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2025 Vol. 45, No. 10 Published: 2025-10-01

	2701	Application of In Situ Infrared Spectroscopy to the Research of Biomass Conversion
		LU Si, CHEN Xiao-li, LIANG Zheng, WANG Xiao-man, SU Qiu-cheng, QI Wei, FU Juan^*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2701-10
		Biomass, as the most abundant bio-renewable resource on Earth, has been recognized as a potential energy source to replace fossil resources and mitigate global energy and environmental crises. As the sole renewable carbon source, biomass can be converted into high-value chemicals and energy-intensive biofuels through various catalytic methods and conversion pathways. In the process of biomass conversion, it is essential to analyze the structure and composition of reactants, intermediates, by products, and products for the study of reaction pathways and reaction mechanisms to improve conversion efficiency. Based on the principle of Fourier infrared spectroscopy, in situ infrared spectroscopy technology with high sensitivity and real-time capabilities is used to monitor online chemical reactions by assembling an in situ reaction tank. It is capable of obtaining the characteristic spectra of substances changing with the reaction conditions, and has a wide range of application prospects in the research of biomass conversion. In this paper, the application of in situ infrared spectroscopy in recent years to research biomass conversion is reviewed. The transformation mechanism of biomass is analyzed using in situ infrared spectroscopy, which includes transmission, diffuse reflection (DRIFTS), and attenuated total reflection (ATR) modes. A categorized review of various conversion strategies for the high-value utilization of biomass is presented, focusing on recent advances in in situ infrared spectroscopy in the study of pyrolysis, chemical catalysis, and electrocatalysis reaction mechanisms. The reaction process involves a variety of catalytic methods, including hydrolysis, hydrogenation, oxidation, reduction, and isomerization, among others. The reaction route, reaction mechanism, and reaction kinetics are studied in depth by tracking the changes in reactant structure and functional groups. It also summarizes the advantages and limitations of various testing modes in in situ infrared spectroscopy when applied to investigate the reaction mechanisms of pyrolysis, chemical catalysis, and electrocatalysis. Finally, addressing the current challenges and difficulties in applying in situ infrared spectroscopy to biomass conversion processes, this study proposes the future directions and potential of this technology. These include technical optimization of in situ reaction cells to enhance compatibility with extreme reaction conditions, as well as the development of multimodal coupling techniques, such as integrating in situ infrared spectroscopy with mass spectrometry and Raman spectroscopy. Such advancements are expected to provide deeper insights into biomass conversion mechanisms and foster more significant breakthroughs in this field.
		2025 Vol. 45 (10): 2701-2710 [Abstract] ( 7 ) PDF (11289 KB) ( 4 )

	2711	Application and Progress of Nuclear Magnetic Resonance and Infrared Spectra in the Study of the Mechanism of Ionic Liquid-Catalyzed Reaction of CO₂ With 2-Aminobenzonitrile
		SUN Zhong-yuan, GUO Yu-jun, XU Ying-jie^*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2711-09
		Nuclear magnetic resonance (NMR) and infrared (IR) spectra are two commonly used spectroscopic methods, which can not only be used for the analysis of substance content and identification of molecular structure, but also characterize important microscopic information such as intermolecular interaction sites, types and strengths at the molecular level through the intensity and position of the absorption peaks, and have been expanded to be used in the study of chemical reaction mechanisms. The resourceful utilization of CO₂ is one of the current hotspots in green chemistry research. Ionic liquids (ILs), with low vapor pressure, high thermal stability and adjustable structure and property, are widely used as a new type of green catalysts for the reaction of CO₂ with 2-aminobenzonitrile, which can simultaneously activate CO₂ and 2-aminobenzonitrile, and efficiently convert atmospheric CO₂ into biologically active quinazoline-2,4(¹H, ³H)-dione and its derivatives under metal-free and mild conditions, showing excellent catalytic performance and potential application value. Therefore, its chemical reaction mechanism has garnered significant interest from a diverse range of researchers. Understanding the chemical reaction mechanism is a prerequisite for effective regulation of the reaction. Characterizing the interaction information between ILs and reaction substrates, aided by suitable detection methods, is an effective way and a key step in studying the chemical reaction mechanism. For this reason, to better understand the application and progress of NMR and IR spectroscopic methods in the study of the mechanism of IL-catalyzed reaction of CO₂ with 2-aminobenzonitrile, based on briefly description of the history and current development of the reaction between CO₂ and 2-aminobenzonitrile, we firstly introduce the characteristics of NMR and IR techniques and their roles in the study of this reaction mechanism. Secondly, we focus on ¹H NMR, ¹³C NMR, ¹⁵N NMR, ¹⁸³W NMR, FTIR and in-situ FTIR in the characterization of the interaction of ILs with CO₂ or 2-aminobenzonitrile, the newly formed (disappeared) groups and the formation of possible reaction intermediates, etc., and combined with the results of the literature, and then the unique advantages and problems of the NMR and IR techniques in the study of the intermolecular interactions and the mechanism of chemical reactions are analyzed, and possible solutions are proposed. Finally, reasonable suggestions are put forward for the further promotion of the application of NMR and IR spectra in the study of chemical reaction mechanisms, namely, using two-dimensional NMR and IR spectra to obtain more refined molecular structure and interaction information, further determining the interaction sites and types between catalysts and substrates, and combining theoretical methods such as DFT calculation and molecular dynamics simulation to obtain the electronic structure of substrates and energy changes of the system during the reaction process, accurately obtaining the structural information of reaction intermediates and transition states, and thus clarifying the reaction mechanism more clearly. This will usher in new developments in NMR and IR spectra in the study of chemical reaction mechanisms.
		2025 Vol. 45 (10): 2711-2719 [Abstract] ( 5 ) PDF (10692 KB) ( 3 )

	2720	Research Progress of Miniaturized Vis/NIR Spectrometers in Leaf and Fruit Nondestructive Detection
		ZHANG Xu¹, XIE Zhuo-jun¹, QIN Zi-quan¹, ZHAO Rui-jie¹, LIU Wen-zheng¹, BAI Xue-bing¹, XIONG Xiao-lin^2*, LIU Xu¹
		DOI: 10.3964/j.issn.1000-0593(2025)10-2720-10
		The physiological indicators of leaves reflect crop growth status, while the physicochemical parameters of fruits characterize their quality attributes. Efficient detection of key indicators in leaves and fruits is a crucial prerequisite for achieving precision agriculture. Visible/near-infrared (Vis/NIR) spectroscopy can non-destructivelydetect material composition and internal structures by capturing molecular vibrations and electron transition signals, synchronously acquiring spectral information across both visible and near-infrared bands. Currently, benchtopspectrometers are expensive, bulky, and power-consuming, making them difficult to use for on-site detection. The prices of commercial portable spectrometers and handheld spectrometers have decreased, but the level of intelligence is not high, which limits the widespread adoption of miniaturized spectrometers. To achieve more economical, efficient, and flexible spectral detection, the miniaturization of Vis/NIR spectrometers has become a critical research direction. In recent years, the development of sensor technology and microelectromechanical systems (MEMS) has driven the miniaturization of spectrometers. The advancement of data analysis optimization and machine learning modeling has further improved spectral detection accuracy. This paper compared key technologies in the development of miniaturized Vis/NIR spectrometers, including structural design, spectrometer integration, and model transfer. It analyzed spectral data processing optimizing methods, such as preprocessing, outlier removal, and feature extraction. Extraction. The construction of qualitative and quantitative prediction models, as well as evaluation indicators for these models, was discussed. Furthermore, it reviewed the latest domestic and international research progress in applying miniaturized Vis/NIR spectrometers to detect leaf physicochemical parameters (e.g., chlorophyll content, nitrogen levels, water content) and fruit quality indicators (e.g., sugar content, titratable acidity, color attributes). Current drawbacks in non-destructive detection of leaves and fruits were summarized, and future research directions for miniaturized Vis/NIR spectrometers were proposed. These research results provide directional guidance for the development of Vis/NIR spectrometer technology and have important reference value for the application and promotion of crop leaf and fruit detection.
		2025 Vol. 45 (10): 2720-2729 [Abstract] ( 5 ) PDF (11419 KB) ( 4 )

	2730	Research on Lung Tumor Diagnosis Method Based on Laser-Induced Breakdown Spectroscopy Combined With Deep Learning
		SUN Hao-ran¹, ZHAO Chun-yuan¹, LIN Xiao-mei², GAO Xun³, FANG Jian^1*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2730-07
		Lung cancer is the most deadly form of cancer worldwide, with a high morbidity and mortality rate. The accuracy of a patient's diagnosis directly impacts their treatment plan and the likelihood of recurrence post-surgery. Conventional diagnostic methods are often dependent on the subjective assessment of medical professionals and are time-consuming. Consequently, there is an urgent need for a lung tumor diagnostic method that can provide objective and quantitative metrics, facilitating rapid detection. The objective of this study is to assess the viability of a novel approach that integrates laser-induced breakdown spectroscopy (LIBS) with deep learning network models for the expeditious and in situ diagnosis of diseased lung tissues. The LIBS technique was employed to quantitatively analyze the elemental composition of cancerous tissues, lung tumors, and normal tissues from 45 patients. This analysis enabled the rapid detection of lung tumors and the acquisition of elemental differences between diseased and normal tissues. A total of 12 characteristic spectral lines of 6 elements, including Ca, Na, K, etc., were selected as inputs to the model through multivariate analysis. To address the challenges posed by the intricate preprocessing and feature extraction in tumor and normal tissue spectral data, a deep learning spectral feature processing system with ResNet18 as the primary network was developed. In conjunction with this system, 3 machine learning models were designed to be utilized in conjunction with the Random Forest feature extraction method. To facilitate a comprehensive comparison, a quadratic recursive machine learning spectral feature processing system was also established. The findings indicate that the deep learning network model exhibits a substantially superior recognition capability compared to the other three machine learning models. Its accuracy, sensitivity, and specificity reach 99.6%, 100%, and 99.3%, respectively. The model demonstrated a 99.6% accuracy, 100% precision, and 99.3% recall in the recognition of spectral data from 140 tumor tissues and 139 normal tissues. The model's balanced ability to recognize different spectral data types while ensuring recognition accuracy, in conjunction with its demonstrated generalization and robustness, is noteworthy. The study above demonstrates that, in the context of high-dimensional and abstract spectral feature information in tumors and normal tissues, the convolution and pooling regions in the deep learning network model exhibit a superior capacity to extract nonlinear features in comparison to traditional chemometric methods. This enhanced capability enables the expeditious identification of similarity and difference information in tumors and normal tissues. The integration of LIBS with deep learning has been demonstrated to facilitate the acquisition of objective, quantitative data concerning diseased tissue in the context of lung cancer diagnosis. This approach has been demonstrated to provide a rapid, precise, and robust method for identifying lung tumors.
		2025 Vol. 45 (10): 2730-2736 [Abstract] ( 5 ) PDF (6743 KB) ( 3 )

