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2025 Vol. 45, No. 12 Published: 2025-12-01

	3301	Ex vivo Analysis of Skin Barrier Damage Using Terahertz Imaging Technology
		QI Ji^{1, 2}, HUANG Jun-kai³, CHEN Yu-ang^{1, 2}, HE Ming-xia^{1, 2}, QU Qiu-hong^{1, 2}, HU Li-zhi^3, ZHANG Yi-zhu^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3301-06
		Terahertz technology has made significant strides in recent years due to its non-ionizing nature and high sensitivity to water content, gradually finding applications in fields such as tumor detection and burn assessment. As a result, the potential of terahertz imaging in pathological diagnosis and biomedical research has garnered increasing attention, indicating substantial value for further development and utilization. The skin, as the largest organ of the human body, serves key functions in protection, thermoregulation, and immune response. Maintaining the integrity of the skin barrier is crucial for preventing or mitigating skin diseases. To evaluate and detect changes in skin barrier function, it is imperative to develop an accurate and reliable new method. Using mouse skin as a model, the experimental group was prepared with barrier-damaged skin through repeated tape stripping, while normal skin served as the control group. To better reveal varying degrees of damage and distinct structural layers, the experiment employed multiple sample preparation techniques, including direct sectioning, water-bath scraping, and enzymatic digestion. These approaches allowed the skin layers to be localized and compared from multiple perspectives. A terahertz two-dimensional translation scanning imaging system was then used to measure the samples. The scan results not only visually illustrated differences between damaged sites and normal areas in the images but also enabled quantitative analysis of average gray values in specific regions, thereby reflecting changes in terahertz absorption at both the skin surface and deeper levels. To improve the localization of skin layers, time-domain tomography was used to analyze reflection signals at different depths. By correlating the terahertz reflections with corresponding skin structures, the study verified the link between changes in terahertz absorption and actual skin barrier damage. The results showed that the barrier function of mouse skin repeatedly treated with tape was significantly compromised. Compared with the control group, the damaged areas in the terahertz imaging displayed notably distinct absorption characteristics; quantitative comparisons of the gray values further confirmed that these differences were statistically significant. These findings not only provide direct evidence for the rapid identification and assessment of skin damage using terahertz technology but also offer a viable strategy for future clinical monitoring of changes in skin barrier function.
		2025 Vol. 45 (12): 3301-3306 [Abstract] ( 6 ) PDF (20754 KB) ( 2 )

	3307	Quantitative Analysis of XRF Iron Ore Grade Combining Morphology and Optimization Algorithms
		WANG Lan-hao¹, ZHU Zhen-yu², ZHONG Xiao³, WANG Hong-yan^3*, LI Zhao-peng⁴
		DOI: 10.3964/j.issn.1000-0593(2025)12-3307-10
		This study addresses the limitations of existing X-ray fluorescence spectroscopy (XRF) technology for the online detection of iron ore grade, including strong background interference, difficulty in resolving overlapping spectral peaks, and insufficient modeling accuracy. It proposes a comprehensive solution that covers the entire process, from signal pre-processing and spectral peak decomposition to grade modeling. In the background subtraction stage, a method combining mathematical morphology and derivative-iterative polynomial fitting (Mor+DIPF) has been developed. This method significantly improves baseline smoothness and fitting accuracy, while also ensuring spectral fidelity in the region of characteristic peaks. This addresses the deficiency of traditional morphological algorithms, which do not sufficiently smooth this region. To address complex peak overlap, a particle swarm optimization (PSO) approach with adaptive parameter updates (APU-PSO) is combined with an expectation-maximization (EM) Gaussian mixture model (GMM) decomposition framework. This enhances global optimization capabilities and enables high-precision analysis, providing accurate peak parameters for subsequent quantitative analysis. To address non-linear errors caused by matrix effects, a fusion model combining transformers and bidirectional long short-term memory networks (BiLSTM) is constructed. Transformers capture long-range dependencies among process variables, while BiLSTMs enhance the learning of temporal dynamics. By fusing multi-source data, key factors influencing grade fluctuations are thoroughly identified, overcoming the challenge of accurately predicting iron ore grades using XRF. Experimental results demonstrate that the Mor+DIPF algorithm outperforms traditional methods in terms of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) across four different baselines (linear, sinusoidal, Gaussian, and exponential), with R² achieving a maximum value of 99.96%. The APU-PSO-EM-GMM algorithm outperforms the comparison algorithm.
		2025 Vol. 45 (12): 3307-3316 [Abstract] ( 5 ) PDF (10560 KB) ( 3 )

	3317	Rapid Identification of Fresh Meat Based on Laser-Induced Breakdown Spectroscopy Combined With Deep Learning Methods
		SUN Hao-ran¹, WANG Si-wen¹, ZHAO Chun-yuan¹, LIN Xiao-mei², GAO Xun³, FANG Jian^1*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3317-07
		Meat is an important source of protein and nutrients in the human diet, and the adulteration of meat has become a major problem in the field of food safety in China. To address the problems of complex operations, time-consuming procedures, and high equipment costs in traditional meat detection methods, this paper proposes using Laser-induced Breakdown Spectroscopy (LIBS) combined with a deep learning network to detect and classify multi-variety meat tissues quickly. Spectral data for beef, mutton, and pork were collected using LIBS. Nine spectral lines of five elements were selected as the analysis spectral lines and model input for modeling and recognition. A ResNet18 backbone network was designed, and three machine learning models were designed to model and recognize the spectral data.The results show that the deep learning network achieves the best recognition performance, with an accuracy of 98.1%. Among the 120 groups of spectral data, 117, 119, and 117 groups of beef, mutton, and pork spectral data were identified correctly, respectively. In the horizontal comparison using the same deep learning model, the ResNet18 model was superior to the three deep learning models, GoogLeNet, Vgg16, and ResNet50, in the recognition of meat spectral data. On this basis, the model's generalization was verified using re-collected data, and the accuracy reached 98.9%, indicating that the model maintains strong cross-data-set recognition ability. It has good generalization and consistency. The above research shows that the combination of LIBS and a convolutional neural network can provide objective, quantitative information on differences between meat varieties in multi-variety meat classification and recognition tasks and has the potential to quickly and in situ diagnose different types of meat tissue.
		2025 Vol. 45 (12): 3317-3323 [Abstract] ( 4 ) PDF (10705 KB) ( 2 )

	3324	Study on Infrared Spectral Radiation Characteristics of Exhaust Plumes From Nuclear-Like Cruise Missiles Based on Modified Narrow-Band Method
		YANG Jie, BAI Lu^*, LI Jin-lu, LIU Rui-xi
		DOI: 10.3964/j.issn.1000-0593(2025)12-3324-08
		This paper proposes an improved narrow-band model based on a correction function for calculating infrared radiation from two types of nuclear cruise missile plumes. The model replaces narrow-band parameters in the Curtis-Godson (CG) approximation with path-equivalent narrow-band parameters to address accuracy degradation in non-uniform combustion systems. Compared with experimental data from Reference^[1], the improved narrow-band model with correction function demonstrates better alignment with experimental results than traditional CG-based narrow-band models, showing accuracy improvements of 13.29%, 18.01%, and 8.4% in the 2.7, 4.3, and 3～5 μm bands, respectively. Building on this foundation, flow field parameters varying along trajectory points for AGM-86B-type and AGM-158B-type missiles are calculated. By solving the radiative transfer equation using the Line of Sight (LOS) method, an infrared radiation calculation model for nuclear cruise missile plumes is established, enabling computation and analysis of infrared radiation characteristics at flight altitudes ranging from 1 to 20 km. Results indicate that for AGM-86B-type missiles, radiation intensities in the 2.7 and 4.3 μm bands exhibit similar altitude-dependent trends, reaching peak signals at 5 km altitude during the latter half of the flight trajectory. For AGM-158B-type cruise missiles during their flight trajectory from 20 to 1 km, the radiation intensity in the 4.3 μm band is consistently higher than that in the 2.7 μm band. These findings provide theoretical support for early-stage missile type identification and interception of these two missile categories.
		2025 Vol. 45 (12): 3324-3331 [Abstract] ( 4 ) PDF (8611 KB) ( 2 )

	3332	Research on Fractional-Order Hyperspectral Diagnosis of Rubber Tree Leaf Powdery Mildew Based on TabPFN Model
		HU Wen-feng¹, CHEN Zhou-yang¹, LI Chuang¹, LUO Xiao-chuan¹, ZHAO Yong-chen¹, HE Yong², TANG Rong-nian^1*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3332-10
		Powdery mildew (PM) is a common foliar disease that negatively impacts the health of rubber trees and the yield of natural rubber. Rapid and accurate disease diagnosis is essential for implementing precise control measures and ensuring optimal rubber production. This study employed hyperspectral imaging technology to analyze infected leaves in Hainan rubber plantations. Samples of rubber leaves at various infection levels were collected, and hyperspectral reflectance data ranging from 965.4 to 1 668.0 nm were obtained using hyperspectral imaging equipment. The hyperspectral data contained noise and redundant information. Three traditional models, namely Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP), as well as the Tabular Prior Data Fitting Network (TabPFN), which incorporates automatic feature weighting, were used to model and analyze both the raw spectral data and the full-band data preprocessed by Savitzky-Golay smoothing, Standard Normal Variate (SNV), and Fractional Order Differentiation (FOD). A multi-model evaluation identified the optimal spectral preprocessing method. To assess the feature weighting capability of TabPFN, Principal Component Analysis (PCA), ReliefF, Maximum Relevance Minimum Redundancy (mRMR), and HSICLasso algorithms were employed for feature selection, extracting sensitive bands associated with powdery mildew grading. The performance of the whole band and feature subsets was compared across different classifiers to determine the optimal model architecture. Finally, Shapley Additive Explanationswas used to analyze the key features and their influence on disease grading. The results showed that TabPFN outperformed all other models, demonstrating superior robustness and effective feature weighting selection. FOD preprocessing effectively reduced spectral noise and enhanced the extraction of essential detail features, resulting in the highest data quality improvement. The full-band TabPFN model with FOD preprocessing achieved a classification accuracy of 95.27%, surpassing traditional methods by 3.24%～13.24%. After applying HSICLasso to select 20 critical features, the accuracy remained at 94.31%, while reducing model complexity by nearly 90% and only decreasing accuracy by 1.01%. SHAP analysis identified the 1 160 nm and 1400 nm regions as key discriminatory bands, linked to C—H and O—H chemical bond vibrations. These bands correspond to the leaf's carbohydrate, lignin, and water content, indicating the model's ability to capture spectral responses related to physicochemical changes caused by powdery mildew. This study validates the integration of FOD and TabPFN for PM detection, providing an accurate model for assessing disease severity, which can aid in precise pesticide application and promote the health of rubber trees, ultimately improving rubber production.
		2025 Vol. 45 (12): 3332-3341 [Abstract] ( 7 ) PDF (7462 KB) ( 3 )

