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2025 Vol. 45, No. 06 Published: 2025-06-01

	1501	The Setting of Parameters in XPS Peak Fitting
		JIANG Zhi-quan, LI Qiu-hua, LIU Jian-yi, LIU Wan-ting
		DOI: 10.3964/j.issn.1000-0593(2025)06-1501-07
		X-ray photoelectron spectroscopy (XPS) has been widely employed in scientific research and engineering technology. The test data typically require handling for peak fitting; consequently, an accurate peak position in binding energy and the relative contents of the surface species are required. However, a serious human factor emerges during the process of XPS peak fitting, leading to distinct mistakes in the fitting results. The reason for the fitting errors is primarily that no constraints or incorrect constraints were applied to the fitting parameters of the XPS signals. There are four essential elements in XPS photoelectron signals: the separation distance and intensity ratio of spin-orbit splitting, the shape and symmetry of XPS signals, and the full width at half-maximum (FWHM) of XPS signals. In this text, the physical natures of these four essential elements are elucidated, and a reasonable suggestion is proposed for the setting of parameters in XPS peak fitting. In the process of XPS peak fitting, it is not simply a matter of mathematical calculation. However, association and constraintmust be considered in the above four essential elements of XPS photoelectron signals, resulting in a large extent of coincidence between the fitting envelope and the original curve. Furthermore, the components after peak fitting all possess explicit physical and chemical meanings.
		2025 Vol. 45 (06): 1501-1507 [Abstract] ( 32 ) RICH HTML PDF (3013 KB) ( 33 )

	1508	Research Progress on Analysis Methods of Blood Residues in Ancient Heritages
		HUANG Ya-wen^{1, 2, 3}, YAN Bing-bing^{1, 2, 3}, DONG Jia-ning^{1, 2, 3}, LIU Yan^{1, 2, 3}, YANG Fu-wei^{1, 2, 3}, ZHANG Kun^{1, 2, 3}, WANG Lu^{1, 2, 3}, WEN Rui^{1, 2, 3}, YANG Lu^{1, 2, 3}, WANG Li-qin^{1, 2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1508-06
		Blood residues in artifacts are closely connected to ancient people's production and daily life. Analyzing these residues can offer valuable insights into ancient social and religious rites, societal development, technological advances, customs, and practices, making it highly significant for academic research. Currently, methods for analyzing blood residues in artifacts are generally divided into chemical and instrumental categories. Chemical methods rely on the peroxidase activity of the heme structure in hemoglobin, which can catalyze color reactions in compounds such as phenolphthalein and tetramethylbenzidine, allowing for preliminary screening of archaeological samples that might contain blood residues. The hematochromic crystal test has also been used to analyze blood residues on artifacts, but its reliability has been widely debated. Although traditional chemical methods are straightforward, quick, and cost-effective, they are susceptible to false positive results due to external interferences. In addition, these methods lack accuracy and specificity. With advancements in science and interdisciplinary approaches, instrumental methods, particularly spectroscopic techniques, have become increasingly prevalent in scientific archaeology. Instruments like mass spectrometry, Raman spectroscopy, UV-visible absorption spectroscopy, and X-ray fluorescence spectroscopy can further identify compounds such as hemoglobin and heme in blood. Spectroscopic methods offer high accuracy and minimal sample consumption and can even be non-destructive, though they face challenges such as complex sample preparation and high costs. Overall, current analyses of blood residues in artifacts are mostly limited to isolated case studies, with methods lacking completeness and systematic integration. This paper reviews domestic and international research and application of blood residue analysis methods, clarifying each method's advantages, limitations, and suitable applications to provide useful references for developing a comprehensive blood residue detection and analysis system in the future.
		2025 Vol. 45 (06): 1508-1513 [Abstract] ( 29 ) RICH HTML PDF (851 KB) ( 30 )

	1514	Neural Network Filtering Method for Carbon Dioxide Gas Detection in TDLAS
		ZHAO Yu-ying¹, WANG Le², HUANG Tian-he¹, SONG Yu-xiao³, BI Wen-hao¹, LI Wen-xuan¹, JIANG Chen-yu^{1, 4*}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1514-07
		Carbon dioxide (CO₂) gas detection has important research significance in various fields, such as environmental monitoring, agricultural production, and microbial detection. Tunable diode laser absorption spectroscopy(TDLAS/WMS) based on wavelength modulation detection systems has become an important means of precision gas detection due to its significant advantages of high sensitivity, low cost, non-invasiveness, and real-time monitoring. However, the system is susceptible to interference from various environmental noises, significantly impacting gas detection accuracy and stability. The commonly used traditional time-frequency analysis algorithm cannot effectively filter out the low-frequency signal noise coupled with the absorption signal, which will interfere with the subsequent gas concentration retrieval task. With their powerful feature mapping capabilities, deep learning algorithms can project signals into a new feature space, learn the distribution of spectral signal background structures, and thus overcome the limitations of time-frequency domain filtering algorithms. Therefore, a deep learning-based TDLAS carbon dioxide gas detection filtering algorithm (TGDF) is proposed to reduce the influence of full-frequency noise in the gas detection system and improve the accuracy of gas measurements. The TGDF takes a fully connected neural network as the infrastructure and adds sampling blocks to remove noise in the feature domain; in addition, the singular value decomposition is introduced to further adjust the harmonic signals. The model is trained, tested, and tuned by simulating different concentrations of CO₂ absorption spectra with noise under experimental conditions, and the model performance is tested on the experimental dataset. In the simulation experiments, the average signal-to-noise ratio of the TGDF-filtered spectra increased by a factor of 3.05 from 7.34 dB to 22.41 dB. It kept the lowest noise residuals in the frequency domain. In real experiments, there is a good linear relationship between the second harmonic amplitude and preset concentrations of CO₂ (R²=0.998); the average absolute error (MAE) of five CO₂ detections is divided into 0.27%, 0.20%, 0.23%, 0.28% and 0.32%. Compared with the commonly used filtering algorithms such as EMD, SG, Wavelet transform, and MLP neural networks in these two datasets, the TGDF showed the best performance in suppressing systematic noises of different frequencies and phases. The results fully proved that TGDF could effectively reduce the systematic noise of each frequency band in the harmonic signal of gas detection and improve the accuracy and stability of TDLAS CO₂ concentration detection, which provides a feasible technical means for high-sensitivity measurement of CO₂ and other trace gases.
		2025 Vol. 45 (06): 1514-1520 [Abstract] ( 27 ) RICH HTML PDF (6702 KB) ( 10 )

	1521	Design and Optimization of H-Type Resonant Photoacoustic Cell for Composite Buffer Cavity
		JIN Hua-wei^{1, 2, 3}, TANG Yu-hao², SHI Jia-sheng², FANG Lei², LIU Wen-jian³
		DOI: 10.3964/j.issn.1000-0593(2025)06-1521-06
		In photoacoustic spectroscopy detection technology, the photoacoustic cell is where the “light-heat-sound” coupling occurs. The performance of the photoacoustic cell directly affects the accuracy and sensitivity of the detection system. To improve the performance of the photoacoustic cell, a H-type resonant photoacoustic cell is simulated and analyzed based on the traditional cylindrical photoacoustic cell, and a novel H-type resonant photoacoustic cell is proposed.Through the interface of the thermal, viscous, and acoustic physical fields in COMSOL software, this paper simulated and analyzed the influence law of the height and radius of the bottom circle on the sound field in the photoacoustic cell. The results show that the sound pressure of the photoacoustic signal increases initially and then decreases with the increase in height of the platform, when other parameters remain unchanged. At the same time, the resonance frequency of the photoacoustic chamber changes significantly when the platform height is altered, and the resonance frequency initially decreases before increasing with the platform height. Similarly, when the other parameters remain unchanged, the sound pressure value of the photoacoustic signal decreases with the increase in the radius of the circle at the bottom of the circle. The resonant frequency of the photoacoustic cell changes with the variation in the radius of the bottom circle, and the resonant frequency increases with the increase in the radius of the bottom circle. The quality factors of the photoacoustic cell, before and after optimization, were 58.84 and 43.87, respectively, representing a 34.12% increase. The optimized photoacoustic signal increased by 43.15% compared to the signal before optimization. It can be seen that optimizing a single cylindrical buffer cavity into a composite buffer cavity, which combines a cylinder and a circular table, can effectively improve the quality factor of the photoacoustic cell and the photoacoustic signal. At the same time, the optimized photoacoustic cell volume is further reduced, which facilitates the miniaturization of the photoacoustic cell. This research presents a novel approach to the optimal design of traditional cylindrical photoacoustic cells.
		2025 Vol. 45 (06): 1521-1526 [Abstract] ( 25 ) RICH HTML PDF (3619 KB) ( 22 )

	1527	In vivo Rapid Multispectral Endoscopic Imaging System for Visualizing Morphological and Physiological Characteristics of the Animal Model
		REN Dan-dan, LU Yi-xin, LIU Jing, CHU Jia-hui, ZHANG Zi-xuan, WANG Shuang^*
		DOI: 10.3964/j.issn.1000-0593(2025)06-1527-07
		Endoscope, a commonly used multi-disciplinary fusion instrument, is important in screening, clinical diagnosis, intraoperative detection, and prognosis assessment of major malignant diseases. Compared with other optical analysis techniques, Multispectral Imaging (MSI) provides both imaging and spectral information and has more advantages in clinical endoscopy optical detection. We developed a fast multi-spectral endoscopy imaging system with Xenon light source, hard tube endoscope, and imaging CCD. We evaluated its imaging performance using living rat testicular tissue as the study object. The system uses six 3-channel narrow-band filters as spectral splitting devices, which can simultaneously achieve three 15 nm narrow-band lighting (~ 15 nm bandwidth) in blue, green, and red wavelengths. After the illumination light is transmitted to the hard tube endoscope through the optical waveguide fiber, the CCD collects the narrowband image and video information corresponding to each filter in real-time (24 MSI data sets per second). The Adaptive Histogram Equalization (AHE) algorithm and CIELab color space model improve the contrast of the obtained multi-spectral images. Combined with the tissue optical transmission model, based on reconstructing tissue multispectral image information, physiological characteristics of living biological tissues (blood content, oxygenation index, scatterer size distribution images, etc.) are further visualized. The experimental results showed that processing MSI images significantly enhanced the visibility of surface tissue blood vessels, subcutaneous microvessels, and other tissue structures and was more conducive to observing tissue texture and capillary distribution. The reconstructed physiological features visualized the differences in blood volume distribution between different tissue parts. By analyzing tissue reflectance spectral characteristics, differences in blood content within tissues can be further quantitatively assessed. The MSI-based endoscopy technology and its detection system in this paper can further quantitatively analyze the biochemical composition information inside tissues on the premise of visually describing the morphological characteristics of tissues without labels, which is helpful to explore and form a new endoscopy optical detection technology that meets the requirements of early detection of clinical cancer.
		2025 Vol. 45 (06): 1527-1533 [Abstract] ( 26 ) RICH HTML PDF (23971 KB) ( 20 )

	1534	Detection of Moisture Content in Strawberry Leaves Using Hyperspectral and Broad Learning System
		LI Ze-qi¹, YANG Zheng¹, ZHOU Zhuang-fei¹, PENG Ji-yu¹, ZHU Feng-le^1*, HE Qing-hai²
		DOI: 10.3964/j.issn.1000-0593(2025)06-1534-09
		Moisture content is a critical factor influencing the growth and development of strawberries, and it holds significant importance for their cultivation. Traditional methods of moisture measurement, although precise, are cumbersome and destructive. Hyperspectral Imaging (HSI) has emerged as an ideal technique for plant moisture detection due to its efficiency, non-destructive nature, and multi-attribute detection capabilities. However, the large volume and redundancy of HSI data present challenges. While deep learning methods can extract deep features from the data, their reliance on large-scale annotated datasets limits their application. To address this issue, our study introduces a Broad Learning System (BLS) to solve the training problem with small sample sizes. It proposes a BLS-based method for detecting moisture content in strawberry leaves. The study first prepared samples of healthy and drought-stressed strawberry leaves, obtaining their hyperspectral images and moisture content data. By analyzing three hyperparameter tuning methods and four preprocessing algorithms, a BLS moisture determination model was constructed and its performance was evaluated against comparative models, including Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVR), Gradient Boosting Decision Tree Regression (GBDTR), and Residual Network (ResNet). The results showed that the BLS model achieved a coefficient of determination (R²_p) of 0.797 4 and a Root Mean Square Error (RMSE) of 0.004 5 on the test set, outperforming the other models and exceeding the ResNet model by 0.039 4, demonstrating its superior generalization ability and prediction accuracy. Additionally, the optimal model was used to visualize the moisture content in strawberry leaves by generating false-color maps, providing an intuitive display of the leaves' moisture status. The findings suggest that the BLS model is suitable for analyzing hyperspectral data and detecting moisture content in strawberry leaves using small samples, providing a theoretical basis for the online detection of moisture content in strawberry leaves.
		2025 Vol. 45 (06): 1534-1542 [Abstract] ( 33 ) RICH HTML PDF (21155 KB) ( 15 )