	2737	Resolution Method of Overlapping Peaks in Soil XRF Spectrum Based on WPS-GMM
		LI Tang-hu², GAN Ting-ting^{1, 4}, ZHAO Nan-jing^{1, 2, 3, 4, 5}, YIN Gao-fang^{1, 4, 5}, WANG Ying^{1, 3, 4}, LI Xing-chi^{1, 3, 4}, SHENG Ruo-yu^{1, 3, 4}, YE Zi-qi^{1, 3, 4}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2737-10
		X-ray fluorescence (XRF) spectroscopy is an important technique for rapid and on-site detection of heavy metals. However, when it is used for detecting heavy metals in soil, due to the variety of elements contained in soil, there are some overlapping peaks in the soil XRF spectrum, which affects the accuracy of characteristic peak extraction and quantitative analysis of heavy metals. For this problem, this paper proposed a method for resolving the overlapping peaks in soil XRF spectra based on wavelet peak sharpening and Gaussian mixture model (WPS-GMM). This method first sharpened the overlapping peaks by a discrete wavelet transform to enhance the important local features of the spectral signal and clarify the peak positions of subpeaks. Then, using it as a prior constraint, a Gaussian mixture model of the overlapping peak was constructed. Finally, the relevant information for each subpeak in the overlapping peak was obtained by maximizing the likelihood to estimate the model parameters, thereby achieving the resolution of the overlapping peak. The established WPS-GMM method was applied to the resolution of typical overlapping peaks of Ni Kα-Co Kβ, Cu Kα-Ni Kβ, and Zn Kα-Cu Kβ in soil XRF spectra, as well as the quantitative analysis of Ni, Cu, and Zn corresponding to the main peaks in the overlapping peaks, to verify it saccuracy by comparing with the traditional resolution method for overlapping peaks based solely on the Gaussian mixture model (GMM). The results showed that compared with the GMM method, when the established WPS-GMM method was used for resolving the three overlapping peaks in soil XRF spectra, for the sub peaks of Ni Kα, Ni Kβ, Cu Kα, Cu Kβ and Zn Kα, the accuracy of resolved peak position increased by an average of 77.55%, 47.03%, 52.65%, 22.07%, and 8.43%, respectively, and the accuracy of resolved integral area increased by an average of 74.05%, 80.17%, 61.62%, 28.29%, and 43.59%, respectively; Moreover, the accuracy of quantitative analysis of Ni, Cu and Zn increased by an average of 73.23%, 68.47% and 47.62%, respectively. The established method demonstrated better universality for the accurate quantitative analysis of Ni, Cu, and Zn in soils with three different uses, including industrial, agricultural, and construction, by resolving the overlapping peaks in XRF spectra. Therefore, the established WPS-GMM method can more accurately obtain sub-peak information of overlapping peaks in XRF spectra of different soils, which is more conducive to improving the accuracy of quantitative analysis of heavy metals in soils by XRF. This study will provide an important methodological foundation for the rapid and accurate on-site detection of heavy metals in soil using XRF spectroscopy.
		2025 Vol. 45 (10): 2737-2746 [Abstract] ( 4 ) PDF (9057 KB) ( 3 )

	2747	Preparation of Zinc-Based Coordination Polymers and Dye Adsorption Properties
		WANG Ce
		DOI: 10.3964/j.issn.1000-0593(2025)10-2747-07
		In this study, a new three-dimensional (3D) porous metal-organic Frameworks (MOFs) {[Me₂NH₂][Zn(btc)]·DMF (1)} was constructed with 1,4 terephthalic acid ligands. The structural characteristics of compound 1 were studied by infrared spectroscopy, single crystal X-ray diffraction (XRD), powder X-ray diffraction (PXRD) and thermogravimetric analysis (TGA). Firstly, the excitation spectrum and emission spectrum of compound 1 were tested, and the fluorescence response of compound 1 to different cations in aqueous solution was explored. The experimental results showed that compound 1 could effectively identify Fe³⁺ and NB ions from other ions. The detection limits of Fe³⁺ and NB were 5.52×10^-7 and 6.94×10^-7 mol·L^-1, respectively. The adsorption effect of compound 1 on water-soluble dyes was further tested, and the adsorption capacity of CR dye was 302.33 mg·g^-1. After 5 cycles of experiment, the removal rate of CR by compound 1 was still 88.2%. Therefore, compound 1 has significant potential advantages in the treatment of printing and dyeing wastewater.
		2025 Vol. 45 (10): 2747-2753 [Abstract] ( 7 ) PDF (7112 KB) ( 2 )

	2754	Helium Atmospheric Pressure Dielectric Barrier Discharge Plasma Emission Spectroscopy Analysis and Voltage-Flow Co-Control Mechanism
		CHEN Xing-wang, SSEKASAMBA Hakim, REN Kai-wen, LI Wei-xing, WANG Zi-yan, TANG Xiao-liang^*, QIU Gao
		DOI: 10.3964/j.issn.1000-0593(2025)10-2754-06
		Atmospheric pressure dielectric barrier discharge (APDBD) has attracted significant interest in various fields as a result of its gentle and uniform generation of active species over a large surface area. However, the plasma characteristics, such as active species concentration, electron excitation temperature, and density, during different discharge modes are not clearly understood. In this study, a “voltage-flow co-control mechanism” is proposed to systematically explore the evolution of electronic parameters and discharge modes in helium APDBD plasma, aiming to provide theoretical support for the design of industrial-grade plasma sources. In this experiment, a ring-ring DBD reactor was utilized to investigate the dynamic characteristics of active particle concentration and electronic parameters using emission spectrum analysis and electrical diagnosis technology. It is found that the main active particles in atmospheric helium dielectric barrier discharge plasma include excited helium atom He I, hydrogen atom Hα, oxygen atom O I, hydroxyl OH (A-X), nitrogen molecule N⁺₂ (B-X), excited nitrogen molecule N₂ (C-B), and N₂ (B-A). The Boltzmann slope method and Hα line Stark broadening were used to diagnose the electron excitation temperature (T_e) and electron density (n_e) of the plasma. It was found that the coupling mechanism between the discharge mode and the electronic parameters showed three stages of evolution: At a helium gas flow rate of 0.5 SLM and low voltage range of (9~11kV), APDBD demonstrated a uniform discharge mode with a 56% increase and 36% decrease in electron excitation temperature and density respectively. When the voltage was increased to medium range (11~15 kV), asymmetric filament discharge mode was observed with 983% and 221% increase in electron excitation temperature and density respectively. In the symmetrical filamentous discharge mode, the electron excitation temperature decreases rapidly by up to 79%, while the electron density remains in dynamic equilibrium. In addition, the electron excitation temperature decreases with the increase of helium flow, and the electron density maintains dynamic equilibrium with the increase of the flow rate. It is found that the input voltage control can realize the conversion between plasma discharge modes, and the helium flow rate can independently regulate the electron excitation temperature, providing a dual-dimensional collaborative control mode for optimizing the parameters of atmospheric pressure plasma in material preparation, modification, and biomedical applications.
		2025 Vol. 45 (10): 2754-2759 [Abstract] ( 7 ) PDF (3826 KB) ( 3 )

	2760	External Electric Field Effects on the Molecular Structure and Spectra of 1,5-Dinitronaphthalene
		LI Yi-duo¹, FENG Zhi-fang¹, CHEN Dong-ming², ZHANG Qian¹, YAO Ning¹, ZHANG Ping¹, TAO Ya-ping³, ZHAO Wen-lai⁴, DU Jian-bin^1*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2760-07
		1,5-dinitronaphthalene (DNN) is an important chemical raw material, widely used in various fields. To study the effect of external electric field (EEF) on DNN, the B3LYP of density functional theory (DFT) is employed to optimize the ground state structure of DNN at the def2-TZVP basis set level, and its infrared (IR) spectra are obtained. Based on this, time-dependent density functional theory (TDDFT) is employed to calculate the change in UV-Vis spectra of DNN under EEF. The range of the electrostatic field is 0~0.02 a.u. in this work. The results show that the geometric configuration of DNN strongly depends on changes in EEF. The dipole moment increases with the enhancement of EEF, while the change in total energy is opposite. The IR spectra undergo energy splitting, and the vibration Stark effect is obvious. The absorption peaks of the UV-Vis spectra exhibit a red shift; the molar coefficients initially increase and then decrease. In the two-dimensional UV-Vis spectrum of DNN, there is a strong autocorrelation peak at 200 nm on the diagonal of the synchronous graph, which indicates that the peak is very sensitive to changes in EEF. In summary, EEF has a significant impact on DNN. This work provides theoretical guidance for various potential applications of DNN, and also has reference value for the study of other nitration products of naphthalene.
		2025 Vol. 45 (10): 2760-2766 [Abstract] ( 6 ) PDF (7338 KB) ( 5 )

	2767	Synthesis of Novel Fluorescent Organic Porous Polymers and Sensing of Paraquat in Aqueous Solution
		LI Ying^1, SUN Zhi-jing¹, HU Xia¹, REN Guo-jie¹, SUN Lu¹, CHU Shan-shan¹, SUN Li-jun², GONG Wei-tao^2
		DOI: 10.3964/j.issn.1000-0593(2025)10-2767-07
		With the rapid growth of the population, the current society's demand for food is also surging, which simultaneously leads to the excessive use of pesticides such as insecticides and herbicides. Among them, with the circulation of the ecosystem, some pesticides flow into the natural environment, causing serious environmental problems, including air pollution, water pollution, and soil contamination, as well as ecosystem destruction. In addition, long-term exposure to pesticides can also lead to different diseases, such as leukemia, lymphoma, and various cancers, seriously endangering human health. Therefore, the development of efficient residual pesticide detection materials and methods is a pressing problem that needs to be addressed. Although traditional chromatographic and electrochemical analysis methods offer advantages such as high sensitivity and accuracy, there are objective factors, including the expense of instrumentation and complex pretreatment steps, that hinder the realization of rapid and in-situ detection. Fluorescence spectroscopy technology has undergone rapid development in recent years, showing promising prospects in the field of rapid detection. However, there are problems, such as poor stability of small-molecule fluorescent probes and easy photobleaching. Metal-organic framework (MOFs) materials have also been widely attempted in the field of pesticide detection. However, poor chemical stability, particularly in terms ofwater stability, limits their further development and promotion in the field of pesticide detection. Porous organic polymer (POPs) materials are a new type of porous materialcomposed of light elements such as C, H, O, and N connected by stable covalent bonds, and have been widely used in fields such as sensing, catalysis, environmental treatment, and energy. However, the effective fluorescence detection of residual pesticides in the aqueous phase has been rarely reported to date. Therefore, in this paper, we successfully prepared a porous organic polymer TPE-OMe with strong fluorescence emission performance by connecting the tetraphenylethylene unit with aggregation-induced emission (AIE) characteristics and the nitrogen-rich hydrazide unit in an aqueous acetic acid solution system through a simple ultrasonic synthesis method, and studied the fluorescence detection performance of the polymer TPE-OMe for paraquat in the aqueous phase using paraquat pesticide as a model. The research results show that the polymer TPE-OMe has a highly sensitive detection ability for paraquat, with a Stern-Volmer coefficient of 2.82×10⁴ （mol·L^-1）^-1, and its detection limit can reach 4.84×10^-7 mol·L^-1.
		2025 Vol. 45 (10): 2767-2773 [Abstract] ( 5 ) PDF (8055 KB) ( 3 )

