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2025 Vol. 45, No. 04 Published: 2025-04-01

	901	Application of Infrared Spectroscopy in Hydrogen Analysis of Hydrogen Fuel Cell Vehicles
		LIU Dan, YUAN Hui^*, WAN Wei, XU Guang-tong
		DOI: 10.3964/j.issn.1000-0593(2025)04-0901-07
		Hydrogen energy, as a clean energy source, is one of the important forms of replacing fossil fuels in the future, and hydrogen fuel cell vehicles are the most promising development direction for hydrogen energy applications. Different hydrogen production processes can lead to the presence of various trace impurities in hydrogen fuel, such as formaldehyde, formic acid, carbon monoxide, carbon dioxide, water, methane, ethane, propane, ethylene, halides, sulfur compounds, and particulate matter, which can cause varying degrees of damage to the battery system and affect the safe operation of fuel cell vehicles. Therefore, extremely strict standards have been established for domestic and international hydrogen purity and trace impurity limits. Both ISO14687—2:2019 and GB/T 37244—2018 standards recommend Fourier transform infrared spectroscopy to analyze some key impurities. After practice and improvement by our research group, it has been proven to be reliable and effective. In recent years, based on the principle of infrared spectroscopy, some emerging infrared spectroscopy technologies have been developed, including tunable semiconductor laser absorption spectroscopy (TDLAS), photoacoustic spectroscopy (PAS), cavity-enhanced absorption spectroscopy (CEAS) and cavity ring-down spectroscopy (CRDS), breaking through the limitations of traditional infrared spectroscopy technologies such as light sources and lasers.It is expected to be applied in the future analysis of trace impurities in hydrogen fuel.This article summarizes the analysis methods of 8 types of impurities in hydrogen fuel and deeply explores the application progress of Fourier transform infrared spectroscopy technology in analyzing these impurities. It summarizes the advantages and disadvantages of emerging infrared spectroscopy technology in trace gas analysis, and further prospects the methodological development trend of infrared spectroscopy.
		2025 Vol. 45 (04): 901-907 [Abstract] ( 35 ) RICH HTML PDF (1437 KB) ( 77 )

	908	Terahertz Spectroscopy of the Structural Isomers of Pyridine Dicarboxylic Acid
		LUO Guo-fang¹, QI Ji², LIU Ya-li², HE Ming-xia², HUANG Hua³, QU Qiu-hong^2, ZHANG Yi-zhu^2
		DOI: 10.3964/j.issn.1000-0593(2025)04-0908-06
		In the production processes of food and pharmaceuticals, the identification and differentiation of stereoisomers constitute a crucial and complex issue, particularly when their molecular formulas are identical, but their crystal structures differ. Pyridine dicarboxylic acids serve as key intermediates widely used in pharmaceutical synthesis, where effective discrimination between different isomers is essential due to their varied physical and chemical properties. The technique of terahertz spectroscopy has emerged as a novel approach for detecting stereoisomers, employing high-resolution terahertz time-domain spectrometers to measure 2,3-pyridine dicarboxylic acid, 2,4-pyridine dicarboxylic acid, 2,5-pyridine dicarboxylic acid, 3,4-pyridine dicarboxylic acid, and 3,5-pyridine dicarboxylic acid, revealing their unique terahertz absorption features at different mass fractions. These isomers exhibit distinct absorption characteristics in the terahertz range. Through density functional theory calculations, a comparison between theoretical simulations and experimental measurements has been conducted, elucidating the primary vibrational modes of these compounds, encompassing the torsional vibrations of carboxyl groups and pyridine rings, bond angle bending, and their coupling effects. The research has demonstrated the unique advantages of terahertz spectroscopy in material identification, highlighting its significant differences from infrared spectroscopy. The findings not only offer insights into the terahertz spectral features of pyridine dicarboxylic acid derivatives but also showcase the potential applications of terahertz spectroscopy in the analysis and identification of chemical substances, providing new foundations for the synthesis and discrimination of pharmaceutical intermediates.
		2025 Vol. 45 (04): 908-913 [Abstract] ( 19 ) RICH HTML PDF (6134 KB) ( 37 )

	914	Ring-Shaped Wearable Optical Sensors Enhancing the Stability of Human-Sensor Contact State
		GE Qing, LIU Jin, HAN Tong-shuai^*, LIU Wen-bo, LU Yue
		DOI: 10.3964/j.issn.1000-0593(2025)04-0914-08
		In non-invasive wearable spectral sensors, ensuring the stability of the contact state between the human body and the sensor is essential for optimal sensor performance. Human movements and changes in body posture can cause variations in muscle and skin tension, altering the contact state at the interface between the sensor and the skin, which results in shifts in the angle of incident light. Such changes affect the path of light propagation within the skin, subsequently influencing the intensity of the diffusely reflected light received by the sensor. To address this issue, this study developed a wearable near-infrared optical sensor specifically designed for non-invasive analysis of body components. The photosensitive part of the sensor features a ring-shaped design, which not only increases the area that receives light but also allows for the average of the signal at 360 degrees, effectively reducing the anisotropic interference caused by variations in the angle of incident light. Monte Carlo simulations were used to analyze the performance differences between point detectors and the ring-shaped detector under conditions where the light incident angles varied randomly within the ranges of 1.2°, 2.5°, 5°, 15°, and 45°, as well as in a monotonic increase from 0° to 5°. The results demonstrated that the ring-shaped detector's signal-to-noise ratio is significantly higher than that of the point detector—approximately ten times greater. Accordingly, its detection limits for glucose are also lower, one-tenth those of the point detector. The ring-shaped detector not only reduces the anisotropic interferences caused by random variations in the angle of incidence but also shows stronger common-mode characteristics in interference received at two different source-detector separations. Therefore, using a pair of concentric ring-shaped detectors and differentially canceling the signals can further suppress interferences caused by changes in the angle of incidence. In human trials, subjects used ring-shaped and point detectors to collect signals under changes in body posture while fasting. The experimental results indicate that the ring-shaped detectors, combined with the differential method, effectively suppress interference caused by changes in the human-sensor contact state due to body posture changes, with signal variation ranging between 0.000 5 and 0.001 a. u., thus meeting the non-invasive blood glucose detection requirements of 0.5 to 1 mmol·L^-1. In summary, the dual-ring shaped wearable detector proposed in this paper exhibits outstanding high signal-to-noise ratio performance in human testing, demonstrating its broad potential for application in non-invasive human body component analysis.
		2025 Vol. 45 (04): 914-921 [Abstract] ( 18 ) RICH HTML PDF (7624 KB) ( 19 )

	922	Imaging Spectral Tracking and Detection of Ship-Polluted Gas Emissions
		REN Hao¹, ZENG Yi^2*, LU Xiao-feng², WU Lu-yao², DONG Jian², LI Hao-ran², SONG Run-ze², HAN Yun-kun², XI Liang², SI Fu-qi²
		DOI: 10.3964/j.issn.1000-0593(2025)04-0922-10
		Ship transportation has promoted the development of trade and logistics while also emitting pollutants into the atmosphere. This study developed a ground-based rapid imaging spectral detection system for tracking and monitoring real-time ship emissions. The system uses both visible light and ultraviolet telescopic lenses. The former works with the gimbal to locate and track target ships, while the latter collects specific spectral data of ship emissions. Adjusting the camera orientation based on the ship's real-time position, and continuously capturing multiple images to establish a time series background and spectral dataset. Improvements have been made to the fiber optic spectrometer, which improves the system's spectral imaging speed and enables the acquisition of high temporal resolution monitoring data. At the same time, it can adapt to ship movements and environmental changes, ensuring the accuracy and continuity of data. Using imaging differential absorption spectroscopy technology, the concentrations of SO₂ and NO₂ in ship emissions can be identified and quantified. Field observations were conducted on ship pollution gas emissions in the Yangtze River Basin of Tongling City to verify the system performance. The results showed that the inclined column density of SO₂ in ship emissions was 5.64×10¹⁶ molecule·cm^-2, and the inclined column density of NO₂ was 8.75×10¹⁶ molecule·cm^-2, which verified the feasibility of the detection system.
		2025 Vol. 45 (04): 922-931 [Abstract] ( 18 ) RICH HTML PDF (41484 KB) ( 12 )

	932	Improved Convolutional Neural Network Quantification of Mixed Fault Characterization Gases in Transformers Based on Raman Spectroscopy
		CHEN Xin-gang^{1, 2}, ZHANG Wen-xuan¹, MA Zhi-peng^1*, ZHANG Zhi-xian¹, WAN Fu³, AO Yi¹, ZENG Hui-min¹
		DOI: 10.3964/j.issn.1000-0593(2025)04-0932-09
		Laser Raman spectroscopy has obvious advantages in detecting transformer fault characteristic gases. With the development of intelligent transformer condition monitoring, it is of great significance to study the fast and accurate quantitative analysis method of mixed fault characteristic gases.Conventional Raman spectral analysis requires a preprocessing process that greatly relies on human experience and spectral feature extraction. Although it can reduce the signal dimensions, it can also result in partially missing or altered spectral features. Aiming at the above problems, a method for quantitative analysis of Raman spectra based on the fusion of improved 1DCNN and LSSVR is proposed; that is, the introduction of global mean pooling and least squares support vector regression improves traditional CNN, and the use of the Dropout method to improve model generalization performance and prevent over-fitting. Design and build the transformer fault characteristic gas Raman spectroscopy detection platform, collect the Raman signal of 7 kinds of fault characteristic gases and N₂, O₂ mixed gases, in the spectrogram near 2 900 cm^-1 frequency shift, CH₄, C₂H₆ gases show the overlap of the spectral peaks, and when transformer overheating or partial discharge fault occurs, it will produce the main fault characteristic gas CH₄, choose different content ratio of CH₄, C₂H₆ mixed gas as a representative research object, 146 groups of mixed gas samples with different contents of CH₄ and C₂H₆ are prepared according to different ratios. Nitrogen is chosen as the standard gas for detection, the Raman spectral data of the mixed gas samples with different content ratios are collected, and the spectral data enhancement method is utilized to construct the gas sample dataset suitable for deep neural networks. Through continuous experiments, we optimize the network structure parameters and network weights, complete the model training and test its prediction effect, compare and analyze with multiple quantitative models, study the effect of spectral preprocessing on different quantitative models, and then evaluate the model performance. The results show that when using the original data set for modeling, the improved CNN model has the best prediction accuracy and regression fitting goodness, the R² can reach 0.999 8, and the RMSE is only 0.000 5 MPa; using preprocessed data. When modeling the set, the RMSE of the improved convolutional neural network model is 0.002 3 MPa, which is an increase of 0.001 8 compared to the modeling error using the original data set. In contrast, the errors of traditional methods have declined.The results of this study show that the proposed method integrates the spectral pre-processing, feature extraction, and quantitative analysis processes compared with the traditional Raman spectroscopy quantitative method, which simplifies the spectral analysis process based on ensuring the prediction accuracy and provides new ideas and references for the fast and accurate analysis of transformer mixed fault characteristic gases.
		2025 Vol. 45 (04): 932-940 [Abstract] ( 23 ) RICH HTML PDF (9874 KB) ( 34 )

