid-infrared spectroscopy technology combined with LDA and Stacking integrated learning can effectively achieve qualitative and quantitative analysis of emulsified oil spills, providing new ideas for research in emulsified oil spills.〖HJ0〗

Offshore oil spills can cause serious environmental damage. Timely and accurate detection and identification are crucial for controlling oil spills. Solar-blind ultraviolet (UV) monitoring for oil spills is less affected by solar background radiation, which can improve the accuracy of oil spill monitoring. A reflection spectroscopy test system was designed and constructed to measure the reflectance spectra of four types of simulated marine oil spills (-20# Diesel, -35# Diesel, 95# Gasoline, and Light crude oil) in the solar-blind UV, medium-wavelength UV, and long-wavelength UV bands. The variation trend of reflectance with film thickness was analyzed. Meanwhile, the reflectivity of oil films with different thicknesses was simulated and calculated using the single-layer feature matrix at different wavelengths. The experimental and simulation results showed that refined oil and crude oil have higher reflectivity than seawater in the UV band, and the reflectivity oscillates periodically with changes in the oil film thickness and eventually approaches the reflectivity of pure oil. The period and reflectivity peak is mainly determined by the oils complex refractive index and the oil films thickness, and the experimental and numerical simulation results are consistent. This research shows that in the solar-blind UV band, the range of reflectance oscillations with oil film thickness is smaller, and oil films have higher discrimin ability from the water background. Therefore, using active solar-blind UV reflection spectroscopy to detect oil spills can reduce the interference of solar background radiation and provide the possibility for all-day and all-weather monitoring of oil spill targets.

Absorption spectroscopy measurements of gas molecules in high-temperature and high-pressure environments are crucial for studying absorption line parameters and laser absorption spectroscopy-based combustion diagnostics. Because of the lack of high-temperature and high-pressure measurement environments, this work established an experimental setup for spectroscopy measurements in the temperature range of 25~1 000 ℃ and pressure range of 0~100 atm for the first time. The absorption path length was 20 cm, and the temperature uniformity within the path length was better than 5%. The feasibility of the experimental setup was demonstrated by measuring H2O absorption spectroscopy under conditions of 1 000 ℃ and 75 atm using a swept laser whose wavelength can cover 7 370~7 450 cm-1. The measured broadband spectra were by the simulated spectra based on the HITRAN database. This experimental setup can provide stable environments for the research of spectroscopic parameters of different molecules (H2O, CO2, etc.) at elevated pressures and temperatures and has high potential to promote the development of absorption spectroscopy-based combustion diagnostics.

Infrared-polarized BRDF at variable temperatures can accurately reflect the radiation characteristics of the material surface, provide basic data for studying the optical properties of the surface, and have a wide range of applications in thermal imaging and infrared target detection. However, existing polarized BRDF measurement systems mainly use a discrete point-by-point scanning strategy, with a single-angle scanning time of more than 5 minutes and a complete BRDF measurement time of several hours, and the resolution is relatively low. When BRDF systems are applied to variable temperature measurements, high temperatures must be maintained on the sample surface for a long time, which leads to increased energy consumption and is difficult to implement. Therefore, there is an urgent need to investigate fast measurement methods. In this work, an infrared polarization BRDF measurement system with a portable sample heater from room temperature to 1 000 ℃ was developed, and the mechanical arms load and temperature resistance problems were overcome. A continuous scanning method of polarized BRDF based on a robotic arm was proposed. Fast and continuous 3D BRDF measurements in different polarization states were realized in two ways: discrete rotation of the robotic arm with continuous scanning of the rotary table and discrete rotation of the rotary table with continuous scanning of the robotic arm, improving the measurement speed and resolution. The single-angle scanning time was less than 1 minute, and the complete BRDF measurement time was shortened to about 1 hour, which is more suitable for variable-temperature BRDF measurements. The developed system was applied to measure the BRDF of a frosted stainless steel sample with a high specular reflection surface at variable temperatures. The area of strong reflections was finely scanned continuously, and three-dimensional distributions of polarized BRDF at three solid angles of 6.1×10-6, 1.37×10-5, and 3.81×10-5 were obtained. The larger the solid angle, the stronger the spatial filtering effect, revealing smaller measurement peaks, which was consistent with the physical model of BRDF. Therefore, it is necessary to use a small solid angle to minimize the spatial filtering effect for measurements of highly specular materials. The stainless steel sample in this work was measured with a solid angle of 6.1×10-6 sr. In variable temperature experiments, the oxidation reaction occurred on the stainless steel surface with increasing temperature, and the S- and P-polarized BRDF peaks both decreased. The maximum standard deviations of the measurements in the two polarization states were 0.56% and 0.24%, respectively, compared to the average BRDF value. The repeatability of BRDF measurements was good at different temperatures and the changes of the two polarization states converge to be consistent, indicating that the developed polarized BRDF measurement system was effective.

Metal-organic Frameworks (MOFs) are a kind of molecular-based functional materials that are highly structurally designed by connecting metal ions or cluster nuclei with organic ligands through coordination bonds. Metal-organic frameworks show attractive application prospects especially in new optical devices, and are expected to become environmentally friendly functional luminescent materials with great application value. Therefore, a colorless transparent crystal ［Mn4(OH)2(tpdc)3］·2CH3CN (1) was synthesized under solvothermal conditions using linear benzene-carboxylic acid ligand 1,4-bis (4-carboxyphenyl) benzene (H2tpdc). The compound was characterized by an X-ray single-crystal diffractometer, and its thermal stability and fluorescence properties were studied. Compound 1 crystallizes in the monoclinic P21/c space group. Each misalignment unit of the compound contains four Mn2+ ions and three H2tpdc2-ligand anions in an asymmetric unit. The crystal growth is a rhomboid two-dimensional layered structure. The excitation and emission spectra of the complexes were measured, and the fluorescence responses of the complexes to different metal ions in aqueous solution were investigated. The experimental results show that compound 1 can sensitively detect Fe3+, Cr2O2-7 and CrO2-4 ions from different ions. The detection limits of Fe3+, Cr2O2-7 and CrO2-4 were 8.2×10-7， 6.7×10-7, 9.2×10-6 mol·L-1, respectively.

Hierarchical self-assembly of triblock terpolymers (TTPs) in dilute solution to form multicompartment micelles (MCMs) typically involves two main steps: self-assembly of TTPs chains into intermediate star-like micelles and further self-assembly of star-like micelles into MCMs. The former step, in theory, has two possible pathways: single-stage self-assembly or hierarchical self-assembly, which has not yet been fully revealed. Here, an amorphous linear A-b-C type TTP polystyrene-block-polybutadiene-block-poly(methyl methacrylate) (abbrev. PS-b-PB-b-PMMA or SBM) was used as a model molecule; solvent exchange strategy was employed to induce self-assembly of SBM in dilute solution. Dynamic light scattering (DLS), static light scattering (SLS), and auxiliary transmission electron microscope (TEM) were used for real-time and dynamic monitoring of the self-assembly process of SBM into star-like micelles and further self-assembly of star-like micelles into MCMs. The results revealed that the self-assembly pathway of SBM in dilute solution into star-like micelles was a direct single-stage self-assembly pathway from SBM chain clusters/networks to form star-like micelles, rather than a hierarchical self-assembly pathway involving intermediate states such as single-chain or multi-chain micelles. Additionally, the concentration of SBM dilute solution had no significant effect on this self-assembly pathway, and the self-assembly process reached dynamic equilibrium within 12 hours. The results provide valuable information for the thermodynamic and kinetic studies of the self-assembly process of multi-component block copolymers in dilute solution, and for applying light scattering technology to characterize polymer solutions.

