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2025 Vol. 45, No. 02 Published: 2025-02-01

	301	Research Progress on the Vibrational Spectroscopy Technology in the Quality Detection of Fish Oil
		MA Hu-yishan^{1, 2}, PAN Nan², LIN Zhen-yu³, CHEN Xiao-ting², WU Jing-na⁴, ZHANG Fang^1, LIU Zhi-yu^2
		DOI: 10.3964/j.issn.1000-0593(2025)02-0301-11
		Fish oil is rich in ω-3 polyunsaturated fatty acids, such as Eicosapentaenoic Acid (EPA) and Docosahexaenoic Acid (DHA), which positively prevent and treat cardiovascular diseases. Its efficacy is directly related to its quality. With the increasing demand for the quality and safety of fish oil from consumers, the development and application of rapid detection technology are of great significance for monitoring the production process of fish oil and ensuring the quality of the products. Spectra are closely related to the composition and content of substances. By analyzing the spectral characteristics of the substance, information that reflects its molecular structure can be obtained, thereby achieving qualitative and quantitative analysis of the compound. Compared to traditional chemical detection methods, spectral technology offers the advantages of high efficiency, non-destructiveness, minimal pre-treatment, and environmental friendliness, making them promising in the field of fish oil quality detection. In this paper, near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy (RS) techniques and their application principles in fish oil quality detection were described; the conventional procedures for establishing spectroscopic prediction models were illustrated; various chemometric methods used for pre-processing of spectral information and model calibration were demonstrated; recent research advances and application progresses of NIR, MIR and RS in the detection of nutritional components (fatty acids, phospholipids and astaxanthin, etc.), quality indicators (acid value, peroxide value, etc.) and impurity analysis of fish oil were summarized. Additionally, the application prospects and existing problems of modern spectroscopic techniques combined with chemometrics in the rapid and non-destructive quality detection of fish oil were discussed. The goal is to extend laboratory research to practical production, thereby promoting the sustainable development of the fish oil industry in China.
		2025 Vol. 45 (02): 301-311 [Abstract] ( 69 ) RICH HTML PDF (3617 KB) ( 68 )

	312	Research on Wide Format Hyperspectral Color Measurement System for Printed Matter
		CHEN Jing-xue^{1, 2}, WEI Yu-cheng³, ZHOU Jian-kang^{1, 2*}, ZHU Jia-cheng^{1, 2}, SHEN Wei-min^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0312-10
		Variations in lighting conditions, differences in printing materials, deviations in-camera color filter arrays, or color responses easily affect the colorimetric measurements of cameras when detecting printed colors. Hyperspectral imagers can obtain the intrinsic reflectance curves of objects and coordinate with the chromaticity algorithm to effectively improve the accuracy of chromaticity measurement and can obtain the chromaticity value under various lighting environment conditions. To meet the demands of detecting color differences in printed matter, a tristimulus influence analysis model for each index of imaging chromaticity measurement system was proposed based on the mixed reflectance of printed matter. Key indices of this imaging colorimetric measurement system include signal-to-noise ratio, spectral resolution, smile, keystone, etc. To reduce the effects of non-uniformity, noise, and radiation standard transfer, uniformly distributed standard diffuse reflectance boards are used to directly establish the physical relationship between object reflectance and instrument detector pixel responses instead of traditional relative and absolute radiometric calibrations of imaging spectrometers. A push-broom type wide-format colorimetric imaging device (PBCID) has been developed based on Offner-type spectrometer components, and correction processing is avoided due to its low smile and keystone. According to the analysis model and CIEDE2000 color difference formula, the color difference caused by each device index is less than 0.3. Comparing the color measurement results of the PBCID and the reference instrument CM-700d on a 24-color standard card, spectral and chromatic accuracy can meet the requirements of discerning fine print color differences. After reflectance calibration, the spectral radiance response within the slit field of view of PBCID is uniform, and the measurement chromaticity uniformity is good, with a maximum color difference of 1.02. The average color difference of each color patch measured by the PBCID under different lighting sources is 1.56, indicating that the PBCID's color measurements are not affected by the light source. The analytical model for instrument indices' impact on chromatic measurements and the calibration process based on the reflectance boards provide a design basis and technical foundation for developing online wide-format imaging colorimetric instruments.
		2025 Vol. 45 (02): 312-321 [Abstract] ( 43 ) RICH HTML PDF (20655 KB) ( 27 )

	322	Research on Prediction of Pigment Concentration in Color Painting Based on BOA-FRNN Spectral Model
		LIU Zhen¹, FAN Shuo^2*, LIU Si-lu², ZHAO An-ran², LIU Li³
		DOI: 10.3964/j.issn.1000-0593(2025)02-0322-10
		In recent years, efforts to protect cultural relics and heritage have been intensified, and the strengthening of the preservation and inheritance of historical culture has risen to the level of a national strategy. Painted cultural relics under anthropogenic, sand erosion, and photodamage, the color of cultural relics generally appeared to varying degrees of fading, discoloration, aging, shedding, and loss of disease, so that it is now difficult to see the original face of the mural painting,digital protection and restoration has become an important means of protection and inheritance of painted cultural relics. Based on the spectral reflectance of color fingerprints, this study has taken the spectral reflectance of the external manifestation of pigment composition change as the entry point andused digital methods to map the concentration of painted pigments. To quickly and accurately identify mineral pigment concentration in color painting, the Bayesian Optimization Algorithm (BOA) was used to find the optimal hyperparameters of the Feed-forward Regression Neural Network (FRNN), and a BOA-FRNN spectral model was constructed topredict pigment composition and concentration distribution mapping. Firstly, the color chart of Dunhuang mineral pigments with different concentration gradients was drawn by traditional Chinese painting techniques, and the visible spectral reflectance and chromaticity information of the color chartwas obtained by Ci64UV integrating sphere spectrophotometer.Secondly, the correlation database of pigments' spectral reflectance, chromaticity values, concentration, pigment particle size, and ingredients was constructed based on the measured data. Finally, the pigment concentration was predicted. The results were compared using the two-constant Kubelka-Munk model, BP network model, support vector machine (SVM) regression algorithm, FRNN network model, and BOA-optimized SVM. To improve the accuracy of concentration prediction and model stability, BOA was proposed to optimize the network structure, activation function, and regularization strength of FRNN. Root Mean Squared Error (RMSE) was used as the fitness function, and the optimal regression parameters were selected through iteration to train the model.The results of pigment data prediction showed that the optimal combination model was BOA-FRNN. Experimental data show that the BOA-FRNN proposed in this paper has higher accuracy. The determination coefficient R² of the model test set was 99.55%, theroot mean square error RMSE was 1.805%. The results show that the Dunhuang pigment color database can select the required spectral reflectance more accurately and quickly, thus improving the model's efficiency and simplifying the algorithm's complexity. BOA searches for the optimal hyperparameters of FRNN and can quickly obtain the global optimal solution by iteratively updating the optimal position of hyperparameters. Compared with K-M, BP, SVM, FRNN, and BOA-SVM, the prediction accuracy and model stability are significantly improved, which meets the accuracy requirements of pigment concentration detection and is a feasible new method for fast pigment mapping.
		2025 Vol. 45 (02): 322-331 [Abstract] ( 46 ) RICH HTML PDF (28551 KB) ( 15 )

	332	Investigation of Spectral Characteristics of Xylene Molecules Based on Laser Raman Spectroscopy
		GAO Wen-han¹, CAI Yu-yao¹, HAN Bo-yuan¹, FENG Jun¹, LIU Yu-zhu^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0332-05
		Xylene is one of the important components of atmospheric pollution, and its emission is rapidly rising with the rapid development of industry. The distinguishing of the three isomers of xylene has become the focus of environmental detection. In this study, based on Raman scattering theory, Raman spectroscopy detection experiments were carried out to identify the three isomers of xylene, and the Raman spectra obtained from the experiments were analyzed to identify the isomers of xylene. The detection system consists of an independently developed Raman spectrometer. The optimized molecular structure model of HF/6-31+ was obtained by the ab initio algorithm of the single-electron approximation theory, and the Raman spectra of the three isomers of xylene were obtained on this basis. Combined with Gaussview 5.0 software, the characteristic peak vibrations of the three isomers of xylenes were attributed to the three isomers of xylenes. This led to the rapid differentiation of xylene isomers. This paper used a self-developed Raman spectrometer to characterize xylene, which provided a reliable basis for identifying xylene isomers.
		2025 Vol. 45 (02): 332-336 [Abstract] ( 42 ) RICH HTML PDF (2778 KB) ( 42 )

	337	Terahertz Absorption Spectra Simulation of Anthraquinone Based on Density Functional Theory
		ZHANG Tong-jun, HAO Jian-jun
		DOI: 10.3964/j.issn.1000-0593(2025)02-0337-07
		Anthraquinone is an organic compound with a planar structure and a macrocyclic conjugated system, which has a wide range of applications in various industries such as dyes, papermaking, biology, and medicine. To investigate the relationship between the characteristic absorption of anthraquinone in the terahertz region and its molecular crystal structure, theoretical simulation and experimental research of terahertz absorption spectrum was carried out by using density functional theory (DFT) and terahertz time-domain spectroscopy (THz-TDS). Firstly, the characteristic absorption spectrum of anthraquinone in the frequency range of 0.5～3.0 THz at room temperature was measured using the THz-TDS system. It was found that anthraquinone crystals have six distinct characteristic absorption peaks in this frequency range, located at 0.95, 1.05, 2.09, 2.25, 2.49, and 2.78 THz, respectively. For deeply analyzing the generation mechanism of the characteristic spectra of anthraquinone in the terahertz frequency band, theoretical simulation calculations were conducted on the single molecule model and the cell model of anthraquinone based on density functional theory. Theoretical calculations were conducted on the anthraquinone single molecule model using Gaussian09 software-based DFT theory with the B3LYP hybrid functional method and 6-311G (d, p) basis set. Geometric structure optimization and vibration frequency calculation were carried out at the same level. The simulation results showed significant differences from experimental measurement data, indicating that single-molecule simulation has certain limitations. Theoretical calculations were carried out on the anthraquinone crystal cell model using the CASTEP module, which is suitable for calculating periodic structures in the MS 8.0 software package. Five exchange-related functionals, PBE, PW91, WC, PBEsol, and RPBE, based on plane wave pseudo potential and generalized gradient approximation (GGA), were used. Geometric structure optimization and lattice dynamics calculations were performed at the same level. A detailed comparative analysis was conducted between the simulated structural parameters (bond length, bond angle) of anthraquinone single molecules and crystal cells and the structural parameters measured by X-ray diffraction experiments. It was found that the consistency between the molecular structural parameters and X-ray diffraction experimental data was the best in the solid-state simulation results obtained based on the PBE method. The theoretical simulation spectra obtained by PBE and RPBE methods agree with the experimental absorption spectra. Therefore, the vibration mode assignment of the experimental characteristic absorption peak was carried out based on PBE and RPBE calculation results. The study indicates that the characteristic absorption of anthraquinone crystals primarily originated from the overall vibration of the anthraquinone ring and benzene ring groups dominated by C—H…O intermolecular hydrogen bonds in the crystal, as well as the collective vibration mode caused by weak intermolecular interactions.
		2025 Vol. 45 (02): 337-343 [Abstract] ( 31 ) RICH HTML PDF (5263 KB) ( 15 )