	941	Study of Differential Diagnosis of Early NPC and Nasopharyngitis Based on THz Spectra
		ZHU Yi-feng^{1, 3}, LIU Jin-peng^{1, 3}, SONG Zheng-xun^{1, 3}, ZHOU Xiao-jun^4, ZHOU Mei⁵, REN Jiao-jiao^{2, 3}, YANG Wen-tao², CUI Zong-yu^{2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)04-0941-06
		Early treatment is essential to improve the survival rate of patients. However, due to its hidden location and early symptoms similar to nasal inflammatory diseases, it is easy to ignore, and it is often found in the middle and late stages. In recent years, terahertz technology has attracted much attention in biomedical cancer detection due to its low energy, strong penetration, and fingerprint spectrum characteristics. In this study, nasopharyngeal carcinoma (NPC) and nasopharyngitis tissues were taken as the research objects to explore the application value of terahertz spectroscopy in the differential diagnosis of nasopharyngeal carcinoma and nasopharyngitis. The terahertz time-domain spectroscopy system was used to collect the spectrum of nasopharyngeal tissues in the range of 0.6~5.0 THz, and the absorption spectrum was obtained by parameter extraction. Based on the spectral data, the spectral characteristics of NPC and nasopharyngitis tissues were analyzed and compared. The spectral difference source between the two nasopharyngeal tissues was combined with the pathological H&E staining results. Through the application of the principal component analysis (PCA) method, the original power spectrum data collected in experiments were reduced. The features were extracted, and the scatter plot of samples in the three-dimensional coordinate space composed of the first, second, and third principal components was obtained. Based on the analysis of this scatter plot, a significant differentiation between NPC tissues and nasopharyngitis tissues in the feature space can be observed. The results show that the absorption of terahertz wave in NPC tissue is significantly higher than that in nasopharyngitis tissue in the range of 1.3 to 3.4 THz, and 2.7 THz is the best potential diagnostic frequency to distinguish NPC tissue from nasopharyngitis tissue. After further dimensionality reduction by principal component analysis, the cumulative variance contribution rate of the first four principal components reached 87.45%, which had a good clustering effect on the two groups of nasopharyngeal tissue samples. The principal component plot can clearly distinguishNPCtissue and nasopharyngitis tissue. K-nearest neighbor (KNN) algorithm and support vector machine (SVM) were combined to construct a classification model to realize the discrimination and classification of the two kinds of nasopharyngeal tissue THz spectra. Compared with the KNN algorithm, the accuracy of SVM classification model has an average classification reaches 92%. This study preliminarily verifies the effectiveness of THz spectra used to identify nasopharyngeal carcinoma from nasopharyngitis, which lays a foundation for further exploring their clinical value.
		2025 Vol. 45 (04): 941-946 [Abstract] ( 21 ) RICH HTML PDF (6568 KB) ( 13 )

	947	Toluene Fluorescence Spectroscopy Analysis in Different Temperatures Using Partial Least Squares
		HAN Ming-hong^{1, 2}, YU Xin^{1, 2}, PENG Jiang-bo^{1, 2*}, YANG Chao-bo^{1, 2}, CAO Zhen^{1, 2}, QI Jin-hao^{1, 2}, YUAN Xun^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)04-0947-06
		Temperature measurement, as one of the important parameters in flow fields, is of great significance. Toluene, a commonly used tracer in laser-induced fluorescence, is often used for flow field temperature measurement due to its temperature-sensitive fluorescence intensity. The primary reason for using toluene in temperature measurement is the redshift in its fluorescence spectrum with increasing temperature. However, there is a lack of direct analysis to elucidate the correlation between the toluene spectrum and temperature. This paper analyzes the fluorescence spectra of toluene at different temperatures using partial least squares analysis to establish the correlation between temperature and spectrum. The spectral measurement wavelength range effectively covers the entire fluorescence range of toluene. Using a 266 nm laser as the excitation wavelength for toluene, the fluorescence spectra of toluene at 35 different temperatures are measured, and the spectral measurements are accumulated 100 times to eliminate random noise. The spectra of the 35 temperature points corresponding to the 260～330 nm wavelength range are allocated to the dataset and validation set in a 6∶1 ratio. When establishing the model using the dataset, the number of factors in the model is set to 9, and the coefficient of determination of the obtained model is 0.992 2. The root mean square error of the data validation is 4.59 K. When the validation set data is applied to the model, the relative error of the predicted results is within 1%, and the root mean square error of the validation set is 1.67 K, which is in good agreement with the actual temperature values. The experimental results demonstrate the feasibility of using the partial least squares method to measure temperature through the toluene fluorescence spectrum. The establishment of this model provides new ideas and methods for the study of flow field temperature measurement. Meanwhile, the temperature results obtained from the model can also be used as calibration data for temperature measurement in flow fields where in-situ calibration is inconvenient.
		2025 Vol. 45 (04): 947-952 [Abstract] ( 18 ) RICH HTML PDF (5306 KB) ( 22 )

	953	Analysis of Oxygen Hemoglobin in Nasopharyngeal Carcinoma Based on NIR-SERS With Statistical Methods
		GAO Fei^1*, ZHANG Ming¹, KONG De-ying², ZHANG Jian-ming³
		DOI: 10.3964/j.issn.1000-0593(2025)04-0953-05
		Near-infrared (NIR) Surface Enhancement Raman scattering (SERS) is a highly sensitive and selective surface-matter detection technique that uses oxygenated hemoglobin in human blood to detect the corresponding NIR-SERS spectra. The spectral data were detected and analyzed using a combined multivariate statistical method. Firstly, the NIR-SERS spectra of oxygenated hemoglobin in 30 healthy subjects and 30 patients with nasopharyngeal carcinoma were screened, and it was found that the average NIR-SERS spectra of oxygenated hemoglobin in NPC patients were at the positions of 340, 479, 817, 1 127, 1 215, 1 347, 1 425, 1 587 cm^-1. The area and intensity of the peaks were relatively small, especially at the positions of 479, 817 and 1 587 cm^-1. In addition, when the NIR-SERS peaks of healthy individuals were at 335, 471, 1 338 and 1 429 cm^-1, the red shift to 340, 479, 1 347 cm^-1and blue shift to 1425 were observed in NPC patients.The multivariate statistical model of PCA-T (principal component analysis combined with independent variable t-test) was established to obtain the three most significant PCA scores: PC1, PC3, and PC7, two-dimensional and three-dimensional scatter plots were made for the three PCA scores, and the healthy people and NPC patients were divided into different regions. A working characteristic curve (ROC) was used to evaluate the reliability of the PCA-T analysis method and verify its reliability. The results showed that the vibrational changes of oxyhemoglobin molecules, such as pyrrole ring and vinyl group, were abnormal, and the changes of L-Phenylalanine were also significant. The nasopharynx of the human body likely has pathological changes, resulting in the patient's body's more vigorous metabolism of amino acids, increased sugar consumption, and other causes. In conclusion, the combination of near-infraredsurface-enhanced Tunku Abdul Rahman scattering spectroscopy with multivariate statistical analysis has the potential to accurately, effectively, and rapidly differentiate healthy human samples from those of nasopharyngeal carcinoma, this study provides a preliminary experimental basis for the application of NIR-SERS spectroscopy in nasopharyngeal carcinoma and other medical studies and may be a potentially sensitive and reliable tool for early clinical cancer diagnosis.
		2025 Vol. 45 (04): 953-957 [Abstract] ( 18 ) RICH HTML PDF (4105 KB) ( 14 )

	958	Investigation of Hydrogen Bonding in Aqueous Nitric Acid Solution Under Concentration Perturbation by Two-Dimensional Correlated Raman Spectroscopy
		YANG Bo, ZHANG Ya-ru, CHENG Bi-yao, LI Yu-wei, QU Peng-fei, TANG Hui, LIU Hai-bin, WANG Xiao-zhuo^*
		DOI: 10.3964/j.issn.1000-0593(2025)04-0958-06
		Nitric acid is known as the “mother of the national defense industry”, and the dilution concentration of its aqueous solution is crucial for producing explosives. This paper improves the Raman excitation and collection efficiency by constructing a cavity-enhanced Raman spectrometer. It achieves the measurement of aqueous nitric acid solutions with fine concentration differences ranging from 35.49% to 88.98% in nitric acid mass fraction (ω). Analyzing the Raman spectra of nitric acid within the range of 900～1 400 cm^-1, it was found that the N—OH stretching vibration mode at 965 cm^-1 showed a significant frequency shift with changes in nitric acid concentration. When 35.49%≤ω≤55.10%, an increase in nitric acid concentration induces a rapid red shift in the N—OH stretching vibration mode, attributed to the formation of HNO₃—nH₂O (n=1, 2, …) cluster structures. When ω>55.10%, the degree of redshift in the N—OH stretching vibration mode gradually decreases, which may be because the hydrogen bonds (H-bonds) formed between nitric acid and water molecules decrease. When ω>68.78%, the degree of redshift in the N—OH stretching vibration mode expands again due to increased H-bonds within nitric acid molecules. In addition, the intensity of the N—O fully symmetric stretching vibration mode at 1 050 cm^-1 and the N—OH asymmetric stretching vibration mode at 1 300 cm^-1 as a function of nitric acid concentration were also studied. When 35.49%≤ω≤55.10%, the area ratio of Raman peaks at 1 050 and 1 300 cm^-1 decreases rapidly as the nitric acid concentration increases. However, the decrease in the area ratio at 1 050 and 1 300 cm^-1 slows down atω>55.10%. The area ratio tends to stabilize at ω>68.78%, which is attributed to NO^-₃ gradually decreases and undissociated HNO₃ increases. The above Raman shift and peak intensity indicate that within the available sample range in this paper, the HNO₃—nH₂O cluster structure will transform when ω reaches 55.10% and 68.78%, respectively. Further, explain the mechanism of peak intensity and frequency shift changes by combining density functional theory (DFT). The HNO₃—3H₂O and HNO₃—2H₂O cluster structures are the main forms in aqueous nitric acid solutions at 35.49%≤ω≤55.10%, and these cluster structures gradually transform into HNO₃—H₂O at ω>55.10%, and the undissociated HNO₃ gradually dominates in aqueous nitric acid solution at ω>68.78%. Finally, a two-dimensional correlation analysis was performed on the one-dimensional Raman spectrum to reveal the peak sources of HNO₃/NO^-₃. The results confirmed that the Raman peak at 1 051 cm^-1 represents NO^-₃, while the Raman peaks at 1 308 and 958 cm^-1 belong to the undissociated HNO₃ molecule. These results contribute to understanding intermolecular interactions in aqueous nitric acid solutions with different concentrations and provide a reference for the application of nitric acid in chemical engineering, material chemistry, and the national defense industry.
		2025 Vol. 45 (04): 958-963 [Abstract] ( 17 ) RICH HTML PDF (6937 KB) ( 13 )

	964	Construction of Manganese Doped ZnS Quantum Dots Phosphorescence System and Its Application to Pb(Ⅱ) Sensing Detection
		DONG Yi-fan, AI Qiu-shuang, YU Xi-ren, ZHANG Li, LIANG Jing-tian, ZHANG Da-wen, QIU Su-yan^*
		DOI: 10.3964/j.issn.1000-0593(2025)04-0964-07
		Phosphorescence sensing technology has attracted much attention in recent years due to its long life, wider gap between the excitation spectrum and emission spectrum, and the ability to effectively avoid interference from the system and light scattering. This study synthesized water-soluble Mn-doped ZnS quantum dots in a reactor using the hydrothermal method. When synthesized quantum dots were characterized，it can be concluded from the XRD results, TEM image, and TEM-mapping image that Mn²⁺ was successfully doped into the ZnS lattice. The quantum dots exhibited an obvious phosphorescence emission peak at 580 nm, much higher than fluorescence peak at 420 nm. The phosphorescence properties of the quantum dots under different reaction conditions were also investigated. It was found that the characteristic peak of the quantum dots was the highest when the pH of the precursor solution was 11, the reaction time was 30 minutes, and the Mn doping ratio was 4%. Compared with the traditional preparation method, the hydrothermal method in the reactor can effectively dope Mn into the interior of ZnS quantum dots rather than the surface, leading to phosphorescence emission, which is conducive to improving the selectivity and sensitivity of the sensing system. In addition, this study also found that Pb²⁺ can selectively quench the phosphorescence of Mn-doped ZnS quantum dots, and investigated the effects of buffer pH and reaction time. Under the optimal conditions, a highly sensitive phosphorescence sensing system for identifying Pb²⁺ ions was established with a linear range of 0.02～20 μmol·L^-1, and the detection limit was as low as 6.6 nmol·L^-1. At the same time, other metal ions had no obvious interference with this sensing technology. The determination of Pb²⁺ in water samples from a lake was successfully tested, the recovery rate ranged from 91.9%~114.1%, and the accuracy rate was between 83.0%~109.8%, which proved that this sensitive phosphorescence sensing system had good stability and repeatability.
		2025 Vol. 45 (04): 964-970 [Abstract] ( 16 ) RICH HTML PDF (6638 KB) ( 8 )