	2774	Near-Infrared Spectroscopy Prediction of the Optimal Harvest Date for Autumn Moon Pear: Considering the Correction of Ambient Light Changes and Inter-Instrument Differences
		SUN Xu-dong¹, LONG Tao¹, WANG Jia-hua², FENG Shao-ran³, ZENG Ti-wei⁴, XIE Dong-fu¹, FU Wei¹
		DOI: 10.3964/j.issn.1000-0593(2025)10-2774-09
		Timely harvesting is an important scientific exploration to improve the high-quality rate of autumn moon pear fruit harvest and storage quality. The influence of light changes in the orchard environment and inter-instrument differences can lead to a decline in the performance of the mathematical model established in the laboratory when predicting the quality of fruits on the tree. This study simultaneously considers the influence of light changes in the orchard environment and inter-instrument differences. It utilizes the global model and external parameter orthogonalization (EPO) method to correct for the influence of environmental light and inter-instrument differences, thereby predicting the optimal harvest date of autumn moon pears. The experiment used 599 autumn moon pear samples collected between 2019 and 2020 for modeling, 80 autumn moon pears as difference matrix samples, and 120 autumn moon pears collected from July to September 2023 as the prediction set. After global model and EPO correction, the predictive ability of the partial least squares regression (PLSR) model was improved, with the global model being the best and EPO being the second. After global model correction, the model of instrument A predicted the data of instrument B, and the coefficient of determination increased from 0.11 to 0.68, and the root mean square error of prediction (RMSEP) of soluble solid content (SSC) decreased from 1.2% to 0.69%. After simultaneous correction of inter-instrument differences and ambient light changes using the global model, the coefficient of determination increased from 0.46 to 0.79, and RMSEP decreased from 1.23% to 0.70%. The best model corrected by the global model, was used to predict the optimal harvest date, and the prediction results from the handheld instrument were consistent with the destructive analysis results. The optimal harvest period of the experimental orchard was August 26, 2023, and 55% of the sampled autumn moon pears had an SSC content exceeding 12%, meeting the harvest standard. The results demonstrate that global model correction can effectively mitigate the impact of inter-instrument differences and ambient light changes on the model's predictive performance. At the same time, this study verified that harvest date prediction can improve the harvest quality of autumn moon pears, providing a feasible reference for non-destructive prediction of the optimal harvest date of autumn moon pears.
		2025 Vol. 45 (10): 2774-2782 [Abstract] ( 8 ) PDF (4510 KB) ( 3 )

	2783	Study on the Interaction Between Dehydronorcantharidin Imide-Cinnamaldehyde and Human Serum Albumin by Spectroscopy and Molecular Docking Simulation
		ZENG Zao, ZHANG Hao, FAN Yi-ning, YAN Jin-ling
		DOI: 10.3964/j.issn.1000-0593(2025)10-2783-07
		Dehydronorcantharidin compounds are a class of drugs with anticancer activity. The interaction mechanism between compounds and human serum albumin (HSA) is of great significance for understanding the properties of such drugs. In this study, dehydronorcantharidin imide-cinnamaldehyde (DCIC) was synthesized from furan, cinnamaldehyde and maleic anhydride, and the structure of the target compound was confirmed by NMR spectroscopy. The interaction mechanism of DCIC with HSA was investigated using fluorescence spectroscopy, site marker competition and molecular docking simulations. Fluorescence spectroscopy results indicated that the quenching mechanism of DCIC towards HSA followed a static quenching model. The quenching constants at 298, 303, and 313 K were found to be 8.53×10⁴, 7.05×10⁴, and 6.05×10⁴ L·mol^-1, respectively. At 303 K, the number of binding sites was calculated to be 0.978, and the Gibbs free energy is -25.75 kJ·mol^-1. Both the enthalpy change (ΔH) and entropy change (ΔS) were negative, suggesting that the main interaction forces between DCIC and HSA were hydrogen bonds and van der Waals forces. According to Förster's theory of energy transfer, the binding distance between DCIC and HSA was calculated to be 3.946 nm, which is less than 7 nm, indicating static quenching through non-radiative energy transfer. Synchronous fluorescence spectroscopy results further revealed that DCIC increased the hydrophobicity of the microenvironment surrounding the tryptophan residue. Molecular docking simulations showed that DCIC binds to HSA at site I through a hydrogen bond, with an optimal binding energy of -25.44 kJ·mol^-1, which is consistent with the findings from the spectroscopic and site competition experiments. This study provides a crucial theoretical foundation for research on the storage, transport, and pharmacokinetics of DCIC within the human body.
		2025 Vol. 45 (10): 2783-2789 [Abstract] ( 3 ) PDF (8944 KB) ( 3 )

	2790	Engine Oil Detection and Quantification Based on LED-IF Modulation-Demodulation Technology
		WANG Xin-yi^{1, 2}, MA Wei-xin^{1, 2}, MA Chong-hao^{1, 2}, WANG Wei³, QUAN Jia-xiang³, JI Zhong-hua^{1, 2*}, ZHAO Yan-ting^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2790-06
		To address the demand for engine oil leakage detection in mechanical manufacturing, this study proposes a high-sensitivity detection method combining LED-induced fluorescence (LED-IF) with modulation-demodulation technology, supported by quantitative analysis. A compact engine oil detection system operable under ambient natural light conditions was designed and implemented. Specifically, a 365 nm LED with an optical power of 20 mW at the output port was employed to excite engine oil samples, while a photodetector measured fluorescence emissions within the 410～460 nm range as the detection criterion. Optical filters and dichroic mirrors were utilized to purify the excitation light and fluorescence frequencies, effectively eliminating interference from ambient light sources on engine oil fluorescence signals. To further enhance sensitivity for weak fluorescence detection, modulation-demodulation technology was introduced to optimize system performance through parameter configuration of sliding filter window data volume, modulation amplitude, modulation frequency, and gain factor. This approach achieved a 70 dB improvement in signal-to-noise ratio compared to systems without modulation-demodulation. Based on the LED-IF modulation-demodulation framework, quantitative detection of engine oil volume and film thickness was investigated. Experimental results demonstrated a detection limit of 0.189 μL for engine oil volume at a 10 mm detection distance and a detection limit of 0.2 μm for engine oil film thickness on a water surface with a 32 mm inner diameter. These findings validate the effectiveness of the LED-IF modulation-demodulation technology in trace engine oil quantification and highlight its practical potential in natural light environments. Compared to conventional laser-induced fluorescence combined with spectrometer-based techniques, this solution offers a lower cost, a more compact footprint, and enhanced safety.
		2025 Vol. 45 (10): 2790-2795 [Abstract] ( 4 ) PDF (5930 KB) ( 3 )

	2796	Rapid Quantitative Analysis of Trace Elements in Petroleum Coke by LIBS Combined With Whale Optimization Algorithm
		ZHANG Meng-fan¹, LI Mao-gang^1, LIU Yi-jiang¹, YAN Chun-hua¹, ZHANG Tian-long², LI Hua^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2796-08
		Petroleum coke is a carbonaceous solid product formed by the pyrolysis of heavy hydrocarbons in crude oil. Due to its high carbon content (80%~95%), high calorific value, and excellent electrical conductivity, it is widely used in electrolytic aluminum anode materials, metallurgical fuels, graphite electrodes, and lithium-ion battery anode materials. It has important application value in industrial production. In petroleum coke, transition metal elements such as Fe and Cu not only reduce the conductivity and thermal stability of petroleum coke, but also may cause side reactions of electrode materials, resulting in battery capacity attenuation.In this paper, a hybrid modeling method based on laser-induced breakdown spectroscopy (LIBS) technology, combined with the whale optimization algorithm, is proposed for the rapid quantitative analysis of Fe and Cu elements in petroleum coke. An efficient analysis model was constructed by integrating the high-throughput and non-destructive detection advantages of LIBS, the intelligent feature selection ability of whale optimization algorithm (WOA), and the high-dimensional data processing characteristics of partial least squares (PLS) regression.Firstly, by collecting LIBS spectral data of 19 groups of petroleum coke samples, the preprocessing strategy of spectral data was systematically investigated. It includes the combination optimization of normalization (Nor), multiple scattering correction (MSC), standard normal variate (SNV), first derivative (D1st), second derivative (D2nd), and wavelet transform (WT). Screening the most effective pretreatment combination for quantitative analysis of Fe and Cu elements.Through experimental verification, the SNV-D2nd-WT combined pretreatment significantly improved the model's prediction ability (cross-validation determination coefficient R²_CV=0.946 4, root mean square error RMSE_CV=12.95 mg·kg^-1; R²_CV=0.914 6, RMSE_CV=8.84 mg·kg^-1). The WOA algorithm was further combined to optimize the feature selection and model parameters (number of whales, number of iterations, threshold). After parameter optimization, the characteristic wavelengths of Fe and Cu elements were reduced from 5 784 to 88 and 23, respectively. The prediction accuracy of the final model was further improved: Fe element prediction setR²_P=0.947 0, RMSE_P=7.35 mg·kg^-1; Cu element R²_P=0.953 8, RMSE_P=6.31 mg·kg^-1. This method offers a rapid and efficient solution for detecting trace metals in petroleum coke.
		2025 Vol. 45 (10): 2796-2803 [Abstract] ( 6 ) PDF (7448 KB) ( 3 )

	2804	Feature Analysis and Classification of the Line-Crossing Sequences Between Stamp Inks and Laser Printing Toner Based on Hyperspectral Techniques
		LI Chang-sheng, GAO Shu-hui^*, LI Kai-kai
		DOI: 10.3964/j.issn.1000-0593(2025)10-2804-12
		Determining line-crossing sequences between stamp inks and laser printing toner forms a core component of questioned document examination, offering critical evidence for authenticating questioned documents. Current methodologies are hindered by examiner dependency and limited applicability, failing to meet the demands for non-destructive, automated, and high-precision detection. While spectrochemical techniques combined with pattern recognition show promise in non-destructive analysis, they remain insufficient for investigating ink-toner interpenetration cases and model accuracy. This study proposes a novel approach that integrates visible-near-infrared hyperspectral imaging (Vis-NIR HSI) with machine learning, utilizing 10 000-pixel spectra per sample from three ink types and laser printers. Initially, a systematic multi-angle spectral difference calculation strategy was employed to quantify the temporal characteristics of material deposition sequences accurately. Following this, a new method that combines standard normal variate (SNV) spectral preprocessing with the interval variable iterative space shrinkage algorithm (iVISSA) was used to enhance model performance by minimizing noise and intelligently selecting wavelengths. In conclusion, a comprehensive comparative analysis of six machine learning models, including logistic regression (LR), support vector machines (SVM), and random forests (RF), was conducted to develop a high-accuracy classification system for questioned document examination. Experimental results demonstrate that the log ratio method effectively improves spectral differentiation. The combined application of SNV preprocessing and iVISSA-based characteristic wavelength selection successfully reduces data redundancy while significantly boosting model performance. Among the evaluated algorithms, SVM, XGBoost, and LightGBM emerged as the top models, showing robust capability in determining line-crossing sequences between compatible stamp inks and laser printing toners. Validation confirms the method's superior generalization ability for questioned document examination. This study addresses limitations by demonstrating the feasibility of integrating Vis-NIR HSI with pattern recognition to analyze line-crossing sequences of compatible stamp inks. This approach enhances conventional techniques, adding substantial judicial value in fighting document fraud.
		2025 Vol. 45 (10): 2804-2815 [Abstract] ( 5 ) PDF (13660 KB) ( 3 )