	344	Fast and Adaptive Raman Spectroscopy Baseline Correction Algorithm Based on the Principle of the Minus-Weighted Iterative Adjustment Least Square Method (MWIALS)
		XU Jia-yang¹, MENG Si-yu², ZHANG Zhi-wei², CHEN Hong-yi², MA Yu-ting², WANG Ce², QI Xiang-dong², HU Hui-jie^2, SONG Yi-zhi^2
		DOI: 10.3964/j.issn.1000-0593(2025)02-0344-07
		Raman spectroscopy is a non-destructive spectral analysis technique that obtains molecular structure information of substances by analyzing the frequency changes of scattered light. Baseline correction is a key step in enhancing spectral data quality, as it removes background signals and unrelated noise to highlight and purify the target signal. Traditional Raman spectroscopy applications do not require high timeliness for baseline correction. Still, in recent years, applications such as flow Raman and endoscopic Raman, which require real-time processing of spectral data, have increased, placing higher demands on the speed and accuracy of baseline correction. Traditional methods, such as iterative polynomial fitting and wavelet transform, have time, accuracy, or adaptability deficiencies. This study developed a fast adaptive baseline correction algorithm based on the Minus-Weighted Iterative Adjustment Least Square Method (MWIALS). The main principle is to extract the set of negative values and assign them higher weights, continuously adjust the baseline during the iteration process, and set parameter thresholds to exit the loop to achieve fast and accurate baseline correction. We also proposed two parameter selection strategies: Fixed Parameter (FMWIALS), suitable for rapid processing of batch homogeneous spectra, and Adaptive Parameter (AMWIALS), suitable for adaptive processing of heterogeneous spectra. The algorithm was applied to flow Raman spectral analysis of particulate matter, and the results showed that compared to other mainstream algorithms, it was significantly more efficient in practical spectral processing (average time of 47 milliseconds per spectrum) and achieved higher accuracy and adaptability. This algorithm can meet the real-time spectral processing needs in biological sample detection for flow Raman and endoscopic Raman applications, providing strong support for the further application of Raman spectroscopy technology.
		2025 Vol. 45 (02): 344-350 [Abstract] ( 44 ) RICH HTML PDF (11266 KB) ( 30 )

	351	Spectral Baseline Correction Method Based on Down-Sampling
		HU Ying-hui¹, CAO Zheng¹, FU Hai-jun^1*, DAI Ji-sheng²
		DOI: 10.3964/j.issn.1000-0593(2025)02-0351-07
		Baseline drift is a common phenomenon in the collection process of spectral data, and baseline correction is an important means to combat baseline drift interference. The baseline correction method based on sparse representation can achieve good spectral preprocessing goals. However, when applied to high-dimensional spectral baseline correction, the computational complexity is extremely high and the effectiveness is poor. Moreover, the utility of pure spectral sparse structure is insufficient, and the performance needs to be further improved. This paper proposes a spectral baseline correction method based on down-sampling to utilize sparse structures and fully reduce computational complexity. Constructing a sparse recovery model with multiple snapshots and additional correlation matrices through a down-sampling strategy ensures that each down-sampling snapshot has common sparsity and spatial correlation while reducing the dimensionality of spectral data. Subsequently, in the variational Bayesian inference (VBI) framework, the independent vector decomposition mode is introduced, and the mathematical transformation technique of vector product is used to adaptively decouple the spatial correlation between multiple snapshots, thereby inferring the Bayesian optimal sparse solutions corresponding to each snapshot. In addition, using grid refinement technology to handle off-grid gaps further improves baseline correction performance. The experimental results on simulated and real datasets have verified the superiority of the proposed method.
		2025 Vol. 45 (02): 351-357 [Abstract] ( 41 ) RICH HTML PDF (4805 KB) ( 24 )

	358	Online Measurement of Trace Water Vapor Based on Mid Infrared Absorption Spectroscopy
		ZHONG Xiang-yu, SHI Qing^*, ZHANG Bu-qiang, ZHANG Yu-lu, LIU Xiao-ying, MENG Gui, NIU Hui-wen, SHAO Wen-bo, ZHOU Jian-fa
		DOI: 10.3964/j.issn.1000-0593(2025)02-0358-06
		Trace water vapor detection plays an important role in semiconductor chip manufacturing, atmospheric model research, high-purity gas preparation, etc. Aiming to accurately and rapidly measure trace water vapor concentration, a scheme of real-time online measurement of trace water vapor in high-purity nitrogen by TDLAS technology based on a 5 020 nm mid-infrared laser is proposed. The mid-infrared band has the characteristics of high selectivity and strong absorption capacity in gas absorption. The trace water vapor detection in this band has the advantages of good selectivityand short response time. According to the spectral parameters in the HITRAN database, the water vapor absorption line is simulated. The simulation results show that H₂O has strong absorption at 5 020.36 nm and no other gas interference. In this paper, the spectral line is selected to measure water vapor in high-purity nitrogen. Then, an interband cascade laser (ICL) with a central wavelength of 5 020 nm is selected as the laser source to build a TDLAS experimental system. The laser output laser is divided into two channels. One is a reference optical path to obtain the background water vapor absorption outside the absorption cell; the other is the measurement optical path. The water vapor absorption in the gas to be measured is obtained through the Herriott absorption cell, and the optical path Lin the absorption cell is 10 m. The detector converts the received optical signal into an electrical signal, amplified by the amplifying circuit, which is collected and transmitted to the computer. The sampling frequency is 25 MHz. Baseline fitting and Voigt linear fitting are performed on the reference and measurement optical path signals to obtain water vapor's absorbance curve and integral absorbance. The trace water vapor concentration in high-purity nitrogen gas is obtained by calculating and deducting the background water vapor absorption in the measurement optical path. The algorithm of the above inversion process is embedded in the software platform. The collected transmission light intensity data is directly inverted to obtain the water vapor concentration value in the high-purity nitrogen in the absorption cell in real-time, and the response time is 12 s. After the water vapor concentration value is stable, the average of the 30 measurement results is taken as the experimental result of the water vapor concentration of high-purity nitrogen in each batch. The experimental results show thatthe deviation between the measured trace water vapor concentration of each batch of high-purity nitrogen and the water vapor concentration in high-purity nitrogen required by the national standard is less than ±5%, and the maximum deviation is -3.04%. The error analysis of the experimental results shows that the maximum uncertainty of the water vapor volume fraction is 0.159×10^-6. The experimental device can accurately and reliably measure the concentration of trace water vapor in high-purity gas in the mid-infrared band, which provides a feasible scheme for online monitoring of trace gas in the mid-infrared band.
		2025 Vol. 45 (02): 358-363 [Abstract] ( 31 ) RICH HTML PDF (2414 KB) ( 29 )

	364	Study on the Effect of Different Ratios of Pb Doping on the High-Pressure Structure and Electrical Properties of Tin Dioxide
		HUANG Yu-xuan, WANG Ya-lin, GAO Jin-jin, WANG Xiao-yu, WANG Shi-xia^*
		DOI: 10.3964/j.issn.1000-0593(2025)02-0364-07
		SnO₂, as an important conductive oxide, can be used in solar cells, electrodes, oxidation catalysts, etc. The electronic structures and ionic radii of Sn⁴⁺ and Pb⁴⁺ are similar, and doping Pb into the crystal structure of SnO₂ can change its optoelectronic properties without destroying the structure. Pressure to change the lattice structure and electronic band gap of materials can effectively enhance the material properties. To investigate the effects of elemental doping and pressure on the structural properties of SnO₂, the structural phase transitions and electronic band gap changes of 10% Pb-doped and 25% Pb-doped SnO₂ under high pressure were investigated. Pure SnO₂, 10% Pb-doped and 25% Pb-doped SnO₂ samples were prepared hydrothermal. Scanning electron microscopy showed that the samples were composed of multiple nanorods arranged in the center of the dispersion, and the whole was in the shape of a flower; X-ray diffraction showed that the samples were of a tetragonal rutile structure (space group P₄₂); and the EDS spectra showed that the Pb was completely doped in the SnO₂ lattice. The effects of different ratios of Pb doping on the high-voltage structure and electrical properties of SnO₂ were investigated using a diamond pressure cavity combined with in situ Raman spectroscopy. Raman spectroscopy results show that there are four Raman vibrational modes of SnO₂ at ambient pressure, which are B_1g (88 cm^-1), E_g (480 cm^-1), A_1g (639 cm^-1) and B_2g (775 cm^-1). When the system pressure increases to 14 GPa, the E_g peak splits, a new peak appears at 563 cm^-1 and SnO₂ changes from a tetragonal rutile structure to a high-pressure CaCl₂-type structure; the two Raman peaks at A_1g and 576 cm^-1 of 10% Pb-doped SnO₂ gradually broaden with the increase of the pressure, and then merge to form a packet-like peak at 13 GPa, the degree of atomic disorder on the surface of the crystal increases, the symmetry decreases, the B_1g mode changes to the A_1g mode, the structural phase transition begins to appear, and the system changes to amorphous when the pressure increases to 25 GPa; the Raman peaks of 25% Pb-doped SnO₂ appear at 190 and 775 cm^-1, respectively, with the Pb⁴⁺ and B_2g peaks, and the intensity of the E_g peak becomes weaker when the pressure reaches 10 GPa, the two Raman peaks at 576 cm^-1 and A_1g merge, the structural phase transition occurs, and amorphization occurs at 25.4 GPa. Using first-principles calculations to study the electrical properties of pure SnO₂ and 10% Pb-doped SnO₂ under pressure results show that: increased system pressure will make the forbidden band width of pure SnO₂ from 0.645 to 1.759 eV, the electrons are more difficult to jump to the conduction band, the electrical conductivity is reduced; at the same time, doping will make the Pb into the crystal lattice to form defects, resulting in an increase in the density of defects in the vicinity of the valence band, valence band energy level decreases, the conductivity is enhanced, but the increase in system pressure does not change the conductivity of doped SnO₂. This study provides new ideas in the field of SnO₂ elemental doping. It enriches the study of the properties of SnO₂ under extreme conditions by combining it with in situ high-pressure technology.
		2025 Vol. 45 (02): 364-370 [Abstract] ( 33 ) RICH HTML PDF (5573 KB) ( 22 )