	971	Study on Spectroscopy and Locality Characteristics of the Tremolite Jade in Hanyaozi Grassland
		YU Xuan¹, LIU Ji-fu¹, YANG Ming-xing^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2025)04-0971-09
		The Hanyaozi Grassland Jade Mine site was excavated and surveyed in 2014, yielding pottery shards, stone tools, and jade materials, which possess significant gemological and archaeological value. To enrich the database of Chinese nephrite and provide more accurate data support for future studies on the analysis of unearthed jade artifacts, this paper takes 38 pieces of tremolite jade collected in Hanyaozi grassland as the research object. Conventional gemological tests, as well as infrared and Raman spectroscopy, were conducted on the samples. Infrared and Raman spectroscopy instruments can rapidly determine the samples' chemical composition and impurity minerals. The results indicate that the samples are standard tremolite, with impurity minerals including sphene and amorphous carbon. Qualitative and quantitative analyses of the samples' major and trace elements were conducted using Laser Ablation-Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). The samples' rare earth element (REE) data were standardized using reported data from chondritic meteorites, resulting in a series of parameters. The ∑REE values ranged from 0.40 to 14.28, with a mean of 3.46, indicating an overall low abundance of rare earth elements. The LREE/HREE values ranged from 0.21 to 17.91, with a mean of 2.69, suggesting a slight enrichment of light rare earth elements. The REE distribution pattern exhibited a relatively flat “seagull-shaped” trend. The δEu values ranged from 0 to 3.3, with a mean of 0.58, indicating a negative Eu anomaly. Ce showed no significant anomalies. The tremolite jade from Hanjiaozi exhibited characteristic differences in REE distribution patterns compared to tremolite jade from other origins. Combining the spiderweb diagrams of trace elements of tremolite jade from the northwest mining area can further differentiate them. The uranium enrichment level (U) in Hanyaozi tremolite jade is significantly higher than in other origins. Finally, employing discriminant analysis methods in SPSS software, the trace elements of the tremolite jade in the northwest mining area were used to establish a discriminant model of origin. The results demonstrated a 100% accuracy rate in discrimination, with cross-validation rates of 98.3% respectively, affirming the distinctiveness of Hanyaozi Grassland tremolite jade from other origins. The established origin discrimination model can be utilized in subsequent provenance studies of unearthed jade artifacts.
		2025 Vol. 45 (04): 971-979 [Abstract] ( 18 ) RICH HTML PDF (12593 KB) ( 13 )

	980	Research on Hg²⁺ Detection in Soil Based on EC-LIBS Technology
		DUAN Hong-wei^{1, 2, 3}, ZHAO Si-jie^{1, 2}, GUO Mei^{1, 2}, NIU Qi-jian³, HUANG Jing^{4, 5}, LIU Fei^{4, 5*}
		DOI: 10.3964/j.issn.1000-0593(2025)04-0980-06
		Accurate assessment of Hg²⁺ in farmland soil is significant for constructing high-standard farmland and protecting food security. To solve the problem of low mercury ion concentration and unknown coexisting interference in polluted soil, a method of Hg²⁺ detection in soil based on electrochemical laser-induced breakdown spectrometry (EC-LIBS) was proposed in this study. First, cyclic voltammetry was used for Hg²⁺ electrodeposition. Compared with the gold electrode substrate before deposition, it was found that the gold electrode substrate after deposition had obvious emission signals at Hg Ⅰ 435.835 nm, and the surface brightness of the gold nanoparticles increased and the yellowness decreased after deposition. The results show that electrochemical deposition can complete the liquid-solid conversion of mercury ions by forming gold amalgam on the surface of the gold electrode. Secondly, the influence of solvent pH and deposition voltage on the deposition effect was investigated, and the optimal deposition parameters were pH 7.0 and voltage of -400 mV. Finally, the sample LIBS spectral line was obtained, and the Whittaker baseline correction and PLS model were carried out. It was concluded that when Lambda=1.0 and p=0.01, the baseline correction effect was obvious, and the prediction sets RMSEP and MRAEP of the developed PLS model were 5.24 mg·kg^-1 and 4.86%, respectively. It shows that EC-LIBS technology combined with the standard model can accurately detect Hg²⁺ in soil.
		2025 Vol. 45 (04): 980-985 [Abstract] ( 21 ) RICH HTML PDF (10450 KB) ( 15 )

	986	Study on the Compositional Characteristics and Colour of Oil Spot Glaze of Jian Kiln in the Song Dynasty Based on Spectral Analysis
		JIANG Cai-shui¹, WU Jun-ming^1*, ZHOU Jian-er¹, BAO Qi-fu¹, LIU Kun², ZHENG Nai-zhang¹
		DOI: 10.3964/j.issn.1000-0593(2025)04-0986-08
		Black-glazed porcelain holds a significant place in the history of Chinese ceramics, with Song dynasty Jian kiln oil spot glazes being highly esteemed for their distinctive colors and patterns. To elucidate the compositional characteristics and underlying causes of the varying colors in oil spot glazes, we employed energy dispersive X-ray fluorescence (EDXRF), colorimetry, scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), micro-focused Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) to analyze the composition, microstructure, and firing processes of these glazes. The results indicate that the Si/Al ratio of Song dynasty Jian kiln oil spot glazes ranges from 6.77 to 11.15, distinct from other Jian ware glazes. These oil spot glazes are characterized by high silicon, high potassium, low calcium, low iron, and low titanium content, suggesting a unique formulation. There is a significant difference in elemental distribution inside and outside the crystalline spots, with calcium, iron, titanium, and phosphorus being enriched within them, playing a crucial role in their formation. No significant differences were observed in the chemical composition of oil spot glazes with different colors, indicating that composition is not the primary determinant of coloration. Instead, analysis of iron ion valence states in the glaze and body color suggests that the firing atmosphere is a key factor influencing the type and size of precipitated crystals. When the Fe²⁺/Fe³⁺ ratio is high, the body color tends to be darker, indicating a predominantly reducing atmosphere. The silver-white spots are caused by strong light reflection from aggregated ε-Fe₂O₃ nanocrystals or α-Fe₂O₃ crystalline films. Conversely, when the Fe²⁺/Fe³⁺ ratio is low, the body color tends to be reddish, indicating an oxidizing atmosphere. The brownish-yellow and reddish-brown spots primarily result from the chemical color of 5～10 μm snowflake-shaped ε-Fe₂O₃ and α-Fe₂O₃ crystals. The coupling effect between chemical and structural colors can modulate the glaze's coloration. Yellow-green spots with a blue tint arise mainly from Rayleigh scattering of 100～200 nm ε-Fe₂O₃ particles coupled with chemical color, while silver-white spots with a reddish-brown tint result from total reflection by α-Fe₂O₃ crystalline films coupled with chemical color. This study elucidates the composition and structural characteristics of Song dynasty Jian kiln oil spot glazes, revealing their diverse coloration mechanisms. These findings provide important insights into the technological aspects of Jian black-glazed porcelain and have implications for the innovation and development of iron-based crystalline glazes.
		2025 Vol. 45 (04): 986-993 [Abstract] ( 18 ) RICH HTML PDF (46522 KB) ( 17 )

	994	Pressure Compensation of Industrial Ambient Gases and Their Prediction Based on Infrared Spectroscopy
		TIAN Fu-chao^{1, 2, 3}, ZHANG Hai-long^{1, 2, 3}, SU Jia-hao^{1, 2, 3*}, LIANG Yun-tao^{1, 2, 3}, WANG Lin^{1, 2, 3}, WANG Ze-wen^{1, 2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)04-0994-14
		Infrared spectroscopy is one of the important means of quantitative analysis of industrial environmental gases. Still, the current infrared gas detector's measurement accuracy is greatly affected by ambient pressure changes, resulting in the detection data deviating from the actual gas concentration under different pressure conditions. To improve the accuracy of the infrared gas sensor, this paper chooses a pressure compensation algorithm combining the Whale Optimization Algorithm (WOA) and Wavelet Neural Network (WNN). It combines it with Long Short-Term Memory (LSTM). Memory (LSTM) to predict the compensated data. By building an experimental platform for gas pressure compensation in industrial environments, using a high-precision gas dispenser to configure 100~900 ppm standard CO gas, and conducting hundreds of repetitive experiments in the range of 80~120 kPa, it is found that the measured value of the CO gas sensor is less than the concentration of the standard gas under negative pressure conditions, and more than the concentration of the standard gas under positive pressure conditions, and the absolute error is linearly correlated with the pressure change, with the highest absolute error of 0.5 ppm. A linear relationship was found, with an absolute error of up to 86 ppm. The sensor data was used to reduce the error using a wavelet neural network, and the initial compensated CO error was reduced to 26 ppm. Still, the individual data error was large due to poor parameter portability. After further optimizing the parameters of the wavelet neural network using the whale optimization algorithm, the compensation effect was significantly improved. The difference between the sensor measurement and the true value was kept within 0.004%, and the data were stable. The root mean square error (RMSE) between the predicted and actual values is less than 0.1, and the mean absolute error (MAE) is less than 0.020. The experimental results show that the WOA-WNN-LSTM algorithm can effectively improve the measurement accuracy of the infrared gas sensors and successfully eliminate the influence of ambient pressure on the results, providing a more reliable and accurate measurement of the gases in industrial environments. It provides a more reliable and accurate solution for gas detection in industrial environments.
		2025 Vol. 45 (04): 994-1007 [Abstract] ( 16 ) RICH HTML PDF (27558 KB) ( 11 )