	2816	Kernel Mahalanobis-Driven Clustering for Outlier Detection in Mid-Infrared Spectroscopy
		HU Rui^{1, 2}, LI Yu-jun^{1, 2*}, JIAO Shang-bin^{1, 2}, SUN Peng-cheng^{1, 2}, WU Chen-yan^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2816-06
		In the quantitative analysis of alkane gas mixtures by infrared spectroscopy, the manual calibration sample preparation process is complex (requiring precise control of parameters such as multi-component gas concentration, ambient temperature, and gas pressure), and operational deviations can easily lead to the deviation of spectral data from the calibration concentration, resulting in anomalous samples. The traditional single anomaly detection method is difficult to handle complex anomaly patterns in high-dimensional and nonlinear data effectively. To address this problem, this paper proposes a hybrid anomaly detection framework that synergizes kernel martens distance (KMD) and K-means clustering, which innovatively combines kernelized feature mapping with dynamic density clustering, thereby overcoming the matrix singularity problem and the limitation of insufficient sensitivity to local anomalies in high-dimensional sample scenarios. In this paper, we use the kernel Marginal Distance (KMD) to construct a nonlinear high-dimensional feature space, quantify the anomaly degree of the spectral-concentration mapping relationship through the covariance matrix, and set a 95% confidence threshold (χ²_{0.95}) to screen potential anomaly candidate samples. Combined with the K-means algorithm, the training set is divided into seven optimisation sub-clusters (determined based on the elbow rule), and a dynamic threshold is set to reject anomalous samples by the standard deviation of the distance from the test sample to the nearest centre of mass. The final dual-threshold joint decision-making is achieved through the logical and (AND) mechanism. The experiment was carried out using a German Bruker Tensor27 spectrometer to collect 938 sets of samples (wavelength 2.5～25 μm, resolution 4 cm^-1), with methane and ethane component gases as the focus of analysis. The model was validated by a partial least squares (PLS) regression model and compared with the traditional Marginal Distance (MD) method. The results showed that after excluding the anomalous samples, the relative error (MRE) of methane concentration prediction decreased from 38.29% to 18.77%, which was 11.52 percentage points more than that of the MD method (30.44%). The MRE of ethane decreased from 54.51% to 26.03%, which was 13.39 percentage points more than that of the MD method (39.42%), and the accuracies of the model analyses were both increased by more than 50%. The proposed method not only theoretically breaks the bottleneck of anomaly detection in high-dimensional spaces, but also demonstrates its effectiveness in the quantitative analysis of infrared spectra of complex gas mixtures in practical applications. Compared to traditional methods, the hybrid detection framework of kernel Martens distance and K-means clustering demonstrates significant robustness in handling nonlinear and multidimensional data. The method offers a reliable and effective solution for cleaning anomaly data in the quantitative analysis of infrared spectra of alkane gas mixtures.
		2025 Vol. 45 (10): 2816-2821 [Abstract] ( 4 ) PDF (2203 KB) ( 3 )

	2822	Preparation of Europium Complex@Nano-Calcium Carbonate Composite Fluorescent Materials
		ZHANG Si-hua, TAO Dong-liang^*, XU Zi-qi, DONG Fu-song, JIANG Guang-peng, JIN Feng
		DOI: 10.3964/j.issn.1000-0593(2025)10-2822-06
		To address the critical issues of severe environmental pollution, high production costs, and low utilization efficiency of rare-earth resources in conventional rare-earth fluorescent materials, an eco-friendly and cost-effective europium(Ⅲ) complex-based composite fluorescent material, Eu(TTA)₃(TPPO)₂@CaCO₃, has been successfully synthesized using nano-sized calcium carbonate (CaCO₃) as a carrier. By substituting traditional highly corrosive alkaline reagents (e. g., sodium hydroxide, aqueous ammonia, and triethylamine) with nanoCaCO₃, this europium(Ⅲ) complex composite was fabricated under mild reaction conditions. The reaction system's pH was stably maintained between 6.93 and 7.23, eliminating the generation of highly alkaline wastewater associated with conventional methods. A series of composite fluorescent materials (m₁~m₅) was prepared using EuCl₃·6H₂O, 2-thenoyltrifluoroacetone (HTTA), and triphenylphosphine oxide (TPPO) as precursors by varying the CaCO₃ dosage (1~5 g). Material structures were characterized using Fourier transform infrared spectroscopy (FTIR) and X-ray powder diffraction (XRD). The europium complex Eu(TTA)₃(TPPO)₂(ETT) was successfully incorporated into CaCO₃ while retaining its crystalline structure. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) revealed that at 1 g CaCO₃ loading, micron-sized ETT crystals (2~6 μm) persisted, whereas increasing the CaCO₃ dosage to 5 g (m₅) yielded a uniform nanoscale composite structure. TEM-EDS confirmed the presence of Eu, F, P, and S onCaCO₃ surfaces, verifying ETT attachment. Fluorescence spectroscopy demonstrated that the composite material exhibited maximum excitation intensity at 383 nm and emitted at 617 nm (⁵D₀→⁷F₂ transition), characteristic of Eu³⁺ red emission. The m₁ sample achieved an absolute quantum yield of 48.05%, comparable to pure ETT (48.70%). Even at high CaCO₃ loading (m₅), the quantum yield remained at 40.29%, indicating minimal luminescence sacrifice. Fluorescence lifetime decay analysis indicated three distinct coordination environments for Eu³⁺, influenced by CaCO₃ interaction. Application tests demonstrated that coating the m₁ sample onto a 395 nm LED chip produced a red LED with 99.9% color purity and a brightness of 27 140 cd·m^-2, showcasing excellent performance. This approach exploits the alkaline and carrier properties of nano CaCO₃ to simultaneously neutralize the reaction system's pH and enable efficient rare earth complex loading, reducing organic ligand requirements while suppressing particle aggregation. Thus, it offers a novel approach to developing cost-effective, environmentally friendly rare-earth fluorescent materials.
		2025 Vol. 45 (10): 2822-2827 [Abstract] ( 4 ) PDF (15925 KB) ( 3 )

	2828	Scientific Analysis of Glass and Relative Materials of the Warring States Period to Han Dynasties Unearthed From Huaiyang, Hennan Province
		CHEN Yan¹, LIU Song^{2, 3}, FANG Li-xia¹, YUAN Yi-meng^{2, 3}, YANG Yi-fan¹, LI Qing-hui^{2, 3}, DING Jun-xia¹
		DOI: 10.3964/j.issn.1000-0593(2025)10-2828-09
		In this paper, a batch of glass and related materials of the Warring States Periods to Han Dynasties unearthed from Huaiyang, Henan Province, are analyzed by optical and spectroscopic techniques, including X-Ray fluorescence spectroscopy (XRF), Laser Raman spectroscopy (LRS), Optical Coherence Tomography (OCT), and Optical microscopy. The chemical compositions, phase components, internal structures, and microscopic morphology of the samples are obtained, and the material properties, production process, and origins of the samples are also clarified. The communications reflected by the samples are also discussed. The results obtained indicate that the analyzed samples can be divided into two categories: glass and glazed pottery. The glass type includes lead-barium silicate glass (abbreviated for lead-barium glass), lead silicate glass (abbreviated for lead glass), and sodium-calcium silicate glass (abbreviated for sodium-calcium glass). The glazed pottery is lead-barium glazed pottery. There are obvious differences in the contents of some components of lead-barium glass unearthed from different sites, indicating that there is diversity in the formulation and raw materials of lead-barium glass. Soda-lime glass is a typical Westernglass system. Still, PbO and BaO were detected in the glass matrix, which may be related to the glass remelting process during the Warring States Period to the Han Dynasties. The lead-barium glass found in Henan is closely related to the Chu Culture, while the potassium-rich sodium-calcium glass indicates that it may have come from Central Asia.
		2025 Vol. 45 (10): 2828-2836 [Abstract] ( 2 ) PDF (36412 KB) ( 3 )

	2837	Spectroscopic Analysis of the Mural Pigments in the Zulakang Scripture Hall of E'zhi Temple, Dege, Sichuan
		DING Meng, ZHAO Fan^*, WANG Er, ZHANG Hui-ni, WANG Ya-li, HU Rui, WEN Chun-zi, LI Jun-hao
		DOI: 10.3964/j.issn.1000-0593(2025)10-2837-07
		As an important Tibetan temple situated in the Jinsha River basin of Sichuan Province, E'zhi Temple has murals distributed in its Zulakang Scripture Hall, which date from the Ming Dynasty, Qing Dynasty, to modern times. With a clear chronological sequence, these murals possess significant artistic, historical, and academic research value. The extant murals suffer from damage, including detachment, flaking, and pulverization, necessitating urgent conservation interventions. Understanding the compositional characteristics of mural pigments across different historical periods at E'zhi Temple scientifically is fundamental to implementing effective conservation and restoration. Spectroscopic analysis techniques such as depth-of-field microscopy, X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), Raman spectroscopy (Raman), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) were employed to study pigments of the Scripture Hall murals and the cloister murals. They exhibit distinct painting content,styles, and chronological origins.Results revealed that the Ming Dynasty cloister murals widely used traditional inorganic mineral pigments,including orpiment, cinnabar, hematite, malachite, azurite, and muscovite. At the same time, the Scripture Hall murals were repainted with modern synthetic pigments such as Pigment Red 14, chrome yellow, Basic Blue 26, phthalocyanine blue, and lithopone. There were significant differences in the types of pigments between the two mural groups. The Scripture Hall murals have undergone several rounds of repainting in modern and contemporary times. Inorganic mineral pigments such as muscovite, cinnabar,and malachite have been detected in the remaining original murals at the lower layer. On the use of mixed pigments, orange pigments were obtained by mixing chrome yellow with Pigment Red 14, and cinnabar with muscovite. Green pigments were obtained by mixing phthalocyanine blue with chrome yellow, which reflects the inheritance of traditional Tibetan painting techniques in color formulation. As one of the few scientific investigations on mural pigments in Sichuan's Tibetan regions, this study enhances the understanding of the scientific information of the murals in E'zhi Temple, and enriches the pigment usage cases in Tibetan-area murals of Sichuan.
		2025 Vol. 45 (10): 2837-2843 [Abstract] ( 5 ) PDF (45598 KB) ( 3 )