	371	Theoretical Study on the Structure and Properties of a Novel Diterpenoid Lactone Compound Euphorikanin A
		XU Jia-zhen¹, WANG Chang-jiang¹, WANG Chao-jie^2*
		DOI: 10.3964/j.issn.1000-0593(2025)02-0371-08
		Euphorikanin A, a recently discovered diterpene lactone compound is dated from the roots of Euphorbia reausui, exhibits promising tumor cytotoxic activity. To comprehend its biological effects fully, it is imperative to investigate its structural characteristics, spectral properties, and potential as a therapeutic agent. Consequently, further examination of its structure and properties can serve as a valuable resource for advancing novel pharmaceuticals. The density functional theory B3LYP/6-311++G (2d, p) and ωB97XD/6-311++G (2d, p) methods were applied to calculate the pharmacophoric conformation, geometric and electronic structure, infrared (IR), ultraviolet-visible (UV-Vis), nuclear magnetic resonance (NMR) spectra of the compound Euphorikanin A, and the molecular global reaction index analysis was carried out by using conceptual density functional theory. The pharmacokinetics platform was used to evaluate druggability and ADME/Tox. The calculation results showed that Euphorikanin A has a unique pharmacophoric conformation, and the geometric structure parameters of the compound were similar in both methods and different solvent environments. The calculated values were in good agreement with the crystal parameters. The theoretical infrared spectral characteristics are consistent with the experiment, and the calculated infrared spectra in the water environment are closer to the real values, with a theoretical scaling factor of 0.94. The maximum absorption peak of Euphorikanin A in the UV-Vis spectrum is between 193.88 and 201.75 nm. The theoretical results of the nuclear magnetic data obtained by both methods agree well with the experimental results, with R² greater than 0.94. The hydroxyl group of the six-membered ring of Euphorikanin A may be the reaction's active site. The compound's global reaction parameters and pharmacokinetics showed good performance, especially regarding Caco-2 cell membrane permeability, blood-brain barrier permeability, and human intestinal absorption. The structure and properties of Euphorikanin A have advantages in druggability and are worthy of further development.
		2025 Vol. 45 (02): 371-378 [Abstract] ( 45 ) RICH HTML PDF (4526 KB) ( 11 )

	379	Development and Application of Hemin-Glucose Oxidase Cascade Catalytic System
		LIU Yuan¹, WANG Ruo-xin^{1, 2}, WANG Xiang-feng¹, LIU Hai-ling¹, XIE Meng-xia^1*
		DOI: 10.3964/j.issn.1000-0593(2025)02-0379-07
		Natural biological enzymes have the advantages of high catalytic activity and selectivity, while they are usually sensitive to the matrix and extreme environmental conditions. Nano-enzymes with higher stability can be effective substitutes and supplements for biological enzymes in catalytic reactions. Enzyme catalysis and Fenton-like reactions have shown great potential in environmental pollutant degradation. Metal-organic frameworks (MOFs) are excellent carriers for biological enzymes and other functional molecules. The in-situ precipitation method prepared ZIF-8 composites of hemin and glucose oxidase (Hemin@GOx@ZIF-8). Hemin@GOx@ZIF-8 has good dispersibility in an aqueous solution, and the solution appears dark green. The structure characterization showed that Hemin and GOx could be successfully encapsulated in the ZIF-8 framework, and the skeleton structure of ZIF-8 remained intact. Hemin@GOx@ZIF-8 has a porous structure with an average particle size of 300 nm, a specific surface area of 1 512 m²·g^-1，an average aperture of 1.076 nm, and a pore volume of about 0.607 5 cm³·g^-1. Large specific surface area and pore volume can provide more active sites for catalytic reactions, which improves catalytic efficiency. Hemin@GOx@ZIF-8 has good stability and cascade catalytic performance. Under glucose drive, H₂O₂ is generated from reactions catalyzed by GOx, then catalyzed by Hemin to complete the Fenton-like reaction. Thus, organic pollutants are efficiently catalyzed and degraded in this cascade catalytic system. The catalytic degradation performance of the cascade system was studied using the organic dye Congo Red (CR) as the substrate. The results indicated that the degradation rate of CR in aqueous solution reached over 88%. The cascade catalytic system has good thermal stability, organic solvent resistance, and repeatability. Further research indicated that Hemin@GOx @ZIF-8 has good degradation ability for some different organic pollutants in water. So this cascade catalytic system has important practical significance and good application prospects in environmental pollutant degradation.
		2025 Vol. 45 (02): 379-385 [Abstract] ( 30 ) RICH HTML PDF (10417 KB) ( 11 )

	386	Simulated Estimation of BOD Content in Water Bodies Based on PCA Transmission Spectrum Reconstruction With Noise Reduction
		WANG Yi-ming¹, WANG Cai-ling¹, WANG Hong-wei^2*
		DOI: 10.3964/j.issn.1000-0593(2025)02-0386-08
		Biochemical oxygen demand (BOD) is an important indicator that can directly reflect water bodies' degree of organic pollution. Real-time monitoring of water BOD is significant in water resource protection and water environment improvement. The traditional BOD measurement method will consume a lot of human and material resources, and the measurement cycle is long, which can not quickly reflect the changing conditions of the water body, and can not realize the timely and effective early warning of sudden water pollution events. With the wide application of machine learning in the field of water monitoring, to solve the problem of difficulty in obtaining the input variables of the machine learning model and the existence of missing values, we further combine the hyperspectral technology to realize the accurate and rapid estimation of the BOD content of the water body. The raw spectral data of ten BOD standard liquids with different concentrations were collected, and 100 sets of transmission spectral data were obtained by whiteboard correction. A noise reduction technique based on PCA transmission spectra reconstruction is proposed, which utilizes the PCA algorithm to extract the principal component eigenvectors of the original transmission spectra and then reconstructs the whole dataset by using the first part of the principal component eigenvectors whose cumulative variance contribution rate reaches a certain percentage. The first 2, 10, and 15 principal component feature vectors were used in the experiment to reconstruct the transmission spectral data and compared with the traditional noise reduction methods for spectral data. We combined the SVM model and BP neural network model to establish a model for estimating the BOD content of water bodies. The results showed that the BPNN model was superior to the SVM model regarding regression accuracy and degree of fit, and the noise reduction effect was more significant. The model using the first 2 feature vectors reconstructed for noise reduction did not fit as expected, probably due to the loss of information. The BPNN model with the first 10 feature vectors reconstructed for noise reduction performed the best with an RMSE of 0.040 6 and an R² of 0.980 3. The reconstruction of the first 15 feature vectors did not improve the noise reduction effect, probably because more than 10 feature vectors added redundant information. The experiments verified the feasibility of noise reduction using PCA reconstruction of transmission spectra and provided a new idea for estimating the BOD content of water bodies.
		2025 Vol. 45 (02): 386-393 [Abstract] ( 38 ) RICH HTML PDF (14389 KB) ( 26 )

	394	Improved Particle Swarm Optimization Algorithm Combined With BP Neural Network Model for Prediction of Total Phosphorus Concentration in Water Body Using Transmittance Spectral Data
		ZHANG Guo-hao¹, WANG Cai-ling^1, WANG Hong-wei^2, YU Tao³
		DOI: 10.3964/j.issn.1000-0593(2025)02-0394-09
		The accurate detection of pollution levels in water bodies using transmission spectrum data and fusion algorithms has become crucial for safeguarding water resources. Inaccurate predictions and detection frequently result from the high-dimension of transmission spectrum data and model instability. The Yangtze River water body's total phosphorus concentration content is predicted in this study, and an accurate and environmentally friendly approach is suggested to achieve this goal. In particular, maxi-min normalization and mean-centering are two preprocessing operations carried out on the Yangtze River's measured water quality transmission spectrum data. These operations remove noise while eradicating differences between different data magnitudes, guaranteeing the consistency and reliability of the data. In addition, to solve the problem of the high dimension of the transmission spectrum data, the KPCA method is used to reduce the dimension of the data and extract the features. The KPCA method is used to select the top 6 principal components that represent 99.42% of the information content of the original data for subsequent prediction model training by finding a classification plane in a high-dimension space. Then, on the foundation of the initial particle swarm algorithm, the particle initialization rule, multiple swarm competition strategy, parameter adaptive update strategy, population diversity guidance strategy, and particle variation mechanism are added to improve the particle swarm's capacity for optimization and prevent particles from trapping in the local optimal solution. Additionally, the improved particle swarm algorithm optimizes the initialized weights and parameter values in the BP neural network to accelerate the convergence of the network and improve prediction performances. Finally, the total phosphorus content of the samples in the test set was predicted using the IMCPSO-BPNN model. The experimental results showed an R² of 0.975 786, an RMSE of 0.002 242, and an MAE of 0.001 612. The IMCPSO-BPNN model suggested in this work has a better fitting effect and better accuracy in forecasting the total nitrogen concentration in the Yangtze River water body when compared to other models such as the RF model, the BPNN model, and the PSO-BPNN model. It offers fresh concepts and viewpoints for studying and applying predictive modeling using transmission spectrum data and fusion algorithms to protect water resources and environmental management.
		2025 Vol. 45 (02): 394-402 [Abstract] ( 35 ) RICH HTML PDF (8330 KB) ( 39 )

	403	MC Simulation of Detection Conditions for EDXRF Analysis of Cd Element in Wastewater Solution
		LIAO Xian-li^{1, 2}, LAI Wan-chang^1*, MA Shu-hao³, TANG Lin²
		DOI: 10.3964/j.issn.1000-0593(2025)02-0403-07
		Industrial wastewater discharge is an important factor causing heavy metal element Cd pollution in water systems. Improper discharge may cause serious environmental pollution. Long-term consumption of crops or aquatic organisms in Cd-contaminated environments will cause various diseases and cause serious harm to the body. Therefore, timely detection of the heavy metal element Cd content is very important for treating and discharging industrial wastewater.Compared with traditional detection methods, energy-dispersive X-ray fluorescence (EDXRF) analysis has the advantages of fast speed, no damage to samples, simple operation, and small instrument size. It is more suitable for application in industrial wastewater treatment sites to detect heavy metal elements in industrial wastewater rapidly. Detection provides a basis for the treatment of industrial wastewater. This article conducts research on factors affecting the on-site rapid detection of Cd element content in industrial wastewater using the EDXRF method. The detection object is untreated flowing industrial wastewater. In order not to affect the process flow, the wastewater sample will flow through a section of the processingpipeline, and the EDXRF detection device will be installed. Outside the processing pipeline, this paper deduces the mathematical model of X-ray fluorescence analysis when the pipeline is square and the X-ray source and detector are vertically located on two adjacent planes of the square pipeline. The simulation analyzes different pipeline geometric parameters and “source-sample” - Explore the influence of the geometric position of the element to be measured on the characteristic X-ray irradiation rate of Cd, verify the accuracy of the theoretical analysis through Monte Carlo method simulation, and obtain the optimal excitation-detection device and its optimized parameters for the square pipe sample.This article conducts MATLAB simulation research based on the established mathematical model. The industrial wastewater solution is set to have a Cd element concentration of 100 000 μg·mL^-1, the medium is an HNO₃ solution with a concentration of 1.09 mol·L^-1, and the incident light ray energy is set to 40 keV, which is brought into parameter calculation, the effects of pipe wall material, pipe wall thickness, detector height, and horizontal distance of the X-ray source on the changing trend of the characteristic X-ray intensity of the element Cd in the standard sample were obtained. At the same time, a Monte Carlo model was established to simulate and study the source outlet ofthe sample. The simulation study was conducted to investigate the influence of the horizontal distance from the source outlet to the sample side, collimator diameter, “source-sample” distance, “sample-detector” distance, detector height, pipe wall thickness, and pipe wall material on the net peak area and peak-to-background ratio of the Cd characteristic peak in the sample. It is found that the pipe wall thickness should be as thin as possible under allowable process conditions, and the commonly used pipe material is better to be polypropylene acid ester plastic. The “sample-detector” distance is 1 mm. The “source-sample” distance and detector height should be as small as possible under objective conditions, such as the device's geometric size and the device's fixing conditions. When the horizontal distance from the source outlet to the sample size is 2.8 mm and the collimator diameter is 2 mm, the peak-to-background ratio and net peak area of the Cd characteristic peak will reach an optimal value.
		2025 Vol. 45 (02): 403-409 [Abstract] ( 45 ) RICH HTML PDF (4535 KB) ( 17 )