	1543	Comprehensive Identification and In-Depth Analysis of Zhaotong Apples Facilitated by Multidimensional Fourier Transform Infrared Spectroscopy Integrated With Two Neural Network Models
		MA Dian-xu, CAI Yan, LI Xiao-pan, CHENG Li-jun, YANG Hai-tao, SHAN Chang-ji, DU Guo-fang
		DOI: 10.3964/j.issn.1000-0593(2025)06-1543-08
		The article presents an analysis of eight different varieties of Zhaotong apples using Fourier Transform Infrared Spectroscopy (FTIR) and Two-Dimensional Correlation Infrared Spectroscopy (2D-IR). Additionally, Convolutional Neural Networks (CNN) and Radial Basis Function (RBF) neural networks were utilized for their identification. The Fourier Transform Infrared Spectroscopy (FTIR) analysis of eight Zhaotong apple varieties revealed prominent absorption peaks within the spectral ranges of 3 500～2 850, 1 650～1 400, and 1 250～800 cm^-1, signifying their abundance in sugars, vitamins, amino acids, lipids, organic acids, phenols, flavonoids, and various other compounds. However, a notable similarity was observed across the spectra of these apples, with only subtle variations in peak intensities and positions. Consequently, relying solely on spectral characteristics to differentiate between these eight varieties is impractical. Using temperature as a perturbation, we collected dynamic spectra of eight distinct apple varieties and subjected them to 2D-IR analysis within the spectral range of 800～1 800 cm^-1. The synchronous spectrum derived from this analysis underscores that as temperature escalates, distinct autopeaks emerge in proximity to 1 010 and 1 642 cm^-1. These peaks serve as indicators of varying degrees of decomposition occurring within esters, acids, and proteins present in apples, with esters and acids undergoing more pronounced decomposition compared to proteins. Notably, among these eight samples, Red Fuji Apples exhibit the strongest autopeak at 1 642 cm^-1, accompanied by the weakest negative cross peak at 1 006, 1 642 cm^-1, while Aksoo Apples display only one prominent autopeak at 1 010 cm^-1. Qin Guan Apples exhibit three autopeaks, whereas both 2001 Apples and New Century Apples have their strongest autopeak shifted to 1 020 cm^-1, differing by 10 wavenumbers from those of other varieties. This 2D-IR analysis enables differentiation between certain apple samples based on their unique spectral signatures. An optimized approach to analyzing the spectra of 216 apples from eight varieties utilizing Convolutional Neural Network (CNN) and Radial Basis Function (RBF) neural networks. By randomly selecting 152 sample spectra for model training and through iterative refinement and rigorous training, both models achieved an optimal state, attaining a classification accuracy of 100% on the training set. Following this, predictions were conducted on the spectra of an additional 64 samples, yielding remarkable results: an accuracy rate of 89.06% in CNN analysis and an even higher 90.6% in RBF neural network analysis. Both neural network models have demonstrated outstanding performance in terms of classification accuracy. Hence, the integration of FTIR, 2D-IR, CNN, and RBF neural network analysis methods forms a complementary approach in the study of apple analysis and identification, facilitating precise classification of Zhaotong apples. Additionally, this comprehensive methodology holds significant potential for application in the classification and identification of diverse substances beyond apples.
		2025 Vol. 45 (06): 1543-1550 [Abstract] ( 28 ) RICH HTML PDF (12249 KB) ( 22 )

	1551	Study on Near-Infrared Absorption Characteristics of Horizontal Gas-Liquid Two-Phase Flow of Methane and Water
		KONG Wei-hang^{1, 2}, ZHANG Heng-heng¹, LI Pei-yu¹, LI Yang³, HUI Yao-zhi¹, LI He^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1551-06
		The phase holdup of gas-liquid two-phase flow in shale gas horizontal wells is an important parameter for calculating phase separation velocity, pressure drop, average density，and gas production. To solve the problem of gas-liquid two-phase flow phase holdup measurement in shale gas horizontal wells, this paper studies the absorption characteristics of horizontal gas-liquid two-phase flow based on near-infrared spectroscopy. Firstly, according to the full-band absorption spectra of methane and water obtained from HITRAN and Refractiveindex databases, the characteristic absorption peak ranges of methane and water are analyzed. Then, the near-infrared spectra of methane and water with different liquid surface thicknesses under different pressures are obtained experimentally. The position and variation range of the absorption peaks are analyzed, and the absorption characteristic wavelengths of methane and water are finally determined to be 1 650 and 980 nm, respectively. Finally, the experimental platform of horizontal tube section with 1 650 and 980 nm wavelength lasers was built to analyze the effects of different pressures, gas-liquid ratios, organic matter and total dissolved solids in the flowback fluid on the near-infrared absorption performance. The experimental results show that the 980 nm near-infrared light intensity is only affected by the liquid level height, and the logarithm of the near-infrared light intensity at the receiving end decreases linearly with the increase of the liquid level height. The near-infrared light intensity at 1 650 nm is affected by the height of the water level and the pressure of the methane gas, and as the height of the liquid level increases and the pressure increases, the logarithm of the near-infrared light intensity at the receiving end decreases linearly. The effect of total dissolved solids in the flowback fluid on the near-infrared light transmittance is slightly greater than that of organic matter. Increasing the concentration of organic matter and total dissolved solids, the effect on the near-infrared light intensity is less than 6%. Therefore, 980 nm near-infrared light can be used to measure the change in water level, and 1 650 nm near-infrared light can be used to measure the change in water and methane pressure. Then, according to Lambert-Beer and linear superposition law, the phase holdup of gas-liquid two-phase flow in shale gas horizontal wells can be obtained.
		2025 Vol. 45 (06): 1551-1556 [Abstract] ( 25 ) RICH HTML PDF (4543 KB) ( 7 )

	1557	Polarization Characteristics of Infrared Radiation on High-Temperature Metal Surfaces
		YANG Chen-nan^{1, 2}, FU Yue-gang^{1, 2}, OUYANG Ming-zhao^{1, 2}, YUAN Shuai^{1, 2}, HE Wen-jun^{1, 2}, ZHAO Yu-sen^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1557-09
		Polarization is a crucial dimension in the light field, alongside intensity and spectrum, and holds significant promise in applications such as target recognition, military reconnaissance, and infrared target detection. Artificial objects, such as spacecraft engines and high-speed aircraft, typically reach temperatures of several hundred degrees Celsius, exhibiting high-temperature self-radiation characteristics. However, there has been relatively little research on the infrared polarization characteristics of targets at high temperatures, particularly concerning the polarization properties of different metal surfaces under elevated temperatures. To investigate the polarization characteristics of high-temperature metal surfaces, a bidirectional reflection distribution function model based on microplane theory was systematically developed. This model incorporates both specular and diffuse reflection characteristics, establishing a mathematical framework for the BRDF of metal spontaneous radiation energy and the degree of polarization of infrared radiation. Through derivation and calculation, the influence of various surface roughness and temperature conditions on the polarization degree of infrared radiation from metals was analyzed. Simulation results indicate that at the same temperature, higher surface roughness of the metal leads to a lower polarization degree. Conversely, under the same roughness, the polarization degree increases with rising temperature. In parallel, thermal imaging of spontaneous radiation polarization was performed using an LGC6122 long-wave infrared camera and a WP25M-IRC infrared gate polarizer. The targets studied were iron and 45 steel, within the 8~14 μm wavelength range, at temperatures of 150, 200, 250, 300, 400, and 500 ℃. Experimental observations revealed that, influenced by Planck's blackbody radiation law, the surface temperature of the metal is positively correlated with its spectral integral polarization degree: as the temperature increases, so does the spectral integral polarization degree. Additionally, the polarization degree gradually increases with the observation angle, peaking within the 70°to 80° range. The findings of this study aim to enhance the accuracy and reliability of thermal imaging and optical sensing technologies in infrared target detection and other related applications. Furthermore, they provide a reference for further exploration of infrared detection technologies in complex environments.
		2025 Vol. 45 (06): 1557-1565 [Abstract] ( 32 ) RICH HTML PDF (11654 KB) ( 11 )

	1566	Study on Improving Stability of Near-Infrared Spectra by Normal Distribution Screening Method
		LI Xiao-xing^{1, 2}, XIAO Jin-feng^1, ZHANG Hong-ming^2, LÜ Bo^{2, 3*}, YIN Xiang-hui¹, ZHAO Ming⁴, MA Fei⁵, FU Jia², HU Yan^{1, 2}, LI Zhi-hao^{1, 2}, WANG Fu-di², SHEN Yong-cai⁶, DAI Shu-yu⁷
		DOI: 10.3964/j.issn.1000-0593(2025)06-1566-12
		In the near-infrared online detection of the fermentation process, bubbles are often generated in the fermentation broth due to the need to continuously pass oxygen into the fermentation broth to promote microbial growth and metabolic activities. When the bubbles in the fermentation broth pass in front of the probe, they will interfere with the intensity of the near-infrared (NIR) spectrum. To eliminate the abnormal spectra caused by bubbles collected during the near-infrared online detection of fermentation broth and reduce spectral fluctuations, a normal distribution screening method is proposed in this study. In this study, 600 g of glucose solution with a mass fraction of 10% was prepared, adding 2 g of glucose solution to a reactor containing 600 mL of distilled water every 30 s, stirring well, then calculating and recording the mass fraction of glucose solution in the reactor, and generating bubbles by passing oxygen to the bottom of the reactor, and collecting the NIR spectra of the glucose solution in the reactor by using NIR spectrometer, respectively. After the anomalous spectra affected by the air bubbles were excluded by principal component analysis (PCA) combined with Mahalanobis distance method, Euclidean distance method, isolated forest, and normal distribution screening method, the sample set of spectra was randomly divided into the correction set and the prediction set according to the ratio of 4∶1, and then, after the spectral pre-processing, the glucose concentration prediction model was established for the correction set using the partial least squares method (PLSR) and the prediction set was analyzed by the established PLSR model. The correlation coefficient of the correction set, the root mean square error of the correction set, and the correlation coefficient and root mean square error of the prediction set were compared and analyzed. The results of the constructed model after removing the anomalous spectra affected by bubbles using the four methods are as follows: the correlation coefficient R²_c of the correction set obtained after removing the anomalous spectra by PCA combined with the Mahalanobis Distance Method is 0.998 208, and the root-mean-square error RMSECV is 0.000 764, and the correlation coefficient R²_p of the prediction set is 0.997 994, and the root-mean-square error RMSEP is 0.000 764; The correction set R²_c obtained after removing the anomalous spectra by the Euclidean distance method is 0.998 628, the root mean square error RMSECV is 0.000 652, the prediction set correlation coefficient R²_p is 0.998 628, and the root mean square error RMSEP is 0.000 655; the correction set R²_c obtained after removing the anomalous spectra by the isolated forest method is 0.998 255, the RMSECV is 0.000 739, the prediction set R²_p is 0.998 132, and the RMSEP is 0.000 740; the correction set R²_c obtained after the removal of anomalous spectra by the normal distribution screening method is 0.998 641, with a root mean square error RMSECV of 0.000 645, and the prediction set R²_p is 0.998 628, with a RMSEP of 0.000 636. Comparing the four methods, the normal distribution screening method can effectively reduce the fluctuation of spectral intensity and eliminate abnormal spectra more effectively than other methods.
		2025 Vol. 45 (06): 1566-1577 [Abstract] ( 27 ) RICH HTML PDF (8991 KB) ( 11 )