	2844	On the Spectroscopic Features and Analyzing Method of Raman OH Stretch Band of H₂O
		HU Qing-cheng, MA Ya-jie
		DOI: 10.3964/j.issn.1000-0593(2025)10-2844-05
		The OH stretch band of H₂O shows features of superposing multi-peaks, a broad and asymmetric band, which arises mainly from the complex water structure. The experimental results reveal that the OH stretch band width and main-peak wavenumber change continuously with increasing temperature in the range of -20~300 ℃: with increasing temperature, the band width gradually decreases and the main peak blue shifts; there are still prominent spectral intensities around 3 420 cm^-1 although the main peak shifts from ~3 420 to ~3 130 cm^-1. It is the widespread hydrogen bonding interactions that enlarge the random distribution range of the laser scattering by water, and this distribution can be described with the Gaussian function whose exponential regularity is consistent with the exponential form in the formula of the scattering cross section. However, it is found that the Gaussian peaks used for fitting the OH stretch band of water need to be broadened to achieve a good fitting degree, unlike the circumstances for the common simple molecules controlled by Vander Waals force that narrow Gaussian peaks are reliable for fitting, indicating the collision broadening and doppler broadening are insufficient for explaining the broadening effect in the OH stretch band. The hydrogen bonding promotes the cooperative and delocalized vibrations of modes among water molecules, which enriches the frequencies of OH stretching. Furthermore, the tetrahedral hydrogen bonding configuration brings about intermolecular vibrational couplings, which change the OH stretching frequencies and enhance the abnormal broadening of the OH stretch peak. We classify the five main hydrogen bonding configurations in water: two tetrahedral and three non-tetrahedral, and correspondingly use five broadened Gaussian peaks to fit the OH stretch band. Using this preferred scheme, the OD/OH stretch bands under different conditions of temperature, H↔D substitution, and salt-adding can be satisfactorily reproduced.
		2025 Vol. 45 (10): 2844-2848 [Abstract] ( 4 ) PDF (3557 KB) ( 3 )

	2849	Hyperspectral Prospecting Enlightenment of the Luobu Mineralization Site in Tibet
		DAI Jing-jing^{1, 2}, LIU Zhi-bo^2, BAI Long-yang², SONG Yang^{1, 2}, WANG Nan², CHEN Wei^{1, 2}, YUAN Chang-jiang³
		DOI: 10.3964/j.issn.1000-0593(2025)10-2849-07
		The Gangdise Metallogenic Belt is one of the important copper polymetallic metallogenic belts in Tibet, which develops multi-stage key mineralization events and is characterized by a collisional environment porphyry copper-polymetallic deposits. The research initially reveals the potential of prospecting in the Luobu area based on satellite hyperspectral Gaofen-5 technology. Grid sampling on the surface was carried out for short-wave infrared measurement and refined alteration mapping by ground hyperspectral technology and X-ray fluorescence spectrum, which could contribute to revealing the deep prospecting direction in the Luobu area. The research findings are as follows: (1) The result of satellite hyperspectral shows that the Luobu area is mainly characterized by large-scale advanced argillaceous alteration, which indicates its potential for prospecting porphyry-high-sulfur epithermal deposits. (2) The surface alteration minerals assemblage in Luobu shows five types, including pyrophyllite-disapore-dickite, alunite-kaolinite-muscovite, muscovite-kaolinite-dickite, chlorite-kaolinite, and carbonate alteration; (3) The shift of the diagnostic characteristic peak position of Al-OH in alunite and the crystallinity of white mica group minerals can indicate the changes with the temperature, pressure, and acidity-alkalinity of ore-forming fluid, and then indicate the direction of the hydrothermal center. The comprehensive results of -the geological background, the short-wavelength infrared spectrum of altered minerals, and the superposition of X-ray fluorescence spectrum anomalies have revealed two potential areas for searching for a hydrothermal center in Luobu, which can provide theoretical references for subsequent deep exploration.
		2025 Vol. 45 (10): 2849-2855 [Abstract] ( 4 ) PDF (30769 KB) ( 3 )

	2856	Prediction of EGFR Amplification Status of Glioma Based on Terahertz Spectral Data With Convolutional Neural Networks
		ZHAO Xiao-yan^1, ZHENG Shao-wen¹, WU Xian-hao¹, SUN Zhi-yan^{2, 3}, TAO Rui^{2, 3}, ZHANG Tian-yao¹, YUAN Yuan¹, LIU Xing⁴, ZHOU Da-biao^{2, 3}, ZHANG Zhao-hui¹, YANG Pei^{2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2856-07
		Gliomas are the most common primary central nervous system tumors with high invasiveness. Glioblastoma (GBM) is the most malignant type of brain glioma, with a 5-year survival rate of only 5.6%. The epidermal growth factor receptor (EGFR) plays an important role in the growth, invasion, and recurrence of glioblastoma. EGFR amplification and mutation have been identified as driving factors in glioblastoma. Currently, the integrated diagnosis process for glioma is limited by complex experimental procedures, often with a certain lag, and results can only be obtained approximately 2 weeks after surgery, which does not provide real-time molecular pathological information support for the operator. This article proposes a method for predicting EGFR amplification status based on intraoperative pathological frozen sections using terahertz time-domain spectroscopy (THz-TDS) data combined with convolutional neural networks (CNN). During the operation, spectral data of frozen sections of brain gliomas were collected using the THz-TDS system, and their absorption coefficients were calculated. After smoothing using the Savitzky-Golay filter, the absorption coefficients were converted into two-dimensional image data using the Gram Angular Field (GAF), Markov Transition Field (MTF), and Recursive Plots (RP) as inputs for subsequent CNN models. To fully utilize image data, we employ various methods, including single-image input, front-end fusion, and mid-range fusion, to construct CNN models. By comparing and analyzing the Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves under different models, it was found that the Mid range Fusion Convolutional Neural Network model with Gram Angular Summation Field (GASF) and Gram Angular Difference Field (GADF) had the best prediction performance, with a predicted AUC value of 94.74% in the test set. In addition, the commonly used prediction models based on terahertz spectral data often -employ one-dimensional spectral data for dimensionality reduction and machine learning analysis, which may result in partial loss of data information during processing. Therefore, we also trained and tested the method of combining the absorption coefficient with machine learning. By comparing the results of different models for one-dimensional data and two-dimensional images, it is found that training models with two-dimensional spectral images in convolutional neural networks yields better predictive performance compared to machine learning with one-dimensional terahertz time-domain spectral data. The experimental results -demonstrate that the proposed method, based on terahertz spectroscopy data and a convolutional neural network model, can achieve real-time and rapid prediction of EGFR amplification status, providing new insights for molecular pathological classification of brain gliomas using terahertz time-domain spectroscopy. It is of great significance for the timely adjustment of surgical strategies during surgery and the early development of postoperative adjuvant treatment plans.
		2025 Vol. 45 (10): 2856-2862 [Abstract] ( 4 ) PDF (4158 KB) ( 3 )

	2863	Research on the Inversion of Moisture Content in Rapeseed Silique Peel Based on Hyperspectral Fusion Imaging
		WEI Wei¹, WANG Dan², WANG Bo-tao³, TAN Zuo-jun¹, LIU Quan¹, XIE Jing^1*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2863-12
		To explore the potential of indirectly estimating the moisture content in silique peel based on hyperspectral data, this study took the rapeseed experimental field as the research object. From March to May 2023, the rapeseed spectra and moisture content of rapeseed silique peel were collected from the experimental field. After two spectral preprocessing methods, three feature wavelength selection methods, and their combinations, hyperspectral image spatial texture information was introduced. Partial Least Squares Regression (PLSR), Lasso regression, Support Vector Regression (SVR), and Extreme Learning Machine (ELM) were used to establish a regression model for the moisture content of rapeseed silique peel, and the accuracy of the model results was evaluated. The research results indicate that: (1) Spectral preprocessing techniques can highlight some hidden information in the spectrum, and mathematical transformations such as Multiple Scatter Correction (MSC) and First Derivative (FD) are more conducive to extracting spectral sensitive information; (2) After performing preprocessing, a feature selecting method combining Competitive Adaptive Reweighted Sampling (CARS) and Iterative Retention Informative Variables (IRIV) was used. The Lasso model demonstrated the best prediction performance, with an R² of 0.772 0 for test set 3.In response to the complex structure, small volume, and geometric influence of moisture content distribution in rapeseed silique peel, spatial texture information is introduced based on pure spectral information. Spatial texture quantifies the spatial variation and structural details (such as wrinkles and bumps) related to moisture content on the surface of silique, compensates for the variation caused by the shape and orientation of silique in a single pixel spectrum, improves the regression accuracy and prediction ability of the model, enhances the robustness of the model to noise and outliers, and provides a new effective way to solve the precise inversion of physiological parameters of complex small-scale crop objects.
		2025 Vol. 45 (10): 2863-2874 [Abstract] ( 2 ) PDF (26925 KB) ( 3 )

	2875	Estimation of Leaf Moisture Content of Maize Based on Spectral Index and Wavelet Transform
		XIAO Ya-ting^{1, 2}, TANG Yu-zhe^{1, 2}, BAI Yu-fei^{1, 2}, WANG Lu^{1, 2}, LI Fei^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2875-10
		In the life activities of plants, water plays a decisive role in crop yield. Rapid detection and acquisition of plant leaf water status is of great significance for understanding the physiological water requirements of field crops and corresponding water management. Hyperspectral indices are an important means of non-destructive, real-time estimation of crop leaf water content. However, the commonly used spectral index is significantly affected by the growth period in estimating leaf water content, and the stability is poor. Meeting production requirements is challenging due to the estimation accuracy. To achieve the accuracy of corn leaf water estimation and realize efficient use of corn, this study conducted field experiments with different moisture gradients in typical corn-growing areas in Inner Mongolia from 2023 to 2024, measured the hyperspectral reflectance of corn leaves at three key growth periods, and established a relationship model between leaf water content (LWC) and wavelet function and spectral index to determine the best performing wavelet function and spectral index, and evaluated their stability and robustness in detecting corn leaf water content. The results showed that the correlation analysis of leaf water content using spectral index and wavelet function found that the MDATT index had the best prediction result (R²=0.52) among the 13 selected water indices. Still, the estimation accuracy was greatly affected by the growth period and layer. In contrast, the continuous wavelet transform improved the estimation accuracy of LWC while overcoming the influence of growth period and layer on the prediction accuracy. Among them, the best performing wavelet function and its characteristics were Coif3 (S₆W₁₇₂₅) (R²=0.83). Compared with the spectral index, the Coif3 function in the wavelet function was more stable in estimating the water content of corn leaves. The determination coefficient R² of the independent verification result of the model was 0.76, and the verification error was the smallest, with RMSE and RE of 3.08% and 3.51%, respectively. The research results enable the accurate assessment of water content during the critical growth period of corn and the precise management of irrigation, thereby contributing to the sustainable development of the integrated water-fertilizer corn planting system in central and western China.
		2025 Vol. 45 (10): 2875-2884 [Abstract] ( 3 ) PDF (9044 KB) ( 3 )