	3342	Fast Classification of Black Mass by Handheld LIBS Based on Machine Learning
		CHEN Nan^{1, 2*}, ZOU Zhao-hua¹, LUO Zi-xun¹, SHEN Xin-jian¹, LIU Yan-de^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3342-07
		With the rapid development of new energy vehicles and energy storage devices, the number of waste lithium batteries has surged. Black mass, as the most critical material in the battery recycling process, has a complex and diverse composition, which is very likely to cause resource waste and environmental pollution if it cannot be effectively identified and categorized. Traditional detection methods are time-consuming and costly, making it difficult to meet the demand for real-time classification of black mass in industrialized scenarios. Laser-induced breakdown spectroscopy (LIBS) offers a new approach for rapid identification of black mass, leveraging its advantages of simultaneous multi-element detection, rapidity, and high efficiency. In this study, a handheld LIBS spectrometer is combined with machine learning algorithms to achieve accurate identification and efficient classification of black mass from used lithium batteries. The experiment firstly purchased nine common lithium battery black mass samples from Ganzhou Haohai New Material Co., Ltd. and collected the spectra of the black mass samples by a handheld LIBS instrument; In order to improve the quality of spectral data and the accuracy of the subsequent modeling, maximum and minimum normalization (MMN) and Savutzky-Golay smoothing filter (SG) were used to optimize the preprocessing of LIBS spectral data; In the feature extraction stage, the pre-processed spectral data were subjected to dimensionality reduction by introducing two data dimensionality reduction methods, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), respectively; Finally, three types of classification models, namely, Random Forest (RF), Partial Least Squares Discriminant Analysis (PLS-DA) and Back Propagation Neural Network (BPNN), were established based on the dimensionality-reduced spectral data; The optimal black mass classification model is selected by comparing four aspects: classification accuracy, precision, recall and F1 score of the test set. The experimental results show that the classification model constructed using a combination of Linear Discriminant Analysis (LDA) and a Backpropagation Neural Network (BPNN) achieves the best recognition performance, with an overall accuracy of 99.70% on the test set. The results validate the feasibility and effectiveness of LIBS technology combined with machine learning methods for identifying lithium battery black mass, providing a theoretical basis and practical value for the efficient classification and reuse of waste lithium battery black mass.
		2025 Vol. 45 (12): 3342-3348 [Abstract] ( 5 ) PDF (6527 KB) ( 2 )

	3349	Spectral Open Set Recognition in Agriculture and Forestry Biological Species Based on Fuzzy Rule Binary Classifier Combinations
		HE Bao-xiong¹, ZHAO Peng^1*, LI Zhen-yu²
		DOI: 10.3964/j.issn.1000-0593(2025)12-3349-09
		Open set recognition requires that a classifier can not only identify testing samples from known classes but also reject those from unknown classes, which is rarely investigated in spectral analysis. In this article, we revise the conventional fuzzy rule multi-class classifier proposed by Ishibuchi for the closed-set scenario and apply it to open-set recognition. First, principal component analysis is used to reduce the spectral dimension of the original spectral curves, yielding 4- to 6-dimensional spectral feature vectors. Second, the fuzzy rule multi-class classifier proposed by Ishibuchi is simplified to a binary classifier, using a 1-vs-1 scheme to obtain a vote for each testing instance. Lastly, all votes from all binary classifiers are counted to determine the predicted class of the testing instance in the open-set scenario. If one known class gets the maximal vote and this vote is larger than a predetermined threshold τ, this testing instance is classified as this known class. Otherwise, it is rejected as an unknown class. The comparative experimental results across different groups of wood and mango spectral datasets indicate that our proposed scheme outperforms other state of the art open-set recognition schemes, such as the revised fuzzy rule multi-class classification based on generalized basic probability assignment, in the open-set scenario, with the best evaluation measures such as F-Score, Kappa coefficient, and overall recognition accuracy. Moreover, a dual-tailed McNemar's test is performed on the comparative experimental results from the mango spectral dataset to verify further that our proposed scheme is superior to other state of the art open-set recognition schemes.
		2025 Vol. 45 (12): 3349-3357 [Abstract] ( 5 ) PDF (4800 KB) ( 3 )

	3358	A Convolutional Neural Network With Feature-Space Attention for Online Near-Infrared Detection of Tartaric Acid
		LI Zhi-hao^{1, 2}, XIAO Jin-feng^1, ZHANG Hong-ming^2, LÜ Bo^{2, 3*}, YIN Xiang-hui¹, LI Xiao-xing^{1, 2}, ZHAO Ming⁴, MA Fei⁵
		DOI: 10.3964/j.issn.1000-0593(2025)12-3358-08
		Tartaric acid, as an important organic acid, is widely present in wine, fruit juice, carbonated beverages, and certain confectionery products. Its concentration directly influences the balance between sweetness and acidity as well as the stability of flavor. During food production, the tartaric acid concentration may fluctuate due to variations in raw materials and formulation adjustments. Therefore, establishing a method for real-time online monitoring of tartaric acid concentration is crucial for ensuring product quality and production consistency. However, conventional detection methods (e. g., titration, HPLC) suffer from response delays and are unsuitable for real-time monitoring. Considering the multivariate, nonlinear, and dynamic characteristics of industrial processes, more accurate concentration prediction models are required. To address this, we integrate a one-dimensional convolutional neural network (1D-CNN) with a feature-space attention (FSA) mechanism, resulting in a CNN-FSA hybrid model. By conducting near-infrared (NIR) spectroscopy—driven experiments to detect tartaric acid concentration, this study explores the potential of CNN-FSA to improve prediction speed and model robustness, thereby providing an innovative approach for real-time online monitoring of solution-phase chemical processes. Spectral data were first processed using principal component analysis (PCA) combined with Mahalanobis distance to remove outliers, followed by standard normal variate (SNV) transformation to eliminate scattering and baseline drift. Subsequently, the proposed CNN-FSA model and the traditional partial least squares regression (PLSR) model were trained and evaluated. Model performance was comprehensively assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Six rounds of experiments were designed, with each round starting with 500 g of a mixed solution (water, ethanol, glucose, malic acid, and citric acid) as the initial substrate, supplemented with 500 g of a solution (475 g water+25 g tartaric acid). Data from the first four rounds were randomly split into training and test sets at a 7∶3 ratio. In comparison, data from the last two rounds were used as independent test sets to evaluate the model's generalization ability rigorously. On the independent prediction sets, the CNN-FSA model achieved outstanding performance: R²=0.989 6, RMSE=0.000 702, and MAE=0.000 580. In contrast, the PLSR model yielded R²=0.968 8, RMSE=0.001 214, and MAE=0.001 059. Compared with PLSR, CNN-FSA reduced RMSE by 42.17% and MAE by 45.23% on the independent prediction sets. The CNN-FSA model significantly outperforms PLSR in tartaric acid concentration prediction, demonstrating superior generalization and robustness on independent prediction datasets.
		2025 Vol. 45 (12): 3358-3365 [Abstract] ( 5 ) PDF (4653 KB) ( 2 )

	3366	Preparation and Photothermal Performance Study of Room Temperature Liquid Metal Core-Shell Nanomedicines
		REN Xing-yu¹, CHEN Huai¹, GAO Si-bo², WANG Guang-hua², DUAN Liang-fei^1, YANG Hui-qin^1
		DOI: 10.3964/j.issn.1000-0593(2025)12-3366-07
		Photothermal therapy (PTT) has garnered significant interest as a promising alternative to conventional cancer treatments, owing to its low systemic toxicity, high targeting precision, and minimal invasiveness. Room-temperature liquid metals (LMs)—a class of functional materials exhibiting both metallic conductivity and fluidic processability—have emerged as attractive candidates for PTT applications. Their unique attributes, including fluidity, dispersibility, high thermal conductivity, efficient photothermal response, and biocompatibility, underscore their potential in this field. However, the practical application of LMs is hampered by their high surface tension and reflectivity, which limit photothermal conversion efficiency. Notably, the highly dynamic and dispersible nature of LM surface atoms offers a pathway for modulation via surface chemical modification. In this study, we employ sodium alginate (SA), a nontoxic and biocompatible natural polymer, to functionally tailor the surface of LMs. Through ultrasonication, a uniform SA coating was formed on LM nanoparticles, yielding well-dispersed core-shell nanostructures, termedLiquid metal@sodium alginate (LM@SA). The SA-modified nanoparticles exhibited remarkable photothermal performance: under 808 nm near-infrared laser irradiation at 1.5 W·cm^-2, the heating rate reached 5.4 ℃·min^-1, with a temperature plateau of 63 ℃ attained within 4 minutes. The photothermal conversion efficiency was calculated to be 41.9%. Furthermore, the SA coating significantly enhanced colloidal and thermal stability, as evidenced by consistent heating performance over eight consecutive laser on-off cycles without noticeable decay. In summary, this work demonstrates a biocompatible polymer-based strategy for effectively regulating the surface optical properties of LMs. The resulting LM@SA nanomedicine exhibits efficient, stable photothermal behavior, offering a promising platform for precise, effective tumor photothermal therapy.
		2025 Vol. 45 (12): 3366-3372 [Abstract] ( 5 ) PDF (14324 KB) ( 8 )