	1008	Traceability Analysis of Penicillin G Acylase Genus Classification Using High-Throughput Infrared Spectroscopy Based on the Weighted KNN
		WANG Yan, ZHANG Pei-pei, ZHAO Yu^*
		DOI: 10.3964/j.issn.1000-0593(2025)04-1008-07
		β-lactam antibiotics (BAs) are an important class of anti-infective drugs in clinical practice. Penicillin G acylase (PGA) is a key technology used in the new enzymatic process to produce of BAs. PGAs derived from different bacterial origins have different protein sequence structures, thermal stability, and stereo-selectivity, which cause different catalytic activity and are crucial for antibiotic synthesis and production. Infrared spectroscopy (IR) can be used to characterize the structure of high molecular weight proteins. Proteomics-based mass spectrometry can identify different protein substances at the peptide level, but its complexity makes it harder to operate. The simple IR method provides a powerful analytical tool for rapidly characterizing PGAs. This article explored the selection of ultrafiltration and drying membrane preparation methods for the pre-treatment of PGA samples. This way could purify PGA raw solutions and remove matrix interference, while it could also overcome the problem of low PGA solution concentration to enhance IR signal response. Besides, a high-through put IR method was optimized and established to analyze 11 batches of PGAs from different sources. All IR spectra of PGAs showed classical IR absorption peaks of amide groups at the characteristic region (1 700～1 500 cm^-1). There were still differential IR absorption peaks within the 1 200～750 cm^-1 fingerprint region. A traceability model was established by selecting differentiated absorption peak spectral bands at fingerprint regions (830~795，1 027~1 020，1 085~1 080 cm^-1). Based on the analysis of proteomics mass spectrometry, a weighted k-nearest neighbor (KNN) algorithm was employed to analyze different PGAs. It showed that 11 batches of PGAs were divided into two classes: those including PGA 1, PGA 3, PGA 7, and PGA 8 belonged to class Ⅰ and were identified as the proteins fermented from E. coli, while the rest of those—PGA 2, PGA 4~6, and PGA 9~11 belonged to class Ⅱ and were produced from Achromobacter sp. CCM 4824. This result verified the applicability and feasibility of the established traceability model, which was consistent with the proteomics result. Then, three batches of unknown PGAs were collected for determination by the traceability model to externally validate the accuracy. Finally, the robustness of the established model was further validated by examining 11 batches of PGAs on different days. The results demonstrate that the high-throughput IR method based on the weighted KNN could rapidly trace PGAs from different bacterial origins. This method is simple, accurate, and durable. It provides a new detection tool for the structural characterization and protein classification of the catalytic enzymes used in producing BAs by enzymatic process.
		2025 Vol. 45 (04): 1008-1014 [Abstract] ( 23 ) RICH HTML PDF (7853 KB) ( 13 )

	1015	Nondestructive Identification of Egg Yolk Color Based on Near Infrared Spectrum and Multivariate Data Processing
		WEN Yu-kuan¹, DONG Gui-mei¹, LI Liu-an², YU Xiao-xue², YU Ya-ping^1*
		DOI: 10.3964/j.issn.1000-0593(2025)04-1015-07
		Yolk color is an important indicator of egg quality, and consumers prefer to buy eggs with darker yolk color. Currently, the commonly used method involves physically opening the egg to distinguish the yolk color using the Roche fan method, so the research on non-destructive identification of yolk color is significant. This paper mainly studies the non-destructive identification method of yolk color for eggs with different eggshell colors. The data is collected by near-infrared spectroscopy. Then, the qualitative classification prediction model is established by using a chemometry algorithm. The components affecting egg yolk color are analyzed to find the functional groups corresponding to the spectral absorption peak. This study collected the NIR spectral data of 90 pink and 89 white eggs using the Roche fan method to record yolk color and establish qualitative classification models. The samples were divided into correction sets and prediction sets according to 2∶1, and prediction models were established for single-color and mixed-color samples, respectively. Linear (partial least square discriminant PLS-DA, linear discriminant analysis LDA) and nonlinear (convolutional neural network CNN, extreme learning machine ELM) methods were used to establish the classification models along sidevarious pretreatment and wavelength screening methods. CARS feature wavelength screening method was used to screen 176 wavelength points of spectral data. Combining CARS wavelength screening, MSC, and second derivative pretreatment methods for 2 kinds of color eggshell samples, the accuracy of the test set reached 91.67% by the PLS-DA model. In contrast, the LDA model reached 98.11%. For the pink shell eggs, the accuracy of the test set is 100% by the PLS-DA model. For the white shell eggs, the accuracy of the PLS-DA model is 96.67%, while that of the LDA model is 100%. These results demonstrate the efficacy of linear methods in characterizing the egg yolk color from spectra. This method can not only meet the needs of consumers but also play a guiding role in feed feeding and control of farms.
		2025 Vol. 45 (04): 1015-1021 [Abstract] ( 23 ) RICH HTML PDF (5136 KB) ( 21 )

	1022	Improving LIBS Quantification by Combining Domain Factors and Multilayer Perceptron Method
		CUI Jia-cheng¹, SONG Wei-ran¹, YAO Wei-li², JI Jian-xun¹, HOU Zong-yu^{1, 3}, WANG Zhe^{1, 3*}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1022-06
		Laser-induced breakdown spectroscopy (LIBS) is an emerging atomic spectroscopy technique with promising applications in coal analysis but is limited by its relatively low quantification performance. Various machine learning methods have been applied in coal analysis on LIBS to improve its quantitative performance in recent years. However, most of these machine-learning models were established purely based on statistics. They ignored the physical rules involved in the quantification, resulting in reduced robustness, application range, and a lack of model interpretability. This work proposed a physics-statistics combined regression method based on the dominant factor (DF) and multilayer perception (MLP), called DF-MLP, to incorporate spectral domain knowledge into machine learning. The new proposed method built a physical-based dominant model to predictelement concentration with the characteristic lines selected with spectral knowledge and correct the residual errors using MLP. DF-MLP combines the dominant factor model and residual error correction using the MLP method can utilize the domain knowledge to improve model robustness and interpretability without reducing complexity. DF-MLP was compared with normal MLP, dominant factor partial least squares regression (DF-PLSR), dominant factor support vector regression (DF-SVR), and other baseline methods, and optimal results were obtained. Compared with normal MLP, the proposed method reduces root mean squared error of prediction (RMSEP) by 13.21%, 14.54%, and 21.77% for carbon, ash, and volatile, respectively. Compared with DF-SVR, the proposed method reduces RMSEP by 14.75%, 23.13%, and 5.99%, respectively. We further discussed the impact of different modeling patterns in the dominant factor method. The experimental results showed that combining domain knowledge with machine learning methods was a feasible approach to improve the performance of LIBS quantification.
		2025 Vol. 45 (04): 1022-1027 [Abstract] ( 22 ) RICH HTML PDF (3059 KB) ( 16 )

	1028	Analysis of Pigments of Polychrome Paintings From the Baoguang Hall of Prince Kung's Palace Museum
		ZHANG Wen-jie¹, GAO Shan², CAO Zhen-wei³, HAN Xiang-na^1*
		DOI: 10.3964/j.issn.1000-0593(2025)04-1028-08
		The Prince Kung's Palace Museum is the largest and most well-preserved Qing dynasty princely residence, with its mansion area having been used successively as the residence of “GuLun-HeXiao” Princess (Heshen's residence), Prince Qing's Mansion, and Prince Kung's Mansion. Baoguang Hall served as the private reception hall for Prince Kung during his time. According to archival records, most of the existing polychrome paintings in Baoguang Hall date back to the mid-Qing period. Previous research on the polychrome paintings of the Prince Kung's Palace Museum has primarily focused on their form and aesthetic style, with little scientific analysis conducted on their production techniques and materials. This study utilizes a suite of analytical techniques, including depth-of-field microscopy, polarized light microscopy, laser Raman spectroscopy, and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy, to analyze and identify the pigments used in the polychrome paintings from Baoguang Hall. The results showed that the cyan pigments of the color painting of the Baoguang Hall were azurite [2CuCO₃·Cu(OH)₂], indigo (C₁₆H₁₀N₂O₂), Prussian blue (Fe₄[Fe(CN)₆]₃), lazurite (Na₈Al₆Si₆O₂₄S_n), artificial ultramarine (Na₈Al₆Si₆O₂₄S_n) and Smalt (CoO·nSiO₂). The green pigments were copper chloride hydroxide [CuCl₂·3Cu(OH)₂]. The red pigments were vermilion (HgS), red lead (Pb₃O₄), and iron red (Fe₂O₃). The yellow pigments were orpiment (As₂S₃). The white pigments were chalk (CaCO₃), kaolin [Al₂Si₂O₅(OH)₄] and lead white[2PbCO₃·Pb(OH)₂]. The black pigments were carbon (C). Additionally, the study found a prevalent use of multi-layered polychrome paintings and mixed pigments. The pigments' application dates are mostly concentrated in the mid-Qing period, indicating that most of the extant polychrome paintings are likely remnants from this era, consistent with the documented period of their creation. This study represents the first scientific analysis of the production techniques and materials of the polychrome paintings in the Prince Kung's Palace Museum, providing preliminary findings that enrich our understanding of the application of pigments in Qing dynasty official architecture. These results are expected to be a reference for future research and conservation efforts concerning polychrome paintings.
		2025 Vol. 45 (04): 1028-1035 [Abstract] ( 20 ) RICH HTML PDF (60522 KB) ( 12 )

	1036	Research on the Pigment Layer of Mural Paintings From the Late Tang Tomb M1373 in Baiyangzhai, Xi'an, Shaanxi Province Based on Hyperspectral Image Processing
		YAN Jing^{1, 2}, TANG Xing-jia^{3, 4*}, HE Zhang^{1, 2}, WANG Zeng^{1, 2}, CHEN Ai-dong^{1, 2}, ZHANG Peng-chang⁵, DONG Wen-qiang^{3, 4}, GAO Jing-wei^{3, 4}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1036-09
		The late Tang tomb M1373, unearthed in November 2022 in Baiyangzhai, Xi'an City, Shaanxi Province, is a well-preserved late Tang tomb heritage. Its colorful and varied tomb murals are significant for studying the worship, etiquette, music, clothing, art, and other aspects of the late Tang period, especially providing direct materials for studying the transformation of mural styles and themes during the Tang and Song dynasties. However, due to its age, the pigment layer of the mural still exhibits many typical diseases, such as pigment peeling, nail peeling, and mud and water pollution. In addition, subsequent mural removal will bring human intervention to the mural. The above will have a certain impact on the information and value of the mural. For this purpose, in this study, we utilized hyperspectral imaging technology to obtain integrated spectral and special information before uncovering murals. Then, spectral analysis and hyperspectral image processing techniques were used to analyze the surface pigment layer of murals. Taking the music and dance painting as an example, the pigment and craftsmanship of mural painting were studied. The experimental results show that the mural uses traditional mineral pigments such as ochre, earth yellow/mineral yellow, and mineral green for its red, yellow, and green pigments, and when drawing carpet flowers, red and green pigments are heavily diluted and used. In contrast, the use of yellow pigments is not obvious. When drawing murals, ink or a hard pen starts the draft, and red lines outline it. The baseline lines are mostly offset from the surface outline lines, and some areas of the outline lines are severely offset from the baseline lines, indicating certain changes between the start and final drafts. Some areas of the baseline lines are more obvious, while others are unclear. Through hyperspectral image processing methods, it was discovered that the starting lines were hidden beneath the pigment layer surface or lines. Moreover, these murals mainly adopted a distributed non overlapping two-dimensional direct view layout, resulting in a slightly poor spatial stereoscopic sense. However, for multiple locations where the fingers of the musician intersect with the flute, some had adopted a two-dimensional direct view drawing method where the foreground and background patterns do not overlap. In contrast, others have adopted a two-dimensional perspective drawing method where a small amount of foreground and background patterns overlap, indicating that the mural was well laid out before and during painting, and there were no obvious alterations. In addition, some hidden sewage pollution diseases were found in the mural. The above research provides data, research methods, and preliminary conclusions to support the development of on-site protection plans for the mural and subsequent research, protection, and restoration.
		2025 Vol. 45 (04): 1036-1044 [Abstract] ( 17 ) RICH HTML PDF (71301 KB) ( 12 )