	2885	Prediction of Soil Organic Matter for Farmlands Covered With High Density of Vegetation Based on UAV Hyperspectral Data
		WANG Jie¹, SUN Xiao-lin^2*, WU Dan-hua³, ZHOU Ya-nan¹, LIU Chang⁴, CAO Yue⁴, TANG Ye-tao⁴, ZHANG Mei-wei¹, WANG Xiao-qing¹, ZENG Ling-tao¹, CUI Yu-pei¹
		DOI: 10.3964/j.issn.1000-0593(2025)10-2885-12
		Accurate estimation of soil organic matter (SOM) content and its spatial distribution is crucial for sustainable agriculture and ecological management.Traditional SOM content measurement methods are insufficient, especially for high-density vegetation areas in tropical and subtropical regions. The widespread use of unmanned aerial vehicles (UAVs) and numerous studies predicting soil information based on vegetation remote sensing data provide a solution to this problem.To evaluate the proposed approach, a 75-hectare agricultural field in Shaoguan City, Guangdong Province, was selected as the study area. UAV hyperspectral imagery of vegetation at crop maturity was first acquired, and 103 soil samples were collected and transported to the laboratory for hyperspectral measurement and SOM content analysis. Subsequently, the continuous wavelet transform (CWT) was applied to extract features from both the UAV vegetation and laboratory soil hyperspectral data. Finally, SOM content estimation and mapping were performed using random forest algorithms on the hyperspectral data before and after feature extraction, with the results compared to those obtained using Ordinary Kriging-based mapping. The results indicate that: (1) There is a significant correlation between UAV vegetation hyperspectral data and SOM content, although the accuracy of SOM content estimation using UAV vegetation hyperspectral data was slightly lower than that using soil hyperspectral data; (2) After CWT, the accuracy of SOMcontent estimation using UAV vegetation hyperspectral data was superior to that of soil hyperspectral data, though still slightly lower than that of soil hyperspectral data after CWT; (3) The mapping accuracy of SOM content inversion using UAV vegetation hyperspectral data was better than that of the traditional Ordinary Kriging method, and was highly refined. Considering the significant advantages of UAV vegetation hyperspectral data in terms of cost and efficiency, this study suggests that the method of SOM contentestimation and mapping using UAV vegetation hyperspectral data is promising for providing abundant and detailed soil information for smart agriculture and other fields.
		2025 Vol. 45 (10): 2885-2896 [Abstract] ( 2 ) PDF (15810 KB) ( 3 )

	2897	Non-Destructive Detection of Soybean Seed Thermal Damage Based on Hyperspectral Imaging and MSC1DCNN
		TAN Ke-zhu¹, SUN Wei-qi¹, ZHUO Zong-hui¹, LI Kai-nuo¹, ZHANG Xi-hai^{1, 2}, YAN Chao^3
		DOI: 10.3964/j.issn.1000-0593(2025)10-2897-09
		Soybean seeds are prone to heat damage due to improper storage and transportation. Heat damage affects the seed quality and germination rate, making it crucial to accurately detect heat-damaged soybean seeds for improving seed quality and agricultural production. This paper proposes a non-destructive detection method for heat-damaged soybean seeds based on hyperspectral imaging and a Multi-scale Cross-channel One-dimensional Convolutional Neural Network (MSC1DCNN). Firstly, hyperspectral imaging systems were used to capture spectral data of soybean seeds in the 400～1 000 nm wavelength range. The spectral curves of different heat-damaged soybean seeds (normal, mild heat damage, and severe heat damage) were compared and analyzed. It was found that the spectral reflectance in the 420～500 nm blue light region and the 750～1 000 nm near-infrared region gradually increased with the degree of heat damage. These spectral variations provided effective spectral features for subsequent heat damage detection. Secondly, the MSC1DCNN model was applied for classification. The model achieved an accuracy, recall, and F1 score of 99.07% on the test set, outperforming Support Vector Classification (SVC) (F1 score of 88.32%), k-Nearest Neighbor (KNN) (F1 score of 84.39%), and One-dimensional Convolutional Neural Network (1D CNN) (F1 score of 92.90%). Notably, the MSC1DCNN model had a misclassification rate of 1.39% in distinguishing mild heat-damaged seeds from normal seeds, which was significantly lower than SVC (12.04%), KNN (15.74%), and 1D CNN (9.72%). Finally, a germination experiment was conducted to verify the effect of heat damage on the germination rate of soybean seeds. The experimental results demonstrated that heat damage significantly reduced the germination rate of soybean seeds, further confirming the potential harm of heat damage to soybean growth. In conclusion, the MSC1DCNN model proposed in this study offers an effective solution for the non-destructive detection of heat-damaged soybean seeds, providing new insights for seed quality detection and automated screening.
		2025 Vol. 45 (10): 2897-2905 [Abstract] ( 2 ) PDF (24685 KB) ( 3 )

	2906	Accurate Estimation of Maize Above-Ground Biomass Using Integrated Multispectral and LiDAR Data
		WU Qiang¹, YANG Mo-han¹, DUAN Feng-hui¹, WANG Zan-pu², KANG Jia-kun¹, YANG Hao³, YANG Gui-jun³, ZHANG Zhi-yong¹, MA Xin-ming¹, CHENG Jin-peng^1*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2906-09
		Accurate estimation of maize Above Ground Biomass (AGB) is a core task in precision agriculture management. Spectral remote sensing technology, by capturing the reflectance characteristics of crop canopies across different wavelengths, can effectively reflect the physiological state of maize but is susceptible to interference from complex canopy structures. In contrast, Light Detection and Ranging (LiDAR) technology can acquire high-precision three-dimensional structural information of maize. Still, it has difficulty revealing the physiological characteristics of crops, leading to limitations when using single data sources for biomass estimation. Therefore, this study developed a method for estimating maize above-ground biomass that integrates multispectral and LiDAR data. The experiment was conducted from 2021 to 2022 at the Beijing Xiaotangshan Precision Agriculture Demonstration Base, collecting data from 140 sample plots covering 7 maize varieties. A P4M multispectral UAV was used to acquire canopy reflectance spectral data during key growth stages. In contrast, an M600 UAV equipped with a Riegl VUX-1 LiDAR was used to obtain three-dimensional point cloud data. Above-ground leaf biomass (AGLB), above-ground stem biomass (AGSB), and total above-ground biomass (AGB) were measured, respectively. Twelve commonly used vegetation indices including NDVI and OSAVI were extracted from multispectral data. At the same time, nine structural features including maximum height (HMax) and average height (AspAvg) were calculated from LiDAR point clouds based on Triangulated Irregular Networks (TIN). Random forest algorithms were employed to construct biomass estimation models, and feature importance was evaluated using SHapley Additive exPlanations (SHAP) method. Results showed that compared to spectral estimation models, the fusion model significantly improved biomass estimation accuracy. The coefficient of determination (R²) of the fusion model reached 0.80 (AGLB), 0.78 (AGSB), and 0.73 (AGB), representing improvements of 5.2%, 27.8%, and 12.3% respectively; root mean square error (RMSE) decreased to 61.67, 248.61, and 356.78 g·m^-2, representing reductions of 8.2%, 43.6%, and 15.4% respectively. Both spectral indices and structural features showed significant correlations with biomass (r=0.52~0.83), with HMax, AspAvg, RVI, and OSAVI being key variables. SHAP analysis revealed that structural features contributed most significantly to stem biomass, while spectral indices had a greater impact on leaf biomass. This study demonstrates the complementarity and synergistic effects of the two technologies in biomass estimation, providing reliable methodological support for crop growth monitoring and above-ground biomass estimation in precision agriculture, and promoting the digitalization and intelligent development of agricultural management.
		2025 Vol. 45 (10): 2906-2914 [Abstract] ( 2 ) PDF (16199 KB) ( 3 )

	2915	Research on Noise Reduction Method for TDLAS Signal Detection Based on BOA-VMD-AWTD Algorithm
		ZHANG Fu^{1, 2}, LIU Zhi-hua¹, YAN Bao-ping¹, WANG Jia-jia³, FU San-ling^4*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2915-07
		To reduce the influence of noise in the second harmonic signal on signal quality and concentration inversion accuracy in tunable semiconductor laser absorption spectroscopy, an optimization objective function was constructed by combining the mean square error and correlation loss between the reconstructed signal and the reference signal. The key parameters of VMD, including the penalty factor α, the number of decomposition layers k, the number of wavelet decomposition layers, and the threshold coefficients, were optimized using the Butterfly Optimization Algorithm (BOA), so that the optimal parameter combinations could be obtained and the accuracy of VMD signal decomposition could be improved. Based on the energy distribution of the intrinsic mode functions and the correlation index, a scoring mechanism that combines energy and correlation was designed to enhance the adaptability of the algorithm under various signal characteristics. In this study, the absorption spectrum of CO gas at 1 567 nm was taken as an example. Five noise reduction algorithms, namely EMD, VMD, BOA-VMD, PSO-VMD, and BOA-VMD-AWTD, were selected to validate the effectiveness of the proposed method through simulation. The simulation results showed that the BOA-VMD-AWTD algorithm achieved the best noise reduction performance, with an SNR improvement of 14.70 dB and an NCC value of 0.999 3. PSO-VMD, BOA-VMD, and BOA-VMD-AWTD were applied to reduce the noise in the second harmonic signals obtained from the experiment. The experimental results demonstrated that the linear fitting coefficient R² of the signal amplitude after noise reduction, for CO concentrations ranging from 0.01% to 0.10%, reached as high as 0.999. To verify the stability of the BOA-VMD-AWTD algorithm, the second harmonic signals corresponding to a pre-set CO volumetric concentration range of 0.01%～0.10% were denoised, and an NCC value of 0.999 was achieved. Furthermore, to further confirm the stability of the BOA-VMD-AWTD algorithm, a pre-set CO volumetric concentration of 0.05% was continuously sampled, and the stability of the resulting concentration data was analyzed. The standard deviation σ after noise reduction was 0.000 5%, indicating that noise was effectively suppressed, while the mean value of the signal before and after denoising remained unchanged. These results provided effective technical support for TDLAS signal processing.
		2025 Vol. 45 (10): 2915-2921 [Abstract] ( 5 ) PDF (5679 KB) ( 3 )