	3373	Differential Diagnosis of Breast Cancer and Ovarian Cancer Based on ATR-FTIR Spectroscopy Coupled With Machine Learning
		SONG Ao^{1, 2}, CAI Yi-sa^{1, 2}, CAI Li-zheng^{1, 4}, YANG Wan-li¹, PANG Nan¹, YU Rui-hua¹, WANG Shi-yan^3, YANG Chao^1, JIANG Feng^1*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3373-08
		Breast cancer and ovarian cancer are common malignant tumors in women, and the differences in their metabolic activity and protein structures reveal unique pathological mechanisms. However, due to the overlap in symptoms and molecular characteristics between the two, clinical diagnosis and differentiation remain challenging. A systematic study of the metabolic processes and protein conformational changes in breast cancer and ovarian cancer provides scientific evidence and guidance for disease diagnosis and personalized treatment. This study, based on attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with machine learning methods, explores the differential spectral markers of breast cancer and ovarian cancer and evaluates their diagnostic and discriminative potential. A total of 157 female participants were included in the study, including 67 breast cancer patients, 41 ovarian cancer patients, and 49 healthy controls, with serum samples collected for spectral analysis. The results show that at the 1 450 cm^-1 band, the absorbance of the breast cancer group was significantly higher than that of the ovarian cancer group (p<0.05), accompanied by a blue shift in the wavenumber, suggesting lipid metabolism and cell membrane synthesis abnormalities. Peak fitting analysis of the Amide I region revealed that the α-helix proportion in the breast cancer group was significantly lower than that in the ovarian cancer group (p<0.05). In comparisor the β-sheet proportion in the ovarian cancer group was significantly higher than that in the breast cancer group (p<0.05), revealing specific differences in protein conformation changes between the two cancers. The Linear Discriminant Analysis (LDA) model constructed using the relative intensity ratio of 1 450/1 650 cm^-1 and Amide Ⅰ spectral data showed a reasonable differentiation performance (AUC=0.851, Specificity=73.2%, Sensitivity=80.3%). The results of this study indicate that ATR-FTIR spectroscopy combined with spectral feature analysis and classification models can provide effective support for the diagnosis and differentiation of breast cancer and ovarian cancer, laying the foundation for future cancer subtype diagnostic research.
		2025 Vol. 45 (12): 3373-3380 [Abstract] ( 5 ) PDF (2932 KB) ( 3 )

	3381	Spectral Classification Using Fuzzy Hyperbolic Cosine Discriminant Analysis
		WU Bin^{1, 2*}, LIU Fu-bei³, WU Xiao-hong³
		DOI: 10.3964/j.issn.1000-0593(2025)12-3381-06
		Traditional linear discriminant analysis (LDA) has a small sample problem when directly processing high-dimensional spectral data, while hyperbolic cosine discriminant analysis (HCMDA) can solve the small sample problem. To further improve the classification accuracy of HCMDA and process noisy spectral data, a fuzzy hyperbolic cosine discriminant analysis (FHCMDA) algorithm was proposed by combining fuzzy set theory with hyperbolic cosine similarity. Furthermore, a model based on FHCMDA and K-nearest neighbors (KNN) was built to classify meat mid-infrared (MIR) spectra and apple near-infrared (NIR) spectra, respectively, and was compared with HCMDA to contrast their classification accuracies. FHCMDA calculates the fuzzy membership values by using the training samples and their means, the fuzzy intra-class scatter matrix and the fuzzy inter-class scatter matrix, the fuzzy intra-class hyperbolic cosine function and the fuzzy inter-class hyperbolic cosine function, and the eigenvector through feature decomposition. At first, MIR spectra were obtained for three meat variaties (chicken, turkey, and pork) and NIR spectra for four apple varieties (Red Fuji, Huaniu, Huangjiao, and Jiana), with 40 samples for each meat type and 50 for each apple type. Secondly, multivariate scattering correction (MSC) was applied to preprocess the NIR spectral data of apples, eliminating spectral differences caused by varying scattering levels and enhancing their correlation. Thirdly, the initial clustering centers for meat and apple were determined, and the fuzzy membership degree of each sample was calculated. The feature decomposition was completed by HCMDA and FHCMDA, respectively, using the calculated fuzzy hyperbolic cosine function to extract features from spectral data. Finally, KNN was used for classification, and the classification accuracies of HCMDA and FHCMDA were obtained and compared. The final results of this experiment were as follows: HCMDA+KNN achieved classification accuracies of 90.48% for the meat variety and 76.67% for the apple variety. The classification accuracy of FHCMDA+KNN was 97.62% for the meat variety and 91.67% for the apple variety. The above experimental results show that fuzzy hyperbolic cosine discriminant analysis combined with KNN is an effective method for identifying food varieties, with identification accuracy significantly higher than that of HCMDA+KNN.
		2025 Vol. 45 (12): 3381-3386 [Abstract] ( 6 ) PDF (1687 KB) ( 4 )

	3387	Simultaneous Determination of Ten Elements Including Aluminum in Bauxite by Mixed Flux Fusion-ICP-OES With Internal Standard Method
		ZHANG Ling-huo^{1, 2}, YANG Guo-yun^3, MA Na^{1, 2}, ZHANG Peng-peng^{1, 2}, CHEN Hai-jie^{1, 2}, XU Jin-li^{1, 2}, BAI Jin-feng^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3387-07
		Bauxite is one of the strategic minerals in China. Accurate and efficient analysis of the contents of major, associated beneficial, and harmful elements is of great significance for evaluating and comprehensively utilizing bauxite. At present, the systematic analysis of bauxite is still dominated by traditional chemical methods and atomic absorption spectrometry, with relatively low analytical efficiency. It is necessary to develop a simple, efficient, and simultaneous multi-element analysis method for bauxite. The key points of simultaneously determining the multi-element content in bauxite by inductively coupled plasma optical emission spectrometry (ICP-OES) include selecting efficient decomposition methods for refractory bauxite samples, improving the precision and accuracy of determination under high dilution conditions, and eliminating matrix and coexisting element interferences. This study proposes a method for 10 elements (Al, Si, Fe, Ti, Sr, Li, Cr, V, Zr, Sc) in bauxite using mixed-flux fusion-ICP-OES with an offline internal standard. The effects of mixed flux dosage and melting temperature on sample decomposition efficiency were systematically investigated. 0.1 g sample was added to 0.7 g mixed flux and melted at 1 000 ℃ for 20 min to ensure complete decomposition of the sample; the extraction conditions of the fused samples were optimized. Ultrasonic extraction with 15% hydrochloric acid was selected, as it offers high extraction efficiency without silicic acid precipitation. Based on the content of various elements in bauxite, the sensitivity and interference of spectral lines, the analytical spectral lines of the determined elements were selected. By comparing the correction effects of Cd 214.438 nm, Cd 228.802 nm, Co 228.616 nm, and Co 345.351 nm on the 10 elements, Co 228.616 nm and Co 345.351 nm were selected as internal standard lines to correct different elements, respectively, which significantly improved the precision of the determined elements. The interference of aluminum at different concentrations on other elements was systematically investigated, and it was found that the degree of interference varied among elements. The concentration of aluminum in the sample solution (dilution of 2 500 times) was generally not more than 200 μg·mL^-1, and the interference on other elements could be ignored. The detection limit for each element in this method ranged from 0.5 μg·g^-1 to 0.1%. According to the verification of certified reference materials, the relative standard deviation (RSD, n= 12) of all elements except lithium was less than 5%. The relative error (RE) of major elements (Al₂O₃, SiO₂, Fe₂O₃, TiO₂) ranged from -1.81% to 1.61% (e. g., Al₂O₃: -0.65%~0.28%), while the relative error of other elements ranged from -10.53% to 12.78%. The determination values were basically consistent with the certified values. This method is convenient and fast, with a low detection limit, high accuracy, and good precision, and is suitable for the simultaneous analysis of multiple elements in bauxite with different contents.
		2025 Vol. 45 (12): 3387-3393 [Abstract] ( 6 ) PDF (1691 KB) ( 4 )