Ketamine is a common new type of drug. According to the chirality of the C atom connected to 2-chlorophenyl, ketamine has chiral isomers ofR and S-ketamine. Due to the similarity of physical and chemical properties of the two isomers, it is difficult to distinguish them by traditional chromatographic methods. To reveal the differences between ketamine isomers and the rapid identification and characterization of ketamine,experimental combined theoretical calculations were used to investigate their properties in this article.The structure optimization of R and S-ketamine chiral isomers was carried out by B3LYP/6-311++G (d,p), and the single point energy was calculated at the MP2/aug-cc-pVTZ level. The calculation of surface electrostatic potential and molecular polarizability shows significant specificity in the properties of the two isomers, leading to their different Raman properties. Using standard samples, Ketamine Raman spectroscopy was experimentally obtained, and the characteristic peaks were determined to be 1 044, 654 and 1 590 cm-1, which are in good agreement with theoretical calculations, while the characteristic peaks of 457 and 596 cm-1 can be used to distinguish between R and S-ketamine. The differences in Raman properties of isomers were explained by electronic structure and polarization analysis, and the Raman vibrational peaks were assigned by PED analysis. Due to the isomers and special double hexagonal ring structure, the study of ketamine Raman spectroscopy is relatively complex. This article provides accurate experimental data and reliable theoretical support for the rapid detection of ketamine Raman spectroscopy, which will contribute to the formulation of ketamine Raman detection standards and the establishment of drug Raman spectroscopy databases. It will also provide a reference for the study of differences in the spectral properties of chiral isomers.

Cashmere is characterized by lightness and comfort, smoothness and softness, dilution and breathability, and good warmth. Because it is very expensive, the quality of cashmere products in the market is mixed. Existing microscopy, DNA, chemical dissolution, and image-based methods have shortcomings such as damaged samples, expensive equipment, and high subjectivity. NIR spectroscopy is a rapid measurement method that is non-destructive and allows for modeling operations. Aiming at the problems that traditional modeling methods usually fail to learn universal near-infrared spectral band features, resulting in weak generalization ability, and that the near-infrared spectral band features of cashmere wool fibers are similar and difficult to distinguish, this paper proposes a near-infrared spectroscopy cashmere wool fiber prediction model based on two-way multi-scale convolution. In terms of data preparation, a total of 1 170 near-infrared spectral band data of the original cashmere wool samples are collected for validation, and the range of the near-infrared spectral band data is 1 300~2 500 nm; in terms of model design, two parallel convolutional neural networks are utilized to extract the features of the near-infrared spectral band, and both the original near-infrared spectral band data and the downscaled near-infrared spectral band data are used as simultaneous. The original near-infrared spectral band data and the downscaled near-infrared spectral band data are input simultaneously. The intermediate contributing near-infrared spectral band features are further extracted using the multi-scale feature extraction module, and the path exchange module is used for the information exchange of the two near-infrared spectral band features. Finally, the cashmere wool fiber prediction results are obtained using the class-level fusion. In the experimental process, 80% of the collected near-infrared spectral band data are used for model training and 20% of the near-infrared spectral band data are used for model testing. The average prediction accuracy of the test set of the model proposed in this paper is 94.45%, which is improved by 7.33%, 5.22%, and 2.96%， respectively, compared with the traditional algorithms such as Random Forest, SVM, and 1D-CNN, etc. Ablation experiments are conducted to further validate the structure of the proposed model. The experimental results show that the proposed two-way multi-scale convolutional near-infrared spectroscopy cashmere wool fiber prediction model can realize the fast and nondestructive prediction of cashmere wool fibers, which provides a new idea for the prediction of cashmere wool fibers in near-infrared spectroscopy.

Lung cancer is a serious threat to human health. In recent years, the incidence of lung cancer has been increasing in China. Imaging examination and histopathological examination are the main screening methods for lung cancer. Imaging examinations are widely used as a preliminary screening method, but they have some uncertainties. The result of the histopathological examination is accurate, so the histopathological examination is the “gold standard” of a lung cancer diagnosis. However, the acquisition of tissue samples can cause traumatic lung injury. Therefore, developing a reliable and minimally invasive method for lung cancer diagnosis is necessary. Acquiring serum samples is more convenient and less invasive than pathological tissue samples. Raman spectroscopy has the advantages of a simple operation, rapid sensitivity, and the ability to provide biochemical information on serum samples. This study obtained Raman spectra of the serum in 155 healthy subjects and 92 lung cancer patients. Curve fitting was applied to the Raman spectra data, and characteristic differences between healthy subjects and lung cancer patients were found in the range of 1 800~800 cm-1. The curve fitting results showed that compared with healthy subjects, the area percentages of sub-peaks around 1 005 and 1 091 cm-1 of lung cancer patients increased by 3.36% and 5.32%. On the contrary, the area percentage of sub-peaks around 964, 1 522 and 1 586 cm-1 of lung cancer patients decreased by 2.3%, 2.82%， and 5.6%. The preliminary results of curve fitting showed that the biochemical substances of carotenoids, amino acids, ribose, and nucleic acids in the serum of lung cancer patients were altered. To investigate the Raman spectral characteristics of serum in healthy subjects and lung cancer patients, machine learning methods were used to obtain the hidden information of the Raman spectral data. First, principal component analysis (PCA) was used to extract the characteristic variables of the spectra. The characteristic variables were applied to support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), logistic regression classification (LRC), Decision Tree (DT), and Bayesian algorithm, respectively, to build classification models. The models predictive performance was evaluated by the leave-one cross-validation method. The results showed that the SVM model best classifies serum Raman spectra. The accuracy, sensitivity, specificity, and F1 are 98%, 94.44%, 100% and 97.14%, respectively. The average of values of the 9-fold cross-verification ROC area under the curve for the SVM model was 0.94, which indicated that the SVM model had a good predictive performance. The result showed that serum Raman spectroscopy combined with machine learning methods can effectively diagnose lung cancer. This technique is minimally invasive and highly accurate; it is a potential diagnostic technology for lung cancer.

A model for determining empty grain content in rice seed examination was established based on near-infrared spectroscopy to rapidly and effectively detect empty grains in rice seeds. Firstly, rice samples with different empty grain contents were prepared, and their near-infrared spectral data were collected. To improve the discrimination accuracy of the model, two different combinations of preprocessing methods, including Savitzky-Golay smoothing (SG)+multiplicative scatter correction (MSC)+polynomial baseline correction (PBC) and Savitzky-Golay smoothing (SG)+standard normal variate transformation (SNV)+polynomial baseline correction (PBC) were selected for noise reduction. Besides, three methods of sequential projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA) were used to extract the characteristic wavelength variables of the preprocessed spectra, thereby reducing the impact of redundant information in the spectra on the model computation speed and prediction accuracy. Then, based on support vector machine (SVM), K-nearest neighbor algorithm (KNN), decision tree (DT), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), and naive Bayes (NB), 6 different identification models for empty grain content of rice seeds were established. Experimental results show that after SG+SNV+PBC preprocessing, the performance of the identification model is better than that of without preprocessing and SG+MSC+PBC. The 158 bands were selected based on the CARS combination SG+SNV+PBC preprocessing band selection. The KNN model established using the selected bands has a better prediction effect, where the testing set identification accuracy of the KNN model could reach 98.47%. The research indicates that near-infrared spectroscopy technology provides a feasible method for discriminating rice seed husk content grades, which provides theoretical support for the non-destructive testing of rice seed quality.