	1045	Spatial Distribution Mapping of Debris Flow Site in Xiaojiang River Basin Based on the GEE Platform
		ZONG Hui-lin^{1, 2}, YUAN Xi-ping^{2, 3}, GAN Shu^{1, 2*}, YANG Ming-long¹, LÜ Jie¹, ZHANG Xiao-lun¹
		DOI: 10.3964/j.issn.1000-0593(2025)04-1045-16
		Rapid, accurate and exhaustive research on the distribution of mudslide-hostile areas is of great significance, enabling us to understand and have a deep understanding of the scope of distribution of mudslides, the distribution pattern, the causes of the mudslides, and to further find scientific monitoring, prediction, prevention and management of the technical means by the specific situation, to reduce the problems and losses brought about by the mudslide disaster. To seek an efficient and high-precision method for extracting the spatial distribution of mudslides, this study chooses the Xiaojiang River Basin in Yunnan Province as the study area, employed the random forest algorithm based on the Google Earth Engine (GEE) platform to extract the spatial distribution of debris flow traces efficiently. Firstly, Four types of feature variables(spectral features, index features, topographic features, and texture features)were constructed using the 2022 Sentinel-2 image and topographic data, then the random forest feature variable importance score and the J-M distance were combined for the feature preference research and analysis, explored the importance of each feature variable on the extraction of mudslide traces, and finally, set up various feature combinations to create six schemes, compared and analyzed the accuracy of the debris flow traces extracted by the six experimental schemes, and found the best scheme to increase the recognition accuracy. The study shows that: (1) regardless of feature optimization, the accuracy of debris flow trace identification with the addition of terrain feature variables is higher than that with merely optical image data, indicating the utility of using terrain data for debris flow trace information extraction; (2) classification accuracy is affected differently by different feature variable kinds; topographic, index, texture, and spectral features are the feature types with the highest to lowest feature importance scores; (3)the experimental scheme 6 is the best results of the spatial distribution map of debris flow traces in Xiaojiang River Basin, Yunnan Province, in 2022, which constructed multi-dimensional feature variables and feature optimization based on the multi-source data of Sentinel-2 optical images and topographic data. This resulted in an overall accuracy of 94.95%, a Kappa coefficient of 0.94, a debris flow trace mapping accuracy of 91.01%, and a user accuracy of 95.29%. Furthermore, the scheme effectively reduced data redundancy while improving the classification accuracy. This study makes use of the Google Earth Engine (GEE) platform. These multi-source data combine topographic and optical remote sensing imagery and the Random Forest algorithm, which can quickly, accurately, and efficiently extract information on debris flow traces in areas with complex feature coverage over a large range of terrain and has a large potential for applications.
		2025 Vol. 45 (04): 1045-1060 [Abstract] ( 25 ) RICH HTML PDF (55979 KB) ( 13 )

	1061	Research on Remote Sensing Extraction of Artificial Surface in Lhasa City Based on Spectral Features
		WANG Jin-zhi¹, ZHOU Guang-sheng^2*, LÜ Xiao-min², REN Hong-rui¹
		DOI: 10.3964/j.issn.1000-0593(2025)04-1061-10
		The Qinghai-Tibet Plateau holds a crucial position in the global ecosystem. As its core city, Lhasa City stands as a representative focal point for studying the delicate balance between urban development levels and ecosystem service capacities. This study was conducted on the Google Earth Engine (GEE) cloud platform, utilizing Sentinel-2, VIIRS, and SRTM remote sensing imagery data. Based on spectral data combined with terrain and texture features, the study extracted artificial surfaces in Lhasa City through Pixel-Based (PB) and Object-Oriented (OO)classification methods. To compare the performance of different parts of the method, this study conducted a comparative analysis of three groups: OO or PB classification, inclusion or exclusion of texture features, and using Random Forest (RF) or Support Vector Machine (SVM) classifiers. The results showed that Based on the same spectral features, the best extraction result can be obtained by not using texture features in the RF classifier in the OO method (OO_RF), with an Overall Accuracy (OA) of 98.03%, a Kappa Coefficient of 0.952 0, a User Accuracy (UA) of 0.944 4, and a Producer Accuracy (PA) of 0.988 4. The effect of texture features in extracting artificial surfaces is relatively weak, with only slight improvements observed in PB methods. Specifically, the OA increased by 0.51% when using the RF classifier (PB_RF) and 0.68% when using the SVM classifier (PB_SVM). The RF classifier performed the best in this study, avoiding over estimation and identifying non-artificial surfaces within cities more accurately. In conclusion, this study provides a reference for extracting artificial surface information regarding methods and parameter settings at the urban scale. Using result data allows for further analysis and dynamic monitoring, which has practical application significance.
		2025 Vol. 45 (04): 1061-1070 [Abstract] ( 20 ) RICH HTML PDF (22400 KB) ( 11 )

	1071	Sparse Unmixing of Hyperspectral Images Based on Adaptive Total Variation and Low-Rank Constraints
		XU Chen-guang^{1, 2}, GUO Yu¹, LI Feng¹, LIU Yi¹, LI Yan¹, DENG Chen-zhi^1, LIU Yan-de^2
		DOI: 10.3964/j.issn.1000-0593(2025)04-1071-11
		Hyperspectral sparse unmixing is an image processing technique that uses a spectral library containing rich endmember spectral information as a prior and decomposes the hyperspectral data to obtain the abundance corresponding to each endmember spectrum in the spectral library. However, most of the current sparse unmixing methods have poor unmixing effect under high noise conditions, and many de-noising unmixing methods only make partial use of some characteristics of hyperspectrum and do not fully consider the characteristics of hyperspectrum, thus affecting the accuracy of understanding the mixing algorithm. To solve this problem, an innovative hyperspectral image sparse unmixing method based on adaptive total variation and low-rank constraints is proposed. In this paper, the sparse unmixing algorithm is introduced in detail. Then, the hyperspectral image's adaptive total variation and low-rank constraint sparse unmixing algorithm are modeled. The hyperspectral image's adaptive total variation and low-rank constraint sparse unmixing algorithm is proposed. The algorithm combines the low-rank characteristics of hyperspectral data with the adaptive TV spatial characteristics. While maintaining the low rank and sparsity of abundance, it adaptively adjusts the ratio of horizontal and vertical differences of total variation regularization of the abundance matrix under different structures to achieve a better denoising effect. Then, the ADMM algorithm is used to solve the new model. Finally, several classical algorithms, such as SUnSAL-TV, ADSpLRU, S²WSU, and SU-ATV, are compared with the proposed algorithm, and two sets of simulation data and one set of real data are used to verify the quality of the algorithm. Two sets of simulation data are obtained by adding 10, 15, and 20 dB high Gaussian noise to DC1 with a single background and DC2 with a complex background, respectively. In the simulation data experiment, different algorithms were used to unmix the two data groups, and the three values of signal and reconstruction error ratio, abundance reconstruction accuracy, and sparsity of the unmixing results were compared. Moreover, the abundance image after unmixing several algorithms and the difference graph between the abundance image and the real image was observed and compared to analyze the quality of several algorithms. The real data experiment uses hyperspectral real data from a Cuprite mining area in Nevada to analyze and compare the unmixing results and further verify the advantages of the proposed algorithm with real data. The experimental results show that the proposed method improves SRE by 11.4%~310.2% with better robustness and performance than several popular methods.
		2025 Vol. 45 (04): 1071-1081 [Abstract] ( 15 ) RICH HTML PDF (25123 KB) ( 13 )

	1082	Sensitivity Study of Atmospheric Methane Hyperspectral Detection
		YE Song^{1, 3}, MA Li^{1, 3}, XIONG Wei^{2, 4}, LI Da-cheng^{2, 4}, WU Jun^{2, 4}, LUO Hai-yan^{2, 4}, LI Shu^{1, 3*}, WANG Xin-qiang^{1, 3}, WANG Fang-yuan^{1, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1082-06
		Methane is a gas whose content in the atmosphere is growing fast. With the development of human industrial emissions and animal husbandry, methane gas emissions are increasing, and the value of its impact on global warming is several times higher than that of carbon dioxide, so the accurate detection of methane is a focus and hotspot to cope with environmental problems. This study uses the SCIATRAN radiative transfer model to simulate the radiative transfer process of high-resolution spectroscopy. By controlling a single variable to change different observation geometries surface parameters and aerosol parameters, we simulate the changes of irradiance spectra of methane in the 1.6-band under different conditions of the near-surface detection environment and analyze the sensitivity parameters in the process of methane detection and study the sensitivity parameters for the process of detecting methane spectra. The sensitivity results are analyzed for high and low sensitivity parameters in detecting methane spectra. The results show that the solar zenith angle significantly affects the methane irradiance, and the influence of zenith angle change is as high as 83%. The variation of observation altitude near the ground has the least negligible effect on the methane irradiance spectra. The effect of aerosol type on the methane irradiance spectra is small, and the difference between the methane irradiance spectra under rural aerosol and no aerosol conditions is small; the effect of aerosol optical thickness on the methane irradiance spectra is regular, and the methane irradiance decreases when the value of optical thickness increases. Surface parameters had the greatest influence on the methane irradiance spectra, with a 66% difference in methane irradiance between different surface vegetation cover types. The methane irradiance at different surface albedos also varied regularly, with higher values of surface albedo leading to larger values of methane irradiance spectra. The study also verified the feasibility and accuracy of the SCIATRAN model for simulating methane spectra by comparing the simulated data with the measured data and provided sensitive data analysis for the inversion of methane spectra.
		2025 Vol. 45 (04): 1082-1087 [Abstract] ( 18 ) RICH HTML PDF (4952 KB) ( 15 )

	1088	Application of Enhanced Photoelectric Coupling and Multi Anode Photoelectric Multiplier Tube Detectors in Biological Aerosol Fluorescence Spectrum Lidar
		RAO Zhi-min^{1, 2}, LI Yi-cheng^{1, 2}, MAO Jian-dong^{1, 2*}, ZHAO Hu^{1, 2}, LI Yi-xiu^{1, 2}, ZHOU Chun-yan^{1, 2}, GONG Xin^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1088-08
		Biological aerosols can reproduce and cause large-scale diseases in humans, animals, and plants. Therefore, the research on remote warning and real-time detection technology of biological aerosols is significant. Based on ultraviolet laser-induced fluorescence spectrum technology, the application of enhanced photoelectric coupling detector (ICCD) and multi-anode photomultiplier tube detector (MAPMT) in fluorescence spectrum lidar of biological aerosols are elaborated in detail. Furthermore, the signal-to-noise ratio, concentration resolution, and relative deviation of the fluorescence spectrum lidar system composed of ICCD and MAPMT detectors for measuring biological aerosols were studied through numerical simulation. Results showed that when the laser working pulse was 600 during the day, dusk, and night, (1) the system's signal-to-noise ratio was greater than 10. The concentration of biological aerosols is 108 bacteria·L^-1, and the detection distances of the fluorescence spectrum lidar system composed of ICCD detectors are 1.1, 3.3 and 3.4 km, respectively. In contrast, the detection distances of MAPMT detectors are 1.3, 8.5 and 11.2 km, respectively. (2) Within a range of 1.0 km, the minimum detection concentrations that the ICCD detector can achieve are 3 081 210, 120 223 and 66 768 bacteria·L^-1, while the minimum detection concentrations that MAPMT detectors can achieve are 1 950 637, 71 146 and 37 723 bacteria·L^-1. (3) When the detection error is less than 10% and 1%, the relative deviations of the fluorescence spectrum lidar in measuring the concentration of biological aerosols are 33.9%, 37.8% and 40.2% for ICCD detectors, while 37.2%, 42.5% and 46.5% for MAPMT detectors.
		2025 Vol. 45 (04): 1088-1095 [Abstract] ( 16 ) RICH HTML PDF (4354 KB) ( 12 )