	3394	Effects of Freeze-Thaw Cycles and Wet-Dry Alternation on Spectral Characteristics of Straw-Derived Dissolved Organic Matter
		CUI Song^{1, 2}, LIU Lu^{1, 2}, ZHANG Fu-xiang^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3394-09
		As a major agricultural by-product, straw is a significant source of dissolved organic matter (DOM) in farmland soils. Aging processes modify the quantity, composition, and structural attributes of straw-derived DOM, thereby influencing its environmental functions and ecological behavior. This study systematically explored the evolution of maize straw-derived DOM under two typical processes freeze-thaw (FT) cycles and wet-dry (WD) alternation-using ultraviolet-visible (UV-vis) spectroscopy, three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy, and parallel factor analysis (PARAFAC). The results demonstrated that (1) both aging treatments significantly enhanced dissolved organic carbon (DOC) release (FT: 6.08 → 9.14 g·kg^-1; WD: 6.42→12.39 g·kg^-1), with cumulative DOC release under WD treatment being 95.1% higher than under FT, indicating that higher temperature and changes in moisture conditions accelerate organic matter mobilization; (2) UV-Vis spectral analysis revealed the presence of double bond-containing compounds (C═C, C═O, N═N) in straw-derived DOM. During FT cycles, the shoulder peak at 250～300 nm gradually increased and broadened, indicating enhanced humification. In contrast, under wet-dry alternation, the shoulder peak appeared narrower with stronger absorbance, suggesting a reduced degree of humification and a higher abundance of aromatic compounds and unsaturated conjugated double-bond structures; (3) PARAFAC modeling identified three humic-like components (C1, C2, and C3), with the relative abundance of the aromatic component C2 decreasing progressively with aging cycles; (4) Spectral parameter analysis revealed that SUVA₂₅₄ and SUVA₂₆₀ values declined, while E2/E3 ratios and fluorescence indices (FI) increased (FT: 1.435→1.446; WD: 1.436→1.456), indicating progressive molecular simplification of straw-derived DOM during the aging process; Correlation analysis further revealed that the degradation of aromatic structures was accompanied by a decrease in molecular weight and an increased similarity between chromophore and fluorophore properties. This study elucidates the dynamic evolution of straw-derived DOM content, composition, and spectral properties during aging, offering scientific insights into the sustainable utilization of straw resources.
		2025 Vol. 45 (12): 3394-3402 [Abstract] ( 5 ) PDF (9659 KB) ( 2 )

	3403	Laser Pyrolysis Spectroscopic Detection and Qualitative-Quantitative Analysis of Organic Compounds in Space Dust
		WU Yi-jian^{1, 4}, XU Wei-ming^{1, 2, 4}, XU Xue-sen^{1, 4}, LI Lu-ning^{2, 4}, LÜ Wen-hao^{1, 4}, YAN Peng-peng², SHU Rong^{1, 2, 3, 4}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3403-12
		Detecting trace organic compounds in deep-space minor celestial bodies is crucial for understanding the origins of life. However, conventional spectroscopic techniques often struggle to simultaneously excite and release all the organic compounds in the sample for comprehensive detection. This is particularly challenging for organic compounds that are diffusely distributed, as their signals are often difficult to capture effectively, leading to limitations in detection within complex matrices. To address this challenge, this study proposes a novel analytical approach that combines laser pyrolysis-Fourier transform infrared spectroscopy (LP-FTIR) with machine learning, establishing a high-precision method for both qualitative and quantitative detection of organic compounds in space dust. This work aims to provide a new technical solution for identifying potential biosignatures in deep-space exploration. First, simulated space dust samples containing six typical life-related organic molecules glycine, stearic acid, cytidine nucleoside, ribose, deoxyribose, and soybean lecithin were prepared. Infrared spectral data of pyrolysis gases were obtained using a miniaturized LP-FTIR detection platform. For qualitative analysis, a multi-model ensemble classification algorithm was developed, integrating SVM, RF, XGBoost, RNN, and BPNN, with hyper parameters tuned using Bayesian optimization. Predictions were integrated through a majority voting mechanism. For quantitative analysis, a novel regression model was crafted by integrating a one-dimensional CNN with a multi-head attention mechanism, employing segmented pooling to pinpoint critical spectral regions and improve feature extraction efficiency. The study results indicate that the multi-model ensemble classification method achieved a 90% accuracy rate in identifying six types of organic compounds, representing a significant improvement over the best single model (BPNN at 87%). The improved attention-based CNN achieved a coefficient of determination (R²) of 0.979 and a root mean square error (RMSE) of 0.21 mg in predicting glycine content, showing significant performance enhancement over traditional PLSR (R²=0.969) and the basic CNN model (R²=0.891).
		2025 Vol. 45 (12): 3403-3414 [Abstract] ( 5 ) PDF (21254 KB) ( 2 )

	3415	Qualitative and Quantitative Analyses of Egg Yolks Adulterated With Sudan Red Ⅰ Based on Near-Infrared Spectroscopy
		YIN Wei-jian¹, WEN Yu-kuan¹, DONG Gui-mei¹, YANG Ren-jie¹, LI Liu-an², YU Xiao-xue², YU Ya-ping^1*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3415-07
		Sudan Red Ⅰ is an illegal food colorant that can enhance the color intensity of egg yolks. Rapid detection of Sudan Red Ⅰ in egg yolks is of great significance. In this study, a near-infrared spectrometer was used to collect spectral data from 60 unadulterated egg yolk samples and 102 adulterated samples containing Sudan Red Ⅰ at concentrations ranging from 0.5 to 20 mg·(100 g)^-1. After spectral analysis and data preprocessing, the sample dataset was divided into training and test subsets at a 3∶1 ratio. Qualitative and quantitative models were then built to detect Sudan Red Ⅰ in egg yolks. The models were evaluated using prediction accuracy, calibration, and prediction R? coefficients (R²_c/R²_p), and root mean square errors (RMSEC/RMSEP). For qualitative analysis, the Partial Least Squares Discriminant Analysis (PLS-DA) algorithm was used to classify egg samples as adulterated with Sudan Red Ⅰ. After data preprocessing using the Standard Normal Variate (SNV) transformation, the model achieved optimal performance, with accuracy rates of 98.3% for the training set and 97.6% for the test set. For quantitative analysis, the Competitive Adaptive Reweighted Sampling (CARS) method was first used to select characteristic wavelengths from the spectral data. Then, regression models were established using the linear Partial Least Squares Regression (PLSR) and the nonlinear Back-Propagation Artificial Neural Network (BP-ANN) algorithms to predict Sudan Red Ⅰ content. The PLSR model showed better performance, with R²_c of 0.98, R²_p of 0.98, RMSEC of 0.79, and RMSEP of 0.80. The results demonstrate that near-infrared spectroscopy enables rapid and convenient detection of Sudan Red Ⅰ in egg yolks.
		2025 Vol. 45 (12): 3415-3421 [Abstract] ( 5 ) PDF (4786 KB) ( 2 )

	3422	Identification of Homochromy Inks Based on Improved Convolutional Neural Network and Hyperspectral Imaging Technology
		JIANG Lin-yi, DAI Xue-jing^*, LI Yun-peng, TANG Cheng-qing
		DOI: 10.3964/j.issn.1000-0593(2025)12-3422-09
		With the increasing number of document forgery and economic dispute cases, the accurate identification of homochromy inks is of great significance in judicial expertise. Traditional methods (such as thin-layer chromatography and Raman spectroscopy) have limitations including sample destruction and time-consuming procedures. At the same time, hyperspectral imaging (HSI) has emerged as a promising alternative due to its advantages of image-spectrum integration and non-destructive detection. However, the existing ink classification methods based on “dimension reduction and clustering” are difficult to fully explore the nonlinear characteristics of high-dimensional data, and shallow machine learning models have limited expressive ability and are susceptible to information loss and error accumulation. Therefore, a deep learning model HI-CNN integrating multi-scale convolution and channel attention mechanism is proposed in this paper, and it is combined with hyperspectral imaging for the identification of homochromy inks. The model employs multi-branch parallel convolution to extract spectral features at different scales, comprehensively capturing spectral information across bands. And a channel attention mechanism dynamically enhances discriminative bands, focusing on key spectral information. Residual connection optimization gradient propagation is adopted to avoid gradient explosion and gradient vanishing, thereby reducing error accumulation and improving training efficiency. Experiments were conducted on the UWA Writing Ink Hyperspectral Image (WIHSI) dataset to determine the optimal training data partitioning and parameter settings. Ablation studies were designed to validate the effectiveness of the multi-branch parallel convolution structure, channel attention mechanism, and residual connections in improving model performance. Finally, the performance of the model proposed in this paper was compared with that of other model architectures on the current dataset. The experimental results show that the multi-branch structure and the channel attention mechanism improved the accuracy rates by 4.6% and 1.0% respectively, and the training cycle was shortened by 34% through the residual network connection. For the most challenging identification of the black ink, HI-CNN achieved an accuracy rate of 98.07% (an improvement of 5.3% compared to the optimal model CAE-LR). In comparison, for the identification of blue ink the accuracy rate reached 99.06%, which was generally superior to the existing methods. This study provided an accurate and efficient solution for identifying homochromy inks, thereby reducing reliance on professional expertise in forensic document examination. It had significant application value in the field of judicial expertise and promoted the leapfrog development of homochromy ink identification technology, transitioning from reliance on experience to scientific quantification.
		2025 Vol. 45 (12): 3422-3430 [Abstract] ( 5 ) PDF (11762 KB) ( 2 )