The chemical composition of ceramic materials directly affects their properties, and due to their diverse composition, when measured by X-ray fluorescence spectroscopy after being pressed directly, the matrix greatly influences the determination of samples.In this paper, the fused pelletswerepreparedfor various types of ceramic materials, the accurate correction of the concentration was discussed by the application of loss on ignition(LOI)for the elements to be measured in the sample, the relative error of concentration was analyzed for different dilution ratios and different sampling approaches, and the test of multiple components was studied in various types of matrices by wavelength dispersive X-ray fluorescence spectrometry. Then, the calibration curve is verified by the validation group. When the actual samplesareprepared, the accurate value of the ignition loss can be used to correct the concentration of the element to be measured in the sample. Different correction formulas are required to correct the content when the sample is weighed before or after the ignition. When the loss of ignition is not corrected, the concentration error is significantly lower when weighted before ignition than when weighted after ignition. When the sample dilution ratio is 1∶10 and 1∶20, the relative error caused by 50% burning loss is only 0.43% and 0.12% after using a dilution ratio 1∶20 for sample preparation and applying the estimated standard error to evaluate the improvement of the working curve. After correction, the linear determination coefficient of each component reached more than 0.99. At the same time, the verification group was used to test the correctness of the method.

Raman spectroscopy is widely used to detect drugs, chemical leaks, food safety, explosive residue, and other fields. However, traditional Raman spectroscopy systems using visible or near-infrared light are greatly affected by environmental light. They typically require Raman spectroscopy in closed sample boxes or under nighttime conditions. Conventional UV Raman spectroscopy is mostly based on micro-distances and is difficult to adapt to the remote detection requirements of samples under natural light conditions.This paper designs and builds a UV Raman spectroscopy remote detection experimental system to meet the special remote Raman spectroscopy requirements under natural light environments. It comprises a 266 nm laser light source, front-end optical system, signal reception system, and signal processing system. The optical system design uses a coaxial semi-common optical system for the emission and collection of light paths, ensuring the systems focusing flexibility. It can quickly align and focus on distant substances for detection. The front-end optical system and signal reception system use relay lenses and fibers and are coupled with a blind UV spectrometer for transmission and detection of Raman spectroscopy signals, ensuring the systems overall flexibility. Based on iterative differential auto-regression estimation, the Raman spectroscopy denoising algorithm IDAR is used to denoise the detected Raman spectra, enhancing the resolution of weak Raman characteristic peaks in samples.The paper sets detection points at intervals of 100 mm from 200 to 1 500 mm. It conducts repeated multiple sets of Raman spectroscopy remote detection experiments on five typical substances: Teflon, sodium bicarbonate, calcium gluconate, erythromycin, and ibuprofen. The experiments are conducted at different integration times and distances, and the results are compared with those of a 15 mm micro-distance UV Raman spectroscopy detection system. The experimental results show that the remote detection system can effectively detect Teflon at a distance of 1 500 mm and achieve remote detection distances of 600mm for sodium bicarbonate, ibuprofen, calcium gluconate, and erythromycin samples. This proves that the blind UV Raman spectroscopy remote detection experimental system has good remote detection capabilities under natural light conditions and can meet the requirements of some on-site safety inspections, drug detection, explosive residue detection, and food safety inspection applications.

As a typical nano-insulating material, SiO2 nanomaterial has quantum size limitations. Combined with the unique photoelectric characteristics of different elements, the effect is widely used in biomedicine and nanodevice-integrated electronics. With the advent of the scientific era, the research results are increasing daily. The research work of rare earth-doped nano-luminescent materials is gradually launched. Its broad application scope includes information display, laser materials, optical fiber communication, and fluorescence detection. Sm3+ is an important rare earth oxide ion material. It has potential applications in solar cells, nanoelectronic devices, semiconductor glass, biochemical sensors, and nanomagnets. In this experiment, Sm3+ doped SiO2 nanorods were successfully prepared by thermal evaporation. Characterization tests using scanning electron microscopy, X-ray diffraction, and Raman scattering spectroscopy revealed that Sm3+ doped SiO2 nanorods have a tetragonal crystal structure. With the decrease of deposition temperature, the diameter of the nanorod increases, the deposition density decreases, and the morphology of the sample changes from a nanorod-like structure to a micro-particle gradually; after doping, the diffraction of the SiO2 lattice shifts to a small angle, the lattice constant increases, and the cell volume increases. The growth process of Sm3+ doped SiO2 nanorods was not affected by metal catalysts. Under saturated vapor pressure, gaseous SiO2 will deposit on substrate regions with different temperatures along the direction of carrier gas flow. Regions with higher temperatures tend to deposit and nucleate preferentially. In the low-temperature region, the oxygen diffusion driving force decreases, and the nucleation growth opportunity decreases, inhibiting the growth of one-dimensional nanostructures and making it easier to form nanoparticles. The synthesis of Sm3+ doped SiO2 nanorods follows a gas-solid (VS) growth mechanism. An analysis of the optical performance of Sm3+ doped SiO2 nanorods by UV and PL tests found that the doping affected the light absorption of SiO2 is blue-shifted, and the corresponding band gap is increased by 0.7～0.8 eV. Different from traditional SiO2 nano/micron material, Sm3+ doped SiO2 transferred energy to Sm3+ after being stimulated by radiation, and the material showed a Sm3+ characteristic luminescence performance. This study has important guiding significance for applying SiO2 materials in optical information.

Laser-induced breakdown spectroscopy (LIBS) is a valuable technique for elemental analysis from a laser-induced plasma. The Zhurong rover in the Tianwen-1 Mars exploration mission carries a payload named Mars Surface Composition Detector (MarSCoDe), which can obtain geochemical compositions on Mars. However, the interpretation of MarSCoDe-LIBS spectra will be affected by the complex environment and rock types. With an intent to acquire accurate chemical compositions on Mars using MarSCoDe-LIBS spectra, this work evaluates the performance of several algorithms using the independent third-party LIBS spectral library. This work uses 351 Martian Analogues Library (MAL) to build the LIBS spectral library in a simulated Martian environment. Several models are built based on the LIBS spectra and chemical compositions using nine different algorithms, including machine learning, integrated learning, and deep learning, to derive the major elements (SiO2, TiO2, Al2O3, Fe2O3T, MgO, CaO, Na2O, and K2O). The parameters of these models are confirmed using the cross-validation method, and the performance of these models is evaluated using the RMSE values of the test set. The training set and test set for most models have similar RMSE values except for the ordinary least square method, suggesting no obvious over fitting for these models. In addition, the MLP and GBR models perform better for major elements. Moreover, the RMSE values of the models are similar to those of the published models for ChemCam and SuperCam, suggesting these models have a good performance and can acquire accurate chemical compositions of unknown targets based on their LIBS spectra. This work is valuable for building models suitable for interpreting MarSCoDe-LIBS spectra acquired on Mars.