	2922	Simulation Study on the Influence of Microstructured Arrays on the Emissivity of Surface Blackbody
		CUI Shuang-long, ZHOU Yi-meng, XING Jian^*, LI Yi, LI Wen-chao, HE Xue-lan
		DOI: 10.3964/j.issn.1000-0593(2025)10-2922-08
		As the core of the contemporary earth observation system, infrared remote sensing technology is of great significance for improving its performance. As the benchmark for the calibration of infrared detection equipment, the radiation characteristics of the blackbody radiation source directly affect the accuracy of the data, and the emissivity is one of the core parameters to measure the radiation capability of the blackbody. The traditional cavity blackbody is difficult to meet the requirements of calibration, and the surface blackbody provides a new solution for the radiation calibration of large-diameter systems through the plane structure design. Existing studies primarily focus on typical configurations such as V-shaped grooves, square cone arrays, and conical arrays, with emissivity testing relying heavily on experimental methods (such as Fourier transform infrared spectroscopy), leading to high R&D costs. To break through the bottleneck of experimental research, this article employs a ray-tracing approach to simulate light propagation within surface blackbody microstructures. By constructing an equivalent model with microstructure units and a simulation system incorporating 10⁶-level light rays, it comprehensively analyzes geometric parameters (bottom to height ratio, size) and surface optical properties (coating emissivity, reflection component). Simulation results demonstrate that performance improvements in surface blackbodies primarily stem from structural design rather than size expansion. Reducing the bottom-to-height ratio of square cone arrays from 2∶3 to 2∶5 increased normal emissivity from 0.987 to 0.995, whereas a tenfold size increase yielded only a 0.000 01 emissivity gain. Regarding surface reflection, the near-specular reflection proportion exhibits significant structural dependence on uniformity. Increasing NSR from 0% to 25% improved uniformity by 231% for square cone arrays and 224% for V-shaped grooves, but decreased it by 316% for conical arrays. Comprehensive performance comparisons show that square cone and V-shaped groove structures offer substantial advantages over conical arrays. The analysis method used in this article can provide important theoretical support for the design of a surface blackbody.
		2025 Vol. 45 (10): 2922-2929 [Abstract] ( 2 ) PDF (10526 KB) ( 3 )

	2930	Study of Wide-Band and Large-Angle Spectral Ellipsometry Technique Based on Grating and Fourier Spectrometry
		ZHANG Rui^{1, 2*}, BAI Qin^{1, 2}, XU Cheng-yu^{1, 2}, WANG Sai-fei¹, KONG Quan-huizi^{1, 2}, XUE Peng¹, WANG Zhi-bin¹
		DOI: 10.3964/j.issn.1000-0593(2025)10-2930-05
		With the rapid advancement of semiconductor and optoelectronic technologies, materials operating across the ultraviolet (UV) to mid-infrared (MIR) spectrum have found widespread application. Accurate characterization of thin film parameters, such as thickness, refractive index, and extinction coefficient, is critical for optimizing device performance. Spectroscopic ellipsometry is the most effective technique for such measurements; however, existing methods struggle to achieve wide-band, multi-angle transreflectance measurements across the UV-visible-shortwave infrared (SWIR) range. To address this limitation, we propose a novel wide-band, large-angle spectroscopic ellipsometry system that integrates grating and Fourier spectrometry. Grating-based spectral ellipsometry is employed for the UV-SWIR range (192～2 100 nm), while Fourier spectral ellipsometry covers the SWIR-MIR range (2 000～3 200 nm), extending the measurement capability across a broad spectral window. The detection approach differs between the two bands: grating spectral measurements are performed at the rear of the bias detection arm, whereas Fourier interference occurs at the front. A horizontal rotation mechanism is introduced, allowing large-angle measurements. In this design, the polarizing arms remain fixed. In contrast, the analyzer arm and sample stage rotate over a broad angular range, enabling integrated measurements from 192 to 3 200 nm and incident angles between 15° and 90°. A prototype system was constructed and applied to a variety of thin films on silicon substrates, including SiO₂-Si (dielectric), ZnO-Si (semiconductor), PI-Si (polymer), Si₃N₄-Al₂O₃-Si (dielectric bilayer), and Au-SiO₂-Si (metal-dielectric bilayer). The relationships between N=cos2Ψ, C=sin2ΨcosΔ, and S=sin2ΨsinΔ in the Mueller matrix were measured and used to extract the ellipsometric parameters Ψ and Δ. Film thicknesses were then obtained through spectral ellipsometry modeling and fitting. System repeatability was assessed by performing 30 repeated measurements per sample, yielding a thickness measurement accuracy better than 0.7 nm and a repeatability of 0.04 nm. This technique enables the flexible selection of the optimal spectral range, depending on the material, significantly improving measurement accuracy and versatility. It holds great promise for high-precision, wide-band, and large-angle thin-film ellipsometry applications.
		2025 Vol. 45 (10): 2930-2934 [Abstract] ( 2 ) PDF (7079 KB) ( 3 )

	2935	A Study of Double TV4 Regularization Based Spectral CT Projection Domain Material Decomposition Method
		YU Xin-li^{1, 2}, KONG Hui-hua^{1, 2*}, ZHANG Ran^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2935-07
		Spectral computed tomography (CT) can distinguish different material compositions by utilizing the differences in material attenuation characteristics under various X-ray energies. Projection-based material decomposition is a commonly used method, which consists of two steps: projection-domain decomposition and basis-material image reconstruction. To address the susceptibility of this method to noise contamination during decomposition, this study proposes a double-regularized two-step decomposition framework that simultaneously incorporates four-directional total variation (TV4) regularization priors into both material decomposition and basis image reconstruction. Extending conventional total variation (TV) to four-directional gradients, TV4 demonstrates enhanced capability in comprehensively capturing multi-directional edges within material images while achieving joint optimization for noise suppression, thereby exhibiting superior robustness in low-dose or high-noise scenarios. Experimental validation was conducted using multi-energy channel projection data from both simulated phantoms and preclinical in vivo mice. In the projection decomposition phase, the proposed TV4 algorithm was compared with conventional LS and SR-TF algorithms in terms of denoising performance. To further evaluate the material decomposition accuracy, basis material images obtained through different regularization strategies were quantitatively compared using root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) metrics. Results demonstrate that the proposed algorithm achieves a clear separation of basis material images, attaining the highest PSNR values and the lowest RMSE values among all compared methods. These findings confirm the method's effectiveness in suppressing noise and artifact interference during decomposition while significantly enhancing basis material image quality.
		2025 Vol. 45 (10): 2935-2941 [Abstract] ( 5 ) PDF (22041 KB) ( 3 )

	2942	Hyperspectral Camouflaged Identification Driven by Spatial-Spectral Characteristics
		LIU En-qin^{1, 2}, HUANG Wei³, XU Yong³, YANG Man², GAO Bing², MO Ding-ru⁴
		DOI: 10.3964/j.issn.1000-0593(2025)10-2942-08
		To address the issues of incomplete contour and fragmented spatial information caused by the sole use of spectral features in hyperspectral remote sensing identification of camouflage, this study proposes a method for identifying camouflage in hyperspectral remote sensing by integrating spatial and spectral features. An imaging spectrometer was employed to capture close-range hyperspectral imagery spanning 400~900 nm spectral range with 1 nm resolution under a grassland background. Spectral features (first derivative, second derivative, spectral indices, etc.) and texture features (mean, entropy, second moment, etc.) were extracted, and the differences between camouflaged objects and the grassland background were analyzed. The Mahalanobis distance method was used to screen out sensitive parameters conducive to camouflage identification, and then multiple band combination strategies were proposed, resulting in five datasets being constructed. Three methods, namely, multi-layer perceptron neural network (MLP), support vector machine (SVM), and spectral angle (SAM), were used to identify camouflage. The results show that: (1) Among the spectral feature variables, the red band, near-infrared, “red edge”, and narrow-band spectral indices (CR1, ARI1, and ARI2) are very beneficial for the identification of camouflage. Among the texture feature variables, mean and contrast are sensitive bands for the identification of camouflage. (2) Compared with the identification results using only spectral or texture features, the dataset integrating spatial-spectral features has better integrity and higher accuracy in identifying camouflage. The "red edge" feature alone failed to identify camouflage, but when combined with other bands, it could identify camouflage. (3) Among the five datasets, dataset 4, composed of four bands (red, near-infrared, mean, and contrast), has the highest accuracy in identifying camouflage, with a producer's accuracy of 99.85% and a user's accuracy of 99.34%. This band combination strategy can be extended to the identification of camouflage in multispectral remote sensing images. (4) Among the three identification methods, SVM performs the best overall and can effectively identify camouflage, while SAM performs poorly. The research results can be extended to the identification of new color schemes and camouflage patterns, and the selected sensitive bands and effective identification features can serve as the basis for feature extraction and target identification in hyperspectral remote sensing images from unmanned aerial vehicles and satellites.
		2025 Vol. 45 (10): 2942-2949 [Abstract] ( 3 ) PDF (36266 KB) ( 3 )

	2950	Research on the Selection Method of Visually Significant Band for Ground Object Classification
		YANG Guang¹, HU Hao-wen¹, JIN Chun-bai¹, REN Chun-ying^2, WANG Long-guang¹, WANG Qi¹, LIU Wen-jing^1
		DOI: 10.3964/j.issn.1000-0593(2025)10-2950-10
		The selection of remote sensing image bands is a prerequisite for the application of remote sensing data. It aids in the visualization and interpretation of remote sensing images, enhances image quality, and highlights the differences between different surface features. This provides a foundational basis for target recognition, image classification, and change detection. However, the large number of hyperspectral image bands, that is, the high spectral dimension, brings great problems and challenges to the band combination of bloom images. Therefore, it is necessary to reduce the dimension of hyperspectral data. In research on band combination, to preserve the spectral characteristics of the original band, the feature selection method is the most reasonable dimensionality reduction approach. In the original data set, select a specific band to form a band subset, and then carry out band selection research. In this paper, an Improved adjacent subspace partition (IASP) method is designed, and a band selection model based on visual saliency is constructed. Finally, the Histogram-based Contrast algorithm is selected to select the significant band, and a Contrast experiment is designed to verify the effectiveness of the method using the data of the OrbitaHyperSpectral satellite.
		2025 Vol. 45 (10): 2950-2959 [Abstract] ( 3 ) PDF (41944 KB) ( 3 )