	3431	Bloodstain Recognition Based on Residual Network Integrating SENet Channel Attention Mechanism and Hyperspectral Imaging
		CHEN Shao-yang, DAI Xue-jing^*, LI Yun-peng, TANG Cheng-qing
		DOI: 10.3964/j.issn.1000-0593(2025)12-3431-10
		The extraction and identification of bloodstains left at crime scenes provide an important basis for case investigation, but their rapid and non-destructive development and examination remain a research hotspot in the field of forensic science. To enhance the development efficiency and detection accuracy of bloodstains, hyperspectral technology has gradually been applied to the non-destructive identification of bloodstains. However, existing hyperspectral imaging techniques have limitations, including low recognition accuracy and insufficient efficiency in identifying bloodstains and blood-like substances, particularly when dealing with bloodstains on complex objects. Therefore, a bloodstain identification model based on hyperspectral imaging technology by integrating the SENet channel attention mechanism with a one-dimensional residual network (ResNet18-1D) was proposed in this paper, aiming to improve the accuracy and efficiency of bloodstain recognition by hyperspectral imaging technology. The SENet channel attention mechanism automatically acquired the importance weight of each feature channel through learning, thereby enhancing effective features and suppressing irrelevant ones. In view of the complexity of the trace-bearing object, this paper improved the traditional SENet module and adopts a dual-branch bottleneck module to enhance the applicability of the model. To address the complex and dynamic nature of forensic practice, this paper conducted two sets of experiments on the public blood detection dataset, which contains multiple traceable objects. (1) Hyperspectral Transductive Classification Scenario. Both training and test sets were derived from the same HSI image. This experiment focused on analyzing substrate interference with bloodstain spectral features. Results show the model achieved an overall accuracy (OA) of 96.8% and an average accuracy (AA) of 97.6% in the complex simulated scenario, representing improvements of 1.3% and 1.9%, respectively, compared to the state-of-the-art Hybrid CNN model. (2) Hyperspectral Inductive Classification Scenario. The model trained on the baseline scenario was directly transferred to test on a different image. This experiment focused on the pre-identification capability for bloodstains and blood-like substances, presenting greater challenges but better reflecting real-world application needs. Experiments showed that the overall accuracy and average accuracy of the model were 63.3% and 65% respectively, which were 2.2% and 1.6% higher than the current optimal RNN model. Through error source analysis, it was found that tomato juice, due to its absorption peak near 470 nm being similar to the characteristic absorption peak of blood at 415 nm, has become the main interference source. In addition to making horizontal comparisons of different algorithms, this paper also verified the impact of the SENet channel attention mechanism module on model performance through ablation experiments. The results showed that the improved SENet channel attention mechanism module, compared with the original SENet channel attention mechanism module, had enhanced the overall and average accuracy of the model in both classification scenarios. Meanwhile, the efficiency test showed that, despite having a large number of parameters, the collaborative design of the residual structure and the dual-branch SENet significantly reduced the computational cost. The training time is only 45 ms·epoch^-1, meeting the efficiency requirements of practical combat.
		2025 Vol. 45 (12): 3431-3440 [Abstract] ( 4 ) PDF (32522 KB) ( 2 )

	3441	Real Temperature Inversion Algorithm for Near-Infrared Multi-Wavelength Pyrometer Based on Multi Constraint Optimization Principle
		ZHANG Fu-cai¹, DING Zhi-yu¹, SHENG Zi-liang¹, SUN Xiao-gang^2*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3441-06
		The Multi-wavelength pyrometer is a crucial non-contact temperature measurement instruments that simultaneously detect radiant energy from targets at different wavelengths, enabling real temperature retrieval through data processing. This measurement methodology eliminates physical contact with the measured target, thereby preserving its original thermal characteristics and temperature distribution, making it particularly valuable in high-temperature and ultra-high-temperature applications. The temperature inversion process fundamentally relies on establishing correlations between emissivity and wavelength or radiance temperature. Emissivity, a critical parameter quantifying a radiator's emission capacity relative to blackbody radiation, serves as the bridge connecting real-world radiators with blackbody radiation laws. By determining the emissivity and radiance temperature at specific wavelengths, the real temperature can be computationally derived. Despite decades of international research advancements, two persistent challenges remain: (1) Time-dependent variations in emissivity can lead to significant temperature calculation errors whenever applied models differ from actual conditions; (2) Conventional emissivity-temperature-wavelength relationship models, developed typically through rigorous experimentation and empirical validation, demonstrate limited generalizability and often fail to perform effectively whenever the objects or conditions being measured are altered. The study proposes an innovative emissivity model-independent methodology for rapid real temperature inversion in multi-wavelength pyrometry. By analyzing intrinsic constraints within multi-wavelength measurement theory and integrating them with multi-constraint optimization principles, we developed a novel temperature inversion framework. The approach eliminates traditional dependence on predefined emissivity models while maintaining measurement accuracy. Through rigorous theoretical derivation and experimental validation, we demonstrated the feasibility and universality of this multi-constraint optimization-based method, establishing a new paradigm for multi-spectral pyrometric temperature solutions. This advancement provides enhanced adaptability for diverse measurement scenarios and evolving target characteristics.
		2025 Vol. 45 (12): 3441-3446 [Abstract] ( 4 ) PDF (2263 KB) ( 2 )

	3447	Comprehensive Research on Five Minerals Excavated From the Tomb of the Eastern Han Dynasty in Xianyang City, Shaanxi Province
		ZHANG Ya-xu^{1, 2, 3}, ZHU Yu-ji¹, DONG Qi¹, QIN Yi-wei¹, ZHAO Zhan-rui^1*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3447-08
		Five Minerals are frequently found in Han Dynasty tombs. However, research on their chemical composition remains limited, with few studies providing detailed analyses of their specific components. This study presents a comprehensive chemical characterization of Five Minerals samples collected from a vase inscribed with cinnabar found in Tomb M3300. The analytical methods employed include optical microscopy (OM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RM). The results indicate that the blue-colored minerals are primarily composed of azurite. At the same time, the red-colored minerals consist predominantly of realgar, which can degrade into uzonite and arsenolite. The black minerals are high-purity magnetite; the white minerals are dolomite with trace amounts of quartz; and the gray minerals are mica. This is the first scientific identification of dolomite and mica as key components of the Five Minerals. Furthermore, the inscriptions and Taoist symbols on the vase indicate that the Five Minerals served as protective agents for the tomb rather than for medicinal use. Although literary sources from the Han Dynasty offer specific references to the Five Minerals, a mixture of Cengqing, Dansha, Baiyu, Xionghuang, and Cishi. Their application in tomb protection is more closely associated with their color, physical properties, and symbolic meanings than with their precise chemical composition. This results in a certain degree of variability in the chemical makeup of the Five Minerals. The findings of this study significantly contribute to our understanding of both the material properties and the ritualistic functions of the Five Minerals in Han Dynasty burial practices.
		2025 Vol. 45 (12): 3447-3454 [Abstract] ( 4 ) PDF (19318 KB) ( 2 )

	3455	Research on Closed-Loop Control Algorithm for Fiber Positioning in Fiber Spectroscopy Telescope Based on SMART
		YANG Hao-jie¹, GAN Zhao-xu¹, WANG An-zhi¹, WANG Jia-bin¹, SUI Xiang¹, DOU Zhi¹, XUE Wei-xi¹, LUO Jia-shun¹, YAN Yun-xiang^{1, 2}, GENG Tao¹, SUN Wei-min^1*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3455-10
		A control algorithm for realizing closed-loop fiber positioning was proposed with the structure of the special-shaped micro-lens aimer (SMART) that can achieve real-time fiber positioning, to fit the requirement of the dual-rotary positioning device on the focal plane of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). During the closed-loop control process, if the fiber and the star image were misaligned, the starlight would deviate from the center plate of the SMART and enter the special-shaped micro-lens, which would deflect part of the starlight into the corresponding feedback fiber. Based on the light intensity signals received by the six feedback optical fibers, the light intensity contrast relative to the feedback optical fibers is calculated to obtain the azimuth information of the misalignment, and a control signal is sent back to the optical fiber positioning unit to drive the dual-rotation positioning device to adjust the angle, thereby achieving high-precision alignment dynamically. Its design logic closely aligns with the optical fiber alignment requirements of the dual-rotation positioning device, as the device relies on two independent rotation axes (a central axis and an eccentric axis) to achieve two-dimensional positioning. The closed-loop control dynamically corrects the dual-axis angle using real-time feedback signals to compensate for mechanical errors and environmental disturbances, ultimately meeting the requirements of large-scale optical fiber arrays for high-precision, high-efficiency alignment. The correspondence between the rotation angle and the movement distance of the dual-rotary positioning device was analyzed, and a two-round, multi-step convergence method was developed. The calculation formulas for the alignment path between the fiber and the star image under different dual-axis expansion conditions were analyzed. In the first round of positioning, the single-step length was set to 30 μm, and the contrast threshold was set to 0.9; in the second round, the single-step length was set to 10 μm, and the contrast threshold was set according to the calibration. The fiber needs to undergo multiple movements, from misalignment to alignment. After each movement of the positioning unit, the control system would again obtain the real-time feedback signal from the detection system to determine whether the fiber had reached the threshold. A simulation system of LAMOST was built in the laboratory, and the lengths of the dual axes were calibrated. The positioning accuracy and positioning time of the fiber closed-loop positioning system were tested. The results showed that the positioning system could make any initially misaligned fiber return to the aligned state. The average correction time was 27.6 s. There were 72.5% fibers of 10 μm correction accuracy, and 97.5% fibers of 30 μm correction accuracy.
		2025 Vol. 45 (12): 3455-3464 [Abstract] ( 4 ) PDF (19335 KB) ( 2 )