Black soil is an extremely precious soil resource on Earth. Unfortunately, the black soil layer gradually becomes thinner, leaner, and harder due to long-term high-intensity utilization and soil erosion. Crop residue covering (CRC) is an important way to protect the black soil. Therefore, monitoring the crop residue coverage is one of the key indicators for assessing the implementation of conservation tillage measures. The Chinese Gaofen-6 (GF-6) satellite is the first high-resolution satellite dedicated to precision agricultural observation. Compared with previous Chinese high-resolution satellites, there are four new bands in GF-6 including ultraviolet, yellow, and red-edge bands sensitive to vegetation changes. The main goal of this study is to determine whether these new spectral bands have potential applications in estimating crop residue coverage in black soil regions. The study was conducted in Lishu County where the “Lishu Model” of conservation tillage was set up. The GF-6 WFV multispectral image acquired on November 5, 2020, was used to explore the potential of GF-6 WFV multispectral image for corn residue coverage estimation , including developing spectral indices and applying the Dimidiate Pixel Model. The research results indicate that (1) these 5 spectral indices including NDI87, NDI37, NDI47, NDI32 and NDI38, combined from green, red, near-infrared, ultraviolet, and yellow bands, were found to be more correlated with the measured residue coverage measured in the field, with the determination coefficient R2 greater than 0.5, explaining more than 50% of the corn residue coverage information. (2) There were good correlations between the estimated CRC using GF-6 WFV multi-spectral image and the results using Sentinel-2 MSI and Landsat8 OLI multi-spectral image, with R2 of 0.833 and 0.732, respectively. This demonstrates the reliability and effectiveness of Chinese GF-6 WFV multispectral imagery for crop residue coverage estimation. (3) The estimation accuracy of corn residue coverage was improved by considering the black soil background and using the Dimidiate Pixel Model. Compared with the linear regression model, the correlation coefficient R2 of the Dimidiate Pixel Modelwas improved from 0.740 to 0.769. After considering the soil texture zoning, the R2 was improved furtherly to 0.822. A new way is provided to improve the accuracy of regional crop residue cover estimation in the black soil region.

The pyromorphite in Guilin, Guangxi, has bright colors and complete crystals, favored by mineral and gem collectors. In this paper, six pyromorphite samples from Guilin and Guangxi with different colors were selected. The samples chemical composition and vibration spectrum were tested by energy-dispersive X-ray fluorescence spectrometer, infrared spectrometer, Raman spectrometer, and X-ray powder diffractometer to explore the relationship between the vibration spectrum and the chemical composition (isomorphism) and crystal structure. The results show that the main chemical element of the samples is Pb, followed by P and Cl. The mid-infrared and Raman spectra mainly characterize the bending and stretching vibrations of PO3-4, in which the bands below 300 cm-1 are related to the lattice vibration. The antisymmetric stretching vibration of AsO3-4 is visible at 882/822 cm-1, and the CO2-3 vibration is related at 1 461 cm-1, indicating that CO2-3 and AsO3-4 replace a small amount of PO3-4 in the structure of Guangxi Guilin pyromorphite. Near-infrared spectroscopy tests show spectral bands related to transition metal ions Fe2+and Cu2+ in the sample, indicating that Fe2+and Cu2+ may simultaneously replace Pb2+ and exist in the crystal structure. The 4 000~8 000 cm-1 region mainly displays phosphate ions, arsenate ions, and their overtone and combination tones, as well as the overtone and combination tones of water molecules, hydroxyl groups, hydroxyl, and metal ions. It is confirmed that crystal, structural, and adsorption water are in the sample, with the channel ion Cl partially replaced by OH. The results of the comprehensive analysis of mid-infrared, near-infrared, and Raman spectra show a wide range of isomorphism in the samples of pyromorphite in Guilin, Guangxi. This phenomenon leads to a decrease in the symmetry of phosphate ions, a distortion of the structure, and a splitting and displacement of the spectral band. Meanwhile, X-ray powder diffraction shows that isomorphism has little effect on the cell parameters a0 and b0, while c0 fluctuates within a small range.

In-site measured water spectral data is the basis of remote sensing inversion. Field object spectrometers are usually used to measure water spectral data. However, the usability of field object spectrometers for water measurements is difficult to evaluate. This article uses the data from the water measurement experiments of the field object spectrometer and the water spectrometer to conduct a difference analysis in terms of noise immunity, measurement results, external environment affecting the two types of instruments, and water quality parameter inversion. Before comparing the differences between the two types of spectrometers for water spectrum measurement, a comparison of the anti-noise capabilities of the two instruments found that the water spectrometer has a stronger anti-noise ability, so the data of the water spectrometer is used as a reference standard for analysis. First of all, regarding the difference in solar irradiance, the correlation coefficient of the solar irradiance measured by the two types of instruments varies in the range of 0.5~0.75, and there is a correlation between the solar irradiance of the two instruments; Secondly, by comparing the changes in skylight radiance at the five wavelengths of 361, 411, 461, 511, and 561 nm, we found that the range of changes in solar radiance obtained by the water spectrometer is smaller. The data acquisition is more stable than the field object spectrometer. By comparing the changes in the total radiance of the sea surface at different times, it is found that there are differences in the radiance obtained by the water spectrometer at different times, and the two change curves are different. The signal-to-noise ratio of the field object spectrometer decreases when the sunlight is weak, resulting in drastic fluctuations in the observed values of each band. The calculated reflectivity difference is obvious before 10 am and after 4 pm. Finally, the comparison shows that the external environment under different wind speed conditions impacts the air-water interface reflectance during the processing of the two instruments. The data needs to be corrected according to the value of the air-water interface reflectance corresponding to the wind speed so that the remote sensing reflectance obtained can be more precise. After the spectrometer obtains the water reflection spectrum curve, it is usually used for quantitative inversion of water quality parameters. To compare and analyze the impact of the observation results of the two spectrometers on the inversion accuracy of water quality parameters, this paper uses the water reflection spectrum curves observed by the two instruments to estimate the suspended matter concentration. The inversion is compared with the experimental test results of water samples collected at the corresponding locations. It is found that the two results are quite different. The accuracy of the water spectrometer is higher than that of the field object spectrometer. In addition, the two instruments could not be mixed, making the result error even greater. Through the processing and analysis of data obtained by two spectrometers and the inversion of water quality parameters, this article provides suggestions for water measurement using field object spectrometers to obtain more accurate and reliable water spectrum curves.

Mid-infrared spectroscopy combined with a two-dimensional correlation infrared method will be used to identify Corni fructose from different regions quickly. Mid-infrared technology scanned 30 batches of Corni fructose medicinal materials from seven production areas in three provinces. The original spectra were preprocessed with baseline correction and smoothing, and then the differences in average infrared and second-order derivative spectra were analyzed. At the same time, the infrared spectra of Corni fructose were compared and analyzed with the infrared spectra of three standard substances, namely login, mononucleosis, and ursolic acid, to establish a two-dimensional infrared correlation spectrum. Use analysis software to perform principal component and cluster analysis on infrared spectral data and analyze the causes of differences based on climate factors in different regions. The peak positions and shapes of the average spectra from the seven production areas have high similarity, while the differences in the second derivative spectra and two-dimensional infrared correlation spectrum results showed in three bands: 2 370～2 400, 2 200～2 300, and 1 500～1 650 cm-1. The results of the principal component analysis show that the spatial distribution of Corni fructose from different regions is independent of each other. The clustering analysis results show that the seven production areas can be divided into three characteristic groups, with Shaanxi Danfeng, Shaanxi Foping, Shaanxi Shangluo, and Shaanxi Zhouzhi clustered into one group, Zhejiang Linan clustered into one group, and Henan Xixia and Henan Luanchuan clustered into one group. The combination of mid-infrared spectroscopy with chemical stoichiometry and two-dimensional correlated infrared spectroscopy can rapidly identify Corni fructoses origin from different origins.