	1096	Preliminary Raman Spectroscopic Study of Szaibélyite
		SUI Xin-hao^{1, 2}, ZHAO Xu-wei^{1, 2}, BAO Xin-jian^{1, 2}, HE Ming-yue³, LIU Xi^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1096-07
		Szaibélyite is a hydrated borate mineral with limited research on its Raman spectrum. This study conducted a preliminary Raman spectroscopic analysis of szaibélyite, with the Raman data collected on szaibélyite found in some serpentinized jianite from Ji'an County, Jilin Province, China. Our microscopic observations show that the szaibélyite is associated with serpentine-group minerals, forsterite, brucite, etc. It occurs as fibrous or fine flattened crystals with diameters of up to several tens of micrometers or nanocrystals forming aggregates with minor amounts of nano brucite. Energy dispersive spectroscopy analyses suggest its composition is close to the ideal MgBO₂(OH) formula. In the range of 100~1 600 cm^-1, approximately 28 Raman peaks have been observed, with the strongest peak appearing at ~823 cm^-1. In the range of 3 000~4 000 cm^-1, two sharp strong peaks at ~3 058 and 3 553 cm^-1 and a weak shoulder peak at ~3 562 cm^-1 have been observed. Among them, the Raman peak at ~3 058 cm^-1 is observed for the first time. These peaks are mostly related to the O—H stretching vibrations. Aided with previous single-crystal X-ray diffraction data and infrared spectroscopic data, we propose that the strong Raman peaks at ~3 058 and 3 553 cm^-1 may be caused respectively by the hydroxyl groups O(4)—H and O(6)—Hin szaibélyite. The origin of the weak shoulder peak at ~3 562 cm^-1 is still unclear and requires further investigation.
		2025 Vol. 45 (04): 1096-1102 [Abstract] ( 24 ) RICH HTML PDF (15828 KB) ( 16 )

	1103	Fluorescent Spectroscopic Features of “Trapiche-Like” Sapphire From Mingxi，Fujian Province
		HOU Chao-xin, QU Xin-yue, XIA Su-qin, HAN Hao-chang, WANG Yu-long, ZHANG Hao, LAI Xiao-jing^*
		DOI: 10.3964/j.issn.1000-0593(2025)04-1103-06
		Mingxi in Fujian Province is one of China's most important sapphire origins, and the gemological research on sapphires from this area is still scarce. In addition, there is currently limited research on the fluorescence characteristic of blue sapphires. This study investigated a batch of sapphires with the “trapiche” phenomenon from Mingxi, Fujian, by conventional gemological instruments, laser denudation plasma mass spectrometer, Raman spectroscopy, and three-dimensional fluorescence spectrometer. The chemical composition and Raman spectroscopy results show that the core, arm, and main body of the “trapiche” gem are composed of corundum and should belong to the “trapiche-like” series.The concentrations of Fe, Ti, and other chromogenic elements in the “trapiche-like” sapphire exhibit no significant variation across different regions, suggesting that the “trapiche-like” phenomenon is not correlated with the distribution of chromogenic elements. The analysis of trace elements and their ratios indicates that the samples exhibit geochemical characteristics consistent with basaltic sapphires, however, the chromium content is mostly higher than >40 ppmw in Mingxi, Fujian basaltic sapphires exceeding that of other basaltic sapphires. Both Raman spectroscopy and fluorescence spectrometer detected several Cr fluorescence peaks of the samples at or near 694 nm. In the three-dimensional fluorescence spectroscopy test, the optimal excitation wavelengths for Cr fluorescence peaks are 410 and 560 nm. The fluorescence intensity of the arm is stronger than that of the core and the main body，which may be caused by the lower concentration of Fe element in the arm. The appearance of Cr fluorescence in the three-dimensional fluorescence spectrum may be related to the higher concentration of Cr element compared with basaltic sapphires originating from other places. Combined with previous studies, it can be differentiated from basaltic sapphires from Laos by observing whether it has the fluorescence peaks of Cr, but this cannot be used to distinguish basaltic and metamorphic sapphires. This study indicates that Mingxi, Fujian has “trapiche-like” sapphires, and the fluorescence spectroscopy results in this study not only enrich the spectroscopy data of basaltic sapphires from Mingxi，Fujian but also offer a fresh perspective for origin identification studies.
		2025 Vol. 45 (04): 1103-1108 [Abstract] ( 22 ) RICH HTML PDF (6866 KB) ( 8 )

	1109	Spectroscopic Characteristics of Yunnan Petroleum-Bearing Quartz Crystal
		ZHANG Run-chu¹, CHEN Liu-runxuan², QU Zhi³, SHEN Hong-tao⁴, LIU Yun-gui^{1, 5, 6, 7*}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1109-07
		Petroleum-bearing quartz crystals are rarely produced in nature.They are primarily produced in Pakistan, Madagascar, and Brazil. Due to liquid organic inclusions, it is highly sought after by the international mineral crystal collection market and researchers.In this study, we investigated the spectroscopic characteristics of petroleum-bearing quartz crystals recently discovered in Kunming Yunnan, which used laser Raman spectroscopy, gas chromatography-mass spectrometry, and photoluminescence spectroscopy. The findings indicated that the Kunming petroleum-bearing quartz crystals contain predominantly inclusions of brown-yellow liquid phase and black solid phase. The inclusions of the black solid phase exhibit yellow fluorescence under long-wave ultraviolet light, while the inclusions of the brown-yellow liquid phase exhibit blue-white fluorescence.TheRaman spectrum indicated that the primary component of the black solid inclusions is asphalt, with typical Raman peak shifts at 1 343, 1 606, 2 954, and 3 214 cm^-1. The gas chromatography-mass spectrometry analysis revealed that the inclusions of brown-yellow liquid contain C₉H₂₀, C₁₀H₂₂, C₁₂H₂₆, and C₁₃H₂₈. The photoluminescence spectrum revealed that the asphalt of the solid phase exhibits photoluminescence peaks mainly at 512 nm, while the inclusions of the brown-yellow liquid phase exhibit photoluminescence peaks at 418, 441, 471 and, 505 nm. Based on the inclusions analysis, it is inferred that petroleum-bearing quartz crystals formed in hydrothermal crystal deposits of carbonate rock under low-temperature environments. The obtained composition and spectral characteristics are significant for studying the metallogenic geological environment of Kunming petroleum-bearing quartz crystals and exploring petroleum and gas resources in this area.
		2025 Vol. 45 (04): 1109-1115 [Abstract] ( 20 ) RICH HTML PDF (15269 KB) ( 10 )

	1116	Simulation and Experimental Study on the Fluorescence Characteristics of CDOM in Seawater Surface Based on LIF
		XIE Bei-bei^{1, 2}, WANG Ying-jie¹, GAO Wang¹, MA Kai-jie¹, KONG De-ming³
		DOI: 10.3964/j.issn.1000-0593(2025)04-1116-07
		Colored dissolved organic matter (CDOM), a key substance in the global carbon cycle and climate change, is important in studying marine environmental monitoring for its fluorescence characteristics. Laser-induced fluorescence (LIF) technology, as an active remote sensing optical detection method, provides an effective tool for studying CDOM. This article aims to investigate the fluorescence characteristics of CDOM through a combination of simulation and experimentation, providing a theoretical basis for detecting CDOM in the surface layer of seawater using LIF.Firstly, the Monte Carlo method is utilized to simulate the fluorescence radiation process of CDOM in the surface layer of seawater. This approach allows for the analysis of fluorescence simulation results of CDOM under various conditions, including different concentrations of CDOM, incident light zenith angles, and fluorescence receiving angles. Subsequently, an LIF system is established through an experimental setup to detect CDOM solutions configured with fulvic acid and humic acid as characteristic extracts. This system's fluorescence spectra are obtained for different CDOM concentrations, system detection parameters, and environmental factors such as temperature, pH value, and salinity.The characteristics of these fluorescence spectra are then analyzed. Finally, the impact of environmental factors on the fluorescence spectra of CDOM is studied by combining single-factor variation analysis with multi-factor response surface analysis. Simulation and experimental results indicate a negative correlation between CDOM fluorescence and the incident zenith angle of excitation light. CDOM fluorescence intensity is relatively high when the emission angle is within the range of 0°～60°, which can provide a reference for the selection of incident and receiving angles of excitation light during the actual detection of CDOM using LIF;As the concentration of CDOM increases, the fluorescence intensity continues to rise. However, after reaching a certain concentration, the fluorescence intensity decreases as the concentration further increases; The effects of environmental factors such as temperature, pH, and salinity on CDOM were studied through single-factor analysis. The results indicate that as the temperature rises, the fluorescence intensity of CDOM gradually decreases. When the marine environment is strongly alkaline, the fluorescence spectrum of CDOM undergoes significant changes. Among the effects of salinity on CDOM, the impact on fulvic acid is greater than that on humic acid. Subsequently, the impact of multiple factors on the fluorescence properties of CDOM under their combined effects is analyzed using the response surface method. By observing the steepness of the response surface of the CDOM fluorescence peak, it can be found that the order of the influence of various factors on the fluorescence intensity of CDOM is pH, temperature, and salinity. These results indicate that the influence of environmental factors must be considered when using LIF to detect CDOM.
		2025 Vol. 45 (04): 1116-1122 [Abstract] ( 17 ) RICH HTML PDF (8037 KB) ( 9 )

	1123	Construction and Application of Fluorescence/LSPR Dual Signal Aptamer Sensor for Aflatoxin B1 Detection
		ZHU Ming-ming¹, HU Jian-dong^{2, 3}, ZHANG Shou-jie¹, LI Guang-hui², ZHANG Yan-yan^{2, 3*}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1123-06
		Foodborne infections pose a substantial hazard to public health worldwide because they play a significant role in establishing and reemerging infectious illnesses. Aflatoxin B1 (AFB1) is a very toxic mycotoxin mainly present in contaminated cereal and poses a risk to human and animal health. With the widespread contamination of AFB1 in food and people's increasing attention to food safety, a quick, accurate, and trustworthy technique for detecting AFB1 in food products is desperately needed. Optical biosensors combine biological-specific recognition elements and biological materials to convert biological reactions into measurable signals. Due to the advantages of fast detection, high sensitivity, and simple pre-processing, optical biosensors have broad application prospects in biological detection sensing. Therefore, in this work, we designed a dual-signal output aptamer sensor for quantitatively detecting AFB1 based on the fluorescence quenching capabilities of gold nanoparticles (AuNPs) and Localized Surface Plasmon Resonance (LSPR). The sensor design used auNPs shielded by AFB1-specific aptamers as signal probes. The competitive binding of AFB1 was subjected to cause the aptamers to separate from the AuNPs, allowing the exposed AuNPs to adsorb Rhodamine B Isothiocyanate (RBITC) quickly. Furthermore, the AuNPs aggregation and quenching of the fluorescence of RBITC were exploited to measure the optical index. The fluorescence of RBITC was restored by further oxidation and etching of the AuNPs using potassium ferricyanide (K[Fe(CN)₆]) and potassium iodide (KI) solution, allowing for precise quantification of AFB1. The results revealed high precision as the developed sensor exhibited a wide detection range for AFB1, ranging from 0.000 1 to 1 ng·mL^-1. During actual sample testing, recovery rates from the LSPR-based approach ranged from 95.6% to 105%, whereas recovery rates from fluorescence-based detection were between 92.3% and 118%. Using this novel approach for mycotoxin detection, the LSPR/fluorescence dual-signal aptamer sensor holds tremendous potential for the quick and on-site detection of AFB1, providing a useful instrument to improve food safety.
		2025 Vol. 45 (04): 1123-1128 [Abstract] ( 20 ) RICH HTML PDF (6441 KB) ( 8 )