	1578	Short Time X-Ray Induced Ca₃(BO₃)₂:Pr³⁺ Long Afterglow From Ultraviolet to Red Region
		LIU Run-yao¹, SHI Wen-li¹, LIAO Xiao-bin¹, ZHANG Jia-xu¹, FU Xiao-yan^1*, LIN Tong-yan¹, LIU Ze-wen¹, CHEN Nai-hui¹, ZHANG Hong-wu²
		DOI: 10.3964/j.issn.1000-0593(2025)06-1578-06
		A short-time X-ray-induced long afterglow luminescent material, Ca₃(BO₃)₂:Pr³⁺, has been developed. The results showed that the phosphor exhibited excellent multiband long afterglow luminescence under X-ray excitation, with the afterglow emission peak primarily located at ultraviolet (270 and 302 nm), green (540 nm), and red (610 nm), corresponding to the 4f5d→³H_{4, 5}, ³P₀→³H₄, ³P₀→³H₆ transitions of Pr³⁺, respectively. More importantly, when X-ray irradiation was applied for 30 s, the intensity of the afterglow after 3 h decay remained 3.9 times that of the background. Even with only 5 s of X-ray irradiation, it can also produce a long afterglow persisting for more than 3 h. In addition, Ca₃(BO₃)₂:Pr³⁺ exhibited excellent photostimulated performance, producing stable and intense light under periodic irradiation with a 980 nm laser. The thermoluminescence spectra results showed that there were traps with a depth of 0.8 eV, which resulted in excellent long afterglow and photostimulated properties. These results indicated that Ca₃(BO₃)₂:Pr³⁺ was an excellent X-ray-induced long afterglow luminescent material, which was expected to be used as an in vivo light source for human photodynamic therapy.
		2025 Vol. 45 (06): 1578-1583 [Abstract] ( 18 ) RICH HTML PDF (12934 KB) ( 5 )

	1584	Temperature Dependence of InGaAs/GaAs Quantum Well Growth Characterized by XRD and PL Spectral Analysis
		LI Bo^{1, 2}, MA Shu-fang^{1, 3}, YANG Zhi^{1, 2}, CHENG Rui-si^{1, 2}, LIU Si-min^{1, 2}, WANG Jia-hui^{1, 2}, HAO Xiao-dong^{1, 3}, SHANG Lin^{1, 3}, QIU Bo-cang^{1, 3}, DONG Hai-liang⁴, HAN Dan⁴, XU Bing-she^{1, 4}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1584-08
		InGaAs/GaAs multiple quantum wells (MQWs) structures are widely utilized as active regions in lasers for optical communications and optoelectronic devices, owing to their unique quantum confinement effects and superior optoelectronic properties.The bandgap width can be finely tuned in this structure by modulating the indium (In) content to satisfy specific wavelength requirements. During the molecular beam epitaxy (MBE) growth of multiple quantum wells (MQWs), precise control over the material composition and thickness enables the optimization of their optical properties. Despite considerable progress in the development of InGaAs/GaAs multiple quantum wells (MQWs), the crystal quality and optical properties of high indium content InGaAs quantum wells remain limited by lattice mismatch, which leads to the formation of dislocations and interfacial defects. Therefore, enhancing crystal quality through precise modulation of MBE growth conditions is critical for improving the optical properties of these materials.Improving the interfacial and crystal quality of InGaAs/GaAs MQWs during the MBE growth process depends critically on the utilization of growth temperature to optimize the growth kinetics and hence control the migration of atoms at the interface, particularly the migration of In and Ga atoms. We grew two sets of InGaAs/GaAs MQWs at growth temperatures of 505 and 490 ℃, respectively, using the MBE method. The crystalline quality and optical properties of these samples were characterized and analyzed using high-resolution X-ray diffraction (HRXRD),photoluminescence (PL), and other complementary techniques. This study aims to investigate the impact of growth temperature on the crystal quality, interfacial integrity, and luminescence properties of the MQWs. According to HRXRD results, growth at high temperatures is advantageous for enhancing group III atomic dynamics and increasing the atomic diffusion length. This increases In and Ga atom migration during the growth process, which facilitates atom nucleation to find a lower energy position on the surface of the epitaxial layer. As a result, these samples grown at 505 ℃ have a lower defect density of 1.02×10⁵ cm^-2, fewer defects, less stress, and better crystal and interface quality. Further evidence that the MQWs samples developed at 505 ℃ have superior crystal quality compared to those formed at 490 ℃ is provided by the photoluminescence performance test and analysis findings, which also reveal that the MQWs samples exhibit high luminescence intensity and good luminescence uniformity. It is demonstrated that an optimal growth temperature is beneficial for enhancing the interfacial quality and optical properties of InGaAs/GaAs multiple quantum wells (MQWs). This process parameter provides an important reference value for the preparation of MQWs materials by MBE.
		2025 Vol. 45 (06): 1584-1591 [Abstract] ( 24 ) RICH HTML PDF (8145 KB) ( 9 )

	1592	Research on the Aging Structure of Silicone for Unearthed Ivory Storage
		WANG Ning¹, XIAO Lin¹, BAI Yu-long¹, SUN Jie¹, JIANG Lu-man¹, LI Na², LUO Guang-bing², SONG Yong-jiao^2, YANG Tao¹, ZHAO Li-juan^2
		DOI: 10.3964/j.issn.1000-0593(2025)06-1592-06
		To protect the ancient ivory at the Jinsha site from cracking, deformation, and pulverization due to rapid dehydration, a type of silicone rubber with room-temperature curing, water-locking, and moisturizing properties, as well as good weather resistance, colorlessness, transparency, and easy peeling, was used as the temporary sealing material for unearthed ivory. However, the colorless, transparent silicone rubber turned into a white, opaque rubber after twenty years of storage, which was detrimental to the storage of ivory. In this paper, Fourier transform infrared spectroscopy (FT-IR) and solid-state nuclear magnetic resonance spectroscopy (²⁹Si NMR) were used to analyze the composition of functional groups and characteristic elements of silicone rubber before and after aging. Thermogravimetric analysis (TGA) was carried out to investigate the thermal behavior and thermal stability of silicone rubber before and after aging. Thermogravimetric-infrared analysis (TG-FTIR) was employed to investigate the physical and chemical changes of silicone rubber samples before and after aging. A scanning electron microscope (SEM) and a whiteness meter were used to determine the differences in structure and transparency of silicone rubber before and after aging. The results showed that the cross-linking reaction between the epoxy group and the hydroxyl group, as well as the condensation reaction of the silicon hydroxyl group, occurred in silicone rubber under long-term storage conditions. With the increase in the degree of cross-linking, the residual ratio also improved correspondingly. According to TG-FTIR analysis, CO₂, NH₃, and H₂O were produced during the thermal decomposition process of the silicone rubber before aging. In contrast, the thermal decomposition products after aging were CO₂ and H₂O, which further confirmed the presence of silicomethyl, hydroxyl, silicomethyl, and amino groups in the silicone system. When the temperature was raised to 500 ℃, hexamethylcyclotrisiloxane was produced due to thermal depolymerization of polysiloxane. The cross-linking sites of aging silicone rubber were weak areas, which ledto stress concentration centers and microcracks in the system, changing the refractive index of the silicone. Consequently, whitish parts were observed in the silicone rubber. According to the aging structure and performance analysis of silicone for unearthed ivory storage, the mechanism of the whitening of silicone rubber was revealed. This project provides data support for the improvement of ivory storage materials, which is beneficial in enhancing the conservation level of cultural relics.
		2025 Vol. 45 (06): 1592-1597 [Abstract] ( 26 ) RICH HTML PDF (4726 KB) ( 7 )

	1598	Research on the Wavelength Attention 1DCNN Algorithm for Quantitative Analysis of Near-Infrared Spectroscopy
		CHEN Bei, JIANG Si-yuan, ZHENG En-rang
		DOI: 10.3964/j.issn.1000-0593(2025)06-1598-07
		Near-infrared spectroscopy(NIRS) technology is widely used in petroleum, textiles, food, pharmaceuticals, etc., due to its fast, non-destructive, and efficient characteristics. However, there are problems with traditional analysis methods,such as difficulty in feature extraction, low modeling accuracy when dealing with spectral data with many variables, and high redundancy. Therefore, this paper proposes a quantitative modeling method of one-dimensional wavelength attention convolutional neural network (WA-1DCNN) suitable for near-infrared spectroscopy without variable screening. The modeling method has a simple structure, strong versatility, and high accuracy.This study introduces the wavelength attention mechanism, which enhances the model's ability to capture important wavelength features by giving different weights to different wavelength data, thereby improving the accuracy and robustness of quantitative analysis. Four publicly available near-infrared spectral datasets were used in this paper to verify the feasibility of the proposed method. The proposed algorithm was compared with three traditional modeling methods that added wavelength screening, namely partial least squares (PLS), support vector regression (SVR), extreme learning machine (ELM), and one-dimensional convolutional neural network (1DCNN)modeling method. The model performance indicators root evaluated the model performance mean square error (RMSE) and coefficient of determination (R²). The results show that the performance indicators of the WA-1DCNN modeling method without the wavelength screening algorithm are better than those of the traditional modeling method and the 1DCNN modeling method with the wavelength screening algorithm. The R² of the test set in the 655 tablets dataset is 0.956 3, which is 4.34%, 12.56%, 18.42%, and 11.59% higher than that of 1DCNN and PLS, SVR, and ELM with wavelength screening; the R² of the test set in the 310 tablets dataset is 0.957 4, which is 2.72%, 8.28%, 7.27%, and 1.17% higher than that of 1DCNN and PLS, SVR, ELM, and 1DCNN with wavelength screening; The R² of the test set were 0.980 3 and 0.968 5, respectively, which were 6.24%, 1.48%, 1.75%, 6.08% and 5.81%, 1.85%, 1.58%, 2.96% higher than those of 1DCNN and PLS, SVR, and ELM with wavelength screening; in the wheat protein dataset, the R² of the test set was 0.960 0, which was 8.67%, 5.79%, 7.94%, and 0.56% higher than those of 1DCNN and PLS, SVR, and ELM with wavelength screening. To verify the optimality of the WA-1DCNN model structure in this paper, ablation experiments were conducted on four near-infrared spectral datasets to change the WA-1DCNN model structure. The results show that the wavelength-attention convolutional neural network is a spectral quantitative analysis method with strong versatility, high generalization ability and simple structure, which can promote the quantitative analysis of near-infrared spectra.
		2025 Vol. 45 (06): 1598-1604 [Abstract] ( 23 ) RICH HTML PDF (7985 KB) ( 11 )