Dissolved organic matter (DOM) is an important parameter reflecting forest soils carbon and nutrient cycles. Therefore, describing the composition and structure of DOM from different forest types and soil depths is particularly important for understanding the forest carbon cycling process. This study used a combined method of three-dimensional fluorescence excitation-emission matrix (EEM) and parallel factor analysis (PARAFAC) to analyze the variation characteristics of DOM in Pinus tabuliformis forest (YS) and Quercus acuteserrata forest (ML) at 0～20, 20～60, and 60～100 cm. The average content of dissolved organic carbon (DOC) was higher in ML forest land, while it was lower in YS forest land and varied significantly with changes in soil depth. Four fluorescent components were identified using PARAFAC, among which C1 and C2 are humic and fulvic acid components, belonging to the humic-like fluorescence component. They are mainly concentrated in the 0～20 cm and gradually decrease with increasing soil depth. C3 and C4 are tryptophan and tyrosine-like components, respectively, belonging to the protein-like fluorescence component, and their proportion increases with increasing soil depth. Fluorescence parameter indices of soil profiles indicated that the dominant source of DOM in the 0～20 cm depth was primarily from terrestrial inputs, while in the 60～100 cm depth, it was mainly derived from autochthonous sources caused by microbial activities. Correlation analysis showed significant correlations between two forest stands DOC and four soil enzymes. Additionally, in the YS soil, DOC was significantly positively correlated with TN (p<0.05), whereas in the ML soil, DOC was highly significantly positively correlated with TP (p<0.01). The results of the Partial Least Squares Path Modeling (PLS-PM) indicated that both physicochemical properties and soil enzymes could independently dominate DOM and interact to influence DOM. These results contribute to understanding the dynamic characteristics of soil DOM in the soil profile of Pinus tabuliformis and Quercus acuteserrata forests, and their influencing factors. These results contribute to understanding the dynamics of soil DOM in the Ziwuling regions Pinus tabulaeformis and Quercus acuteserrata forests, providing theoretical insights into carbon cycling in forest ecosystems.

A reasonable water supply is a prerequisite for potatoes to achieve high yield and high-quality tubers. To realize the rapid water diagnosis during the critical period of potato water demand, we used hyperspectral remote sensing and machine learning to study the real-time monitoring of plant water status during the potato tuber formation period to lay the foundation for efficient water management of potatoes in arid areas. This article aims to construct a quantitative estimation model for leaf water content during potato tuber formation with high monitoring accuracy and greater universality. The data of canopy hyperspectral reflectance and leaf water content were measured, and the characteristic spectral parameters that respond to the moisture content of potato leaf finally constructed the Partial least squares regression, Support vector machine, and BP neural network models of leaf water content based on the hyperspectral characteristic parameters. The results showed that: For the monitoring of potato leaf water content, screened the 13 sensitive bands such as 725, 856, 1 000 nm, etc; 11 characteristic spectral first-order derivatives such as 521, 555, 570 nm, etc.; and 7 characteristic spectral indices such as MSI, NDII, PSRI, etc. The three established models that are based on the above characteristic spectral parameters can accurately quantify the leaf water content of potatoes in the tuber formation stage, which means these combined spectral characteristic parameters have strong practicality; Moreover, the use of characteristic spectral parameters screened from full growth stage leaf moisture content and hyperspectral data had higher universality in quantitative monitoring of leaf water content in potato during the critical growth stages. The BP neural network model had the highest prediction accuracy in monitoring leaf moisture content during the tuber formation period. Therefore, this studys results can monitor potato leaves water content in real-time and accurately, which was of great value for evaluating the water status of potato plants and providing technical support for rapid water diagnosis and water-saving irrigation recommendations for potatoes.

As a layered silicate mineral, serpentine is easy to slime. Fine serpentine particles can significantly deteriorate the flotation environment and adversely affect flotation indexes. Ascharite and serpentine are the most important non-metallic ores in paigeite ore. They are closely associated with each other and have fine particle size, so they need to be sorted under fine particle size. However, a large quantity of fine serpentine particles not only reduces the recovery of ascharite but also flows into the concentrate with ascharite, leading to the low quality of the concentrate. Effective inhibition of serpentine is the key to solving the problem of as charite flotation from serpentine. As an anion inhibitor, the effect of calcium lignosulfonate (CLS) on the serpentine surface has rarely been studied, and the inhibitory mechanism is unclear. Flotation test, Zeta potential analysis, XRD, and XPS discussed the inhibition mechanism of CLS on serpentine. Flotation test, Zeta potential analysis, XRD, and XPS discussed the inhibition mechanism of CLS on serpentine. The experimental results show that serpentine can significantly reduce the recovery rate of ascharite flotation when pH>8. As the pH value increased to 10, the recovery of ascharite decreased to 42.8%, and the serpentine recovery was 17.6% in concentrate simultaneously. However, the serpentine recovery was reduced to less than 5% and the ascharite recovery was up to about 66% when the 20~40 mg·L-1 CLS was used in flotation. The mechanism analysis shows that the adsorption of CLS on the serpentine surface can reduce the Zeta potential of serpentine, which is attributed to influences of chemical adsorption and hydrogen bonding. The former is achieved by forming a bond with the Mg atom on the serpentine surface, and the latter is the result of the interaction between the phenolic hydroxyl group of CLS and the hydroxyl group in serpentine.

EMI-NL satellite-borne imaging spectrometer utilizing charge-coupled device CCD275 is applied to monitor the global trace gases of the atmosphere. This spectrometer is the first application of CCD275 in China. Since its overall volume and power consumption are more significant than usual CCD detectors, CCD275 uses the thermal design of radiant coolers with heaters. The satellite-borne imaging spectrometer works in a sun-synchronous orbit. It captures original spectrum images during the day and dark background images at night. The original spectrum and dark background images must be captured under the same temperature and exposure time conditions. During the data processing, the dark background images are subtracted from the original spectrum images to obtain valid spectral images for subsequent retrieval of the trace gases. During the on-orbit operation of the radiant cooler, there is a risk of cooling efficiency decrease or complete failure. The risk leads to the temperature control failure of the CCD275, and collecting original spectrum images and dark background images at the same temperature becomes a problem. This paper proposes a method to calculate the dark background images instead of acquiring one to solve the problem of dark background collection at a specific temperature after thermal control failure. This method could calculate the dark background images at any specific temperature and exposure time by collecting two sets of dark background images with known temperatures and different exposure times. This paper discusses the working principle, the thermal design, and the dark background collection method of the satellite-borne spectrometer. The relationship formula, which the method mentioned above is based on, between pixel value, temperature, and exposure time is deduced from the analysis of dark current characteristics and the continuous frame transfer imaging mode. To verify this method, this paper built an experimental platform that included a vacuum system, cooling system, and imaging circuit. The platform can set the detectors working temperature and exposure time, obtaining dark background images. Statistic results show that the correlation coefficient between the calculated and measured images reaches 99.96% for a single point, and the pixel value deviation range is 0.06%~6.9%. For a full-frame image under 278 K, the mean error between the calculated dark background and the measured one is 0.23%, the variance error is -3.20%, the correlation coefficient is 99.94%, the pixel value deviation range is -0.70%~2.37%. The feature point matching rate reaches 97.9%. When temperature control fails, the calculated dark background can replace the measured one for spectral image processing.