	410	Design and Research of Magnetically Enhanced High-Throughput Glow Discharge Sputtering Source
		SHEN Yi-xuan^{1, 2}, WAN Zhen-zhen^3, YU Xing^{2, 4}, WANG Hai-zhou^{2, 4*}, WANG Yong-qing³, ZHU Yi-fei^{2, 4}, LIU Shao-feng³
		DOI: 10.3964/j.issn.1000-0593(2025)02-0410-10
		High-throughput characterization technology has important applications in metal material analysis. It can realize the cross-scale global analysis of metal materials and evaluate the macroscopic characteristics, microscopic inhomogeneity, and local defects of the material surface. Glow discharge sputtering can etch the sample layer by layer in a large-size, flat, and fast way along the depth direction of the sample surface, and then send the sample exposed to the real microstructure to a variety of analytical instruments for composition, performance, and structure analysis, to be the key equipment for high-throughput characterization of metal materials. Based on the traditional Grimm glow sputtering source, this study built a glow discharge sputtering system designed for high-throughput experiments. The system expands the glow sputtering scale from mm to cm levels. At the same time, it was found that large-size sputtering leads to a decrease in glow discharge intensity and sputtering rate. Because of this phenomenon, combined with the results of glow discharge electron trajectory, electron density, and chemical reaction rate calculated by COMSOL Multiphysics simulation, this study proposes a technical method to enhance the glow discharge spectrum ( GD-OES ) intensity and sputtering rate by applying a scanning magnetic field. The T2 copper sample was used to explore the enhancement effect of the scanning magnetic field. The results show that applying the scanning magnetic field enhancement device can obtain stronger spectral signal intensity and a faster sputtering rate. Under the premise of keeping the discharge voltage and current unchanged, the spectral intensity of Mo and Cu elements increased to 1.43~11.97 times and 1.13~26.50 times that without a magnetic field, respectively. Among them, the maximum spectral intensity of the Mo element can reach 53 421 Cts, and the maximum spectral intensity of the Cu element can reach 76 948 Cts, which are 6.86 times and 4.32 times that without a magnetic field, respectively. In addition, this method can significantly increase the sputtering rate. When the pore size of the anode tube is Φ15 mm, the sputtering rate of the T2 copper sample is increased by 4.35 times, up to 2 662.09 nm·min^-1. The morphology and microstructure images of the sputtering pits were collected by white light interferometer and metallographic microscope. The results show that the pits after sputtering are flat and can clearly show the real microstructure characteristics of the samples. In summary, the experimental results show that the application of a scanning magnetic field enhancement device can improve the spectral signal intensity and sputtering rate, which provides a new method to solve the problem of low sputtering rate caused by large-size sputtering.
		2025 Vol. 45 (02): 410-419 [Abstract] ( 34 ) RICH HTML PDF (27413 KB) ( 21 )

	420	Determination of S and the Correlation With Total Protein in Pet Feeds by Super Microwave Digestion ICP-OES
		CHEN Shu-di¹, LI Jin-cai¹, CHEN Xiao-yan^1, LI Sheng¹, ZHANG Shi-wei^{1, 2}, ZHENG Yan-jie^1
		DOI: 10.3964/j.issn.1000-0593(2025)02-0420-06
		A novel method for determining the sulfur content in pet feeds was established using a super microwave digestion system and inductively coupled plasma optical emission spectrometry (ICP-OES) in the study. For the first time, we systematically investigated the effects of super microwave digestion conditions on residual carbon content (RCC) and residual acid (RA) in the digestion solution. We evaluated the digestion effects effectively and developed a new strategy for optimizing microwave digestion conditions based on these indicators. Furthermore, we thoroughly examined the influence of five different sulfur species on sulfur determination. We found that the emission intensity of sulfur was significantly enhanced by preparing low-valent sulfur standard substances under acidic conditions. However, when high-valent sulfur standard substances were prepared under acidic, neutral, or alkaline conditions, the emission intensity of sulfur remained relatively stable. This provided useful guidance for selecting preparation media and sulfur standard substances. Additionally, our research revealed that the super microwave digestion system effectively eliminated the enhanced emission intensity effect of low-valent sulfur under acidic conditions. In summary, the accurate determination of five different valence states of sulfur in pet feeds can be achieved by preparing high valence sulfate (SO^2-₄) standard substance under acidic conditions using the super microwave digestion system. The recoveries ranged from 86.5%~108%, and the RSD was between 1.69% and 4.18%. The detection limit was 6.2 mg·kg^-1. Compared with the national standard method, this method had high sensitivity, low cost, environmental friendliness, and high work efficiency. Furthermore, a significant positive correlation was observed between sulfur and total protein content in pet feeds (correlation coefficient=0.819, p<0.01). This conclusion can provide new ideas and directions for accurately determining pet feed protein content.
		2025 Vol. 45 (02): 420-425 [Abstract] ( 45 ) RICH HTML PDF (2062 KB) ( 14 )

	426	Analysis Techniques and Optimization Models for the Determination of Au in Ores by Atomic Absorption Spectroscopy
		WANG Peng^{1, 2}, MEN Qian-ni^{1, 2}, GAN Li-ming^{1, 2}, BAI Jin-feng⁴, WANG Xiao^{1, 2}, JING Bin-qiang^{1, 2}, KOU Shao-lei^{1, 2}, LIU Hui-lan⁵, HE Tao^{1, 2}, LIU Jiu-fen^{3, 6}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0426-08
		Gold (Au) in ores typically appears in the form ofgranular gold or lattice gold and occurs in different states in different types of gold ores. The particle effect caused by its unique malleability challenges the analysis technique for gold samples. The preparation process involves numerous difficult-to-control factors, directly impacting the accuracy and stability of gold analysis. Taking the gold element in ores as the research object, an analytical method for determining gold in ores using the foam plastic adsorption-atomic absorption spectroscopy technique is established. Qualitative judgments and quantitative calculations are made for the key factors in the sample testing process, and an optimization model based on grey relational analysis-response surface methodology is proposed. The important steps in the sample analysis technique, such as roasting, digestion, enrichment, and elution, are discussed. The optimization factors of the roasting method, digestion acidity, enrichment time, and thiourea concentration are determined. An orthogonal experiment is designed, and correlation analysis is carried out. Grey relational coefficients are calculated, and range analysis is used to qualitatively determine the significance of each optimization factor. A significant level table is constructed by combining central composite design with response surface methodology. A predictive model is established using a quadratic polynomial regression equation, and significance analysis is performed. Three-dimensional response surface plots and two-dimensional contour plots are used to fit and analyze the experimental data, determining the optimal parameters of the optimization model as follows: roasting method stepwise segmented roasting, digestion acidity-10.58%, enrichment time-40.00 min, and thiourea concentration-11.65 g·L^-1. Experimental results indicate that under the conditions of the optimized model, when preparing and testing national first-class standard gold ore materials, the method has a detection limit of 0.021, a determination limit of 0.077, and spike recovery rates ranging from 91.6% to 104.5%. The accuracy and precision meet the requirements of GB/T 20899.2—2019 quality control. Furthermore, method validation and comparison are performed on external samples from the Xiqinling area in Shaanxi and the Meichuan area in Gansu. The relative deviation does not exceed 10% for all cases, and the evaluation results are excellent, indicating that the proposed detection optimization model remains accurate and reliable for practical samples, demonstrating correctness and scientific validity. This study presents a new method for the rapid, accurate, and convenient analysis of the Au element in geological and mineral laboratories, providing new ideas for optimizing multi-objective parameter combinations in inspection and testing. It also contributes to accurately testing the new round of strategic mineral exploration.
		2025 Vol. 45 (02): 426-433 [Abstract] ( 37 ) RICH HTML PDF (6497 KB) ( 33 )

	434	Study on the Aging Behavior of Transformer Oil Based on Machine Learning and Infrared Spectroscopy Technology
		XIAO Zhong-liang, YUAN Rong-yao, FU Zhuang, LIU Cheng, YIN Bi-lu, XIAO Min-zhi, ZHAO Ting-ting, KUANG Yin-jie, SONG Liu-bin^*
		DOI: 10.3964/j.issn.1000-0593(2025)02-0434-09
		To solve the problems of complexity and large errors in oil aging analysis at the present stage, a technique integrating infrared spectroscopy and machine learning is proposed. With the help of a Fourier-Transform Mid-Infrared (FT-MIR) spectrometer, the sample spectra of three kinds of transformer oils were collected at different aging times. Various preprocessing methods were used to preprocess the sample spectra, and then the peaks were automatically sought and the sum of the characteristic peak areas was obtained. PLSR and PSO-SVR were used to establish a quantitative analysis model of transformer oil aging degree, and the effects of multiple spectral data preprocessing methods on the processing effects of infrared spectral noise reduction and baseline correction, as well as on the quantitative analysis effects of two models were investigated and analyzed. The results show that the best oil spectral preprocessing is the smoothing method, in which the SG+SVR and SG+PLSR model fitting Goodness-of-Fit (R²) are 98.14% and 99.13%, respectively, and the mean absolute error (MAE) is 0.312 4 and 0.288 0, and the root-mean-square error(RMSE) is only 0.097 7 and 0.379 0. Under the appropriate preprocessing conditions, both machine learning algorithms are robust and reliable, and the difference between the predicted and actual values of the models is extremely small.
		2025 Vol. 45 (02): 434-442 [Abstract] ( 38 ) RICH HTML PDF (17784 KB) ( 46 )

	443	Application of in Situ X-Ray Diffraction Spectroscopy in Crystal Structure Analysis of High-Entropy Pseudobrookite Ceramics
		MA Xiao-hui¹, LIU Jia-chen¹, WU Jin-yu¹, MAO Jing¹, HU Xiao-xia², GUO An-ran¹
		DOI: 10.3964/j.issn.1000-0593(2025)02-0443-05
		Present research on high-entropy ceramics focuses primarily on creating high-performance ceramics by element substitution or addition. It often ignores changes in the crystal structure of high-entropy ceramics due to the complexity of the composition and their effects on properties. This work systematically studies the formation process, crystal structure change, and effect on the thermal expansion coefficient of high-entropy pseudobrookite using in-situ X-ray diffraction spectroscopy. The results show that the formation process of high-entropy pseudobrookite ceramics is a slow solid-phase reaction. Compared to its corresponding single-phase ceramic, the lattice constants of high-entropy (Mg,Co,Ni,Zn)Ti₂O₅ change, with the a-axis and b-axis lattice constants slightly increasing and the c-axis lattice constants decreasing. Additionally, as the temperature rises, the increasing lattice constants along each crystal axis weaken, reducing the thermal expansion coefficient and anisotropy of the thermal expansion coefficient. These findings show that the in-situ X-ray diffraction spectroscopy technique effectively elucidates the crystal structure evolution process and its impact on properties during the formation of high-entropy pseudobrookite ceramics. Moreover, all the above results indicate that this technique shows great promise for resolving high-entropy ceramic crystal structures and advancing their application prospects.
		2025 Vol. 45 (02): 443-447 [Abstract] ( 27 ) RICH HTML PDF (10419 KB) ( 13 )