	1605	*Nondestructive Identification of Lonicerae Japonicae Flos* and Flos Lonicerae With Near Infrared Spectroscopy and New Variable Selection-Partial Least Squares Discriminant Analysis**
		LIU Wei¹, TAN Hui-zhen¹, JIANG Li-wen¹, DU Guo-rong^2, LI Pao^{1, 3}, TANG Hui³
		DOI: 10.3964/j.issn.1000-0593(2025)06-1605-07
		Both Lonicerae Japonicae Flos and Flos Lonicerae are plants of the Caprifoliaceae family. They are rather similar in appearance. However, there are differences in chemical composition, content, efficacy, and price. To obtain excessive profits, unscrupulous merchants sell the cheaper Flos Lonicerae as Lonicerae Japonicae Flos. It is difficult for consumers to distinguish them with the naked eye. Currently, there is no study on the non-destructive identification of Lonicerae Japonicae Flos and Flos Lonicerae. Rapid and non-destructive analysis of complex samples can be achieved using near-infrared (NIR) spectroscopy. The identification of samples from different sources can be achieved by combining pattern recognition methods, such as partial least squares discriminant analysis (PLS-DA). However, an excessive number of spectral variables may easily lead to the problem of overfitting in the PLS-DA method. In this study, 643 spectra of Lonicerae Japonicae Flos from three production areas and 200 spectra of Flos Lonicerae from the local area were collected using a grating portable NIR spectrometer. Besides, 50 samples of Lonicerae Japonicae Flos from each production area and local Flos Lonicerae were collected one month later as the external validation set. A new pattern recognition method, named randomization test (RT)-PLS-DA, was proposed. This method was compared with principal component analysis (PCA), PLS-DA, and existing variable selection-PLS-DA methods, such as competitive adaptive reweighted sampling (CARS)-PLS-DA and Monte Carlo-uninformative variable elimination (MC-UVE)-PLS-DA. The accuracies of the models were further improved with the spectral pretreatments. The results showed that there were severe interferences, including peak overlapping, baseline drift, and background, in the original spectra. Even with optimized pretreatment methods, the accurate identification of Lonicerae Japonicae Flos and Flos Lonicerae cannot be achieved using the PCA method. Accurate identification results could be obtained using PLS-DA with either first derivative (1st) or continuous wavelet transform (CWT) pretreatment, while the identification rates for the validation and external validation sets were 100% and 98%, respectively. Among the three variable selection-PLS-DA methods, the CARS method selected the fewest variables. The selection of feature variables and achieving satisfactory identification rates can be done simultaneously with the RT method. The 1st-RT-PLS-DA model was the best, and the identification rates for the validation and external validation sets were 100% and 99.50%, respectively. The above results indicate that the accurate identification of Lonicerae Japonicae Flos and Flos Lonicerae can be achieved using a portable NIR spectrometer and a variable selection-PLS-DA method, providing a new approach for the rapid detection of adulteration in traditional Chinese medicinal materials.
		2025 Vol. 45 (06): 1605-1611 [Abstract] ( 26 ) RICH HTML PDF (6464 KB) ( 19 )

	1612	Effect of Amino Acids on Abiotic Synthesis of Humic Acid
		KANG Zi-tong^{1, 2, 3}, ZHANG Sa-sa¹, JIAO Zi-wei¹, GAO Xin², CHANG Yuan², ZHANG Long-li⁴, CHEN Wen-jie², XU Ting², LI Ji², WEI Yu-quan^{1, 2, 3*}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1612-10
		The process by which organic matter is transformed and polymerized into humus through biological or chemical conversion is called humification. Both biological and abiotic pathways can drive this process. The abiotic pathway offers several advantages, including environmental friendliness, excellent catalytic performance, and controllable reaction conditions. It plays an important role in enhancing soil fertility, regulating pollutants, and improving compost quality. However, because microorganisms in compost are the primary driving force for organic matter transformation and play a dominant role, the abiotic pathway is often overlooked. Amino acids are the core structural components of humic acid substances and act as precursor substances in the abiotic pathway, participating in the phenolic protein and Maillard systems, and promoting the polymerization of small organic monomer molecules to form humic acid substances. Previous studies have shown that the formation of humus generally requires the participation of amino acids in the synthesis reaction, continuously condensing to form large molecular polymers. However, the effects of different amino acid types and concentrations on abiotic humification remain unclear. Therefore, in this study, catechol and glucose were used as the reaction substrates for the phenolic protein pathway and the Maillard pathway, respectively. The extraction solutions of the reaction products were analyzed by ultraviolet-visible spectroscopy and three-dimensional fluorescence spectroscopy. The aim was to elucidate the mechanism of amino acid precursor substances on the abiotic formation of humic acid and to investigate the effects of various types and concentrations of amino acids on the humification process. The results showed that at the end of the reaction, the concentration of humic acid-like substances (HLA) in the phenolic protein theoretical system with lysine participation was the highest, reaching 120 mg·L^-1. In the Maillard systems with different amino acids, only the tryptophan addition system showed accumulation of HLA, indicating that aromatic amino acids are conducive to the direct synthesis of humic acid-like substances in the Maillard system. Ultraviolet-visible absorption spectroscopy and three-dimensional fluorescence spectroscopy indicated that lysine made a greater contribution to the aromatization degree of humic acid in the abiotic synthesis pathway. With the increase in lysine concentration, the content of fulvic acid-like substances (FLA) in each abiotic humic acid formation system also increased. When the lysine concentration was 0.025 and 0.05 mol·L^-1, HLA was produced in the phenolic protein system at 24 hours into the reaction. Under the same lysine concentration, the Maillard theoretical pathway had the highest FLA content at the end of the reaction. In contrast, the phenolic protein system yielded the highest HLA and had the highest humic-to-fulvic acid ratio (DP). A comprehensive analysis indicated that the phenolic protein system had a more effective impact on the abiotic synthesis of humic acid compared to the Maillard system. This study revealed the influence of amino acid types and concentrations on the humification process of compost, providing theoretical support for further regulating the degree of compost humification and enhancing the synthesis of humic acid under the phenolic-protein theory.
		2025 Vol. 45 (06): 1612-1621 [Abstract] ( 25 ) RICH HTML PDF (11092 KB) ( 5 )

	1622	A Study of Model for Calculating Mid-Infrared Reflectance of Vegetation
		HE Li-qin^{1, 2}, JING Xin², YAN Lei², ZHONG Yi-gen¹, XIAO Zhi-feng¹, ZENG Fan-gang^3*
		DOI: 10.3964/j.issn.1000-0593(2025)06-1622-07
		Mid-infrared reflectance has significant application value for vegetation. However, due to the coupling of the emitted energy from land objects and the reflected energy from the sun in the mid-infrared band, it is challenging to determine the reflectance of land objects in this band. This article presents a model for calculating mid-infrared reflectance in vegetation, and the sensitivity analysis demonstrates that the model exhibits strong reliability. This study employed this model to calculate the mid-infrared reflectance of vegetation at multiple research sites. The values obtained were consistent with reality and showed a certain negative correlation with NDVI. A new vegetation index was established using the near-infrared reflectance and mid-infrared reflectance obtained from this model, rather than the infrared reflectance. The results showed that this new vegetation index can represent vegetation coverage and has the advantage of being insensitive to aerosols.
		2025 Vol. 45 (06): 1622-1628 [Abstract] ( 31 ) RICH HTML PDF (4005 KB) ( 13 )

	1629	Research on Non-Destructive Detection of Moisture Content in Xuan Paper Based on Near-Infrared Reflectance Spectroscopy
		WANG Jian-xu¹, TAN Yin-yu¹, QIN Dan^2, TANG Bin¹, TANG Huan², FAN Wen-qi², YANG Wen¹, ZHONG Nian-bing¹, ZHAO Ming-fu^1
		DOI: 10.3964/j.issn.1000-0593(2025)06-1629-10
		Water content is a critical factor affecting the preservation of paper cultural relics. To establish a rapid, non-destructive method for detecting the moisture content of paper artifacts, this study focuses on four-foot single-layer Xuan paper made of cotton. We utilized near-infrared (NIR) spectrometry combined with chemometrics for non-destructive moisture detection. Seven different humidifying salts were placed in a sealed environment box to create humidity conditions ranging from 37% to 97% relative humidity (RH). The Xuan paper samples were equilibrated in this controlled environment for seven days. The water content of the samples was measured to range between 6.35% and 15.55% using the drying method. NIR spectra were collected over the range of 900 to 1 700 nm. The raw spectral data were divided into 168 training sets and 42 validation sets using the spectral-distance joint method (SPXY) at a ratio of 4∶1 for a total of 210 samples. The data were preprocessed using Standard Normal Variate (SNV), Baseline Correction (BC), and normalization, both individually and in combination. Feature bands were selected using Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS). Subsequently, linear partial least squares regression (PLSR) models were established for the full spectrum and selected feature bands, as well as a nonlinear double-layer backpropagation neural network (DL-BPNN) model. The results indicated that the best prediction model for the full spectrum was SNV-PLSR, with a root mean square error (RMSEP) of 0.644 5 and a coefficient of determination (R²_p) of 0.928 3. For the feature bands, the original spectrum-CARS-PLSR model performed best, with an RMSEP of 0.570 7 and an R²_p of 0.943 8. Among the DL-BPNN models, the WT-Normalize-CARS-DL-BPNN model yielded the best results, with an R²_p of 0.942 4 and an RMSEP of 0.577 6. Comprehensively comparing the prediction effects of the three models, the original spectrum-CARS-PLSR model exhibits the best prediction ability, indicating that the CARS feature extraction method effectively retains important features while eliminating redundant information. This study confirms the feasibility of using NIR spectroscopy for non-destructive moisture content detection in Xuan paper, establishes the relationship between NIR spectra and moisture content, and provides a reliable technical means for measuring the moisture content of paper cultural relics in China.
		2025 Vol. 45 (06): 1629-1638 [Abstract] ( 30 ) RICH HTML PDF (15981 KB) ( 11 )

	1639	Application of Solid-Liquid Mixing Sample Preparation With Standard Addition Method in EDXRF Detection of Heavy Metal Content in Soybeans
		MA Jia-hao¹, YANG Jian-bo^{1, 2*}, LI Rui¹, XU Jie², LI Hui², WU Jia-hui²
		DOI: 10.3964/j.issn.1000-0593(2025)06-1639-09
		The energy-dispersive X-ray fluorescence (EDXRF) method for detecting heavy metal Cd in soybeans faces challenges due to the complex composition of soybeans, difficulties in obtaining standard samples, and high production costs. Leveraging the sulfhydryl groups in soy protein that complex with Cd²⁺, we propose a mixed solid-liquid sample preparation method based on the standard addition method. This, combined with intensity correction for matrix correction, aims to improve the accuracy and reliability of EDXRF for Cd detection in soybeans. First, we optimized seven parameters affecting Cd excitation in soybeans through single-factor experiments. The optimized conditions were as follows: tube voltage of 70 kV, tube current of 700 μA, detection time of 1 200 s, sample mass of 12.00 g, sample mesh size of ≥100 mesh, tablet pressure of 25 MPa, and holding time of 20 s. Next, we prepared soybean powder samples with Cd spiked at concentrations of 0.000, 0.100, 0.200, 0.300, 0.400, and 0.500 mg·kg^-1 by the standard addition method. Under the optimized conditions, a standard curve was established between the resolved spectral area and the spiked concentration of Cd in soybean. This curve exhibited excellent linearity with an R² value of 0.996 22. This indicates a significant linear relationship between the fluorescence intensity of Cd in soybean and its elemental concentration, demonstrating that soybeans have an effective complexation effect on Cd²⁺. Finally, comparing the Cd content calculated by the standard addition and incremental methods revealed a deviation due to the absorption enhancement effect of other elements, particularly Sn, on Cd. Based on GFAAS results for Cd content in soybeans, a correction equation was derived using the intensity correction model. After intensity correction, the average deviation of the EDXRF method for four 100-mesh soybean samples decreased from 0.048 30 to 0.006 73. Testing ten randomly selected samples validated the universality of the calibration method, with the average deviation decreasing from 0.035 48 to 0.010 94 and the overall deviation reducing from 36.32% to 3.31%. The mixed solid-liquid sampling method proposed in this paper is simple to operate. It effectively overcomes the limitation of the lack of corresponding standard samples for EDXRF in soybean detection. Combined with the intensity correction mode, the correction effect is remarkable, providing a new and effective method for detecting cadmium (Cd) in soybeans.
		2025 Vol. 45 (06): 1639-1647 [Abstract] ( 23 ) RICH HTML PDF (5374 KB) ( 10 )