	3465	Micro-Inclusion Composition and Raman Spectral Characteristics of Pyroxene From Mingxi, Fujian
		ZHOU Hui-wei¹, ZHANG Lu², YANG Li-yi³, XU Shuang³, SHEN Hong-tao⁴, LIU Yun-gui^{5, 6, 7, 8}, SONG Yan-jun^{5, 6, 7, 8*}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3465-07
		Pyroxene megacrysts are common minerals in Cenozoic alkaline basalt bodies in the eastern region of China. These pyroxenes and their internal inclusions provide important evidence for studying the composition of deep-mantle materials and deep-seated processes in eastern China. This study employs electron probe microanalysis (EPMA) and laser Raman spectroscopy to investigate the chemical composition and Raman spectral characteristics of pyroxene megacrysts and their microscopic inclusions in basalt bodies from the Mingxi area, Fujian Province. The results indicate that the Mingxi pyroxenes are augites, characterized by well-developed fractures and relatively high Ca and Mg contents. Their calculated chemical formula is (Ca_0.66～0.68Na_0.04～0.05Mg_0.27～0.30)(Al_0.25～0.26Fe²⁺_0.18～0.21Fe³⁺_0.04Ti_0.02～0.03Mg_0.48～0.50）[(Si_1.86～1.92Al_0.08～0.14)O₆], with Raman characteristic peaks located at 1 008, 670, 545, 396, 338, and 232 cm^-1. The pyroxenes contain abundant linear, dotted, or dashed metallic mineral inclusions, primarily composed of pyrrhotite and hematite mixtures, while fractures exhibit goethite impregnation. Among these, pyrrhotite is a mantle-derived inclusion with a chemical formula of Fe_{（0.81~0.88）}S, whereas hematite and goethite are its later oxidation and hydration products. The fluid inclusions in Mingxi pyroxenes are mainly CO₂, with a Fermi resonance doublet peak separation (Δσ) of 104.643 cm^-1 in Raman spectroscopy. Based on the linear relationship between the Fermi resonance doublet and gas density, as well as the ideal gas equation of state, the formation depth is estimated to be greater than 72 km. The findings of this study provide valuable data for further research on the formation of basalt bodies and deep-seated processes in southeastern China.
		2025 Vol. 45 (12): 3465-3471 [Abstract] ( 4 ) PDF (34006 KB) ( 2 )

	3472	The Driving Force of Chemical Weathering Intensity on Slope Geological Hazards Identified by Using XRF
		QING Zhan-hui
		DOI: 10.3964/j.issn.1000-0593(2025)12-3472-08
		Geological hazard prevention and control is a crucial national policy in China. Understanding the intrinsic driving factors of geological hazards is fundamental for disaster mitigation. As a province prone to geological hazards, previous studies in Guangdong have primarily focused on the physical and mechanical properties of rock and soil masses. At the same time, insufficient attention has been paid to the underlying causes of mechanical property deterioration—chemical weathering. This study conducted statistical analyses on 6 841 slope hazard sites (collapses and landslides) in typical igneous and sedimentary rock regions of northern and eastern Guangdong. From the perspective of “chemical water-rock interaction (CWRI)”, major chemical elements (including SiO₂, Al₂O₃, Fe₂O₃, CaO, MgO, K₂O, Na₂O, and loss on ignition (LOI)) in 40 samples (weathered rocks, residual soils, and slope-residual soils) from 11 hazard sites were analyzed using X-ray fluorescence spectrometry (XRF). Classic chemical weathering indices—silica-alumina ratio (Si/Al), silica-sesquioxide ratio (Si/R₂O₃), chemical index of alteration (CIA), and a newly proposed CIA-rate by the author—were calculated to explore the driving mechanisms of chemical weathering intensity on slope hazards. Here, the CIA-rate is equal to the CIA value of the weathered product minus the CIA value of the fresh parent rock, then divided by the CIA value of the fresh parent rock. XRF results revealed that the average CIA-rate of igneous rock weathering products (71.28) was significantly higher than that of sedimentary rock products (21.26), consistent with the 1.4 times higher hazard density in igneous rock areas. In igneous rock regions, slope hazards predominantly occur in fully weathered layers and residual soils with CIA values of 75~85 and CIA-rates of 50~70, particularly at sites with spheroidal weathering bodies or heterogeneous interlayers. In sedimentary rock regions, hazards are often observed in lower fully weathered layers and residual soils with CIA values of 75~85 and CIA-rates of 15~25, where bedding structures and “soft-hard interlayers” facilitate multi-stage or deep-seated landslides. This study demonstrates that the hot-humid climate and intense rainfall in South China enhance surface water and groundwater erosion and corrosion, accelerating chemical weathering. During desilication and enrichment of aluminum/iron, elements such as K, Na, Ca, and Mg are leached. In contrast, clay newbornminerals and iron hydroxides accumulate, weakening the mechanical properties and structural integrity of rock and soil masses. These processes serve as intrinsic drivers of geological hazards. The findings provide a scientific basis for early identification and warning of geological hazards.
		2025 Vol. 45 (12): 3472-3479 [Abstract] ( 4 ) PDF (9805 KB) ( 2 )

	3480	Experimental Teaching Reform of Soil Analysis Under the Background of New Agricultural Science: Comparative Study of ATR-FTIR and FTIR Technique
		LÜ Feng-lian^{1, 2}, SHAN Xiao-ling^{1, 2}, LI Li-min¹, ZHAO Ran^{1, 2}, ZHENG Wei^1*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3480-08
		Against the background of new agricultural construction, improving the practical innovation ability of undergraduates in agricultural colleges and universities has become the core goal of experimental curriculum reform. The traditional experimental course content is obsolete, and the technical method is single, which makes it difficult to meet the needs of new agricultural science for compound talents. In this study, Fourier transform infrared spectroscopy (FTIR) was used as a starting point to systematically compare the operational procedures, data characteristics, and teaching applicability of attenuated total reflection (ATR) and transmission (FTIR) for the first time in the determination of soil functional groups under different fertilization treatments. Based on the soil analysis experiment, a three-stage teaching mode of “basic cognition-method comparison-comprehensive application” was developed, with teaching conducted through group experiments and comparative analysis. The results showed that: ① The average peak height of the —OH functional group (3 400 cm^-1) detected by the FTIR method was 0.32±0.04 (n=3), and the ratio of the C—H vibration peak area at 1 440 cm^-1 was 3~4 times that of the ATR method, which was more suitable for accurate quantitative analysis. ② After the reform, the score rate of students in the final “ATR/FTIR method selection application question” increased from 62% to 89%, the success rate of FTIR tableting increased from 65% to 88%, and the error points of the self-recognition experiment increased from 1.8 to 4.2. ③ Under different fertilization treatments, SNPK treatment significantly reduced the content of soil aromatic compounds (ATR method: 3.16%±0.14% vs. CK: 4.27%±0.01%), and 50% NPK+50% M treatment significantly increased the content of phenols and alcohols (FTIR method: 15.35%±1.93% vs. CK: 13.12%±1.29%). Through the logical closed loop of “technical comparison-practical verification-innovative application”, this teaching mode significantly improves students' scientific research cognitive depth, operational innovation ability and scientific research thinking, more effectively connects the principle of FTIR technology with the needs of new agricultural soil research, optimizes the structure of agricultural resources and environment experiment course, and provides a replicable practical path for the cultivation of new agricultural cross-disciplinary talents.
		2025 Vol. 45 (12): 3480-3487 [Abstract] ( 5 ) PDF (2384 KB) ( 2 )

	3488	Hyperspectral Inversion of Soil Organic Matter Content Using a Discrete Wavelet Coupling Algorithm
		LI Xiao-fang¹, WANG Jin-gao¹, HUO Jian-hong¹, LI Zi-tong¹, HAO Hong-chun¹, HAN Rui-xin¹, GU Xiao-he^2*, ZHU Yu-chen⁴, WANG Yan-cang^{2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3488-10
		Soil organic matter content in the plow layer is a key indicator for evaluating soil quality. It not only provides crops with abundant nutrients but also improves the soil environment in the plow layer, making it an essential component of the plow layer. This study proposes a spectral data mining algorithm to enhance the sensitivity of spectral data to soil organic matter content and improve its estimation capability. The study first employed discrete wavelet algorithms to sequentially perform separation, correlation analysis, and model construction on soil spectral data, thereby establishing a model for estimating soil organic matter content. Subsequently, coupled algorithms were used to sequentially perform data mining, correlation analysis, and model construction on soil spectral data, with evaluation metrics used to assess the accuracy of the resulting model. Finally, the sensitivity and estimation capability of spectral data toward soil organic matter content were compared before and after coupling. The research results indicate: (1) The spectral information mining algorithm proposed in this study can integrate the advantages of various wavelet bases, significantly enhancing the sensitivity of the spectra to soil organic matter content, with the correlation coefficient increasing by an average of 15.33%. (2) A comparison of model accuracy before and after coupling indicates that the spectral information mining algorithm proposed in this study can significantly enhance the estimation capability of spectra for soil organic matter content and reduce estimation errors. The conclusions of this study can support the mining and analysis of spectral data across different locations and serve as a reference for the development of related algorithms.
		2025 Vol. 45 (12): 3488-3497 [Abstract] ( 5 ) PDF (13049 KB) ( 2 )