	448	Research on the Chemical and Mineral Composition of Jian Wares in the Song Dynasty Using Multiple Spectroscopic Techniques
		WANG Tian¹, YANG Shao-xiong¹, XIA Sen-wei¹, WANG Fen¹, WANG Ying¹, SUN Jian-xing², SUN Li², LI Qiang³, LUO Hong-jie^{1, 4}, ZHU Jian-feng¹
		DOI: 10.3964/j.issn.1000-0593(2025)02-0448-08
		Jian kilns in Nanping City, Fujian Province, were famous during the Song Dynasty (CE 960-1276), producing black glazed tea-bowls. Jian kilns were created during the late Tang and Five Dynastiesand lasted through the Song, Jin, Yuan, Ming, and Qing dynasties. Jian wares havenumerous varieties and various glaze colors, mainly including pure black glaze, hare's fur (HF) glaze, and persimmon red (PR) glaze. These types not only occupied an important position in the history of ancient Chinese ceramics but also profoundly influenced the history of ancient Asian ceramics, especially the history of ancient Japanese ceramics. Much research was devoted to analyzing Jian wares' chemical composition and microstructure to understand color generation and manufacturingtechnology. However, the differences betweenthese representative types (black glaze, HF glaze, and PR glaze) concerning the raw materials and preparation process are still unclear. In this work, various spectroscopic techniques such as X-ray fluorescence spectrometer (XRF), micro confocal Raman spectrometer (μ-RS), X-ray diffraction (XRD), and spectrophotometer combing optical microscope (OM) were applied to study the microstructure, chemical and mineral composition, and glaze appearance of black glaze, HF glaze, and PR glaze of Jian kilns. The results showed no significant difference in the content of most major elements, except for the CaO content in black glaze, which was ~1 wt% higher than that in HF glaze and PR glaze. The coloring elements are Fe₂O₃. The Fe₂O₃ content of black glaze and HF glaze is similar (6.0~7.0 wt%), while the iron content of PR glaze is the highest (10.5 wt%). The high content of Fe₂O₃ results in a reddish-brown color on the PR glaze surface. Many micro-scale branched metastable phase ε-Fe₂O₃ crystals were detected in the glaze cross-sections of all samples, the brown stripes on the HF glaze surface, and the PR glaze surface. Meanwhile, several hematite crystals (α-Fe₂O₃) were found in both brown stripes of HF glaze and PR glaze surfaces. From this, it can be inferred that -the combined coloring of ε-Fe₂O₃ and hematite colors is brown HF and PR glaze. In addition, quartz, zircon, pseudobrookite, and rutile crystals were detected in the Jian glazes. The chemical composition of the three bodies is similar, with the main phases being quartz, quartz, and mullite. A small amount of hematite was also found in some HF samples. This article uses various spectroscopic techniques such as X-ray fluorescence spectroscopy (XRF), micro confocal Raman spectroscopy (μ-RS), X-ray diffraction (XRD), and spectrophotometer combined with an ultra depth of field microscope (OM) to efficiently and accurately analyze the microstructure, chemical and mineral composition of Jian wares. This work also provides a theoretical basis for revealing the differences in raw materials and preparation processes of Jian black glaze, HF glaze, and PR glaze and provides certain reference significance for the study of similar ancient ceramics.
		2025 Vol. 45 (02): 448-455 [Abstract] ( 43 ) RICH HTML PDF (17249 KB) ( 29 )

	456	Accuracy Analysis of Simultaneous Wavelength Calibration for LiJET Spectrograph
		WANG Jia-qi^{1, 2}, JI Tuo⁴, CHANG Liang^{1, 2, 3*}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0456-07
		The LiJET spectrograph equipped with the Lijiang 2.4-meter telescope has two observation modes: DFDI and DEM mode, of which DEM mode can carry out simultaneous calibration observations. Simultaneously, calibration technology is one of the key technologies used to achieve high-precision radial velocity measurement. To carry out simultaneous calibration, it is necessary to evaluate the wavelength calibration accuracy and instrument stability. The difference in data processing software and processes is very likely to cause a difference in data processing results, so it is necessary to develop standardized data processing procedures for instruments. Based on the measured data of LiJET, a data processing program for LiJET is developed in this paper, which can realize the functions of image preprocessing, spectral order positioning, and spectrum extraction. The wavelength solution and wavelength calibration accuracy of the LiJET-DEM model are obtained by combining atmospheric absorption line cluster, thorium argon lamp spectrum, and other reference spectra. Iodine absorption spectra verify the calibration accuracy obtained by the thorium argon lamp at typical spectral orders. The calibration accuracy obtained by the thorium argon lamp and iodine absorption spectra is close, 3.3×10^-4 and 4.2×10^-4 nm, respectively. Then, the thorium argon lamp spectrum data of the same typical spectral order for 10 days was used for instrument stability analysis, and the drifting result of the instrument for 10 days was stable at 3.7×10^-5 nm, which was converted to the radial velocity of 19.8 m·s^-1.
		2025 Vol. 45 (02): 456-462 [Abstract] ( 35 ) RICH HTML PDF (13360 KB) ( 13 )

	463	Spectral Binary Star Analysis Based on Rough Set and Cluster Voting Mechanism
		WANG Qi¹, YANG Hai-feng^2, CAI Jiang-hui^3
		DOI: 10.3964/j.issn.1000-0593(2025)02-0463-06
		Spectral binary star usually refers to the spectra that show double dominant component characteristics. Due to the double component's complexity and diversity, its formation is complicated. At the same time, the spectral signal-to-noise ratio is relatively low. Many of the existing analytical methods separated two-component system spectra into two spectra. Still, the separation method can't guarantee the accuracy of the spectra, and the reliability of the existing clustering methods of the single clustering is relatively low. This paper proposes a binary star spectrum analysis and evaluation method based on a rough set and cluster voting mechanism. Using the idea of multiple clustering and voting, the gradient reliability of each spectrum belongs to the corresponding category. The method consists of two parts: First, the spectral binary star data set is reconstructed by using clustering algorithms with different ideas, and each clustering algorithm label is aligned with the Hungarian algorithm as a spectral attribute to reconstruct the data set. Secondly, the voting mechanism is used to reflect the consistency of the clustering results and give the category of each spectrum. At the same time, rough sets are defined to trace the characteristics of each spectrum, and the reliability of the classification of each spectrum is given by using the up/down approximation set. LAMOST DR10 was selected to publish the spectral set of binary stars as the analysis object. Four clustering algorithms, partition-based K-means, model-based Gaussian mixture model (GMM), Spectral clustering, and Agglomerative clustering, were used to reconstruct the spectral data set. Select the lower bound of votes as 2 and obtain clustering results with reliability gradients of 1, 0.75, and 0.5 through voting. About 1/3 of the samples have a reliability of 1, indicating that the four clustering results of this batch of samples are completely consistent. The SNR of each spectrum and the number of votes arestatistically analyzed. The SNR of the samples with the low number of votes is relatively low, which is one of the reasons why they are divided into different categories by different clustering algorithms. We analyzed the physical origin of 6 spectral samples with a reliability of 1, among which binary stars, Hanoi Nebula, and target stars were the main ones. The difference in clustering labels may be caused by the difference in the flow rate of the two components or data processing such as splicing and calibration. In addition, factors may lead to pipeline misjudgment due to low spectral quality, and its sky location distribution is consistent with the research on the distribution characteristics of low-quality data.
		2025 Vol. 45 (02): 463-468 [Abstract] ( 39 ) RICH HTML PDF (9642 KB) ( 15 )

	469	Study on the Leaching and LIBS Spectral Detection Method of Rare Earth Elements in Deep-Sea Sediments
		HAN Yan^{1, 5}, DU Zeng-feng¹, TIAN Ye³, LU Yuan³, SHI Xue-fa^{2, 4}, LUAN Zhen-dong^{1, 5}, YU Miao⁴, ZHANG Xin^{1, 2, 5*}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0469-07
		Deep-sea rare earth, refers to the sediments rich in rare earth elements in deep-sea basins. It is the fourth deep-sea metal mineral discovered after polymetallic nodules, cobalt-rich crusts, and polymetallic sulfides, and has great resource potential. The research on the investigation and detection technology of deep-sea rare earth resources in China is very weak. There is a lack of complete technical means to detect rare earth elements (REY) in deep-sea sediments in real-time, and rare earth elements cannot be accurately detected from deep-sea sediments. Laser-induced breakdown spectroscopy (LIBS) has unique advantages, such as in-situ, real-time, continuous, and non-contact. In recent years, LIBS has been gradually applied to underwater elemental analysis. Therefore, this paper proposes a new method for deep-sea rare earth detection, that is, the rare earth elements in deep-sea sediments are leached by inorganic acid. Then, the ionic rare earth elements in the leaching solution of deep-sea sediments are detected using LIBS underwater analysis. After the pretreatment of deep-sea sediments, the leaching experiments of different inorganic acid types and concentrations, solid-liquid ratio, and time conditions were carried out, and the effects of various conditions on the leaching process of rare earth elements were studied. By comparing the leaching rate of rare earth elements in each leaching solution, the optimum leaching conditions were obtained, that is, HNO₃ concentration 1.5 mol·L^-1, liquid-solid ratio 2∶1, leaching time 5 min. LIBS was used to analyze the rare earth elements (Y, La, Nd) in the leaching solution. After the spectrum was averaged and corrected by wavelength shift difference algorithm (WASS), univariate regression (UVR) and partial least squares (PLS) analysis were performed. The regression coefficients of the best linear regression results of rare earth elements (Y, La, Nd) obtained by UVR analysis were 0.87, 0.83, 0.80, respectively, and the corresponding detection limits were 3.55, 4.09, 5.71 μg·g^-1; PLS obtained significantly better regression results than UVR and obtained better regression coefficients, which were 0.97, 0.99, 0.97, respectively. The results show that PLS is more suitable for quantitatively analyzing deep-sea sediments than UVR. It also proves that LIBS can detect deep-sea, in-situ rare earth elements. It is feasible to use LIBS combined with multivariate regression analysis to detect and evaluate rare earth elements in deep-sea sediments and provide data support for LIBS deep-sea rare earth detection.
		2025 Vol. 45 (02): 469-475 [Abstract] ( 35 ) RICH HTML PDF (6473 KB) ( 17 )

