Researching | GPXYGPFX Volume 45 Issue 1,2025

YE Yan-qing, ZHANG Hai-yu, SHEN Di, LE Zhi-wei..., WU Yu-ping, KONG Guang-hui, ZHANG Jian-rong, TIAN Meng-yu, CHEN Jian-hua, ZHANG Cheng-ming and WANG Jin|Show fewer author(s)

GC-MS and FTIR analysis, combined with multivariate analysis methods such as PCA and PLS, were used to evaluate and identify moldy tobacco leaves. GC-MS analysis screened 9 markers of tobacco leaf mold, including 2-ethylhexanol. The linear discriminant equation constructed by 9 key compounds, such as 4-hydroxybutyrolac

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 88 (2025)

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Measurement of Absolute Quantum Yield of Weak Luminescence Samples by Four-Line Method

ZHENG Yong-li, ZHANG Xiao-dong, and ZHOU Yong-feng

Aiming at the difficulty of the absolute quantum yield measurement of weak luminescence samples, a Four-Line method is proposed, mainly using the neutral density attenuator to realize the relative enhancement of the sample emission signal. The fluorescence spectrometer and integrating sphere measure the absolute quantu

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 95 (2025)

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Determination of Vanadium and Nickel in Crude Oil by ICP-MS/MS Based on Reaction Gas Mixture

CAI Song-tao, HUANG Ying-xiu, YUAN Liang, DAI Yu-xuan..., MO Yao-kun and HUANG Jian-hua|Show fewer author(s)

The content and ratio of metal elements V and Ni in crude oil play an important role in understanding the characterizing depositional environment and organic matter types of crude oil. However, in the process of crude oil refining, V and Ni, as harmful metal elements in the catalytic cracking process of crude oil, will The content and ratio of metal elements V and Ni in crude oil play an important role in understanding the characterizing depositional environment and organic matter types of crude oil. However, in the process of crude oil refining, V and Ni, as harmful metal elements in the catalytic cracking process of crude oil, will reduce the activity of the catalyst, and even lead to poisoning of the catalyst and serious corrosion of equipment. Before refining, V and Ni must be removed from the crude oil. In addition, V and Ni are mutagenic and carcinogenic, and it is becoming increasingly important to identify or monitor the potential danger of V and Ni in crude oil entering the environment. Therefore, conducting highly sensitive and accurate determination of V and Ni content in crude oil is of great significance. This paper proposes a new strategy for determining V and Ni in crude oil by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). ICP-MS/MS directly analyzes the crude oil sample after diluting aviation kerosene. For the spectral interferences of V and Ni, in the MS/MS mode, the reaction gas mixture NH₃/He/H₂ is composed of NH₃/He (He is the buffer gas), and H₂ is used as the reaction gas. Based on the fact that V⁺ does not react with NH₃/He/H₂, but the interfering ions of V⁺ react with NH₃/He/H₂, the on-mass method was used to eliminate the interference, and V⁺ was selected for determination. Based on the mass shift reaction between Ni⁺ and NH₃/He/H₂, the interference was eliminated by the mass shift method, and the interference-free cluster ion

N i {(N H_{3})}_{3}^{+}

was selected for determination. By comparing it with the NH₃/He reaction mode, it is found that for the determination of V, adding H₂ as a reaction gas can quickly remove the interference of large ions and cooperate with NH₃ to eliminate the interference of oxide ions. For the determination of Ni, adding H₂ as reaction gas can promote the formation of fully hydrogenated adducts (—NH₃), thus increasing the yield of

N i {(N H_{3})}_{3}^{+}

. In the NH₃/He/H₂ reaction mode, the background equivalent concentration (BEC) of V and Ni is lower, the sensitivity of Ni is higher, and the limit of detection (LOD) of V and Ni is as low as 0.83 and 3.76 ng·kg^-1, respectively. The limit of quantification (LOQ) is 2.77 and 12.5 ng·kg^-1, respectively. The accuracy and precision of the method were evaluated by analyzing the standard reference material NIST SRM 1634c and the spiked recovery experiment, respectively. The results showed that the method's determination results were consistent with the certified values of the standard reference material. The spiked recoveries of V and Ni were 97.7% and 103%, respectively, and the relative standard deviation (RSD) was 1.8% and 1.9%, respectively. The developed method avoids the complicated sample pretreatment process and has the characteristics of simple and rapid operation, with high sensitivity, accuracy, and good precision. It is suitable for detecting V and Ni in crude oil and can provide a theoretical basis for removing V and Ni in the deep processing of crude oil..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 101 (2025)

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Prohibition Mechanism Between Dihydromyricetin and Pancreatic Lipase by Multiple-Spectroscopy and Molecular Docking