	1129	Construction of Near-Infrared Detection Models for Peanut Protein and Their Components With Different Seed Coat Colors
		SHANG Yan-xia, HOU Ming-yu^, CUI Shun-li, LIU Ying-ru, LIU Li-feng, LI Xiu-kun^
		DOI: 10.3964/j.issn.1000-0593(2025)04-1129-08
		The protein and its component content in peanut seed is an important quality trait of peanuts. Exploring non-destructive and efficient content detection methods is an important research direction of peanut breeding and production. Constructing near-infrared models according to the color of the sample's appearance is beneficial for improving detection accuracy. This study used 282 peanut germplasms with black, red, and pink coats to detect protein content by the Bradford method and the near-infrared spectral value. The Partial Least Squares Regression (PLSR) method was used to construct the near-infrared prediction model. A total of 11 near-infrared prediction models were constructed, including black seed coat crude protein, black seed coat albumin, black seed coat reaching, black seed coat contracting, red seed coat crude protein, red seed coat albumin, red seed coat reaching, red seed coat contracting, pink seed coat albumin, pink seed coat reaching, pink seed coat contracting, etc. The spectral value preprocessing method was a variety of composite processing methods. The best pretreatment methods for black seed coat crude protein, black seed coat albumin, black seed coat reaching, and black seed coat contracting models were Baseline + Detrend, Detrend + MSC, 2^nd-der+Detrend+1^st-der, Baseline + SNV, respectively. The best pretreatment methods of red seed coat crude protein, red seed coat albumin, red seed coat reaching, and red seed coat contracting model were Baseline + SNV, Baseline + SNV + MSC, SNV + MSC + Baseline, SNV + MSC + Baseline, respectively. The best pretreatment methods of pink seed coat albumin, pink seed coat reaching, and pink seed coat contracting model were 2^nd-der+1^st-der, 2^nd-der+Detrend+1^st-der and 2^nd-der+Baseline+1^st-der, respectively. The model's correlation coefficient (R_c) was 0.825~0.925, and the root means standard error of calibration (RMSEC) was 0.110 ~1.383. The correlation coefficient (R_p) of the external validation set of the 11 models ranged from 0.822 to 0.971, and the root mean standard error of prediction (RMSEP) ranged from 0.102 to 0.954. The peanuts with different seed coat colors were detected by other color models and fitted with their chemical values. The correlation coefficients were in the range of 0.002~0.877, and the standard errors were in the range of 0.257~9.464. The correlation coefficients were lower than the correlation coefficients of the external validation set, and the best detection model was the model corresponding to the seed coat color. In this study, a model of peanut protein and its component content with different seed coat colors was constructed, which can quickly and non-destructively detect the content of peanut protein and provide the basis for the selection of raw materials for peanut protein processing and the breeding of specials peanut germplasm.
		2025 Vol. 45 (04): 1129-1136 [Abstract] ( 21 ) RICH HTML PDF (9953 KB) ( 15 )

	1137	Study on the Occurrence Forms and Distribution Patterns of Lithium in Clay-Type Lithium Ore
		LI Yan^{1, 2}, HU Wen-bin^{1, 2}, WANG Chen-ye^{1, 2*}, LI Hui-quan^{1, 2}, SUN Zhen-hua^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1137-06
		Clay-type lithium ore, as an integral part of strategic lithium resources, has garnered attention due to its widespread distribution, abundant reserves, and ease of extraction. However, the uncertainty surrounding the occurrence forms and distribution patterns of lithium within ore presents challenges for large-scale development using traditional pyrometallurgical or hydrometallurgical processes. This study conducted a sequential chemical extraction of clay-type lithium ore from a specific region in Yunnan, China, coupled with characterization techniques including mineral liberation analysis (MLA), X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), inductively coupled plasma optical emission spectroscopy (ICP-OES), scanning electron microscopy (SEM), and time-of-flight secondary ion mass spectrometry (TOF-SIMS), to analyze in detail the distribution patterns and binding states of lithium. SEM images unveiled that the ore surface exhibits granular and lamellar structures, accompanied byminute granules and pores. XRD and MLA analyses indicate that the primary mineral components include diaspore, boehmite, anatase, and clay minerals such as kaolinite [Al₂Si₂O₅(OH)₄] and lithium chlorite [LiAl₅Si₃O₁₀(OH)₈], identifying the ore as a bauxitic carbonate-hosted clay-type lithium ore. The total lithium content in the ore is determined to be 3 293 μg·g^-1, with 88.61% of lithium existing in the form of silicate/aluminosilicate, with carbonate/phosphate-bound lithium comprising 9.64%, and the remaining part existing in free, ion-exchange, and sulfide-bound states. TOF-SIMS results further confirmed the overlapping distribution of lithium with aluminum, silicon, potassium, and other elements within the ore, predominantly existing in stable, less substitutable states within clay-type mineral phases. The disappearance of clay mineral phases following concentrated hydrofluoric acid leaching further validates that lithium is predominantly distributed within clay-type aluminosilicate minerals. This study provides an in-depth analysis of lithium occurrence forms and distribution patterns in clay-type lithium ore, offering theoretical guidance for achieving selective and efficient lithium extraction from clay-type lithium ore.
		2025 Vol. 45 (04): 1137-1142 [Abstract] ( 16 ) RICH HTML PDF (10401 KB) ( 8 )

	1143	Research on CO₂ System Detection Based on 2.8 μm Band Er³⁺∶ZBLAN Laser Source
		LIU Yong-yan¹, WANG Kun-yang¹, TIAN Ying^1*, CAI En-lin^{2, 3, 4}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1143-07
		Absorption spectroscopy based on mid-infrared light sources is one of the effective methods for CO₂ gas detection and analysis, and it has significant potential applications in environmental science and medical fields. However, most of the light sources used for the detection of CO₂ in the 2.8 μm band are commercial diode lasers or semiconductor lasers, which have a single output wavelength and poor beam quality, and the laser output power is small, which is susceptible to environmental fluctuations during the gas testing process. In this paper, a CO₂ gas detection system based on a mid-infrared fiber laser light source is constructed, which uses a 980 nm semiconductor laser as the pump light source and 1.9 m Er³⁺∶ZBLAN fiber as the gain medium to realize a high-quality mid-infrared laser output at 2.8 μm. It analyzes the output characteristics of the light source under this structure. When the maximum pump power is 6 W, the center wavelength of the laser output spectrum is 2 783.09 nm, the spectral width is 5.69 nm, the average output power is 1.21 W, and the conversion efficiency is 20.3%. The output power fluctuation of the laser is less than 4.5% within 30 min, and the output energy is relatively concentrated. The fitted beam waist radii D_x and D_y are 0.035 mm and 0.038 mm, and the beam quality factors M²_x and M²_y are 1.096 and 1.224, respectively. The better quality and stability of the laser's output beam dramatically reduces the effects of environmental fluctuations during gas detection. On this basis, the signal spectra before and after CO₂ fluxing were obtained using direct absorption spectroscopy. The center wavelengths of the absorption peaks of CO₂ in the 2 778.5~2 784 nm band were measured to be 2 779.36, 2 780.70, 2 782.13, 2 783.55 nm, and the difference in the center wavelengths of the absorption peaks with the HITRAN standard library was less than 0.05 nm. The peak and half-height width of the CO₂ absorption spectrum at the center wavelength of 2 780.7 nm was 5.33 cm·mol^-1 and 0.18 nm, obtained by inversion of the Lambert-Beer law. The corresponding peak absorption spectra and half-height width values of the HITRAN standard library under the same conditions were 5.38 cm·mol^-1 and 0.12 nm, respectively, with a difference of only 0.05 cm·mol^-1 and 0.06 nm. The high accuracy of the experimental test results proves that the detection system can be used in CO₂ gas detection.
		2025 Vol. 45 (04): 1143-1149 [Abstract] ( 14 ) RICH HTML PDF (4422 KB) ( 5 )

	1150	Extraction of Impervious Surfaces in Towns Based on UAV Hyperspectral Imagery
		ZHANG Yi-ting^{1, 2}, LU Dong-hua^{1, 2*}, WU Ding^{1, 2}, GAO Yan^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1150-09
		Remote sensing images acquired by UAV-mounted hyperspectral sensors have the advantages of rich spectral information and high spatial resolution, which can provide more effective data for extracting impervious surfaces in towns and cities. However, hyperspectral images contain many bands, information redundancy increases the complexity of model training, and the volume of data space grows exponentially with data dimensions. The limited sample size will be sparsely distributed in high-dimensional space, easily leading to model overfitting. In addition, the traditional extraction method has limited feature learning capability, is ineffective in dealing with high-dimensional data, and fails to focus on the specific material information of the impervious surface. To make more effective use of UAV hyperspectral data to obtain information on impervious surfaces in towns and assess the development of town construction, this study selects Donghuayuan Town, Huailai County, Zhangjiakou City, Hebei Province, as the study area and acquires 150 effective bands from airborne hyperspectral remote sensing data. On this basis, the hyperspectral feature bands applicable to extracting impervious surfaces in towns were selected using stepwise discriminant analysis, validated, and comprehensively analyzed using principal component analysis, band standard deviation, and inter-band correlation, and 14 representative bands were finally identified. Subsequently, a remote sensing impervious surface extraction method based on a convolutional neural network was proposed. By improving the AlexNet network architecture, a deep learning network model containing four convolutional layers, one pooling layer, and two fully connected layers was constructed. Finally, two sets of comparison experiments were designed in the study area to compare the information extraction accuracy of impervious surfaces in hyperspectral raw images with selected feature bands and the information extraction accuracy of the proposed network model with common impervious surface extraction methods, respectively. The experimental results show that the selected combination of feature bands can be used as the best combination of bands for impervious surface extraction, which significantly improves the extraction accuracy of various methods. Meanwhile, the network model proposed in this study is the optimal method for impervious surface extraction, and combined with the optimal band combination, the overall accuracy and Kappa coefficient of the final classification reach 99.07% and 0.988 3, respectively, showing excellent performance. The research results in this paper are of great significance for the sustainable development of town construction and ecological and environmental protection and can provide strong support for research in related fields.
		2025 Vol. 45 (04): 1150-1158 [Abstract] ( 14 ) RICH HTML PDF (33090 KB) ( 10 )

	1159	Application of Multispectral Index Features Based on Sigmoid Function Normalization in Remote Sensing Identification and Sample Migration Study of Camellia Oleifera Forest
		ZHANG Hai-liang¹, WANG Yu¹, HU Mei³, ZHANG Yi-zhi¹, ZHANG Jing¹, ZHAN Bai-shao¹, LIU Xue-mei^2, LUO Wei^1
		DOI: 10.3964/j.issn.1000-0593(2025)04-1159-09
		In remote sensingimage analysis, the normalization process of multispectral index features is crucial to improve the model's classification accuracy and generalization ability. This paper was based on the Google Earth Engine (GEE) platform, Sentinel-1 SAR radar remote sensing images and Sentinel-2 A optical remote sensing images were used as the data sources, and different classification scenarios were constructed by calculating the multispectral exponential features before and after the normalization of the texture features, topographic features, polarization features, Sigmoid function, and employing four machine learning classifiers, namely, Random Forest, Gradient Boosting Tree, Support Vector Machine and Simple Bayes which were used to conduct classification experiments to analyze whether normalization of spectral index features was beneficial to the recognition of Camellia oleifera forests. Subsequently, the constructed convolutional neural network (CNN) and deep learning model combined with the Bi-LSTM module were compared with the machine learning classifiers to analyze the effect of different models on Camellia oleifera forest recognition. Based on the sample points of five land types in Ji'an City in 2021, the classification scenarios suitable for Camellia oleifera forest recognition were applied to the sample migration in different years (2019, 2020, 2022, 2023) to analyze the incremental and spatial distribution of Camellia oleifera forest area in each year. The results show that the normalized spectral index feature combined with random forest achieve the highest recognition accuracy in identifying Camellia oleifera forests, with an overall accuracy (OA) of 99.02%, a Kappa coefficient of 0.983 7, a user accuracy (UA) of 95.31% for Camellia oleifera forests, and a producer accuracy (PA) of 93.74% for Camellia oleifera forests; The deep learning model of CNN series in the study has slightly lower accuracy than random forest classifier for Camellia oleifera forests recognition, in which the overall accuracy (OA) of the deep learning model combined with the Bi-LSTM module is 98.69%, the Kappa coefficient is 0.971 3 The user accuracy (UA) of the Camellia oleifera forests is 94.96%, and the producer accuracy (PA) of the Camellia oleifera forests is 93.17%; 2021 The planted area of Camellia oleifera forests in Ji'an reached 1 844 881 000 mu, of which Suichuan County accounted for 27.67%, the largest county in area; the distribution of Camellia oleifera forests planting decreases from high terrain to low terrain, and the planting sites are mostly located in the hillside land and self-retained land near the family farms and the planted area of Camellia oleifera forests is increasing year by year. The extraction method of Camellia oleifera forests proposed in this study can help realize the dynamic monitoring and management of Camellia oleifera forests, and the proposed sample migration method can effectively reduce the cost of sample collection and labeling.
		2025 Vol. 45 (04): 1159-1167 [Abstract] ( 20 ) RICH HTML PDF (22578 KB) ( 12 )