	1648	Research on Rapid Detection With Machine Learning-Based LIBS for Occupational Chronic Lead Poisoning
		ZHANG Rui¹, KANG Li-zhu², HUANG Zhi-jie², YAN Wen-hao², LIN Zhan-jian², CHEN Ji², LU Bing², XUE Zhi-dong^1, LI Xiang-you^2
		DOI: 10.3964/j.issn.1000-0593(2025)06-1648-09
		Occupational chronic lead poisoning is a gradually developing disease.It is easy to accumulate lead and develop into severe lead poisoning due to atypical early symptoms, which seriously threaten the health and quality of life of occupational groups. Mainstream analytical techniques have problems, such as requiring high precision but complicated and time-consuming operations, or being easy to operate but having poor applicability, which cannot achieve rapid in-situ detection. Laser-induced breakdown spectroscopy (LIBS), a new detection method, has demonstrated great potential and promising applications in the field of elemental analysis. In this paper, the feasibility of rapidly diagnosing occupational chronic lead poisoning is demonstrated using LIBS technology combined with machine learning algorithms. The whole blood sample preparation method has been optimized, and it is proposed that ultrasonic treatment can make the whole blood matrix more uniform, thereby alleviating the sample fragmentation problem when the laser is applied to dry blood. A glass slide is the most suitable substrate type compared to filter paper, graphite, and boric acid substrates. The effects of various LIBS experimental parameters on the signal intensity and signal-to-background ratio of the characteristic spectral lines of the element lead (Pb) were investigated. Simulated blood LIBS data for different types of occupational chronic lead poisoning were collected, and data dimensionality reduction was achieved by extracting features using principal component analysis (PCA). A 10-fold cross-validation and support vector machine (SVM) and back-propagation neural network (BPNN) were used to construct a diagnostic model for the chronic lead poisoning category. The recognition accuracies of both models reached more than 90%, which solved the problem of weak spectral difference of Pb elements in low concentration, which was difficult to be distinguished by traditional methods, and the BPNN model showed excellent diagnostic effect, with the classification accuracy and precision rate of 95.56% and 96.08%, respectively. The results demonstrate that machine learning-based LIBS technology can facilitate the timely detection of lead elemental excess in whole blood, providing an auxiliary method for the rapid and accurate screening of blood lead abnormalities, and supplementing the clinical basis for the diagnosis of occupational chronic lead poisoning.
		2025 Vol. 45 (06): 1648-1656 [Abstract] ( 28 ) RICH HTML PDF (18165 KB) ( 4 )

	1657	A Method for Predicting the Emission Spectrum of High-Power X-Ray Tubes Based on Neural Network Models
		WU Yun-long¹, XING Li-teng^{2, 3}, SUN Ai-yun¹, CHENG Can^{3, 4}, YIN Li-lei¹, JIA Wen-bao^1*
		DOI: 10.3964/j.issn.1000-0593(2025)06-1657-06
		The energy spectrum of X-rays plays a critical role in computed tomography (CT) applications. Accurate spectral information is crucial for implementing spectral CT and effectively correcting artifacts resulting from beam hardening in conventional CT reconstruction. In the fields of medical imaging and industrial inspection, high-power X-ray tubes are commonly used due to their ability to produce a large flux of X-rays in a short time, thereby improving imaging efficiency and enabling the acquisition of high-resolution and high-contrast images. However, directly measuring the energy spectrum of high-power X-ray tubes becomes challenging due to their excessively high photon flux.Currently, energy spectrum estimation is the predominant method for obtaining spectral information. This approach involves acquiring projection data from phantoms of varying thicknesses and establishing a system of equations that relate the spectrum, attenuation coefficients, and projection data. By solving this system, the energy spectrum can be obtained. However, the accuracy of this method relies heavily on the precision of algorithms and data. Due to the severely ill-posed nature of the equations, the reconstructed spectrum often lacks critical spectral features when suitable initial values are not provided. To address this limitation, this study proposes a novel method that combines direct measurement with neural network-based prediction. The photon flux of the X-rays is reduced to a detectable level by introducing a certain thickness of iron filters. A series of spectral data is then acquired by sequentially adding thin iron filters of varying thicknesses. These data are used to train a neural network model, enabling precise prediction of the emitted energy spectrum. Simulation results demonstrate the outstanding performance of the proposed neural network model in predicting energy spectra, achieving a root mean square error (RMSE) of only 0.000 031 between the normalized predicted spectrum and the ground truth. However, due to experimental limitations, the amount of measured data is relatively small. To enhance the model's generalizability and prediction accuracy, this study integrates transfer learning. Specifically, a neural network is first trained on a large dataset generated using GEANT4 simulations, and then fine-tuned using a small amount of experimentally measured spectral data. Prediction results based on experimental data show an RMSE of 0.000 194 between the normalized predicted spectrum and the ground truth. Compared to traditional spectral estimation methods, the proposed approach not only achieves significantly higher accuracy but also effectively captures critical spectral features. The advantages of this method lie in leveraging the powerful learning capabilities of neural networks, thereby overcoming the dependency on initial values and ill-posed equation solving in traditional methods, and greatly improving prediction stability and accuracy. Both simulation and experimental results validate the feasibility of the proposed approach.
		2025 Vol. 45 (06): 1657-1662 [Abstract] ( 24 ) RICH HTML PDF (11391 KB) ( 5 )

	1663	Construction and Performance Control of Low Infrared Emissivity Coating Based on Visible and Near-Infrared Reflectance Spectra of Green Vegetation
		ZHANG Jia-lun^{1, 2}, ZHANG Wei-gang^2*, ZHUANG Yue-ting^{1, 2}, ZHANG Qian-feng¹
		DOI: 10.3964/j.issn.1000-0593(2025)06-1663-07
		In recent years, green vegetation-like camouflage materials have become a research hotspot in multi-spectrum compatible stealth. In this paper, polyurethane resin matrix, yellow and blue paste, and flake aluminum powder were used as the main raw materials, a kind of low infrared emissivity coating with visible near-infrared reflectance spectral characteristics similar to green vegetation was prepared by adjusting the ratio of yellow and blue paste, the amount of flake aluminum powder and total pigment (color paste+aluminum powder). The effects of the ratio of yellow and blue paste, the amount of flake aluminum powder, and the amount of total pigment on the visible near-infrared reflectance spectrum, color characteristics, infrared emissivity, and mechanical properties of the coating were systematically studied, and the infrared stealth effect was evaluated. The results show that the color of the coating varies from emerald green to yellow-green by adjusting the ratio of yellow and blue polyurethane paste in the range of 9∶1~9.8∶0.2, and the reflection peak of the coating in the visible band varies from 541 to 545 nm, which is consistent with the color change of the coating. When the ratio of yellow and blue polyurethane paste is adjusted to 9.6∶0.4, the coating has the characteristics of a green peak similar to that of green vegetation in the visible near-infrared reflectance spectrum, which makes the coating match the yellow-green characteristics of green leaves. The emissivity of the pure color paste coating under different color paste ratios is greater than 0.894, so the coating has no infrared stealth effect. Under the optimum ratio of yellow and blue paste, the emissivity of the coating can be significantly reduced by adding a small amount of flake aluminum powder to the coating. With the increase of flake aluminum powder in the coating, the emissivity of the coating decreases, and the yellow-green color characteristics become lighter. However, the reflection spectrum can still show a yellow-green reflection peak at 545 nm. When the amount of flake aluminum powder in the coating is 10%, the infrared emissivity of the coating can be reduced to 0.679, which is 24% lower than that of the pure paste coating (0.894), and the coating still retains the obvious green peak and yellow-green visible light characteristics. The color characteristics of the coating and the spectral peak characteristics at 545 nm are unchanged when the total pigment content in the coating is adjusted under the optimal ratio of aluminum powder and color paste. When the total amount of pigment in the coating does not exceed 50%, the infrared emissivity of the coating decreases with the increase of the total pigment content. By adjusting the total amount of pigment, it is found that when it is 50%, the emissivity of the coating can be as low as 0.677, and the infrared stealth performance is the best. At the same time, the coating has excellent mechanical properties (adhesion strength is grade 1, flexibility is 2 mm, and impact strength is 50 kg·cm).
		2025 Vol. 45 (06): 1663-1669 [Abstract] ( 23 ) RICH HTML PDF (29569 KB) ( 8 )

	1670	RN-SM：Stellar Spectral Classification Algorithm Based on ResNet Feature Extraction
		YANG Jia-ming¹, TU Liang-ping^{1, 2*}, LI Jian-xi², MIAO Jia-wei¹
		DOI: 10.3964/j.issn.1000-0593(2025)06-1670-10
		The advent of the Large Survey Telescope made it possible to build spectral databases of stellar. To study the massive stellar spectrum data in the database more efficiently and effectively, it is necessary to develop a fast and efficient automatic stellar spectrum processing algorithm. Based on the deep learning model ResNet, a hybrid deep learning algorithm named RN-SM is built in this paper. The algorithm consists of five steps: ① Normalization processing: A linear normalization function is used to normalize the stellar spectrum, ensuring it has a uniform scale. ② Denoising processing: The Ces algorithm is used to denoise the stellar spectrum, removing noise from the data. ③ Composite RGB image: Three channels of an RGB image, corresponding to the gray image generated by the same spectrum. The superposition of the same spectrum makes the main features of the stellar spectrum more pronounced and easier to work with in subsequent analysis. Here, we normalize the continuous spectrum of the stellar spectrum so that the content shown in the RGB image is the spectral line information of the stellar spectrum. At the same time, we analyze the feasibility of data conversion (synthetic RGB image) by using the main spectral line information of the stellar spectrum as a reference and investigating whether the relevant pixel position of the synthetic RGB image contains these features. It is proven that the method of data conversion (synthesizing an RGB image) proposed in this paper is feasible. ④ Feature extraction: To facilitate the connection of the SoftMax algorithm, the ResNet algorithm was used to extract features from stellar spectra. The 1×2 048 feature vector from the 64×64 RGB image was extracted. The ResNet algorithm contains 49 convolution layers in total. Automatic classification: The feature vector is transferred to the SoftMax module for automatic classification. The loss function used by SoftMax is the sum of the dataset loss and the regularization term loss. After 10000 iterations, the loss function becomes stable. When the RN-SM algorithm uses the spectra of A, B, dM, F, G, gM, and K-type stars with R-band signal-to-noise ratio greater than 30 for classification, the classification accuracy is 0.91. This classification accuracy is also higher than that of the CNN+Bayes, CNN+Knn, CNN+SVM, CNN+AdaBoost, and CNN+RF algorithms, at 0.862, 0.876, 0.894, 0.868, and 0.889, respectively.
		2025 Vol. 45 (06): 1670-1679 [Abstract] ( 20 ) RICH HTML PDF (32879 KB) ( 11 )

	1680	Study on Gas Pressure Measurement Based on Spatial Heterodyne Spectroscopy
		ZHANG Ya-fei^1*, DONG Meng¹, HAN Bin², ZHAO Heng-xiang², JIA Wen-jie¹, WU Xiang-min¹, CHENG Yong-jun¹
		DOI: 10.3964/j.issn.1000-0593(2025)06-1680-07
		With the quantization transformation of traditional metrology standards, quantum vacuum metrology technology based on optical methods has been developed. Combining the quantum vacuum metrology technology based on optical interference and spatial heterodyne interference spectroscopy, this paper proposes a new method for measuring gas pressure using refractive index. The two-arm passages of the spatial heterodyne interferometer are set as closed chambers that can be evacuated and de-aerated. When the gas pressure in the chamber changes, the refractive index of the gas changes accordingly. The change in refractive index changes the propagation path of light in the two arms, causing changes in the spatial frequency, phase and other characteristics of the interference fringes, from which the gas pressure can be inverted. In this paper, based on the analysis of the influence of refractive index on the grating diffraction angle, the optical path difference is calculated to obtain the theoretical expression of spatial heterodyne interference fringes containing refractive index. Through theoretical analysis, it is obvious that the change in refractive index brings about the drift of the Littrow wavelength of the interferometer, thereby causing the change of the spatial frequency and phase of the fringes. Then, numerical simulation is carried out based on the theoretical expression. When the air pressure changes from 0 ATM to 1 ATM, the maximum changes of the sampling fringe period and phase are 6.21 periods and 19.50 rad, respectively. An optical model is established with the same parameters for ray tracing simulation, and the simulated interference pattern is inverted with the same Fourier method. The changes of the fringe period and phase under the same air pressure change in the inversion result are very close to the numerical simulation, with a difference of only 5.6×10^-3 periods and 0.014 8 rad, which verifies the theoretical expression of spatial heterodyne interference fringes with refractive index effect in this paper, and illustrates the feasibility of refractive index inversion and gas pressure calculation based on this theory. Finally, two methods of changing Littrow wavelength and increasing optical path difference scanning range to further improve phase response sensitivity are discussed, and simulations of changing grating diffraction order and increasing optical path difference offset are carried out. In the simulation, compared with the first-order diffraction, changing the order can increase the diffraction order by times, and increasing the offset of 1 mm compared with the symmetrical structure also improves the phase response by 27.65%, which proves that the feasibility of using spatial heterodyne interferometer for gas pressure measurement can be further improved after optimized design.
		2025 Vol. 45 (06): 1680-1686 [Abstract] ( 24 ) RICH HTML PDF (3816 KB) ( 12 )