	476	Rapid Detection of Bacterial Conjunctivitis Pathogens Using SERS@Au Microarray Chip
		LIU Wen-bo, LI Han, XU Yuan-cong, LIU Meng-dong, WANG Hui-qin, LIN Tai-feng, ZHENG Da-wei, ZHANG Ping^*
		DOI: 10.3964/j.issn.1000-0593(2025)02-0476-07
		Acute bacterial conjunctivitis is a prevalent ocular disease that can lead to severe vision impairment if not promptly treated. The conventional diagnostic method for bacterial conjunctivitis still relies on microbial culture, which, although highly sensitive, is time-consuming, labor-intensive, and unable to meet the demand for rapid detection. In this study, we developed a SERS@Au microarray chip as an enhanced substrate for collecting the SERS spectra of conjunctivitis-associated bacteria, including Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Staphylococcus epidermidis. The results demonstrated that the SERS@Au microarray chip has an excellent enhancement effect, reproducibility, and stability. By selecting the Raman shifts from 400 to 1 800 cm^-1 of pathogenic bacteria to perform an SVM model and OPLS-DA model, the discrimination accuracies achieved 97% and 90%, respectively. Detecting spiked tears using the SERS@Au microarrays chip can quickly, accurately, and conveniently screen pathogenic bacteria with simple culture. Collecting tears for direct testing is simple, painless, and non-destructive for patients. Combining the SERS@Au microarray with a portable Raman spectrometer is suitable for on-site screening of ophthalmic bacterial infections. Moreover, it enables the detection of mixed infections, thereby greatly enhancing overall efficiency in diagnosis and providing a valuable assistive tool for ophthalmic disease screening.
		2025 Vol. 45 (02): 476-482 [Abstract] ( 36 ) RICH HTML PDF (7744 KB) ( 16 )

	483	*Analysis of Mid-Infrared Spectral Characteristics of Soils Cultivated With Salvia Miltiorrhiza* at Different Intervals Based on Infrared Spectroscopy**
		QIAO Lu^{1, 2}, LIU Yong-hong^{1, 2}, XU Ke-ke^{1, 2}, YU Huan-ying¹, CHEN Yuan-jie³, YANG Lin-lin^{1, 2}, DONG Cheng-ming^{1, 2}, WANG Lei^1
		DOI: 10.3964/j.issn.1000-0593(2025)02-0483-09
		The continuous cropping obstacle caused by the differential changes in beneficial and pathogenic bacteria in the rhizosphere mediated by root exudates is the main factor restricting the development of Salvia miltiorrhiza cultivation. Propose a method based on infrared spectroscopy technology combined with two-dimensional correlation spectroscopy to quickly detect the composition and content changes of root exudates of Salvia miltiorrhiza. The spectra of 7 kinds of Salvia miltiorrhiza soil samples in the same area (not planted with Salvia miltiorrhizae), 1 year of rotation (19-ED, 23-ED), 2 years of rotation (23-One), 3 years of rotation (23-Two), 5 years of rotation (19-Five), and Salvia miltiorrhiza (19-ING) were collected, and the characteristics of the extraction were analyzed. The composition of soil compounds was analyzed. The results showed that the infrared spectral peaks and peak shapes of salvia miltiorrhiza cultivated at different intervals were the same. The main characteristic absorption peaks were around 3 622, 3 380, 1 638, 995, 777, 693, 524 and 463 cm^-1, respectively, to characterize the functional groups of phenolic hydroxy —OH, carbonyl C═O, methylene, benzene ring absorption substitution, cyclic ketones and other substances in phenolic acids. The absorbance of the soil with 1 year of rotation was the strongest at each characteristic peak, indicating that the autotoxic substances such as phenolic acids and esters continued to accumulate during the planting process. The position, number, and color of the absorption peaks were different in the bands 3 750~3 600, 2 170~2 145, 2 060~2 030, and 530~585 cm^-1, which clearly characterized the differences in functional groups. This indicates that the combination of infrared and 2D correlation spectroscopy can rapidly detect and monitor organic compounds in soil, and provide a theoretical basis for exploring the formation and reduction mechanisms of continuous cropping obstacles in Salvia miltiorrhiza.
		2025 Vol. 45 (02): 483-491 [Abstract] ( 34 ) RICH HTML PDF (20943 KB) ( 48 )

	492	Establishment and Optimization of the Hyperspectral Detection Model for Soluble Solids Content in Fortunella Margarita
		LI Wei-qi¹, WANG Yi-fan¹, YU Yue¹, LIU Jie^{1, 2, 3*}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0492-09
		To develop a rapid measurement method of SSC in Fortunella margarita, the detection models based on hyperspectral imaging data were established and optimized by employing various preprocess and regression algorithms, and the pseudo-color distribution of SSC with storage time was analyzed. The 307 whole citrus and 227 hemisected citrus samples were involved in hyperspectral data collection and the SSC values. The effects of preprocessing, including standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay (SG) filtering, normalization (NM), first derivative (FD), standardization (SD), and wavelet transformation (WT), on the performance of the partial least squares regression (PLSR) model were compared to select the appropriate preprocessing method. Then, the detection models were established by using PLSR, least absolute shrinkage and selection operator (LASSO) regression, support vector machine regression (SVR), artificial neural networks (ANN), decision trees (DT), random forest (RF) and light gradient boosting machine (Light GBM) algorithms. Furthermore, the models were optimized using genetic algorithms (GA) to select characteristic spectral wavelengths. The results indicated that for the whole citrus samples, the FD preprocessing could extract more features, and the LASSO regression model performed better than other models with 0.925 7 and 0.976 5 as the prediction determination coefficient (R²_p) and root mean square error of prediction (RMSEP), respectively. For the hemisected samples, the RF model based on the spectral after SD preprocessing had higher R²_p at 0.896 3 and lower RMSEP at 1.063 0. The GA could remove 53.85% and 50.58% wavelength variables to reduce the computational complexity for the whole and hemisected sample spectral, of which the SVR model has R²_p at 0.918 9. RMSEP at 1.017 3 RF model having R²_p at 0.895 3 and RMSEP at 1.084 3 performed better than other models. The results provided a feasible solution for high-throughput, non-destructive detection of SSC of Fortunella margarita.
		2025 Vol. 45 (02): 492-500 [Abstract] ( 56 ) RICH HTML PDF (14641 KB) ( 21 )

	501	Assessment of the Preservation Status of Wreck Archaeological Wood From Huai'a Watergate Site Park
		ZHANG Tong^{1, 2}, GUO Hong^{1, 2}, LI Tong³, ZHAO Qing-yao⁴, HAN Liu-yang^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0501-06
		As archaeological endeavors progress in our nation, shipwreck artifacts are discovered and excavated, and their preservation is increasingly prioritized. Various degradation factors in the burial environment cause irreversible changes in the microscopic morphology, chemical composition, cellulose crystallite structure, and physical and mechanical properties of archaeological wood. Therefore, it is crucial to accurately assess the preservation condition of shipwood before implementing conservation measures. This study aims to ensure the effective overall preservation and protection of the shipwreck artifacts excavated at the Huai'an Watergate Site Park of the Grand Canal in Jiangsu Province. To this end, representative shipwreck wood samples were selected, and their degradation degree was comprehensively evaluated. First, the wood species of the archaeological wood were identified, and the degradation level was preliminarily classified based on the maximum water content (MWC) and basic density (BD). Additionally, the relative crystallinity of wood cellulose was calculated using X-ray diffraction (XRD), and the thermal stability of the archaeological wood was determined through thermogravimetric analysis (TGA). Based on these analyses, infrared spectroscopy (FTIR) was used to detect changes wood's chemical structure. The results showed that the selected archaeological wood is Cinnamomum sp. and can be classified into three degradation levels: slight, moderate, and severe. The relative crystallinity of cellulose in sound wood is 58.87%, while that in archaeological wood ranges from 17.16% to 36.42%, indicating degradation in the crystalline regions of the cellulose. The maximum pyrolysis temperature of sound wood is 366.73 ℃, whereas that of archaeological wood ranges from 340.38 ℃ to 365.67 ℃, suggesting that large molecules in the archaeological wood gradually decompose into smaller molecules. Infrared spectroscopy revealed that hemicellulose degradation was the most severe during the degradation process, with the characteristic peak at 1 735 cm^-1, attributed to the acetyl groups in the side chains of hemicellulose, completely disappearing in moderately and severely degraded samples. As the degradation level increased, the intensity of the characteristic peak at 897 cm^-1 gradually decreased, and the characteristic peak at 1 424 cm^-1 shifted to a lower wavenumber, indicating that some hydrogen bonds in the cellulose were gradually destroyed as the degradation level increased. The lignin structure remained relatively stable, with the intensity of the characteristic peaks related to the aromatic skeleton significantly increasing. The ratio of lignin to cellulose further confirmed the above results. This study provides a more intuitive evaluation of the preservation status of archaeological wood and offers reliable fundamental data for the subsequent conservation work of the shipwreck artifacts.
		2025 Vol. 45 (02): 501-506 [Abstract] ( 55 ) RICH HTML PDF (10842 KB) ( 32 )

	507	Porosity Prediction by Emission Spectra During Narrow Gap Laser Wire Filling Welding
		SHE Kun^{1, 2}, LI Dong-hui¹, YANG Kai-song³, YANG Li-jun^3, LIU Jin-ping⁴, HUANG Yi-ming^3
		DOI: 10.3964/j.issn.1000-0593(2025)02-0507-08
		As an advanced joining process for large thick components in nuclear power fields, narrow gap laser welding has the advantages of low heat input, high welding efficiency, and high joint quality. However, attributing to the complex welding environment on site, porosity defects are prone to be generated due to inadequate cleaning of pollutants. The traditional post-welding nondestructive testing is time and labor-consuming, and the part size and the subjective judgment of the testers restrict the test results. Therefore, developing an online detection technology for porosity defects is urgent. In this study, a narrow gap laser welding detection system based on the emission spectrum was designed and developed. The effect of process parameters and pollutants such as water and oil on welding quality was investigated. The action mechanism of water on the electron temperature and spectral intensity of laser-induced plasma was analyzed. An online warning software system for porosity defects caused by pollutants was developed. The results showed that the spectrum intensity of narrow gap laser welding was weak due to the strong reflection and scattering of high plasma density caused by the side wall constraint of the workpiece. Due to the loss of laser energy, the measured spectral intensity during wire-filling welding was less than that of self-fusion welding. The electron temperature and electron density of plasma induced by narrow gap laser filling wire welding were 7 201.1 K and 5.279 7×10¹⁵ cm^-3, respectively, which were both lower than the thermodynamic parameters of self-fusion welding. Dense porosity defects were not detected by X-ray inspection in the self-fusion welding. When water was on the base material surface, pores on the weld surface were observed, and many dense pores were detected by X-ray inspection. The relative light intensity in all bands was reduced compared with the spectral data obtained under the normal process. The electron temperature also reduced from 6 900 to 7 200 K, but the electron density increased. Using a neural network model to train the spectral data after dimensionality reduction of principal component analysis, the porosity defects caused by water and oil in narrow gap laser wire filling welding can be predicted with high accuracy. The developed detection system can effectively identify porosity defects caused by pollutants with an accuracy of 90 % and a response time of 0.1 s.
		2025 Vol. 45 (02): 507-514 [Abstract] ( 40 ) RICH HTML PDF (26697 KB) ( 10 )