Hyperspectral images (HSI) have been widely used in various fields of production and life due to their rich spectral and spatial information. This paper proposes a tensor dictionary learning-based sparse representation classification (Tensor-DLSRC) algorithm, which directly takes the spatial-spectral tensor as the basic unit to exploit the spectral and spatial information and improve the accuracy of hyperspectral image classification. Firstly, the spatial-spectral tensor comprises the spectral vectors of all pixels in the spatial neighborhood of the central pixels. Secondly, the mean vectors of each order fiber of the training spatial-spectral tensor are used as dictionary atoms to generate an initialized dictionary. The tensor-based dictionary learning (TDL) algorithm is proposed to train a set of structured dictionaries from the training samples. Then, a tensor-based sparse representation model is constructed based on the sparsity constraints of the tensor, and the sparse representation coefficient tensor corresponding to the test spatial-spectral tensor is obtained by solving the model. Finally, the class of the test sample is determined according to the minimization of the reconstruction residuals. To analyze the impact of parameters on the classification accuracy of the proposed algorithm, a series of experiments were conducted to discuss the effects of parameters such as training sample size M, neighborhood window size (2δ+1)×(2δ+1), sparsity μ1 in dictionary learning stage, and sparsity μ2 in sparse representation stage on overall accuracy (OA) before conducting classification comparison experiments. To verify the effectiveness of the proposed algorithm, a series of experiments were conducted on three HSIs, (e.g., Indian Pines, Salinas, and Xuzhou) to compare and analyze the classification results of our algorithm with five comparative algorithms: SRC and DLSRC algorithms based on spectral vectors, JSRC and DLSJSC algorithms with added neighborhood spatial information, and Tensor DLSRC algorithm based on spatial-spectral tensor. The classification results were quantitatively analyzed using Average Precision Rate (APR), Average Accuracy (PA), OA, and Kappa coefficients based on the confusion matrix. The proposed Tensor-DLSRC algorithm has the highest average level of OA and Kappa coefficients among the six algorithms. It has the smallest standard deviation, indicating that compared with the comparative algorithms, this algorithm can provide more accurate and stable classification results.

Inductively coupled plasma mass spectrometer (ICP-MS) is one of the most powerful analytical methods for trace elements due to its high sensitivity and low detection limit. In the design of ICP-MS instruments, effectively introducing the ion beam of the analyte into the post-mass analyzer system is a major challenge. To achieve efficient transmission of ion beams, the ion beam should have a small angle divergence and energy divergence after entering the deflection lens, and be emitted as parallel as possible. That is, the velocity in the vertical direction should be as low as possible. At the same time, to remove neutral particles and photons to the maximum extent and reduce background noise, the vertical deflection displacement should be made larger. This paper draws on the theory of electrostatic fields in electron optics, starting from the motion process of ions in an electrostatic field, and theoretically derives and establishes a model for the motion process of ions in a polarized suspended off-axis deflection lens. Then, using SIMION software, the motion of Li, In, and U ion beams representing low, medium, and high mass numbers in this deflection lens system is simulated and analyzed. The key electric field parameters acceleration voltage Ua, deflection voltage U1, deflection voltage U2, and mechanical parameter L2 in theoretical analysis are examined for their effects on ion beam transmission, including ion deflection displacement sy, vertical velocity v′y at the exit hole, and ion pass rate. Combined with simulation results and design, the effects are investigated. Based on the simulation results and design requirements, this article presents the key parameter values and size optimization design scheme for the off-axis deflection lens. According to the optimized design scheme provided by software simulation, three sizes of deflection lens systems were applied to ICP-MS instruments for comparative testing of background noise and sensitivity to verify the reliability of the simulation. The experimental results showed that the background noise and sensitivity were significantly improved with the optimized structure, especially for low-mass numbers. The background noise was reduced by about 2 times and the sensitivity was increased by about 4 times. Although there may be some errors between simulation and actual results, the overall trend is correct. It can guide the structural design and electrical parameter optimization of deflection lens systems in practice.

The detection of wear particle content in engine lubricating oil is crucial for preventing engine wear. Accurately and rapidly detecting the wear particle content in lubricating oil can timely assess the wear condition of mechanical equipment. To rapidly and efficiently detect the wear particle content in the solid-liquid two-phase flow formed by lubricating oil wear particles, a method combining near-infrared spectroscopy with mathematical modeling algorithms for predicting the wear particle content is proposed. Through the near-infrared absorption spectroscopy experimental system built, the spectral data of wear particle concentration in the range of 6～15 μg·mL-1 were collected by using a near-infrared spectrometer with a wavelength detection range of 900～2 500 nm under a total of 10 groups of working conditions under two kinds of metal wear particles, Fe and Cu, and five different particle sizes. To address the issue that spectral information at single-wavelength points cannot adequately explain the changes in wear particle concentration within the lubricating oil, the sample set partitioning based on joint x-y distances (SPXY) algorithm was employed to segment the spectral dataset. The partial least squares (PLS) model for predicting the wear particle content of lubricating oil was established, and the model prediction results under each working condition were analyzed. The results showed that wear particles could be effectively detected under each working condition, the highest coefficient of determination (R2) for the model was 0.831 8. Various data preprocessing methods were employed to correct the raw spectral data before modeling to address the issue of less-than-ideal prediction performance when using PLS modeling alone. The results showed that, except for a few abnormal conditions, the coefficient of determination R2 for the models under other conditions was greater than 0.8, optimizing the predictive performance of the PLS model. To further optimize the prediction effect of the lubricating oil wear particle model, the lubricating oil wear particle genetic programming (GP) model and the lubricating oil wear particle genetic program-partial least squares (GP-PLS) model were established, respectively. Compared with the PLS optimization model, the GP model for predicting lubricating oil wear particle content was more robust and had a better prediction effect, and the highest R2 reached 0.956 2. The mean fiducial error (MFE) was 14.73%. The GP-PLS model, compared to the GP model, achieved the highest R2 of 0.943 0 and a maximum MFE of 10.86%, improvement in MFE, thereby enhancing the predictive accuracy of the model. Through research and analysis of wear particle content prediction models, it has been concluded that various models can effectively predict changes in wear particle content in lubricating oil. Among them, the GP-PLS model performs better predicting wear particle content changes. The research results indicate that using spectroscopic analysis combined with model algorithms to predict wear particle content in the solid-liquid two-phase flow of lubricating oil is feasible, providing an effective detection method for detecting mechanical wear faults in engine equipment.

When wear occurs between the internal transmission components of the engine, fine metal wear particles will fall off between the internal components of the equipment, which will seriously affect the normal operation of the engine and even cause serious accidents. Therefore, it is necessary to monitor the information of wear particles in lubricating oil online. In this paper, the detection experiment of lubricating oil wear particle content was carried out based on the quantitative analysis of the reflection spectrum. By building the experimental platform for detecting the wear particle content of lubricating oil by reflection spectrum, two kinds of Fe particles and Cu particles with particle sizes of 300 mesh (50 μm) fatigue wear particles and 80 mesh (175 μm) severe wear particles were selected. In the visible light band (450～760 nm) and the ultraviolet band (200～435 nm), 31 sets of reflection spectrum data of lubricating oil wear particle concentration in the range of 6～15 μg·mL-1 with an interval of 0.3 μg·mL-1 were obtained. Firstly, a partial least squares (PLS) linear model was established for the reflectance spectral data, but the prediction effect of the model was poor. Therefore, the data preprocessing correction method is used to screen and correct the original data. The interference factors in the modeling data are reduced, and the PLS optimization model is established. However, it is found that although the PLS optimization model improves the prediction effect, it is still poor under some working conditions. To further optimize the prediction effect of the model, a genetic programming model and a genetic programming-partial least squares (Genetic Programming-PLS) model are established. Finally, the following conclusions are drawn: the model determination coefficient R2 is in the range of 0.71～0.80 in the PLS linear model, 0.80～0.94 in the PLS optimization model, 0.72～0.96 in the genetic programming model, and 0.84～0.98 in the genetic program-PLS model. The results showed that the genetic programming-PLS model had the best prediction effect. The study of the reflectance spectroscopy of wear particle content in lubricating oil is expected to provide a new method for engine oil monitoring.