	1687	Mineralogical and Spectroscopic Characteristics Comparison of Green Jadeite From Myanmar and Guatemala
		TONG Zi-da, YAN Xiao-xu^*, LIU Xi-feng, HE Rong-rong
		DOI: 10.3964/j.issn.1000-0593(2025)06-1687-06
		Gemstone jadeitite primarily consists of jadeite, other pyroxene minerals, and albite, among other minerals. It is widely renowned for its vibrant color, particularly its green hue. To address the lack of understanding of mineralogical characteristics and color mechanisms in green jadeitite, this study conducted EPMA, XPS, mid FTIR, and UV-Vis analyses on six specimens selected from Myanmar and Guatemala. The results revealed that the chromogenic minerals in the Myanmar samples were primarily pure Cr³⁺bearing jadeite or a combination of Cr³⁺ bearing, jadeite and omphacite. In contrast, the Guatemala samples consisted of pure Cr³⁺ bearing omphacite. Absorption peaks at 1 165 and 1 097 cm^-1 were associated with jadeite and omphacite, respectively. Additionally, the Guatemala samples exhibited high levels of Ca and Mg but lacked Na, resulting in a shift of absorption peaks towards lower wavenumbers at 580, 526, 462, and 420 cm^-1. Furthermore, they exhibited higher concentrations of Fe²⁺ and Fe³⁺, as indicated by narrow absorption bands at 330, 391, and 437 nm in the UV-Vis spectrum, which aided in distinguishing their origin from Myanmar. The combined results of UV-Vis and XPS attributed absorption bands at 380, 437, 636, 660, and 690 nm to the d—d orbital forbidden transitions of Fe³⁺ and Cr³⁺. In the Guatemala samples, absorptions at 333, 391, 437, and 640 nm were attributed to Fe³⁺-O^2- charge transfer, Fe³⁺ d—d orbital spin-allowed transition, Fe³⁺ d—d orbital forbidden transition, and Cr³⁺ d—d orbital forbidden transition, respectively. Despite differences in chromogenic minerals between Myanmar and Guatemala, the color mechanisms were fundamentally similar, likely due to [AlO₆] octahedral lattice isomophic substitution of Fe³⁺ and Cr³⁺, resulting in absorption bands in the ultraviolet (~380 nm) and violet-blue (380~450 nm) regions and broad absorption bands in the red region, leading to a transmission window in the green region. Notably, stronger absorptions in the violet-blue region in the Guatemala samples, indicated a grayish hue compared to the Myanmar samples.
		2025 Vol. 45 (06): 1687-1692 [Abstract] ( 24 ) RICH HTML PDF (7666 KB) ( 8 )

	1693	Scientific Exploration of Jade Artifacts From the Dawenkou Culture Unearthed at the Jiaojia Site, Shandong, China
		WANG Qiang¹, WANG Fen^1, GU Xian-zi^{2, 3}, ZHENG Xin-yu², BIAN Rong-wei¹, DENG Zi-xu³
		DOI: 10.3964/j.issn.1000-0593(2025)06-1693-07
		The Jiaojia site in Zhangqiu, Shandong Province, was one of the “National Top Ten Archaeological Discoveries” in 2017, featuring a large number of jade artifacts dating from the middle to late Da Wenkou culture. In this study, 85 representative samples excavated from the Jiaojia site were analyzed by Fourier Transform Infrared Spectroscopy (FTIR) and portable X-ray fluorescence spectroscopy (p-XRF). The results show that there are seven kinds of materials used: tremolite, serpentine, turquoise, mica jade, marble, talc, and silicomalachite, and the material combinations are relatively consistent in each archaeological cultural section, and tremolite jade accounts for a relatively small proportion of the material; the tremolite jade used is of the marble type, and the characteristics of the Sr content do not match with the known Meiling jade from Liyang in Jiangsu Province, which may have come from Hebei, Liaodong, and other places; the serpentine jade used can be seen as both marble-type and ultrabasic rock type, which are multi-origin sources, and part of the ultramafic serpentine jade is consistent with the characteristics of Shandong “Taishan Jade”, so it is a nearby material; Jiaojia site used turquoise for a long time. Considering that there is no turquoise mine in Shandong and the results of archaeological research, we speculate that the turquoise raw material may originate from the eastern part of the Qinling Mountains. It is assumed that the acquisition of turquoise raw materials resulted from material and cultural exchanges. In conclusion, this study makes up for the lack of scientific research on jade materials in Dawenkou culture, confirms the ability of the Haidai region in prehistory to obtain scarce resources represented by jade, and is of great historical significance for a complete understanding of the level of productivity development and regional exchanges in the Dawenkou culture period.
		2025 Vol. 45 (06): 1693-1699 [Abstract] ( 22 ) RICH HTML PDF (14530 KB) ( 10 )

	1700	A Study on the Identification and Application of Bastnäsite in Carbonatite-Related REE Deposits Based on Hyperspectral (VNIR-SWIR-TIR)
		GUO Dong-xu^{1, 2, 3}, SHI Wei-xin^{2, 3*}, GUO Rui⁴, GAO Qing-nan^{2, 3}, REN Xiao-sa^{2, 3}, LIU Jun-yuan^{2, 3}, GUO Xue^{2, 3}, MEI Xiao-min^{2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1700-12
		Bastnäsite is one of the most economically important rare-earth element (REE)- bearing minerals in carbonatite-related REE deposits. It contains extremely important geological information regarding the processes of diagenesis, alteration, and mineralization. It is one of the focuses that receives wide attention to accurately identify the concentrations of this rare earth mineral for geologists. Visible, near-infrared, short-wave infrared, and Thermal infrared spectroscopy (VNIR-SWIR-TIR) is characterized by an environmentally friendly, rapid, and non-destructive determination of material composition, through laboratory, field, drill core, airborne spectrometers, as well as spaceborne instruments. The qualitative identification of REE and their compounds, as well as rare earth minerals, along with the quantitative analysis of REE, has been systematically studied using this technology. However, the quantitative inversion study of rare earth minerals using infrared spectroscopy is relatively limited, and further work is needed. In this paper, typical bastnäsite-bearing ore samples collected from the Maoniuping, Dalucao, and Bayan Obo deposits were measured using a HyLogger instrument, supplemented by XRD and EMPA, to investigate the identification and application of bastnäsite by infrared spectroscopy (VNIR-SWIR-TIR) in carbonatite-related REE deposits. The conclusions can be obtained as follows. There are similar characteristics among ore mineral assemblages, including the major compositions of calcite and bastnäsite, between the Dalucao and Maoniuping deposits. Ore minerals are mainly -composed of bastnäsite, while gangue minerals primarily contain calcite, fluorite, celestite, barite, quartz, mica, aegirine-augite, and arfvedsonite in both deposits. The major components of calcite are CaO, along with minor concentrations of FeO, MnO, and MgO, while the bastnäsite is composed of higher contents of La₂O₃, Ce₂O₃, Pr₂O₃, Nd₂O₃, and F in these two deposits. A systematic summary of the spectral parameters, including relative absorption depths (or relative reflection heights), absorption areas, and full width at half maximum (FWHM), is established in this study. The positions of strong absorption (511, 522, 580, 677, 742, 865, 890, 1 094, and 1 255 nm) for bastnäsite in the wavebands of 400~1 300 nm remain unchanged with changes in bastnäsite concentration (≥10%). Furthermore, the relative absorption depths and absorption areas for these wavebands of bastnäsite exhibit strong correlations with its concentration, which has been used to establish various categories of quantitative inversion models for bastnäsite concentrates. Models of quadratic regression with one variable through characteristic absorption depths (and/or absorption areas) yield the best predictions of bastnäsite, along with high accuracy, as indicated by R² values ranging from 0.988 5 to 0.997 6. According to the comparison of bastnäsite and REO concentrations, predicted from established inversion models, as well as the chemically analyzed contents of REO within the drill core ZK3303 from the Dalucao carbonatite-related REE deposits, there is a high consistency in the trend with depth for the three variations as a whole. This study indicates that infrared spectroscopy (VNIR-SWIR-TIR) technology, with its relative advantages of accurate identification and content detection for bastnäsite, has broad applications in mineral and regional exploration, as well as mineral resource forecasting in the deep and marginal areas within carbonatite-related REE deposits.
		2025 Vol. 45 (06): 1700-1711 [Abstract] ( 23 ) RICH HTML PDF (11498 KB) ( 8 )

	1712	Early Detection Method of Mechanical Damage of Yuluxiang Pear Based on SERS and Deep Learning
		XU De-fang¹, GUAN Hong-pu², ZHAO Hua-min³, ZHANG Shu-juan³, ZHAO Yan-ru^2*
		DOI: 10.3964/j.issn.1000-0593(2025)06-1712-07
		Yuluxiang pear is loved by consumers because of its crisp flesh and sweet juice, but it is prone to mechanical damage during transportation. Once it does not occur in time, it will lead to internal decay of the fruit, which will affect the whole batch of fruits and cause economic losses. It is difficult to detect early damage quickly and accurately depending on the human eye. Modern optical technology has been widely used in fruit tree quality inspection because of its non-contact and rapid advantages. Raman spectroscopy has emerged in fruit and vegetable quality detection due to its fast molecular fingerprint characteristics and non-sensitivity to water. To solve the problem of Yuluxiang pear being vulnerable to mechanical damage during picking and transportation, this study proposes a method based on surface-enhanced Raman scattering (SERS) technology combined with a deep learning method. The change law of Raman optical characteristics of Yuluxiang pear in the early stage of mechanical damage was explored, and the coupling relationship between the early damage stage and the spectrum was mined through the deep learning algorithm to detect the early mechanical damage of Yuluxiang pear. Specific research contents: (1) The SERS spectrum data of Yuluxiang pear surface at different damage stages were obtained by constructing a highly sensitive SERS silver sol nano substrate combined with Raman spectrometer; (2) The S-G smoothing algorithm and the iterative adaptive weighted penalized least square method are used to preprocess the original spectrum to eliminate the fluorescence noise and baseline drift. (3) Data enhancement technology was used to expand the training data, a fast Fourier transform was used to extract features, and a one-dimensional Convolutional neural network (1D-CNN) model was constructed to detect the mechanical damage of Yuluxiang pear. The results showed that the model achieved ideal accuracy, precision, recall, and F1 score, especially when the injury was only 4 hours old. At the same time, the Raman characteristic peak of the protein in the injured part of pome fruit shifted from 1 607 to 1 589 cm^-1. The study shows that SERS combined with deep learning has a strong discrimination ability in detecting mechanical damage of Yuluxiang pear early. This study provides a new research idea for the early detection of fruit damage and data support for developing high-sensitivity fruit quality detection sensors.
		2025 Vol. 45 (06): 1712-1718 [Abstract] ( 27 ) RICH HTML PDF (7960 KB) ( 9 )