MENG Xiao-hui, HUANG Xu-bo, XIA Zhang-chen, XU Juan..., WANG Yan-bin, CHENG Jun-wen, YANG Liu and HE Liang|Show fewer author(s)

A natural plant named vine (Ampelopsis grossedentata) has been proven to exsert various bioactivities due to its major component of dihydromyricetin (DMY), but there is little information on its hypolipidemic function. In this study, the inhibition behavior of DMY based on pancreatic lipase (PL) assay was investigated A natural plant named vine (Ampelopsis grossedentata) has been proven to exsert various bioactivities due to its major component of dihydromyricetin (DMY), but there is little information on its hypolipidemic function. In this study, the inhibition behavior of DMY based on pancreatic lipase (PL) assay was investigated by ultraviolet spectroscopy followed by a series of multiple-spectroscopy measurements including fluorescence spectroscopy, synchronous fluorescence spectroscopy and 3D fluorescence spectroscopy as well as the DMY-PL interaction mechanism by molecular docking. The half inhibitory concentration (IC₅₀) of PL detected by UV spectroscopy was 2.6×10^-4 mol·L^-1, showing its satisfactory lipid-lowering capacity on PL. The calculation of the Lineweaver-Burk equation indicated their interaction type was competitive inhibition with the inhibition constant of 6×10^-4 mol·L^-1. The Stern-Volmer equation and static quenching double logarithmic formula analyzed the fluorescence spectra. The results suggested that DMY could significantly quench PL's self-luorescence and its fluorescence quenching constant K_SV was negatively sensitive to temperature, revealing that the fluorescence quenching process belonged to static quenching. The value of 1 for the binding site and positive relation of K_a to temperature demonstrating PL might combine one DMY to produce a stable complex, which was further evidenced by K_q values exceeding 2.0×10¹⁰ L·mol^-1·s^-1. According to the Van't Hoff equation, the results of thermodynamic parameters ΔS=0.201 4 J·mol^-1·K^-1, ΔH=32.311 kJ·mol^-1 and ΔG<0 under 293 and 310 K elaborated that the binding force was mainly hydrophobic force and a spontaneous and exothermic process. The binding distance r=1.475 nm reflected the possible non-radiative energy transfer from PL to DMY based on the theory of Förster's non-radiative energy transfer. Both synchronous fluorescence spectroscopy and UV spectroscopy results ascertained that the amino acid residue microenvironment and secondary structure of PL changed after the interaction with DMY. The former displayed DMY could bind to the surroundings of tryptophan (Trp) residue in PL by 2 nm red-shifts of the spectrum (Δλ=60 nm), while the latter uncovered the π→π^* transition of PL interacted with DMY. 3D fluorescence spectroscopy found the polarity of PL increased after hydrophobic interaction with DMY by 10 nm red-shifts of peak1 causing 51.38% decrease of fluorescence intensity and 5 nm bathochromic shifts of peak2 with 41.93% loss of fluorescence intensity. Moreover, the molecular docking results showed that the DMY binding site was located in the pocket of PL, which was formed by PHE77, PHE215, TYR114, ILE209, and PRO180 amino acids. The hydrogens in amino acids of HIS263, TYR114, SER152, and PHE215 could be linked to —OH of DMY on A ring C5, B ring C4′, C ring C3 and C ring —C＝O via hydrogen bonds, and other amino acids including THR78, LRU213, GLU179 and ALA178 may formed van der Waals forces with DMY. The experimental data obtained to a deeper understanding of the lipid-lowering molecular mechanism of DMY and its unique structure provides a theoretical basis for drug synthesis and screening of natural inhibitors..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 107 (2025)

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Research on a Mixed Gas Detection Method Based on KNN-SVM