	1168	A Robust Characteristic Spectrum Construction Algorithm Based on Spectral Domain Interpolation
		LI Xu-sheng^{1, 2, 3}, WANG Da-ming^{1, 2, 3*}, WANG Fei-cui^{1, 2, 3}, TONG Yun-xiao^{1, 2, 3}, CAO Si-qi^{1, 2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)04-1168-07
		In the traditional characteristic spectrum extraction algorithm, the arithmetic mean value of spectra is often used to indicate the characteristic spectrum. However, by strengthening the extreme value information and weakening some characteristic information, the indication capability of the mean value is easily affected by the degree of internal differences between objects, Based on the first theorem of geography and the idea of spatial interpolation, a characteristic spectrum extraction algorithm of spectral domain interpolation is proposed. First, the spectral domain of the objects, maximum and minimum reflectances of the objects at each wavelength，are calculated on several object spectra. To obtain single-feature spectral domain spaces, normalized inverse distance interpolation is performed at the center of a single object spectrum with the range of spectral domain. Finally, as multiple spectral domains are added, the cumulative spectral domain space of ground objects is obtained, and the maximum value in the cumulative spectral domain space, which is calculated by wavelength, is taken as the reflectivity, forming the characteristic spectrum of ground objects.To verify the validity and superiority of the spectral domain interpolation characteristic extraction algorithm's performance on the construction of characteristic spectral shape and amplitude, tree species' spectra measured from aerial hyperspectral remote sensing images and ASD are used as data sources to calculate the mean characteristic spectrum (MCS) and spectral domain interpolation characteristic spectrum (ICS). To explore the ICS's ability to characterize the overall shape and reproduce detail features, spectral angle mapping (SAM) of aerial hyperspectral data, feature parameter extraction importance evaluation, and linear discriminant analysis (LDA) of ASD-measured data were performed.The experimental results show that ICS improves the overall accuracy by 4.24% in the SAM when indicating characteristic spectral morphology compared with MCS when it comes to the amplitude feature parameter importance evaluation and LDA, which reveals the characteristic spectral details, the amplitude parameter importance score increased by 0.35 on average, the discrimination accuracy of each tree species increased by 2.51%, and the overall accuracy increased by 2.5%. Studies have shown that ICS is superior to traditional MCS in characterizing the spectral features' overall shape and reproducing detailed features. ICS can be used to refine the feature spectrum extraction process of target objects in classification scenes and improve the separability between classes. Moreover, ICS can also be used to optimize the selection of feature parameters in inversion scenes to improve the ability to characterize spectra.
		2025 Vol. 45 (04): 1168-1174 [Abstract] ( 20 ) RICH HTML PDF (7327 KB) ( 11 )

	1175	River Inputs as Determining Factor for the Spatiotemporal Variations of DOM Composition in Drinking Water Source Reservoirs
		ZHANG Chen-xue^{1, 2}, DUAN Meng-wei³, YAN Nuo-xiao^{2, 4}, QIU Zhi-qiang^{5, 6}, TANG Deng-miao², LIU Dong^2*
		DOI: 10.3964/j.issn.1000-0593(2025)04-1175-08
		A watershed's economic development can significantly impact the water quality of drinking water reservoirs, with one important aspect being the exacerbation of dissolved organic matter (DOM) pollution through river inputs. However, there is a lack of systematic studies on the spatiotemporal variability of DOM composition in drinking water reservoirs due to river inputs. Based on synchronous field sampling of the Shahe Reservoir and its inflowing rivers during different seasons, this study explored the spatiotemporal variation characteristics of DOM composition and the influence of river inputs using methods such as parallel factor analysis of three-dimensional fluorescence spectra of colored dissolved organic matter (CDOM). We obtained the following results. ①Reservoir DOM contained four components: humic substance C1, protein-like tyrosine C2, protein-like tryptophan C3, and terrestrial humic substance C4, with C2 accounting for over 50% of the proportion at most sampling points. ②The composition of reservoir DOM exhibited significant seasonal variation, with humic substance DOM components being lowest in spring; for the main component C2, the content proportions in spring, summer, autumn, and winter were 46.66%, 34.58%, 54.74%, and 56.00%, respectively. ③River inputs had a decisive impact on the spatial distributions of reservoir DOM, with the contents of components other than tyrosine, the main source of which was domestic sewage, being higher in the river mouth area. Although the DOM input from rivers to reservoirs was mainly terrestrial humic substance C1 (with proportions of 28.80%, 30.51%, 27.11%, and 22.19% in spring, summer, autumn, and winter, respectively), the degree of humification of DOM in Shahe Reservoir was low, indicating that autochthonous sources dominated the DOM in the reservoir. Autochthonous DOM in the reservoir was mainly related to algal proliferation, and the component of protein-like tryptophan C3 was linearly significantly positively correlated with chlorophyll-a content (R²=0.51, p<0.01). This study is of great significance for improving the water quality of drinking water reservoirs, reducing organic pollution, and ensuring the safety of residents' drinking water.
		2025 Vol. 45 (04): 1175-1182 [Abstract] ( 19 ) RICH HTML PDF (10735 KB) ( 6 )

	1183	Spectral Simulation and Characteristic Analysis of the Lower Limit Concentration Value of Iron Sulfate in Remote Sensing Inversion Under the Background of Different Kinds of Water
		LIANG Ye-heng¹, DENG Ru-ru^{1, 2, 3*}, CHEN Jin-lin¹, LIU Xu-long⁴, TONG Tian-ren⁵, LI Jia-yi¹, LI Yi-ling¹, LAO Xiao-min⁶
		DOI: 10.3964/j.issn.1000-0593(2025)04-1183-07
		Using satellite remote sensing technology to monitor heavy metals in water is of great research significance. However, due to the low content of heavy metals in natural water, the feasibility of remote sensing inversion is still doubted by the academic community, resulting in relatively slow development in this field. Because of this, taking the retrieval of iron sulfate concentration in water by the Chinese HJ-1A Satellite Hyperspectral Imager (HSI) as an example, the remote sensing sensitivity analysis model (DDE model) was used to numerically simulate the lower limit concentration spectrums in remote sensing inversion of iron sulfate under the background of four kinds of water, including: theoretical clear deep water and three kinds of common natural water: eutrophic water, turbid water and heavy metal polluted water. The minimum value of the remote sensing inversion lowers the limit concentration, and its wavelength is given. The variation pattern of remote sensing sensitivity under the background of different kinds of water is then analyzed. The research results found that under the background of different kinds of water, the simulated minimum lower limit concentration of iron sulfate and its wavelength position changed. Under the background of theoretically clear deep water, the minimum lower limit concentration is 4.63×10^-4 mg·L^-1, appearing at 468.530 nm (the fifth band of the HSI sensor, abbreviated as Band 5, the same below). After expanding to the minimum 10% increment, the covered band range is 460.040~479.600 nm (Band 1—10). However, the lower limit concentration value no longer exists in the wavelength range 721.605~951.540 nm (Band 81—115). Similarly, under the background of three kinds of natural water: eutrophic water, turbid water, and heavy metal polluted water, the minimum lower limit concentrations are: 6.30×10^-2, 2.78×10^-2, and 1.64×10^-1 mg·L^-1; the corresponding wavelengths are: 577.865 nm (Band 46), 587.900 nm (Band 49), 669.285 nm (Band 70); the corresponding coverage band ranges after expanding the minimum value by 10% are: 555.725~587.900 nm (Band 39—49), 568.160~612.740 nm (Band 43—56), 627.895~687.410 nm (Band 60—74). The above characteristic bands are all important references for the future sensitive band collection of iron sulfate remote sensing inversion models. The relationship between the minimum lower limit concentration under the background of four kinds of water is: theoretical clear deep water<
		2025 Vol. 45 (04): 1183-1189 [Abstract] ( 150 ) RICH HTML PDF (2327 KB) ( 10 )

	1190	Research on Chlorophyll-a Water Quality Parameter Inversion Based on Multi-Scale Attention Fusion Network Model
		SUN Bang-yong^{1, 2}, GONG Kai-jie¹, YU Tao^2*, BIE Qian-wen³
		DOI: 10.3964/j.issn.1000-0593(2025)04-1190-11
		Water resources are one of the core elements of the ecological environment, and there is currently a large number of water bodies being polluted by industrialization or nutrient enrichment, making real-time monitoring of water quality parameters crucial for maintaining the health of water bodies. Traditional water quality monitoring methods often use on-site sampling measurement or linear regression prediction methods. Still, due to the significant non-linear characteristics of water quality parameters, traditional water quality monitoring methods are time-consuming and inaccurate in their predictions. In recent years, deep learning methods have shown good performance in dealing with complex non-linear problems and have been applied by many scholars to the inverse estimation of water quality parameters. However, water quality inversion models based on deep learning still suffer from inaccurate analysis of remote sensing spectral images and poor model generalization capabilities. Therefore, this paper proposes a water quality inversion network model based on a multiscale attention fusion mechanism, which can accurately predict water quality parameters such as chlorophyll-a, providing a basis for assessing the health of water bodies. The network integrates advanced attention mechanisms and feature fusion strategies, combining the advantages of local feature learning from CNN and global feature extraction capabilities from Transformer to construct a Dense ASPP module for obtaining multiscale features of remote sensing images. It uses a channel attention decoder and pooling fusion modules to extract detailed features. Then, it estimates the concentration of chlorophyll-a by fusing different scales and levels of feature information, achieving higher inversion accuracy and generalization performance. To validate the performance of the proposed inversion model, experiments were implemented in Python 3.7 and the PyTorch framework, using ocean remote sensing spectral images and chlorophyll-a concentration data from January 2021 to December 2022 for network training. The experiment compares the proposed method with seven other water quality inversion methods, achieving the best performance in all objective indicators. It improves the R² index by 0.09 compared to the best method in the comparison, and reduces the RMSE, MAE and MAD indices by 11.99, 0.089, and 0.029, respectively, and improves the E_var index by 0.098, and the NSE index by 0.041. Meanwhile, in subjective evaluation, the proposed method obtains more precise chlorophyll-a concentrations, smaller errors, and higher generalization ability in different waters.
		2025 Vol. 45 (04): 1190-1200 [Abstract] ( 23 ) RICH HTML PDF (17630 KB) ( 22 )

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