	1719	Remote Sensing Monitoring of Nitrogen Nutrient Index in Winter Wheat by Integrating Hyperspectral and Digital Imagery
		YANG Fu-qin¹, LI Chang-hao¹, ZHANG Ying-fa¹, CHEN Ri-qiang², LIU Yang², GUO Liang-dong³, FENG Hai-kuan^{2, 4*}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1719-10
		Rapid, real-time, and accurate acquisition of the nitrogen nutrition status of winter wheat is crucial for evaluating winter wheat growth, estimating yield, and guiding agricultural modernization and production. This study utilized hyperspectral cameras and digital cameras mounted on drones to collect canopy spectral data during the three critical growth periods. Simultaneous ground experiments were conducted to determine the physical and chemical properties of biomass and plant nitrogen content. Four characteristic parameters, including the vegetation index, the red edge index, the red edge parameter, and the three-band parameter of the hyperspectral image, as well as the color index of the digital camera and its fusion parameters, were selected. Partial Least Squares Regression (PLSR), Stepwise Regression (SWR), Random Forest (RF), and Back Propagation (BP) algorithms were used to establish a winter wheat nitrogen nutrition index monitoring model. The accuracy of the model was evaluated, and the optimal estimation model was selected. The results showed that (1) In univariate modeling, the nitrogen nutrition index model constructed with the red-edge parameter DIDRmid was the best, achieving a modeling R² of 0.66, RMSE of 0.11%, and a validation R² of 0.55, RMSE of 0.13%. (2) In the multivariate modeling, the nitrogen nutrient index model constructed with the red edge parameter as the independent variable was superior to the nitrogen nutrient index model constructed with the vegetation index, the red edge index, the three-band parameter and the color index as the independent variables, where the nitrogen nutrition index model constructed by the BP algorithm based on red edge parameters was optimal (modeling R²=0.75, RMSE=0.10%, validation R²=0.60, RMSE=0.12%).(3) In fusing hyperspectral parameters and digital index variable modelling, the nitrogen nutrient index model constructed with the multimodal variable red edge parameter + color index was superior to the nitrogen nutrient index models constructed with the red edge parameter + vegetation index, red edge parameter + red edge index and red edge parameter + triple band parameter, where the nitrogen nutrient index model constructed using PLSR with the fusion of the red edge parameters and color index was optimal (modeling R²=0.77, RMSE=0.09%, validation R²=0.65, RMSE=0.11%). Its model accuracy was better than that of univariate modeling and multivariate modeling. This study can provide an important reference for estimating the nitrogen nutrition status of winter wheat.
		2025 Vol. 45 (06): 1719-1728 [Abstract] ( 27 ) RICH HTML PDF (15348 KB) ( 12 )

	1729	Monitoring Potato Biomass and Plant Nitrogen Content With UAV-Based Hyperspectral Imaging
		YANG Fu-qin¹, CHEN Ri-qiang², LIU Yang², CHEN Xin-xin¹, XIAO Yi-bo¹, LI Chang-hao¹, WANG Ping³, FENG Hai-kuan^{2, 4*}
		DOI: 10.3964/j.issn.1000-0593(2025)06-1729-10
		Above-ground biomass and plant nitrogen content play a crucial role in crop growth, development, and yield formation. Therefore, dynamic monitoring of crop growth and nutritional status is of considerable importance. The study used unmanned aerial vehicles to obtain hyperspectral data and above-ground biomass during the budding stage, tuber formation stage, tuber growth and starch accumulation stage, to analyze the correlation and the importance of variable projection between vegetation indices and biomass and plant nitrogen content, and to screen out vegetation indices that are sensitive to biomass and plant nitrogen content combining deep neural network (DNN), partial least squares (PLSR), elastic network regression (ENR), ridge regression (RR) and support vector machine (SVR) to estimate biomass and plant nitrogen content and comparing the effectiveness of different models in estimating biomass and plant nitrogen content. The results showed that (1) the correlation between vegetation indices and both biomass and plant nitrogen content reached 0.01 significant level, and the importance of the variable projection was used to screen out the vegetation indices that were sensitive to biomass and plant nitrogen content; (2) Comparing the remote sensing estimation models for the five growth stages, the best model for biomass and plant nitrogen content was constructed at the tuber formation stage, the worst model for biomass was estimated at the present bud stage, and the worst model for plant nitrogen content was estimated at the tuber growth stage. (3) The optimum biomass model constructed in the tuber formation stage using the PLSR method was modelled with R², RMSE and NRMSE was 0.60, 235.65 kg·hm^-2 and 0.15 kg·hm^-2 respectively, and validated with R², RMSE and NRMSE was 0.58，344.72 kg·hm^-2 and 0.26 kg·hm^-2，The optimum plant nitrogen content model constructed during tuber formation stage using RR method was modelled with R²，RMSE and NRMSE was 0.74, 0.31% and 0.15%，validated R², RMSE and NRMSE was 0.77, 0.58% and 0.28%. Comprehensively comparing the DNN, PLSR, ENR, RR, and SVR algorithms for estimating biomass and plant nitrogen content models, the accuracy of the estimated plant nitrogen content model is found to be better than that of the estimated biomass model. The plant's nitrogen content can be used to more effectively monitor crop growth and nutritional characteristics, providing a reference for informed agricultural management.
		2025 Vol. 45 (06): 1729-1738 [Abstract] ( 28 ) RICH HTML PDF (22338 KB) ( 11 )

	1739	Study on the Effect of Cosine Response Error on Spectral Irradiance Measurement and Its Correction Method
		WANG Ling-ling¹, LI Ling^2*, PAN Jiang¹, DAI Cai-hong², WU Zhi-feng², WANG Yan-fei²
		DOI: 10.3964/j.issn.1000-0593(2025)06-1739-05
		Accurate spectral irradiance measurement is very important in the field of Earth observation. In optical radiation measurement, a cosine diffuser is often used to improve the accuracy of irradiance measurement results at different azimuths. The cosine response characteristic is the key factor of the spectral irradiance sensor in the field measurement. This paper studied the cosine response characteristics of the commonly used spectral irradiance instrument for ground object observation, and a spectral irradiance experimental measurement system was established. The variation rules of the instrument's spectral irradiance measurement results at different angles were obtained through experimental methods, and the cosine curve was fitted by MATLAB software. The influence of the traditional angle response normalization method and the offsetting angle response normalization method on the instrument cosine error was analyzed. Compared with the traditional normalization method, the maximum cosine error of the spectral irradiance instrument was reduced from 11.2% to 7.7% by using the offsetting normalization method, and it was almost independent of the wavelength changes. This is mainly due to the limitation of the optical path structure inside the instrument and the diffused material. When the incidence angle θ changes, the spectral irradiance angle response deviates from the cosine function. Therefore, based on the cosine response characteristics of the instrument and the experimental results of cosine error, a method for correcting the cosine error of the spectral irradiance was proposed. By extrapolating the single wavelength fitting function to all bands for correction processing, the deviation between the correction value of the instrument's spectral irradiance and the standard cosine response curve is less than 1.7%, and the cosine error is reduced from 8% to less than 2% in the range of -55~55°. The correction method greatly reduces the influence of cosine error on the instrument's measurement results, improves the instrument's measurement accuracy under different azimuths, and meets the high precision application requirements of field measurement, such as earth observation and ocean remote sensing.
		2025 Vol. 45 (06): 1739-1743 [Abstract] ( 21 ) RICH HTML PDF (4390 KB) ( 8 )

	1744	Rapid Quantitative Analysis of Acidic Ions in In-Situ Leaching Solution Using Cavity-Enhanced Raman Spectrometry
		LI Wen¹, LUO Cheng-kui^1, CHEN Shi-heng^2, JIN Hao-shu², LI Jie¹, LI Yi-bo¹
		DOI: 10.3964/j.issn.1000-0593(2025)06-1744-08
		A cavity-enhanced Raman spectrometer equipped with an automatic sampling and waste disposal device was designed to address the challenge of rapid quantitative analysis of ions in uranium ore in situ leaching solutions. Standard solutions of SO^2-₄, NO^-₃, CO^2-₃, and HCO^-₃ were tested to establish standard quantitative analysis models for these anions. This enables rapid quantitative analysis of these ions in in-situ leaching solutions from a uranium mine in China. Compared to other enhancement cavities, near-concentric cavities offer better cost-effectiveness and structural simplicity, enhancing Raman scattering signals by increasing the number of reflections of the incident laser light. The near-concentric cavity used in this experiment enhanced the Raman scattering signal by 23.77 times for single-beam incident laser light. To avoid the cumbersome nature of manual operations and the introduction of experimental errors, a dedicated automatic sampling and waste disposal device was designed for the cavity-enhanced Raman spectrometer, capable of functions such as selective sampling, sampling liquid level monitoring, air purging, and automatic waste disposal. The inclusion of the automatic sampling and waste disposal device aids in expanding the functionality of the cavity-enhanced Raman spectrometer in online detection. Compared to traditional quantitative analysis methods (such as titration), cavity-enhanced Raman spectroscopy offers advantages such as high sensitivity, ease of operation, no need for reagent pretreatment, non-destructive detection, rapid detection, and the ability to detect multiple molecules and ions simultaneously, providing a novel and efficient analytical tool for fields such as chemical analysis. Experimental results show that the detection limits of this technique for SO^2-₄, NO^-₃, CO^2-₃, and HCO^-₃ are 50, 50, 17, and 30 mg·L^-1, respectively, with correlation coefficients (R²) of the established standard quantitative analysis models exceeding 0.999, demonstrating excellent analytical performance and linear response capabilities. Five sets of tests were conducted on actual in-situ leaching sample solutions from two mining areas using the models, revealing that the acid leaching solution contained SO^2-₄ and NO^-₃, with average ion concentrations of 10 743.10 and 1 253.52 mg·L^-1, respectively, and relative standard deviations (RSD) of 0.39% and 1.39%, respectively. In contrast, the neutral leaching solution contained SO^2-₄, CO^2-₃, and HCO^-₃, with average ion concentrations of 1 400.87, 98.31, and 550.04 mg·L^-1, respectively, and RSD of 1.42%, 2.13%, and 1.69%, respectively. The comparison of experimental results using cavity-enhanced Raman spectroscopy showed much smaller errors than those using titration, further demonstrating the great application value of cavity-enhanced Raman spectroscopy in accurate and efficient quantitative analysis of ions in solutions.
		2025 Vol. 45 (06): 1744-1751 [Abstract] ( 26 ) RICH HTML PDF (6379 KB) ( 7 )

	1752	Simulating Lead Pollution Environment Based on Geological Data of Mining Areas LDI Diagnosis of Sensitive Spectral Range in Maize
		ZHANG Chao^{1, 2, 3}, YANG Ke-ming⁴, SHANG Yun-tao^{1, 3*}, NIU Ying-chao^{1, 3}, XIA Tian⁵
		DOI: 10.3964/j.issn.1000-0593(2025)06-1752-07
		To effectively obtain effective spectral response sub intervals of maize leaves under heavy metal lead pollution, and support heavy metal monitoring of crops. This article used hyperspectral remote sensing as the core technology and set up a maize pot experiment to collect a complete set of hyperspectral remote sensing data for maize leaves under heavy metal lead pollution using the SVC land cover spectrometer. A Lead Detection Index (LDI) was designed based on an improved Red Edge Normalization Index to obtain effective spectral response sub intervals of maize leaves under heavy metal lead pollution. Firstly, the original reflectance spectral data of maize in the training set was denoised by using the Db5 wavelet in the Daubechies wavelet series, resulting in the d5 component of the high-frequency component in the 5th layer of the wavelet decomposition. Then, we divided the entire spectral range of mazie leaves from 350 to 2 500 nm into 11 subband intervals and established LDI using the d5 wavelet coefficient values corresponding to the middle wavelength of each subband interval. Using the Pearson correlation coefficient r, LDI was compared with three conventional spectral indices (Photochemical Reflection Index, PRI; Meris Territorial Chlorophyll Index, MTCI; Modified Red Edge Simple Ratio Index, mSR). The effective spectral response sub-intervals of maize leaves under heavy metal lead pollution obtained from the training set data are purple valley, green peak, near-infrared platform, and near edge. The absolute values of Pearson correlation coefficients are all greater than 0.9, which are 0.911 0, 0.915 5, 0.905 1, and 0.907 6, respectively. In contrast, the absolute values of Pearson correlation coefficients between the three conventional spectral indices and the heavy metal lead content in the leaves are all less than 0.9, indicating high LDI effectiveness. Finally, we used the validation set data to obtain the effective spectral response subintervals of maize leaves under heavy metal lead pollution: purple valley, green peak, near-infrared platform, and near edge. The absolute Pearson correlation coefficients were all greater than 0.9. The Pearson correlation coefficients r for validation set one and validation set two were -0.999 9, -0.973 0, 0.914 2, 0.905 7, and -0.999 9, 0.911 7, -0.914 6, and 0.910 3, respectively. However, the absolute Pearson correlation coefficients between the three conventional spectral indices and the heavy metal lead content in the leaves were all less than 0.9. The results showed that under heavy metal lead pollution, the effective spectral response subbands of maize leaves were purple valley, green peak, near-infrared platform, and near-edge four subbands. The research results can provide technical support for monitoring heavy metal pollution in other crops.
		2025 Vol. 45 (06): 1752-1758 [Abstract] ( 30 ) RICH HTML PDF (2320 KB) ( 6 )