	3498	Probing Dynamic Interfacial Evolution in Al(OH)₃-Coated CaCO₃ Composite Materials via Multispectral Synergy
		XU Yan¹, BAO Wei-jun^1*, WEI Fu-guang², JIANG Zong-chen²
		DOI: 10.3964/j.issn.1000-0593(2025)12-3498-10
		Using ordinary ground calcium carbonate (D₅₀≈38 μm) as the core phase, a CaCO₃/Al(OH)₃ core-shell composite material was constructed in a supersaturated sodium aluminate solution via heterogeneous nucleation. By multiple spectroscopic techniques, including X-ray diffraction (XRD), scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), the dynamic evolution of phase composition, interfacial chemical states, and bonding structures during the coating process was systematically revealed. The coating pathway was regulated by reaction time gradients (2 h, 12 h, 36 h). Combined with SEM-EDS cross-sectional morphology analysis and XRD phase identification, a three-layer model of the core-shell structure was established: a calcite-type CaCO₃ core (≈38 μm), a C₃AH₆ intermediate layer (300～500 nm), and a dense Al(OH)₃outer shell, following a stepwise reaction path of “CaCO₃→C₃AH₆→Al(OH)₃”. FTIR and Raman spectra further corroborated the interfacechemical bonding path: FTIR detected the characteristic OH stretching vibration peak of C₃AH₆ at 3 664 cm^-1, while Raman identified additional lattice vibration modes of C₃AH₆ in the 3 390～3 640 cm^-1 range, collectively confirming chemical bonding (via Ca—O—Al bonds) rather than physical mixing between calcium and aluminum. XPS quantitative analysis demonstrated that the surface Ca/Al molar ratio decreased from 1.45 at 2 h (close to the theoretical value of 1.5 for C₃AH₆) to 0.08 at 36 h. Combined with the Ca(2p) binding energy shift and the Al(2p) chemical state transition (Al—O→Al—OH), this directly revealed the dynamic formation of the C₃AH₆ intermediate phase and the coverage mechanism of the Al(OH)₃ shell. The innovation of the multispectral coupling strategy lies in: achieving phase-morphology collaborative characterization through XRD/SEM-EDS, analyzing bonding structure evolution with FTIR/Raman, and quantitatively tracking surface chemical state migration with XPS. This integrated approach overcomes the limitations of single-technique characterization and systematically elucidates the pivotal role of the C₃AH₆ intermediate phase in interfacial bonding. The study provides a theoretical foundation for the controllable synthesis of core-shell composites. For instance, optimizing the Al(OH)₃ shell thickness (300～500 nm) and uniformity by adjusting the sodium aluminate concentration and reaction time can enhance the material's weathering resistance and interfacial compatibility. Through multidimensional synergy of spectroscopic techniques, this work establishes a critical theoretical basis for the interfacial chemical regulation and industrial applications of CaCO₃/Al(OH)₃ composites.
		2025 Vol. 45 (12): 3498-3507 [Abstract] ( 4 ) PDF (23585 KB) ( 2 )

	3508	Research on Photovoltaic Cell Defect Detection and Performance Evaluation Based on Photoluminescence Imaging
		PAN Wen-wen^{1, 2}, QIAN Yun-sheng^1, LIU Ya-cheng¹, SUN Xiao-fei^2
		DOI: 10.3964/j.issn.1000-0593(2025)12-3508-06
		EL imaging and PL imaging are commonly used detection technologies in the photovoltaic industry. EL imaging has high spatial resolution, but requires an external power supply, which increases the difficulty of detection; PL imaging does not require electrical contact. After illuminating the photovoltaic cell with a PL light source, a photoluminescence signal is generated on the photovoltaic cell. By using a high-sensitivity camera to collect the signal, high-resolution image information can be obtained non-contact for defect detection. To verify whether the PL imaging method can be applied to the defect detection of photovoltaic cells and the characterization of photovoltaic cells, this paper establishes the corresponding experimental methods and test processes based on a set of self-developed PL imaging experimental platform, obtains PL images of photovoltaic cells under different excitation conditions by adjusting the input current of LED excitation light source, and uses the open circuit voltage obtained from IV measurement results as a reference, calculates the voltage difference with PL images according to the generalized Planck equation, and then verifies the quantitative analysis capability of PL imaging. The research shows that PL imaging can clearly show scratches and defects caused by materials in photovoltaic cells; The voltage difference calculated by PL imaging has a good linear relationship with the voltage difference measured by IV; PL imaging can completely image the entire photovoltaic cell without electrical contact, and its spatial resolution can reach the same level as EL imaging. Multiple defect types can be detected using coexcitation.
		2025 Vol. 45 (12): 3508-3513 [Abstract] ( 4 ) PDF (23401 KB) ( 2 )

	3514	Research on the Near-Infrared Spectroscopy Detection Method of Water Absorption Rate of Silicone Rubber Based on CPO-SVM
		WU Tian^{1, 3*}, WANG Ling-zhi^{1, 3}, QIU Zhong-hua², LI Ming-dian¹, WU Chen^{1, 3}, WU Bin-fan^{1, 3}, GU Tong^{1, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3514-10
		Silicone rubber composite insulators are commonly utilized for external insulation in overhead transmission lines due to their exceptional insulation, weather resistance, and anti-pollution flashover performance. However, traditional methods for assessing the water absorption rate of silicone rubber involve destructive sampling techniques, such as the fuchsin identification method and the weighing method. These methods do not align with the requirements for on-site detection and maintaining the structural integrity of the insulators. Currently, there is a lack of a non-destructive, in-situ method for detecting the water-absorption characteristics of silicone rubber materials used in composite insulators. Therefore, this study introduces near-infrared spectroscopy to detect the water-absorption rate of the silicone-rubber outer sheath of composite insulators. Taking newly manufactured silicone rubber test specimens of composite insulators as the research object, the original data were first subjected to abnormal sample elimination using the Local Outlier Factor (LOF) algorithm and the Mahalanobis distance algorithm (MA), and then the near-infrared spectral data were preprocessed using methods such as Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Savitzky-Golay smoothing filter, first derivative, and second derivative. Subsequently, the Competitive Adaptive Reweighted Sampling (CARS) algorithm, the Interval Combination Optimization (ICO) algorithm, and the Successive Projections Algorithm (SPA) were used to screen for redundant wavelengths among the characteristic wavelengths and to establish SVM models, respectively. Finally, the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Crown Pigeon Optimization (CPO) algorithms were used to optimize the model parameters. The research results show that the SNV-CARS-CPO-SVM model achieves good discrimination of the water absorption rate of silicone rubber test pieces, with an accuracy of 96.64% on the test set. This indicates that CARS can select high-quality features, effectively remove redundancies and noise, and compared with the PSO-SVM and GWO-SVM optimization models, the CPO-SVM model's classification accuracy rate has increased by 2.65% and 3.68%, respectively, demonstrating significant advantages. This study presents a novel approach for identifying the water-absorption rate of silicone rubber and other high-voltage insulating materials.
		2025 Vol. 45 (12): 3514-3523 [Abstract] ( 5 ) PDF (12833 KB) ( 2 )

	3524	An Improved NSGA-Ⅲ Algorithm of Information-Redundancy Bi-Objective Optimization for Hyperspectral Band Selection
		YUAN Bo
		DOI: 10.3964/j.issn.1000-0593(2025)12-3524-09
		Aiming at the issues of strong randomization in the initial population, imbalance between global convergence and local diversity, and low local search efficiency of the Non-dominated Sorting Genetic Algorithm Ⅲ (NSGA-Ⅲ) for hyperspectral band selection, an improved algorithm—INSGA-Ⅲ (Improved NSGA-Ⅲ driven by feature classification)—is proposed. Firstly, Latin Hypercube Sampling and a reference-point guidance mechanism were integrated to generate a high-quality initial population that ensures both comprehensive search space coverage and targeted focus in the objective space. Secondly, a classification accuracy-driven term based on the Adaptive Rotating Forest algorithm and a correlation penalty term based on the Pearson correlation coefficient were designed to construct a multi-objective fitness function that balances global exploration and local exploitation. Finally, the search mechanism of Particle Swarm Optimization was introduced to enhance regional search efficiency. Experiments are conducted on four types of hyperspectral datasets: Indian Pines (agricultural scenes), Pavia University (urban features), Salinas (vegetation monitoring), and Botswana (mineral identification). Widely used algorithms, including Sequential Forward Selection (SFS), Competitive Adaptive Reweighted Sampling (CARS), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and the original NSGA-Ⅲ, are selected as benchmarks to verify the universal advantages of INSGA-Ⅲ. Experimental results show that, in terms of band selection performance, INSGA-Ⅲ improves information entropy by 8.5% and reduces the band correlation metric by 9.7%, compared to the mean values of all benchmark algorithms (p<0.01). In the SVM classification task, INSGA-Ⅲ outperforms the benchmark mean values by 10.3% in Overall Accuracy (OA) and 11.6% in Kappa coefficient (p<0.01). Regarding algorithmic efficiency, INSGA-Ⅲ requires 32% fewer iterations to reach 90% Pareto front approximation than NSGA-Ⅲ, and shows significantly lower accuracy fluctuation (standard deviation ±1.23%) than the benchmark mean (±4.2%) under 25% Gaussian noise (averaged over 10 runs). The proposed algorithm provides an efficient and robust band selection scheme for applications such as agricultural crop monitoring, urban feature classification, and mineral identification, effectively balancing information content, redundancy, and classification accuracy, while significantly reducing the dimensionality and processing cost of hyperspectral data.
		2025 Vol. 45 (12): 3524-3532 [Abstract] ( 4 ) PDF (4279 KB) ( 2 )

	3533	Provenance Study of Oil-Spot Porcelain Excavated From the Jining Road Ancient City Site in Inner Mongolia
		JIA Xiao-lei, CHEN Yong-zhi^*
		DOI: 10.3964/j.issn.1000-0593(2025)12-3533-12
		To explore the kiln origins and technical characteristics of Jin-Yuan period oil-spot porcelain excavated from the Jining Road ancient city site in Inner Mongolia, this study systematically analyzed 20 oil-spot porcelain samples from the site and 10 comparative samples from Linfen Kiln in Shanxi Province using wavelength-dispersive X-ray fluorescence (WDXRF), inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy (SEM), Raman spectroscopy and ultra-depth microscopic analysis. The results reveal significant technical differentiation: Type I samples exhibit low Al₂O₃ and high SiO₂ content in bodies, with chemical composition and microstructure characteristics highly consistent with Linfen Kiln products. The morphological features of iron-rich crystals and elliptical ZrSiO₄ crystals in the glaze layer further confirm their Linfen Kiln origin. Type II samples display high Al₂O₃ and low SiO₂ content in bodies, resembling Jiexiu Kiln's clay formula, though their precise source requires further investigation. This research demonstrates a “dual-source supply” pattern for oil-spot porcelain at the Jining Road site, confirming Linfen Kiln's crucial role as a ceramic supplier along the Steppe Silk Road, while providing scientific evidence for understanding kiln technology transmission and ceramic trade networks during the Jin-Yuan period.
		2025 Vol. 45 (12): 3533-3544 [Abstract] ( 4 ) PDF (47814 KB) ( 3 )