	515	TDLAS-Based Water Vapor Concentration Detection in Battery Drying Process
		QI Jia-lin¹, MA Xin-guo^{1, 2*}, FU Hai-liang³, WANG Mei¹, ZHANG Feng¹
		DOI: 10.3964/j.issn.1000-0593(2025)02-0515-07
		A method based on tunable semiconductor laser absorption spectroscopy (TDLAS) is used to study the problem of trace water measurement error due to pressure change during the drying process of electric core, and realize the contactless, high-precision and real-time detection of water vapor content. Firstly, according to the Lambert-Beer law, it is found that the changes in water vapor absorption spectra, optical range, and pressure will affect the measurement efficiency. The water vapor absorption spectra and the effective optical range of the measurement at 105 ℃ were investigated by the method of HITRAN simulation, and the results show that the water molecules are absorbed best and there is no interference of the background gases when the laser operates in the band of 7 181.17 cm^-1. Absorption coefficients of 10^-3 can be achieved at an optical range of 12 m. The change in pressure will lead to the change of the second harmonic peak and affect the water vapor measurement. By studying the absorption of water vapor under different pressures, a positive correlation between pressure and absorption coefficient is found, and a normalization method is proposed to solve the measurement error caused by the change of pressure by taking advantage of this law. The experimental tests found that the peak fluctuation of the second harmonic compensated by the pressure was reduced from 350 to 6.2 mV. The results of five experiments showed that the water content ratio to the second harmonic's peak voltage was 0.24, and the measurement error after calibration was less than 1.2%. These results are significant in improving the detection accuracy of water vapor content in the battery drying process.
		2025 Vol. 45 (02): 515-521 [Abstract] ( 30 ) RICH HTML PDF (5907 KB) ( 22 )

	522	Study on Detection of Gas Void Fraction in Oil-Gas Two-Phase Flow Based on Ultraviolet Spectroscopy
		LI Min¹, YIN Xiong¹, LIU Xue-jing¹, MA Shi-yi¹, ZHOU Yan^1*, CHONG Dao-tong¹, XIONG Bing², LI Kun²
		DOI: 10.3964/j.issn.1000-0593(2025)02-0522-10
		The accurate and rapid measurement of the gas void fraction in the oil-gas two-phase flow of the engine lubrication system holds significant importance for ensuring the safe operation of industrial processes. The precise determination of the void fraction is particularly crucial for monitoring the operational status of the engine. In light of the limitations associated with traditional gas void fraction detection methods, a method is proposed that combines ultraviolet spectroscopy with a modeling algorithm for predicting the void fraction. Initially, within the range of 185 to 430 nm, 31 sets of absorption spectra data were collected at five different two-phase flow temperatures and three different two-phase flow velocities, encompassing 15 operational conditions. The spectra were obtained for gas void fractions ranging from 0.9% to 3%. A total of 1799 spectral wavelength variables were considered for analysis. The spectral-physicochemical value coexistence distance algorithm (SPXY) was employed to partition the spectral data set into 21 calibration and 10 test sets. Partial least squares (PLS) modeling was conducted for various gas void fraction conditions, resulting in a test set determination coefficient R²) range of 0.63 to 0.91. To address the significant variations in prediction performance across different operating conditions, various data preprocessing methods, including centering, autoscaling, Savitzky-Golay convolution smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, detrending, and orthogonal partial least squares (OPLS) orthogonal signal correction, were applied to optimize the model. The Detrend-center-PLS model exhibited the best predictive performance, with the R² increasing from 0.903 2 to 0.955 7. To address the issue of excessive spectral wavelength variables, three methods, Competitive Adaptive Reweighted Sampling (CARS), Monte Carlo Uninformative Variable Elimination (MCUVE), and Genetic Algorithm (GA), were employed for band dimension reduction. The reduced spectral data were then modeled using Multiple Linear Regression (MLR), Partial Least Squares, and Least Squares Support Vector Machine (LS-SVM). The optimal predictive model was determined to be the Detrend-center-CARS-PLS model, which exhibited an improved R² from 0.955 7 to 0.959 8 compared to the full-wavelength model. Due to the limited improvement in optimization, the intersection of the three-dimensionality-reduction methods was taken, and the modeling was re-conducted. The R² increased from 0.959 8 to 0.967 1, indicating a noticeable enhancement in optimization. Considering the influence of temperature and flow rate on the predictive model, a multi-condition gas void fraction prediction model was established using the same mprocess as the single-condition model- Autoscaling was identified as the optimal preprocessing method, and the R² for the autoscaling-PLS model was 0.948 8. The wavelength dimensionality reduction method reduced the 1799 wavelength variables to 400~500 demonstrating a significant dimensionality reduction. For different modeling methods, the autoscaling-MCUVE-LS-SVM model was identified as the best multi-condition predictive model, achieving an R² of 0.992 6. Finally, by comparing the single-condition predictive model with the multi-condition predictive model, it was found that the gas void fraction prediction performance of the multi-condition model was superior, improving overall predictive accuracy. The results indicate that using spectral analysis combined with modeling algorithms for predicting gas void fraction in oil-gas two-phase flow is feasible and provides an effective monitoring method for the safe operation of engines.
		2025 Vol. 45 (02): 522-531 [Abstract] ( 32 ) RICH HTML PDF (3116 KB) ( 17 )

	532	A Generalized Combustion Characteristic Prediction Model Based on Flame Hyperspectral Technology
		WANG Ming^{1, 2}, HE Hong-juan^{1, 2}, WANG Bao-rui^{1, 2*}, AI Yu-hua^{1, 2}, WANG Yue^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0532-10
		Accurately measuring combustion characteristics can provide important references for optimizing combustion technology. This study predicts multiple combustion properties simultaneously using high spectral images of methane flames, addressing the challenge of monitoring combustion characteristics in engineering, which typically requires various collection devices and on-site sampling. A flame characteristic prediction model based on the 3D-CNN method is proposed. Initially, this method is validated on laminar flame high spectral images and further applied to turbulent flame high spectral images. Subsequently, a universal model and training method are established. Finally, the Grad-CAM method is used to conduct interpretability studies of the model. In the laminar flow model, we investigated the influence of different parameters on the accuracy of flame characteristic prediction models. In the turbulent flow model, we explored the feasibility of using transfer learning and the impact of adding wavelet denoising techniques and altering the number of convolutional layers on the model results. Within the general model, we addressed the issue of inconsistent labels in different datasets, enabling the blending of laminar and turbulent flame data for mixed training of the model. This facilitated the establishment of a combustion characteristic prediction model, ranging from laboratory-scale flames to real-world engineering flames. In the study of model interpretability, the Grad-CAM method was employed to investigate the operational principles and decision-making processes of neural network models. The results indicate that the average accuracy of the laminar flow prediction model for different predictive parameters exceeds 96%, with the Mish+Mae parameter combination achieving the highest average accuracy of 98% and demonstrating enhanced model stability. The average accuracy in the turbulent flow prediction model is consistently above 95%. Transfer learning, wavelet denoising, and altering the number of convolutional layers all contribute to improving model performance to some extent, contingent upon appropriate adjustments. The general model achieves an average of 97% and 94% accuracy for laminar and turbulent parameter predictions, respectively, while exhibiting higher robustness. In the interpretability study, information from Grad-CAM heatmaps suggests that this method can effectively identify flame-sensitive regions, extract crucial component information from spectra, and accomplish flame characteristic predictions. This study supports the intelligent development of experimental testing techniques in combustion science, holding significant implications for investigating combustion characteristics.
		2025 Vol. 45 (02): 532-541 [Abstract] ( 41 ) RICH HTML PDF (25272 KB) ( 10 )

	542	Infrared Thermography Detection of GFRP/NOMEX Honeycomb Sandwich Structure Defects Based on Convolutional Neural Network
		TANG Qing-ju^{1, 2}, GU Zhuo-yan¹, BU Hong-ru², XU Gui-peng², TAN Xin-jie², XIE Rui²
		DOI: 10.3964/j.issn.1000-0593(2025)02-0542-09
		Honeycomb sandwich structure is one of the important structural forms in the field of composite materials. Due to its complex preparation process and harsh service environment, it is easy to produce delamination, debonding, and other defects, which seriously affect the service life of materials. To ensure the performance and quality safety of related components, it is necessary to carry out regular quality monitoring and flaw detection of the honeycomb sandwich structure through appropriate non-destructive testing technology. Therefore, quantitatively detecting defects is the fundamental way to prevent and solve such problems. Based on infrared thermal imaging technology to GFRP/NOMEX honeycomb sandwich structure specimens containing prefabricated delamination and debonding defects as the object of study for pulsed infrared thermal wave nondestructive testing experimental research, the acquisition of several frames of the specimen surface temperature distribution thermograms, take several defective areas and the healthy region of the pixel temperature signals to construct a sample dataset, and randomly divided into a training set and a validation set, take the fourth row of defective The center horizontal line area is taken as the test set data. Combined with convolutional neural network technology, GFRP/NOMEX honeycomb sandwich structure defect detection and depth prediction is completed. Analyze the -one-dimensional convolutional neural network structure, introduce multi-scale dilated convolution, residual module, and attention mechanism to build a one-dimensional convolutional neural network prediction model, and use the constructed temperature signal data set to train the network model. The training results show that the Loss and RMSE trends of the validation and training sets are consistent. The final Loss of the validation set is 1.67×10^-5, the RMSE is 0.005 8, and there is no over-fitting problem. The test set data is input into the trained network. The results show that the constructed network can effectively identify the defects and the depth prediction error at the defect center is controlled within 2%. It can be seen that the combination of convolutional neural network and infrared thermal imaging detection technology can realize the reliable detection of GFRP/NOMEX honeycomb sandwich structure defects and the stable prediction of defect burial depth and also provide a reference for the identification and quantitative detection of defects in other composite materials.
		2025 Vol. 45 (02): 542-550 [Abstract] ( 44 ) RICH HTML PDF (11318 KB) ( 32 )