	2960	Study on the Effect of Oil Extraction Temperature on the Structural Characteristics of Protein Components in Walnut Cake
		FU Chao^{1, 2, 5}, XIAO Xuan-rui¹, BAI Tong-tong¹, ZHU Meng-yu¹, WANG Ping^{1, 2}, BAI Bing-yao^{1, 2}, ZHANG Chun-lan^{1, 2}, ZHANG Rui³, XI Xiang¹, JIAN Tian-tian^4*
		DOI: 10.3964/j.issn.1000-0593(2025)10-2960-08
		Using ‘Wen 185’ paper-skin walnuts as the raw material, walnut albumin, globulin, prolamin, and glutelin were separated using a solubility gradient method. The study investigates the effect of oil extraction temperature on their structural characteristics. Infrared spectroscopy was employed to analyze the changes in the secondary structure of proteins, while fluorescence and ultraviolet spectroscopy were used to examine the changes in the tertiary structure. The results from infrared spectroscopy showed that the secondary structure of albumin was minimally affected by the oil extraction temperature. However, the secondary structure of globulin, prolamin, and gluten underwent significant changes when the extraction temperature reached 130 ℃. Additionally, the total content of α-helix and β-sheet structures in gluten was lower than in the other three protein components, indicating that its secondary structure stability is weaker than that of the other proteins. Fluorescence spectroscopy results revealed that the maximum fluorescence peak of albumin and globulin shifted to a longer wavelength (red shift) after the oil extraction temperature exceeded 130 ℃, indicating that their tertiary structure unfolded, exposing more hydrophobic amino acids on the protein surface. The fluorescence peak of prolamin shifted slightly to a shorter wavelength (blue shift) after the extraction temperature exceeded 100 ℃, suggesting that the hydrophobic amino acids on its surface were buried within the protein molecule. However, when the extraction temperature exceeded 130 ℃, a noticeable red shift occurred, indicating an increase in hydrophobic amino acids on the protein surface. The maximum fluorescence peak of gluten exhibited a red shift within the oil extraction temperature range of 40 to 70 ℃, indicating that changes in its tertiary structure led to increased exposure of hydrophobic groups. However, when the temperature exceeded 130 ℃, a blue shift in the fluorescence peak was observed, suggesting a reduction in the exposure of surface hydrophobic groups. This indicates that the tertiary structure of gluten is relatively more unstable compared to the other three types of proteins. Furthermore, the overall fluorescence intensity of albumin and globulin was higher than that of prolamin and gluten, suggesting that albumin and globulin contain more hydrophobic groups than prolamin and gluten. Ultraviolet spectroscopy results showed that all four protein components exhibited a significant UV absorption around 275 nm. As the oil extraction temperature increased, there was little change in the peak intensity of albumin, globulin, prolamin, and gluten. However, the number of UV absorption peaks in albumin and globulin was greater than in prolamin and gluten, suggesting that albumin and globulin contain more exposed hydrophobic amino acid groups, which is consistent with the conclusion drawn from fluorescence spectroscopy that albumin and globulin have more hydrophobic groups on their surfaces. In conclusion, when the oil extraction temperature reaches 130 ℃, it significantly affects the structure of albumin, globulin, and prolamin. The structural stability of gluten is relatively low, and at an oil extraction temperature of 70 ℃, its structure is already noticeably impacted. This study provides useful insights for understanding the effect of oil extraction temperature on walnut protein structure and for the development of walnut cake protein component products.
		2025 Vol. 45 (10): 2960-2967 [Abstract] ( 3 ) PDF (4396 KB) ( 3 )

	2968	Study on the Rheological Properties, Aeration Performance, and Flavor Quality of Whipping Cream With Different Lipid Composition
		HOU Yi-fei^{1, 2}, LIANG Chao³, CAO Hong-fang³, LI Feng^{1, 2}, LÜ Jia-ping², PANG Xiao-yang², ZHANG Shu-wen², XIE Ning², LI Xu², WANG Xiao-dan², DU Xin-yu⁴, LIU Yan-yan^1, WANG Yun-na^{2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)10-2968-10
		As a typical polymorphic emulsion, the lipid composition of whipping cream played a crucial role in determining its rheological properties, aeration performance, and flavor quality. However, the mechanisms by which different lipids influenced polymorphic behavior and texture remained unclear. This study systematically investigated the effects of milk fat, vegetable fat, and their mixtures on the polymorphism, emulsion stability, flavor characteristics, and aeration performance of whipping cream. The results showed that whipping creams with different lipid compositions primarily consisted of fine, needle-like β′ crystals stacked vertically in a triple-chain length (3L) structure. Among them, whipping creams containing mixed milk/vegetable fat and vegetable fat alone exhibited higher crystallization degrees, leading to β-crystal recrystallization during heating (0～35 ℃). This process caused fat globule coalescence, increased average particle size, bimodal or multimodal particle size distribution, and a higher degree of fat coalescence. Despite this, the average particle size (1～3 μm) of whipping cream remained small, with a unimodal distribution and enhanced emulsion stability (TSI=1～3). Whipping cream with vegetable fat demonstrated higher viscosity, with G′>G″, indicating a stronger fat crystal network. This contributed to improved aeration performance, as evidenced by shorter whipping time (130～170 s), increased overrun (150%～200%), and greater foam hardness (900～1 400 g). The primary flavor compounds in whipping cream included heptanone, pentanone, acetone, and butanone. Notably, the flavor profile of mixed milk/vegetable fat cream more closely resembled that of vegetable fat cream and differed significantly from pure milk fat cream. Additionally, the main volatile compounds in milk/vegetable fat cream—such as methanol, and ethyl butyrate—contributed distinct aroma characteristics. Overall, this study provided essential theoretical insights for quality control in whipping cream production.
		2025 Vol. 45 (10): 2968-2977 [Abstract] ( 4 ) PDF (17436 KB) ( 3 )

	2978	Research on Multispectral Concrete-Mud Boundary Detection Technology
		HAO Xiang-wei¹, XING Jian¹, MA Jia-qiang^1*, LIANG Jian-jun², ZONG Yun-cui³
		DOI: 10.3964/j.issn.1000-0593(2025)10-2978-05
		During construction, it is necessary to determine the classification of concrete and mud within the pouring area. Currently, most projects use manual detection methods. To ensure engineering quality, the height of poured concrete piles often far exceeds the design value, leading to significant waste of concrete. To address this issue, an automatic mud boundary detection technology based on multispectral imaging is proposed.First, a spectrometer was used to analyze the visible light reflection spectra of 11 mixtures of concrete and mud at different ratios. The results show a functional relationship between the mixing ratio coefficient K and spectral reflectance. The calculation formula for reflectance was used to determine that reflected light intensity can be used as a substitute forreflectance. Based on this, a monitoring system was designed using the AS7341 spectral chip, which consists of 8 visible light spectral bands, an STM32 microcontroller, and a JDY-31 Bluetooth communication module, all enclosed in a transparent housing. Multispectral reflected light intensity data were collected for the 11 concrete-mud mixtures at different ratios, forming a dataset of reflected light intensity values for mixtures of loess and black soil with concrete at varying proportions. It was found that the reflected light intensity values fluctuated over time. Therefore, a mud-concrete boundary prediction algorithm based on a Convolutional Long Short-Term Memory Network with Attention Mechanism (CNN-LSTM-Attention) was proposed. The CNN network extracts key features from the input reflected light intensity data of 8 channels to capture more local features and improve subsequent prediction accuracy. The LSTM layer adds interfaces and reverse gates to the CNN layer to enable backpropagation, avoiding gradient disappearance and explosion. Finally, the Attention function focuses on more critical spectral information among the input reflectance values at multiple wavelengths, thereby reducing or ignoring other spectral information to address the problem of information overload and improve efficiency.Simulation results show that the algorithm achieves a precision of 0.952, a recall of 0.944, and an F1 score of 0.938. Compared with other algorithms, it demonstrates higher accuracy and stability, meeting national standards for construction sites. This method eliminates the need for manual measurement. It enables real-time remote control of spectral data collection via a host computer, providing direct detection results for the boundary between concrete and mud.
		2025 Vol. 45 (10): 2978-2982 [Abstract] ( 3 ) PDF (9498 KB) ( 3 )

	2983	Research on Satellite Greenhouse Gas Remote Sensing Retrieval Methods Based on Machine Learning
		SHENG Shu-li¹, ZOU Ming-min^{1, 2*}, LIU Tian-qi¹, CHENG Yong-ping¹, CHEN Zi-zheng¹, WANG Xu-wen¹
		DOI: 10.3964/j.issn.1000-0593(2025)10-2983-09
		Greenhouse-gas satellite remote sensing provides vital data support for climate-change research. Accurately obtaining the spatiotemporal distribution of greenhouse gas concentrations is key for effective carbon emission accounting. This study aims to develop a satellite-based greenhouse-gas retrieval model using machine-learning methods, enabling rapid and high-precision inversion of column-averaged dry-air mixing ratios (XCO₂ and XCH₄). First, a training dataset was constructed using an atmospheric radiative-transfer model combined with measurements from the Total Carbon Column Observing Network (TCCON). Next, a one-dimensional convolutional neural network (1D CNN) was employed as the machine-learning method. The model leveraged Adaptive Moment Estimation (Adam) and Bayesian Optimization (BO) during training. Its performance was compared to that of Random Forest (RF) and Backpropagation Neural Network (BPNN) models. Results showed that the 1D-CNN model achieved correlation coefficients of 0.953 1 for XCO₂ and 0.957 3 for XCH₄ on the test dataset, outperforming both RF and BPNN. Finally, high-spectral-resolution observations from the Chinese GF-5B satellite's Greenhouse gases Monitoring Instrument (GMI/GF-5B) were used to retrieve global XCO₂ and XCH₄ for 2022—2024. Comparisons with data from nine TCCON stations demonstrated strong agreement: the correlation coefficients exceeded 0.938 5 for XCO₂ and 0.959 8 for XCH₄. Overall retrieval errors were within 2 ppm for XCO₂ (with 99.15% of validation samples showing errors below 1.5 ppm) and within 10 ppb for XCH₄.
		2025 Vol. 45 (10): 2983-2991 [Abstract] ( 4 ) PDF (18726 KB) ( 3 )

	2992	Analysis of Three-Dimensional Fluorescence Spectroscopic Characteristics in the Upper Reaches of the Fenhe Reservoir Basin
		WANG Yan¹, SUN Hui¹, YANG Xiao-yu¹, ZHANG Feng^{1, 2*}, WANG Chao-xu^{1, 2}, MAO Li-bo³
		DOI: 10.3964/j.issn.1000-0593(2025)10-2992-09
		The pollution characteristics of chromophoric dissolved organic matter (CDOM) are of great significance for revealing the carbon and nitrogen cycling mechanisms in water bodies, assessing the risk of eutrophication, and formulating precise control strategies. In view of the current situation of diffuse input of non-point source pollution and complex source-sink relationships in semi-arid basins, this study took the upper reaches of the Fenhe Reservoir as the research object. It used three-dimensional fluorescence spectroscopy (EEM) combined with fluorescence region integration (FRI) and parallel factor analysis (PARAFAC) models to systematically analyze the composition, sources, and spatiotemporal differentiation patterns of CDOM at 36 dry and tributary sections in summer and autumn. The results showed that CDOM in the basin contained five characteristic peaks, among which the humic-like peaks (A, M, and C peaks) dominated agricultural non-point source pollution, and the protein-like peaks (T and B peaks) significantly indicated the input of domestic sewage and livestock and poultry breeding wastewater. FRI analysis indicated that the proportion of humic-like substances reached 58.28% in summer; in autumn, the proportion of humic acid-like substances dropped sharply to 19.37%, while the proportions of fulvic acid-like (29.80%) and tryptophan-like protein (28.28%) substances increased. The PARAFAC model extracted three components: humic-like C1 (Ex/Em=255(315)/415 nm), C2 (Ex/Em=255(360)/470 nm), and protein-like C3 (Ex/Em=225(280)/340 nm); humic-like substances dominated the composition of CDOM in the basin, and the proportion of protein-like substances increased in autumn. Fluorescence parameters (FI, HIX, BIX) further verified the seasonal differentiation mechanism of CDOM, with land-based input being the main source in both summer and autumn, and the contribution of microbial activity increasing in autumn. The research results provide a scientific basis for dynamic water quality monitoring and precise control of non-point source pollution in semi-arid basins.
		2025 Vol. 45 (10): 2992-3000 [Abstract] ( 3 ) PDF (16530 KB) ( 3 )

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