	1759	Identification and Adulteration Detection of Lotus Root Starch Using Hyperspectral Imaging Technology Combined With Deep Learning
		PENG Jian-heng¹, HU Xin-jun^{1, 2*}, ZHANG Jia-hong¹, TIAN Jian-ping¹, CHEN Man-jiao¹, HUANG Dan², LUO Hui-bo²
		DOI: 10.3964/j.issn.1000-0593(2025)06-1759-09
		Lotus root starch is highly nutritious, and its production process is complex. Some unscrupulous businessmen, driven by profit, adulterate lotus root starch with cheaper common starch or mix common starch into lotus root starch. Traditional methods for authenticating lotus root starch are time-consuming, labor-intensive, and destructive. Hyperspectral imaging technology, with its advantages of rapid, non-destructive, and accurate, has been widely applied in food safety detection. Therefore, this study proposes a method for quickly distinguishing between lotus root starch and other common starches, as well as identifying adulterated lotus root starch, by combining hyperspectral imaging technology with deep learning. Hyperspectral images of pure lotus root starch, four types of common starches, and adulterated starches were collected in the wavelength range of 900~1 700 nm using hyperspectral imaging technology. Several regions of interest (ROI) were delineated in the hyperspectral images of pure lotus root starch and the four common starches, and the average reflectance of each ROI was calculated from the original spectral data to build classification models. Abnormal bands affected by noise at the beginning and end of the original spectra were removed, leaving 443 bands between 940 and 1 675 nm. Outliers in the spectral data were then eliminated using the Isolation Forest (IF) algorithm. To enhance model training efficiency, the Competitive Adaptive Reweighted Sampling (CARS), Bootstrapping Soft Shrinkage (BOSS), and Channel Attention Mechanism Module (CAMM) were employed to extract 45, 32, and 12 feature wavelengths from the 443 bands, respectively. Partial Least Squares Discriminant Analysis (PLS-DA) classification models were constructed based on the spectral data after feature wavelength extraction, with the CAMM-PLS-DA model showing the best recognition effect, achieving an accuracy of 95.25% in the test set. To determine the optimal classification model, PLS-DA, Support Vector Machine (SVM), and Convolutional Neural Network (CNN) classification models were established using spectral data with different numbers of feature wavelengths extracted by CAMM. The CAMM-CNN model exhibited the best classification performance, with a highest accuracy of 99.69% in the test set. To further verify the ability of the CAMM-CNN model to distinguish adulterated lotus root starch, the spectral data of all pixel points in the hyperspectral images of adulterated lotus root starch were input into the trained CAMM-CNN model for discrimination. Visualization images showed that the model successfully identified various types of common starches in the adulterated lotus root starch. The results indicate that the combination of hyperspectral imaging technology and deep learning methods can effectively be applied to the authentication of lotus root starch, providing a new detection approach to combat the adulteration of lotus root starch and ensure its safety.
		2025 Vol. 45 (06): 1759-1767 [Abstract] ( 29 ) RICH HTML PDF (13001 KB) ( 11 )

	1768	Research on Identification of Non-Directional Doping of Egg White Powder Based on Near Infrared Spectroscopy and LOF
		ZHU Zhi-hui^{1, 2}, LI Wo-lin¹, HAN Yu-tong¹, YE Wen-jie¹, JIN Yong-tao¹, WANG Qiao-hua^{1, 2}, MA Mei-hu³
		DOI: 10.3964/j.issn.1000-0593(2025)06-1768-08
		Egg white powder adulteration identification technology is of great significance to ensure the quality and safety of egg powder, however, the traditional biomolecular detection methods are complicated and time-consuming, and the adulteration identification model for egg white powder is still mainly a directional identification model, which has a limited detection range and can not effectively cover all the possible adulterants, so it is urgently needed to develop a fast, accurate and generalized method for egg white powder adulteration identification. In this study, we introduced near-infrared spectroscopy detection technology and constructed a LOF non-directional identification model. The model is an unsupervised single classification model, and MSC preprocessing and CARS wavelength screening processing are added to the original model to enhance the model's ability to extract spectral features, reduce noise interference, and lower computational requirements. The experimental results show that the detection rate of the LOF non-directional identification model for adulterated egg white powder can reach 93.6%. Its accuracy, precision, recall, and F1 score reach 93.6%, 95.5%, 93.6%, and 94.5%, respectively. For egg white powder with an adulteration concentration of more than 15%, the total accuracy rate (A_AR) of both test sets reaches 100%, and the average detection time (A_ATS) can be as low as 0.001 1 s. Compared to other non-directional algorithms, this algorithm has higher accuracy and is more generalizable than traditional directional models, making it more suitable for identifying egg white powder adulteration with a wide variety of adulteration types in the market. This study can provide a theoretical basis for the subsequent development of a portable near-infrared spectroscopy detector for detecting egg white powder quality.
		2025 Vol. 45 (06): 1768-1775 [Abstract] ( 31 ) RICH HTML PDF (6400 KB) ( 6 )

	1776	Acquisition and Analysis of Raman Signals of Levitated Single Droplet by Optical Tweezers System
		CAO Xue, ZHANG Yun-hong^*
		DOI: 10.3964/j.issn.1000-0593(2025)06-1776-06
		To overcome the limitations of traditional commercial spectroscopic detection instruments, this study focuses on measuring the evolution mechanisms of atmospheric fine particulate matter and its important physicochemical parameters. We have developed a long-duration, spatially resolved, high-sensitivity, and high-spectral-resolution optical tweezers-stimulated Raman spectroscopy device, combining new principles, technologies, and methodologies. This device integrates spontaneous and stimulated Raman scattering, as well as Rayleigh scattering, aiming to address the scientific challenges of real-time monitoring of physical and chemical processes in micron-scale levitated single droplets, as well as the observation of aerosol microdroplets reacting with trace gaseous species. Specifically, this device enables real-time detection of a levitated single droplet, allowing for the observation of reaction processes between the gas phase and the droplet while simultaneously measuring the chemical composition and evolution of both phases. A 532 nm continuous-wave laser serves as the light source for optical tweezers levitation and Raman signal excitation, facilitating the rapid capture and stable levitation of a single droplet. The relationship between the stability of the levitated droplet and the laser power has been established. Additionally, an EMCCD was employed as the detector for spontaneous and stimulated Raman signals, enabling the investigation of the reaction kinetics of an optical tweezers-levitated single droplet with trace SO₂ gas under precisely controlled conditions of trace reaction gas and relative humidity. The research results reveal dynamic changes in the reaction processes within the droplet, providing quantitative data on the variation of droplet radius over time. This device effectively detects and analyzes the chemical reaction processes of aerosol microdroplets, exhibiting high temporal and spatial resolution. It not only provides a new experimental platform and methodology for understanding the evolution mechanisms of atmospheric particulate matter but also lays a foundation for detailed studies of gas-aerosol reaction processes.
		2025 Vol. 45 (06): 1776-1781 [Abstract] ( 25 ) RICH HTML PDF (11692 KB) ( 10 )

	1782	Composition, Distribution and Source Analysis of Dissolved Organic Matter in the Water Body of a Typical Agricultural Watershed in the Headwaters of Chishui River
		LIU Shi-jie^{1, 2}, YANG Juan^{2, 3*}, WANG Ke-qin¹
		DOI: 10.3964/j.issn.1000-0593(2025)06-1782-09
		The Chishui River serves as a crucial tributary to the upper reaches of the Yangtze River. To enhance the protection of the water ecological environment in this area, it is essential to investigate the composition characteristics and sources of dissolved organic matter (DOM) within the upstream region. This study employed UET-visible absorption spectroscopy, three-dimensional fluorescence spectroscopy (EEMs), and parallel factor analysis (PARAFAC) to investigate the composition, distribution, and source of dissolved organic matter (DOM) in the Longjing small watershed, located in the headwater area of the Chishui River (Zhenxiong Section) in northeast Yunnan Province. The findings revealed that concentrations of DOC, CDOM, and FDOM gradually increased from top to bottom within the Longjing small watershed, with DOC concentrations ranging from 4.65 to 15.35 mg·L^-1, which is higher than those in other types of water bodies. Moreover, the humification degree and molecular weight level exhibited a pattern in which they decreased downstream, with lower reaches > middle reaches > upper reaches. DOM consisted of three fluorescent components: humus-like component C1, protein-like component C2, and long-wave humus component C3. Among these components, the tryptophan-based component C2 contributed significantly, accounting for 39.35% of the total fluorescence intensity, while C1 and C3 accounted for 32.17% and 28.48%, respectively.The origin of DOM primarily stemmed from autogenesis, characterized by weak humification; however, there was also some influence from both internal and external sources on certain sections within the Longjing small watershed due to comprehensive load effects.Under conditions such as isohigh reverse slope terrain, combined with nitrogen/phosphorus accumulation and vigorous microbial activity, DOM generation was promoted, altering its molecular structure.The C1 and C3 components exhibited a highly significant positive correlation, indicating their homology. TN and TP also demonstrated a significant positive correlation with the C1 and C3 components. Nitrogen and phosphorus in the Longjing small watershed predominantly existed in the form of organic nitrogen and organophosphorus, which combined with humus to contribute to exogenous DOM. This study provides valuable insights into the water quality of the Yangtze River's source waters, offering guidance for protecting its aquatic environment.
		2025 Vol. 45 (06): 1782-1790 [Abstract] ( 24 ) RICH HTML PDF (6352 KB) ( 8 )

	1791	Spectroscopic Study on the Evolution of Coal Molecular Structure During CO₂ Storage
		GAO Fei^{1, 2}, LIN Wan¹, JIA Zhe¹, BAI Qi-hui¹, LIU Jing¹, WANG Yi-fan¹, LI Wei-ying³
		DOI: 10.3964/j.issn.1000-0593(2025)06-1791-10
		This study examines the impact of CO₂ storage on the pore and molecular structures of coal in unminable coal seams. Low-temperature liquid nitrogen adsorption, low-temperature CO₂ adsorption, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and Raman spectroscopy (Raman) experiments were employed. The evolution rules of pore structure, surface functional groups, macromolecular structure, and microcrystalline structure of coal after CO₂ adsorption at different pressures were investigated. The results showed that, after CO₂ adsorption, the cumulative specific surface area and cumulative pore volume of micropores decreased. In contrast, the cumulative specific surface area and cumulative pore volume of mesopores and macropores increased. The average pore size of micropores increases overall, and the micropores in the pore structure transition to a direction that favors mesopores and macropores. At the same time, the total content of aromatic hydrocarbons in coal decreased, the aromatic layer spacing d₀₀₂ increased, the microcrystalline stacking height L_c, the average number of crystal stacking layers n, the aromaticity f_a decreased, the content of oxygen-containing functional groups and hydroxyl groups increased, the content of —CH₂— decreased, the content of —CH₃ increased slightly, and the CH₃/CH₂ value increased. It shows that after the adsorption of CO₂ by coal, the large aromatic structure developed to the small aromatic structure, the branched chain on the aromatic ring gradually became shorter and increased, the aromatic microcrystalline structure was destroyed, and the aromatic layer was more loosely accumulated, resulting in a decrease in the degree of crystallization. With the increase of CO₂ adsorption pressure of coal, the peak position difference d_(G-D) between D peak and G peak decreases, the intensity ratio I_D/I_G, half peak width of D peak (FWHM-D), half peak width of G peak (FWHM-G) and W_D/W_G values increased, and the peak area ratio A_S/A_total, A_S/A_D, A_D/A_G, A_(GR+VL+VR)/A_D values representing the defect structure also increased, it indicates that after coal adsorbed CO₂ under different pressure, the macromolecular structure in coal expanded and cracked, the proportion of graphite structure decreased and the proportion of impurity structure increased. The coal structure as a whole develops in the direction of increasing disorder. The research results provide a theoretical basis for the influence of geological storage of CO₂ on the microscopic properties of coal. The research results provide a theoretical foundation for understanding the impact of geological CO₂ storage.
		2025 Vol. 45 (06): 1791-1800 [Abstract] ( 25 ) RICH HTML PDF (16208 KB) ( 11 )

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