Different varieties of eggs contain different nutrients and ingredients as a nutritious agricultural product. The phenomenon of inferior quality and adulteration poses a serious threat to food safety, which makes an urgent need to solve the problem of egg variety detection. Four egg varieties as research objects were divided into the training and test sets according to 2∶1 with 160 and 80 eggs respectively. A hyperspectral imaging system was utilized to capture the egg spectral image in the 935.61～1 720.23 nm range. Region of Interest (ROI) with a center size of 30×30 pixels of egg sample was selected after black and white correction, and the average reflectivity of each pixel in the region was extracted as the original spectral data of the sample. The average spectral information in the 949.43～1 709.49 nm range was intercepted for the subsequent study to reduce the influence of random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the effective bands after denoising. The feature wavelengths of the preprocessed spectral data were extracted using a successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) single screening, and combinations of CARS-SPA and CARS+SPA, respectively. Support vector machine (SVM), particle swarm optimization (PSO) optimized SVM model (PSO-SVM), and extreme learning machine (ELM) model were established based on full bands (FB) and feature band, which were compared to find the best variety classification model. The experimental results showed that the SG-SPA-ELM model has the best identification effect with the best classification accuracy of 85.00%. Hyperspectral imaging technology combined with ELM can effectively realize non-destructive, efficient, and accurate identification of egg varieties and provide references for egg adulteration detection and identification of other agricultural products.

Accurate and efficient acquisition of chlorophyll-a (Chl-a) concentration in water is the prerequisite for improving eutrophication and sustainable development of water bodies. This study used the hyperspectral reflectance of the water surface and the measured Chl-a water concentration as data sources. The original spectral reflectance was processed with a step size 0.1 by fractional order differentiation (FOD) technology. The characteristic bands were screened by exploring the correlation between the spectral data and the Chl-a concentration of the water body, and the variable-order spectral dataset was constructed. The Partial Least Squares (PLS) model was used to screen the optimal features to construct the dataset, which was divided into modeling set and verification set according to the ratio of 7∶3, and the support vector machines (SVM) and deep forest (DF) models were used to establish the water Chl-a concentration inversion model. It is also compared with the model constructed using the original data and the model constructed by common dimensionality reduction methods. The results show that FOD technology can reduce hyperspectral noise, mine potential spectral information to a certain extent, and improve the correlation between hyperspectral reflectance and the concentration of Chl-a in water. Compared with the Chl-a inversion model established by using the original data and PCA dimension reduction, the R2 of the water Chl-a concentration inversion model established by FOD combined with PLS first screening features was increased, and the mean square error (MSE) and mean absolute error (MAE) were reduced. DF has a higher degree of fitting and prediction accuracy than the other three models, with R2=0.96, MSE=0.51 μg·L-1, and MAE=0.64 μg·L-1. The validation set R2=0.89, MSE=0.69 μg·L-1, MAE=0.64 μg·L-1. In general, it is feasible to establish a water Chl-a concentration inversion model based on the variable-order spectral dataset after FOD recombination and the preferred features of PLS. Comparative analysis of the inversion results of other models shows that DF has a good inversion effect on water Chl-a. This work provides a theoretical basis and technical support for the inversion of hyperspectral remote sensing of Chl-a in inland second-class water bodies, helps the continuous monitoring of water quality in Baiyangdian Lake, and also provides new ideas for the inversion of Chl-a from hyperspectral satellite remote sensing images in the future.

Separating the surface contribution is crucial in aerosol optical depth (AOD) inversion. Traditional retrieval algorithms developed for single-angle sensors typically assume that the surface is homogeneous Lambertian, which ignores surface anisotropy and introduces errors. However, considering surface properties can create an ill-posed inversion, so it is necessary to introduce a priori knowledge to characterize the surface anisotropy parameters. Using the mean value over a certain time range as a fixed value for the surface parameter is a common practice. However, surface conditions vary over time, and averaging can lead to inaccuracies in estimating the surface contribution. This paper analyzes the anisotropic scattering kernel coefficients of various surface cover types to evaluate the effect of the mean value on inversion. The analysis is based on the MODIS bidirectional reflectance distribution function product (MCD43C2) for 2022 and the MODIS surface classification product (MCD12Q1). The statistical analysis indicates that when forested land is the dominant cover, the parameter is generally lower than 0.05. In nearly 90% of the cases where grassland, cropland, and towns are the dominant covers, the parameter is lower than 0.1. This paper presents a simulation of the potential errors in apparent reflectance under diverse surface types, intending to invert the AOD based on the simulation findings. A comparison of the results with the preset “true value” of the AOD revealed that the quarterly mean value of the BRDF shape parameter was less prone to producing errors in the location of the quarterly mean value of the BRDF shape parameter than the bright surfaces with less vegetation cover and the more highly covered surfaces. The shape parameter of the bi-directional reflectance distribution function (BRDF) at this location produces fewer errors than brighter surfaces with less vegetation cover. In other words, at a solar zenith angle of 50° and a true value of AOD of 0.4, the mean absolute error of retrievals is 0.053 for evergreen broadleaf forests and 0.089/0.083/0.113 for nearly 90% grasslands, croplands, and towns, respectively. However, the mean absolute error of retrievals of AOD is significantly higher in the bright surface area, reaching a maximum value of 0.145. This is the case for nearly 90% of the grassland, cropland, and urban regions. However, the mean absolute error of retrievals is only 0.078/0.107 for the remaining areas. This indicates that using quarterly-averaged BRDF as a surface constraint in AOD retrievals becomes less reliable with increasing surface reflectivity. This is due to the inherent uncertainty in using scalar satellite observations, where the surface signal dominates. Consequently, the synergistic inversion of BRDF and AOD using multi-angle polarization observations represents a promising avenue for enhancing the accuracy of aerosol inversion in the future. The findings of this study offer insights into the distribution of BRDF kernel coefficients across different surface types globally. Additionally, this research delves into the sources of errors in AOD inversion based on the non-Lambertian forward radiative transfer model. The study also presents the potential error ranges associated with this process.

Finger motion gesture recognition has broad application prospects in remote medical care, intelligent wearable devices, and human-computer interaction. However, many challenges still exist in achieving natural and fluent finger motion gesture recognition. In this paper, we proposed a finger motion gesture recognition system based on a double-clad fiber Bragg grating (FBG) sensor array. Six double-clad FBG sensors with different central wavelengths were deployed at various positions on the subjects forearms. The system can recognize the small deformations of different muscle tissues caused by finger motion in real-time, and it will not affect the natural and fluent motion of the fingers. The system achieved high accuracy recognition of nearly 100% of unknown simple gestures and about 85% of complex gestures based on the double-clad FBG center wavelength shift of six known static gestures. The same experiment was conducted using single-mode FBG. The results showed that the double-clad FBG could effectively recognize simple gestures of single-finger motion and complex gestures of multiple-finger motion. In contrast, the single-mode FBG can only recognize gestures of single-finger motion. The double-clad FBG sensor array finger motion gesture recognition system proposed in this paper not only ensures high-precision deformation measurement to recognize different gestures but also has good stability and anti-interference ability. These results indicate that the research work in this paper has great potential in gesture recognition.