	551	Transformer-Based Method for Segmentation of Gastric Cancer Microscopic Hyperspectral Images
		ZHANG Ran^{1, 2}, JIN Wei^{1, 2}, MU Ying¹, YU Bing-wen², BAI Yi-wen², SHAO Yi-bo^{1, 2}, PING Jin-liang^3*, SONG Peng-tao³, HE Xiang-yi³, LIU Fei³, FU Lin-lin³
		DOI: 10.3964/j.issn.1000-0593(2025)02-0551-07
		Gastric cancer is the third leading cause of cancer-related deaths globally, posing a serious threat to human life and health. Therefore, early identification of gastric cancer lesions is crucial for early diagnosis of gastric cancer. As an emerging technique, microscopic hyperspectral imaging technology can simultaneously obtain rich spectral information and spatial information of biological tissues at the microscopic level, providing a new approach for early pathological slice diagnosis. In this paper, gastric cancer microscopic hyperspectral images in the range of 400~1 000 nm were collected using a microscopic hyperspectral imaging system. The gastric cancer microscopic hyperspectral dataset containing 230 images was constructed through preprocessing, such as spectral calibration. Although spatial attention-based methods have achieved significant results in image classification, segmentation, and other fields, they still face challenges of high computational complexity and insufficient utilization of spectral information when dealing with hyperspectral images. Therefore, this paper proposes a backbone network model based on convolution and attention mechanism called Mixing Dual-Branch Transformer (MDBT). This model achieves spatial and channel feature aggregation between blocks and within blocks by alternately applying spatial and channel mixing modules. Specifically, this paper designs window attention, convolution dual branches, and spatial and channel interaction structures. This design not only reduces computational complexity but also achieves window-to-window information interaction and feature fusion through convolutional interaction, overcoming the limitation of window attention's receptive field and further improving the global modeling ability of the Transformer. In the image segmentation experiments, we adopt the UperNet model as the decode head network to reconstruct the features extracted by the backbone network to obtain the final segmentation results. Five-fold cross-validation experiments were conducted on the collected gastric cancer hyperspectral dataset, and the results show that the average priceand mIoU of this paper's model reach 85.39 and 74.66, respectively, outperforming mainstream image segmentation network models such as UNet, Swin, PVT, and VIT. Meanwhile, ablation experiments are designed to verify the optimization effects of the proposed spatial and channel dual mixing modules, convolution, window attention dual branches, and other structures on experimental results. Experimental results demonstrate that the proposed MDBT model can effectively utilize hyperspectral images' rich spatial and spectral information, improve the accuracy of gastric cancer image segmentation, and prove the research significance and application value of microscopic hyperspectral imaging technology in gastric cancer diagnosis.
		2025 Vol. 45 (02): 551-557 [Abstract] ( 36 ) RICH HTML PDF (14284 KB) ( 21 )

	558	Improved Fusion Algorithm for Infrared and Visible Images Based on Image Enhancement and Convolutional Sparse Representation
		ZHU Rong¹, ZHENG Wan-bo^{1, 2, 3*}, WANG Yao^{2, 3}, TAN Chun-lin^{2, 3}
		DOI: 10.3964/j.issn.1000-0593(2025)02-0558-11
		Infrared and visible light images have become important source images in the field of image fusion research due to their complementary characteristics, and the current infrared and visible light image fusion methods have the problem of limited ability to preserve the details of texture information in the image. In this paper, firstly, the histogram equalization (HE) method is used to dynamically expand the range of gray values of infrared and visible images after alignment to achieve image enhancement, which makes the texture information in the image more prominent, and at the same time, the contrast between the background of the image and the details of the texture is also improved. Secondly, the gradient minimization filter is used to smooth the enhanced image to obtain the background layer of the image, and then the source image and the background layer are used to obtain the detail layer by difference operation to realize the decomposition of the infrared and visible light images. Again, the convolutional sparse representation (CSR) is combined with feature similarity analysis for infrared and visible image fusion: the two detail layers containing rich texture information are fused using the fusion strategy based on the convolutional sparse representation, and in this process to reduce the mismatch sensitivity of the convolutional sparse representation method, a window-based averaging strategy is adopted to process the activity level map of the image, to make the convolutional sparse representation insensitive to mismatches; For the problem of large amount of redundant information in the background image, the feature similarity analysis of the two background layers is carried out, and this is used as the basis for determining the degree of importance of the two in the fusion process. Finally, the preliminary fused detail and background layers are reconstructed by the inverse transform of gradient minimization image decomposition, and the fusion results of infrared and visible light images are finally obtained. Two sets of images, scenes 1 (buildings) and 2 (woods), from 21 scenes in the TNO dataset are used for subjective visual analysis. The observation results show that the HE-CSR-based fusion method visually retains the image's texture details better than the existing eight typical image fusion methods, including CVT, DTCWT, FPDE, GTF, IFEVIP, LP, RP, and CSR. At the same time, the objective index evaluation of the image fusion effect of all scenes in the TNO dataset is further conducted. The results show that the six evaluation index values of SF, SD, SCD, AG, EN, and CC for the HE-CSR-based fusion results are 7.316 6, 37.350 5, 1.704 1, 5.571 4, 6.756 3, and 0.744 6, which are respectively improved by 19.54%, 21.87%, 13.11%, 31.31%, 2.17%, and 8.23%. The experimental results show that the HE-CSR fusion method proposed in this paper outperforms other typical methods in subjective visual analysis and objective index evaluation and provides a new and more effective model and method for infrared and visible image fusion.
		2025 Vol. 45 (02): 558-568 [Abstract] ( 43 ) RICH HTML PDF (86248 KB) ( 21 )

	569	Hyperspectral Inversion Model of Forest Soil Organic Matter Based on PCA-DBO-SVR
		DENG Yun^{1, 2}, WANG Jun^{1, 2}, CHEN Shou-xue^2*, SHI Yuan-yuan³
		DOI: 10.3964/j.issn.1000-0593(2025)02-0569-15
		Soil Organic Carbon (SOC) is the carbon component of Soil Organic Matter (SOM) and is crucial for maintaining the balance and stability of forest ecosystems. Traditional methods for analyzing the organic matter content in soil involve chemical analysis, which is time-consuming and labor-intensive, and generates chemical wastewater that pollutes the environment. Hyperspectral technology offers a non-contact, efficient means of detecting soil nutrient information. Addressing the limitations in the accuracy and computational efficiency of existing machine learning models for soil organic matter prediction, this study uses soil samples from Guangxi State-owned Huangmian Forest Farm and State-owned Yachang Forest Farm. Using full-spectrum data, Principal Component Analysis (PCA) was employed to select the optimal wavelength number for feature bands. Fractional-order differentiation, which processes data more precisely than first-order differentiation and balances spectral noise and resolution, was used as one of the preprocessing methods to transform the spectral data. Finally, the Dung Beetle Optimizer (DBO), known for its higher robustness and fault tolerance compared to traditional centralized algorithms, was used to optimize the parameter combination of the Gaussian kernel function in Support Vector Regression (SVR). The results indicated that the PCA-DBO-SVR model effectively improved the coefficient of determination (R2) for soil organic matter prediction and reduced the Root Mean Square Error (RMSE). The PCA-DBO-SVR model demonstrated the best generalization performance and accuracy among the compared prediction models, with a validation set R² of 0.942 and an RMSE of 2.989 g·kg^-1, showcasing excellent accuracy.
		2025 Vol. 45 (02): 569-583 [Abstract] ( 34 ) RICH HTML PDF (25162 KB) ( 42 )

	584	Hyperspectral Method for Tracing the Origin of Hawthorn Using the Weighted Combination Model
		FANG Ao, YIN Yong^*, YU Hui-chun, YUAN Yun-xia
		DOI: 10.3964/j.issn.1000-0593(2025)02-0584-07
		Hawthorns from different origins have uneven quality due to the differences in growth environment and geographic climate, so determining the geographic origin of hawthorns is of great significance. A combined identification model based on error reciprocal weighting was proposed to improve the stability and accuracy of the hawthorn origin traceability model. Firstly, the hyperspectral information of 456 hawthorns was collected using hyperspectral imaging technology; and by comparing Savitzky-Golay Convolutional Smoothing (SG), Multiplicative Scattering Correction (MSC), and Standard Normal Variables (SNV) three preprocessing methods, and used the preprocessed data and the original data to construct BP Neural Network (BPNN) and Random Forest (RF) models, the preprocessing method with SNV as the average spectral value was determined based on their accuracy. Then, the hyperspectral image of the hawthorn was subjected to principal component analysis, and the 1st principal component image was selected; at the same time, six feature wavelengths were screened based on the weight coefficients under the full wavelength band, and then the corresponding average spectral value was used as the representation value of the spectral information. Secondly, the texture features corresponding to the 1st principal component image and the feature wavelengths grayscale images were extracted, respectively, and the spectral representation values of the feature wavelengths were combined with the texture representation values of these feature wavelengths grayscale images as well as the texture representation values of the principal component image to construct the input vectors of the origin traceability identification model. Finally, three methods of BPNN, RF, and weighted combination model (BPNN-RF) were selected for the identification model construction, and two evaluation indexes, namely, accuracy (Acc) and macroF1 score (macroF1) were selected to evaluate and analyze the hawthorn origin identification models constructed by different input vectors. The results showed that the accuracy and macroF1 score of the BPNN-RF model with the same input vector were mostly better than those of the BPNN model and the RF model, in which the accuracy of the actual test data set of the BPNN-RF model with the input vector consisting of three kinds of representation values was increased from 89.01% to 98.90%. The macroF1 score was also increased from 89.32% to 98.95%. This indicates that the combined BPNN-RF model based on the error inverse assignment has the strongest discriminative ability and the best effect on the identification of hawthorn origin, which is better than the single discriminative model such as BPNN or RF. This study provides methodological support for the traceability of hawthorn origin without relying on physicochemical analysis and only relying on hyperspectral information.
		2025 Vol. 45 (02): 584-590 [Abstract] ( 36 ) RICH HTML PDF (7060 KB) ( 13 )

	591	Characterization of DOM Sources and Distribution in Changdang Lake Based on 3D Fluorescence Combined With PARAFAC Approach
		HUAN Juan¹, ZHENG Yong-chun¹, XU Xian-gen², ZHANG Hao¹, YUAN Jia-long¹, LI Xin-cheng¹, ZHOU Li-wan^2*
		DOI: 10.3964/j.issn.1000-0593(2025)02-0591-10
		Lake water quality directly affects the surrounding ecology, human health, and economy. Pressures from modern industrialization and urbanization pose significant challenges to lake water quality, necessitating a thorough understanding of its changes, identification of pollution sources, and implementation of effective measures to maintain ecological health and ensure safe drinking water. Therefore, this study utilized Three-Dimensional Excitation Emission Matrix Spectroscopy (3DEEMs) and Parallel Factor Analysis (PARAFAC) to analyze the fluorescence spectra of dissolved organic matter (DOM) in the water from Changdang Lake and its surrounding surface sources in 2022. It explored the sources and spatiotemporal distribution of DOM fluorescence components in Changdang Lake and compared the similarity between the fluorescence of surface sources and lake water. Results revealed that the DOM in Changdang Lake primarily consists of two fluorescence components: protein-like (C1) and humic-like (C2, C3, C4). These components exhibit high similarity with fluorescence from nearby printing, domestic sewage, and aquaculture sources. Different fluorescence distributions were observed in Changdang Lake during different hydrological periods, particularly during high-flow periods when fluorescence intensity mainly concentrates at the downstream lake outlet. The fluorescence characteristics of Changdang Lake, with FI values ranging from 1.68 to 1.75, BIX values ranging from 0.92 to 0.93, and HIX values ranging from 0.56 to 0.7, suggest that endogenous sources predominantly drive DOM increment. Comprehensive analysis indicates that organic matter in Changdang Lake mainly originates from endogenous increments, such as the decomposition of aquatic plants and algae. Redundancy analysis reveals that environmental factors significantly correlated with lake DOM, including Chl-a and COD. This study contributes positively to addressing local ecological and environmental issues and provides valuable practical experience for studying other lake ecosystems.
		2025 Vol. 45 (02): 591-600 [Abstract] ( 23 ) RICH HTML PDF (20070 KB) ( 19 )

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