	3545	Microstructure Characteristics and Thermal Expansion Behavior of Coal Measures Graphite
		LI Huan-tong¹, ZHANG Qian¹, ZOU Xiao-yan^2*, ZHANG Wei-guo¹, LIN Ke-jin¹
		DOI: 10.3964/j.issn.1000-0593(2025)12-3545-11
		Graphite is a layered carbon material composed of sp2 hybridized carbon atoms. Coal-measure graphite, a cryptocrystalline form of graphite derived from coal through high-temperature metamorphism, exhibits significant anisotropy. In this study, four samples from the anthracite-coal measure graphite series were selected and analyzed for their microstructural characteristics and thermal expansion behavior using X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), and ultraviolet-visible-near-infrared diffuse reflectance testing (UV-Vis-NIR). The quantitative relationship between these properties was also investigated using in situ high-temperature XRD. The results show that as the graphitization degree increases, the microcrystalline size and stacking height along the c-axis direction of coal-measure graphite significantly increase, and the anisotropic characteristics become more pronounced. The I_D1/I_D2 ratio, which corresponds to different defect types, varies, with the basal plane exhibiting a higher I_D1/I_D2 ratio than the edge plane. HRTEM reveals that the aromatic layers of highly metamorphosed anthracite exhibit local orientation domains, with fewer stacking layers and unstable extension lengths, and that amorphous carbon is present on the microcrystalline surface or edges. Oxygen-containing functional groups are mainly concentrated in the defective regions of graphite microcrystals. As the graphitization degree increases, the oxygen content decreases, and the proportion of carbonyl groups（C═O） decreases. The presence of polycyclic aromatic hydrocarbons in coal-measure graphite -shifts the π—π^* transition absorption peak to approximately 208 nm, with a simulated color distribution ranging from Cool Gray 9 CP to 2 CP. Higher graphitization degrees are associated with stronger reflectivity. Regarding thermal expansion behavior, the thermal expansion coefficient of graphite shows significant anisotropy, with the coefficient parallel to the basal plane being lower than that perpendicular to it. As the temperature rises, the expansion of graphite microcrystals along the c-axis is regulated by the corresponding increase in-the interlayer spacing. In contrast, in the a-axis direction, the formation of lattice defects decreases the average microcrystalline size (W-H_size). The higher the graphitization degree, the more complete the microstructure, the more pronounced the anisotropy, and the more significant the anisotropic thermal expansion behavior.
		2025 Vol. 45 (12): 3545-3555 [Abstract] ( 4 ) PDF (40272 KB) ( 2 )

	3556	Study on Quantitative Estimation of Soil Dry Density by Hyperspectral Method After Removing the Influence of Moisture
		LI Xiao-fang^1*, HUO Jian-hong¹, JIANG Nan⁵, WANG Yan-cang^{2, 3, 4}, GU Xiao-he³, HAO Hong-chun¹, LI Zi-tong¹, HAN Rui-xin¹, WANG Jin-gao¹, GAI Xiao-kai¹, WANG Yao-xin¹
		DOI: 10.3964/j.issn.1000-0593(2025)12-3556-10
		Soil dry density directly influences the mechanical properties of compacted soil. To explore a hyperspectral-based detection method for compacted soil dry density, this study thoroughly analyzed the influence patterns of soil moisture content and dry density on soil spectra. A spectral dewatering method was proposed to improve the accuracy of soil dry density estimation. This study obtained relevant parameters and corresponding spectral data through integrated soil moisture gradient experiments, soil static compaction tests, and soil spectral measurements. By combining spectral processing analysis methods and correlation analysis algorithms, the spectral response characteristics of soil moisture content and dry density were analyzed, leading to the proposal of the spectral dewatering method. Subsequently, optimal feature bands were screened and extracted using correlation analysis algorithms and optimal subset construction algorithms. A soil dry density estimation model was constructed using partial least squares regression. Key findings include: (1) Soil moisture content is the primary factor influencing compacted soil spectra, with dry density being a secondary factor; both significantly affect the overall compacted soil spectral signature. (2) Compared to raw spectra and soil compaction coefficients, spectra corrected by the Spectral Dewatering method exhibit significantly higher sensitivity to soil dry density. The maximum correlation coefficient R reached 0.858, with an average correlation coefficient improvement of 33.7% (wavelet transform). This indicates that the spectral dewatering method employed in this study effectively mitigates moisture's influence on soil spectra and enhances spectral sensitivity to soil dry density. (3) Compared to the soil compaction coefficient, the model constructed based on SD showed an average increase of 3.36% in R² and an average decrease of 9.985% in RMSE. The optimal model (7 scales) constructed using the Spectral Dewatering method achieved R²=0.792 and RMSE=0.184, demonstrating that the proposed Spectral Dewatering technique further enhances the ability of spectral data to estimate soil dry density. The conclusions drawn in this study provide fundamental theoretical and methodological support for rapid, non-destructive monitoring of soil dry density in engineering foundations.
		2025 Vol. 45 (12): 3556-3565 [Abstract] ( 5 ) PDF (7405 KB) ( 2 )

	3566	Monitoring Atmospheric Ammonia Column Concentrations in the Hefei Region Using Ground-Based High-Resolution Fourier Transform Infrared Spectroscopy
		TONG Ji-ping^{1, 2}, XIE Yu^{1, 5*}, WANG Shi-yi^{2, 3}, YUE Yang¹, LI Long¹, TAN Wen-zhuo¹, SHAN Chang-gong², QIAN Zheng-wei^{1, 2}, WANG Wei^{2, 4}
		DOI: 10.3964/j.issn.1000-0593(2025)12-3566-09
		Ammonia (NH₃) readily reacts with acidic pollutants in the atmosphere to form secondary inorganic aerosols, which constitute an important component of particulate matter and indirectly affect the environment and health. Thus, detecting the concentration and variation of ammonia in the atmosphere is of great significance for the study of the causes of particulate matter and pollution prevention and control. This study analyzed the variation characteristics of atmospheric NH₃ column concentrations and the driving factors behind the variations in Hefei, China, from 2019 to 2023 using high-resolution Fourier Transform Infrared (FTIR)spectroscopy at a ground-based remote sensing site. The reliability of Infrared Atmospheric Sounding Interferometer (IASI) satellite data was also validated. Results showed a significant interannual increasing trend in NH₃ column concentrations, especially with an annual growth rate of 35.96% in 2022. Pattern of seasonal variation was clear, with the summer peak (3.74×10¹⁶ molec·cm^-2) being 4.2 times higher than the winter minimum (0.89×10¹⁶ molec·cm^-2), driven by intensified agricultural emissions and temperature-enhanced volatilization. Further analysis was conducted on the relationship between the ammonia column concentration and meteorological factors such as atmospheric temperature, wind direction, and wind speed. It was found that only in the spring did the ammonia column concentration show a moderate correlation with atmospheric temperature; in the other seasons, the correlation was weak. Moreover, the observed ammonia column concentration was mainly influenced by the local wind direction, while the influence of wind speed did not show a clear pattern. Diurnal analysis identified dual-peak characteristics during morning (08:00) and evening(16:00) rush hours in spring, summer, and autumn, reflecting contributions from non-agricultural sources such as traffic emissions. Furthermore, comparisons between ground-based FTIR and IASI satellite data demonstrated high consistency, with a correlation coefficient from 0.65 to 0.76. In contrast, satellite data exhibited systematic bias, with relative bias from -1.0% to 8.7%, due to retrieval algorithm limitations and cloud interference. This study revealed the variation characteristics of ammonia column concentrations in the Hefei area and the influencing factors, providing a scientific basis for regional ammonia emission reduction strategies, and verifying the accuracy of satellite data for observations in China.
		2025 Vol. 45 (12): 3566-3574 [Abstract] ( 6 ) PDF (11910 KB) ( 5 )

	3575	Research on Denoising Method of Agricultural Product Terahertz Spectroscopy Based on Adaptive Signal Decomposition
		WU Jing-zhu¹, LIU Yu-hao¹, YANG Yi^1*, XIE Chuan-luan², LÜ Zhong-ming¹, LI Yi-can¹
		DOI: 10.3964/j.issn.1000-0593(2025)12-3575-10
		To address the issues of peak overlap caused by complex matrices in agricultural product terahertz (THz) spectral signals and the dynamic, nonlinear interference induced by environmental and system noise, this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality. THz time-domain spectroscopy (THz-TDS) combined with an attenuated total reflection (ATR) accessory was used to collect THz absorbance spectra from 48 peanut samples. Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example, wavelet transform (WT), empirical mode decomposition (EMD), local mean decomposition (LMD), and its improved methods—segmented local mean decomposition (SLMD) and piecewise mirror extension local mean decomposition (PME-LMD)—were employed for spectral denoising. The applicability of different denoising methods was evaluated using a support vector regression (SVR) model. Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance, with a root mean square error (RMSE), coefficient of determination (R²), and mean absolute percentage error (MAPE) of 0.010, 0.912, and 0.040, respectively. Compared with traditional methods, PME-LMD significantly improved spectral quality and model prediction performance. The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals, providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products. This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
		2025 Vol. 45 (12): 3575-3584 [Abstract] ( 5 ) PDF (10225 KB) ( 3 )

	3585	《光谱学与光谱分析》2025年(第45卷)总目次(第1～12期)
		《光谱学与光谱分析》编辑部

		2025 Vol. 45 (12): 3585-3600 [Abstract] ( 7 ) PDF (1187 KB) ( 5 )

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