Ziziphi spinosae semen is an important raw material of health care products and traditional Chinese medicine preparations because it nourishes the heart and the liver, making it ideal for calming the nerves and helping sleep. At present, the adulteration of ziziphi spinosadsemen in the market is serious, which greatly damages the interests of consumers and disrupts the market order. Traditional manual detection or laboratory-based high-performance liquid chromatography methods have problems of low efficiency and difficult promotion. In this study, a hyperspectral imaging method for ziziphi spinosadsemen authenticity identification was proposed based on convolutional neural network and partial least squares discrimination, and the key spectral features in the two types of models were discussed and studied. The study will reference the subsequent development of multispectral systems and portable instruments. The average spectra of all single kernels in the hyperspectral images (400~1 000 nm) of ziziphi spinosae semen and its common counterfeits (Ziziphus mauritiana lam, Hovenia dulcis Thunb. and Lens culinaris) were extracted. The partialleast squares discriminant analysis (PLSDA) model and the one-dimensional convolutional neural network (1DCNN) model were respectively established based on the average spectra. The competitive adaptive reweighting algorithm (CARS) selects characteristic wavelengths before PLSDA modeling. A custom wavelength selection layer was added to the 1DCNN model. T-distributed stochastic neighborhood embedding (t-SNE) was applied to the outputs of convolutional and fully connected layers for visual analysis. To effectively compare with the CARS-PLSDA model, a 5W-1DCNN model based on five wavelengths was constructed. The results showed that both the CARS-PLSDA and1DCNN models could achieve precision prediction results, and the classification accuracies of both the calibration set and the prediction set are above 0.99. Comparing the feature wavelengths selected by CARS and custom layers, wavelengths near 670, 721, and 850 nm play important roles in both models. The research results provided a reference for multispectral systems and portable equipment for rapid detection of the authenticity of ziziphi spinosad semen.

A dual-polarization spontaneous Raman spectroscopy measurement system based on volume phase holographic transmission grating (VPH) has been developed, which enables synchronous quantitative measurement of the concentration and temperature of major gaseous species components (carbon dioxide CO2, oxygen O2, nitrogen N2, water H2O, fuel, and intermediates) under a single laser pulse in a combustion field. The temperature obtained synchronously can correct the influence of temperature on the Raman scattering cross-section of species. Two sets of ICCD cameras synchronously collect Raman scattering signals in two mutually perpendicular directions, effectively eliminating fluorescence interference. This system can also achieve a joint testing technology of Rayleigh temperature measurement and Raman concentration measurement. The concentration and temperature were calibrated on the gas sample pool with controllable pressure and temperature and the McKenna standard burner; the temperature measurement accuracy is less than 1.06%, and the accuracy of component concentration measurement is less than 1.10%. In the combustion field of an aero-engine model combustion chamber, the simultaneous measurement for a single pulse (20 ns) of component concentration and temperature at three multi-measurement points in a single working condition was completed.

The fluorescence components of dissolved organic matter (DOM) in the water bodies of a mining area were studied using three-dimensional fluorescence spectroscopy (EEMs) combined with parallel factor analysis (PARAFAC), and their types, distribution, and sources were analyzed and discussed. The results showed 5 natural components in the water samples and sediments of the study area. Components C1 (275/325(335) nm) and C2 (290/345 nm) in the water samples were identified as protein-like substances (tryptophan-like). In comparison, components C3 (320/405 nm) and C5 (285/505 nm) were identified as visible region humic acids, and component C4 (265/430 nm) was identified as ultraviolet region humic acid. Components C1 (270(300)/340 nm), C2 (290/340 nm), C3 (300/360 nm), C4 (285/320(360) nm), and C5 (270/435 nm) in the sediments were identified as protein-like substances (tryptophan-like), and component C5 was identified as ultraviolet region humic acid. The contents of protein-like substances in the water samples and sediments of the mining area were 302.23 and 1 976.83, respectively, and the contents of humic acids were 96.72 and 41.11, respectively, indicating that both the water samples and sediments were mainly composed of protein-like substances. Additionally, since sediments are one of the main sources of protein-like fluorescence, the content of fluorescent substances in the sediments was much higher than that in the water samples. The spatial distribution of fluorescent substances in the water samples was higher at the mining wastewater outlet and the Shuangji River and lower along the river and at the Hutuo Ditch and the Shuangji River confluence. On the other hand, the spatial distribution of fluorescent substances in the sediments was higher at the Shuangji River and lower at the mining wastewater outlet along the river and at the confluence of the Hutuo Ditch and the Shuangji River, which was the opposite of the distribution in the water samples. The fluorescence index (FI), biological index (BIX), and humification index (HIX) in the water samples and sediments indicated strong autochthonous characteristics and weak humification characteristics. The correlation analysis between the components and the fluorescence characteristic parameters further indicated that the protein-like substances in the sediments were mainly derived from autochthonous sources rather than exogenous sources. The research findings can better understand the organic matter composition in the wastewater discharged from mining areas and serve as a reference for treating mining wastewater discharge.

The overlying water is the interface between water and the atmosphere, and its water quality is an important indicator for evaluating the environmental quality of park landscape water. This study collected landscape overlying water samples from eight parks in Harbin urban and applied the three-dimensional fluorescence spectroscopy parallel factor analysis method to detect the fluorescence spectral characteristics of dissolved organic matter (DOM). DOMs source and composition characteristics and the correlation between fluorescence spectral index, component fluorescence intensity, and physicochemical index were analyzed. This provides a reference basis for the environmental quality evaluation and pollution prevention of urban park landscape water. The study results showed that DOMs fluorescence index (FI) ranged from 1.71 to 1.98, with an average value of 1.185, suggesting that the source of DOM has both exogenous and endogenous characteristics. The biological index (BI) ranged from 0.97 to 1.34, averaging 1.09; the freshness index (β/α) ranged from 0.91 to 1.19, averaging 1.101, indicating strong characteristics of the recently endogenous composed of DOM. The humification index (HI) ranged from 0.186 to 4.126, averaging at 2.143, indicating a low degree of humification. Five kinds of DOM fluorescent components were identified in the water samples: fulvic-like acid substance (Ultraviolet fulvic-like acid component C1, visible fulvic-like acid component C2), protein-like substance (tryptophan-like component C3, tyrosine-like component C5), and humic-like acid component (C4). The relative concentration of humic-like substances (C1, C2, and C4) was higher than that of protein-like substances. There was a significant (p0.05). Various physicochemical indices impact on FI was insignificant (p>0.05). The value of pH and BI, β/α (p<0.01) exhibited a significant positive correlation and a significant negative correlation with HI (p<0.05), respectively. The negative correlation between DO and C3 and the positive correlation between DO and C5 were significant (p<0.05). Humus-like substances were significantly positively correlated with TOC, TP (p<0.01), and COD (p<0.05), while fulvic-like acid substances were also significantly positively correlated with TN (p<0.05). Conversely, C5 negatively correlated with TOC and TP (p<0.01). Based on the principal component analysis results of the overlying water physicochemical indices DOM fluorescence spectral index and component fluorescence intensity, it can be concluded that compared to the fluorescence spectral index, the fluorescence intensity of humic-like substances and C5 can better distinguish different water samples and be used to evaluate the environmental quality of landscape water.