SUN Chao, HU Run-ze, WU Zhong-xu, LIU Nian-song, and DING Jian-jun

In the current research on mixed gas detection, various mathematical algorithms for classifying and predicting data of multiple gas components have emerged. Rapid and accurate gas composition and concentration detection has gradually become a hot topic. However, in some studies, the features of gas data are difficult t In the current research on mixed gas detection, various mathematical algorithms for classifying and predicting data of multiple gas components have emerged. Rapid and accurate gas composition and concentration detection has gradually become a hot topic. However, in some studies, the features of gas data are difficult to capture and judge, and the classification and prediction of gas data exhibit poor accuracy and efficiency due to data bias and unbounded generalization errors. In response to challenges like data bias and unbounded generalization errors, this paper proposes a KNN-SVM algorithm. This algorithm performs secondary classification on ambiguous gas data that is challenging to classify. It combines K-nearest neighbors and Support Vector Machine algorithms to make more comprehensive data feature assessments. The algorithm determines the weights of each algorithm based on experiments, thereby improving the accuracy of discriminating gas categories. The integration of the two algorithms also enhances the efficiency of the overall algorithm, providing stable adaptability to different types of gases. The experimental gas composition consists of cylinders containing C₂H₂, NO₂，and SF₆ at concentrations of 12 mg·L^-1, NO₂, SF₆ at 10 mg·L^-1, and C₂H₂ at 5 mg·L^-1(all diluted with N₂ as a background gas), as well as two bottles of pure N₂. The experiment involves mixing these gases and adjusting their ratios to set the required gas concentrations for detection. By detecting individual gases,60 sets of training data are obtained for each of the three gases. Linear fitting of these 60 data sets yields fitted lines for each gas, establishing the relationship between gas concentration and absorption peak. The accuracy of gas detection is confirmed through the adjusted R-squared values for the fitted lines: 0.991 for C₂H₂, 0.981 for NO₂, and 0.987 for SF₆. Subsequently, 40 sets of training data are obtained by detecting mixed gases. The KNN-SVM algorithm is then applied to classify and predict mixed gases, and the concentrations of each gas in the mixed gas are inferred from the fitted lines. Comparisons with traditional SVM algorithms using various classification metrics demonstrate the effectiveness and superiority of the proposed algorithm. Experimental results indicate that the KNN-SVM algorithm exhibits outstanding performance in gas classification and prediction, achieving an accuracy of 99.167% and an Area Under the Curve index of 99.375%. This algorithm enhances the accuracy of gas detection and improves generalization capabilities to adapt to diverse gas compositions, providing robust support for real-time gas detection systems..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 117 (2025)

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Comparative Study of Hyperspectral Preprocessing Methods and Multiple Models in Classification and Discrimination

JU Lei, YU Jie, WU Yan-miao, LI Li..., LU Tian, DING Ya-ping and SHU Ru-xin|Show fewer author(s)

Identifying different parts of Solanaceae plants is crucial for their product formulation design and quality control. Hyperspectral technology, which can quickly and non-destructively acquire rich information, has become a widely used tool in plant research and monitoring. As important economic crops, Solanaceae plants Identifying different parts of Solanaceae plants is crucial for their product formulation design and quality control. Hyperspectral technology, which can quickly and non-destructively acquire rich information, has become a widely used tool in plant research and monitoring. As important economic crops, Solanaceae plants have great research potential when combined with hyperspectral technology. This study employs hyperspectral technology to classify different parts of Solanaceae plant leaves after initial roasting. Firstly, hyperspectral sampling was conducted on 293 powder samples from different parts of Solanaceae plants using the Field Spec 3 spectroradiometer. Subsequently, data preprocessing was performed using S-G smoothing and first-order and second-order derivatives to enhance information and remove noise. To minimize redundant features, partial least squares (PLS) were then used for data dimensionality reduction. Finally, based on the dimensionality-reduced data, six machine learning classification models-support vector machine (SVM), logistic regression, K-nearest neighbors (KNN), decision tree, random forest, and gradient boosting decision tree—were used for modeling and analysis. The results showed that for the classification task, the SVM model performed best after first-order derivative processing, achieving an accuracy of 100.0% on the training set and 84.7% on the test set. After grid parameter optimization, the optimal parameters were determined: no restriction on maximum depth, a minimum sample split of 4, and 200 estimators. The accuracy of five-fold cross-validation after parameter optimization was 88.1%, with the training set accuracy at 100% and the test set accuracy at 86.4%. The study results indicate that preprocessing methods combined with dimensionality reduction can enhance data information, enabling classification models to capture the characteristics of Solanaceae plant samples better. This study is of great significance for the rapid, accurate, and non-destructive differentiation of parts of Solanaceae plants..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 125 (2025)

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DMD-Based Hadamard Transform Near-Infrared Spectrometer Design and Implementation of Fast Processing System

WANG Shuo, XIE Zhen-kun, and WEI Zhi-peng

The near-infrared spectrometer based on a digital micromirror device (DMD) has the advantages of good wavelength repeatability, high resolution, and good vibration resistance. It is widely used in the fields of food safety and agricultural production. With the development of micro near-infrared spectrometers based on D The near-infrared spectrometer based on a digital micromirror device (DMD) has the advantages of good wavelength repeatability, high resolution, and good vibration resistance. It is widely used in the fields of food safety and agricultural production. With the development of micro near-infrared spectrometers based on DMD becoming more and more mature, the cost and performance of the instrument are still the key to research and development. Although most researchers have focused on software development and detection methods, the processing speed of the instrument hardware is also crucial. The spectral analysis can be effectively realized only by ensuring that the spectrometer can collect and transmit data quickly and accurately. In addition, in most studies, the generation and decoding of the Hadamard matrix is usually completed by the host computer, and the template is imported by FLASH storage. However, this method may limit the time efficiency of collecting complete spectral data. A method of high-speed driving DMD and fast data acquisition is proposed to improve the spectral acquisition speed and signal-to-noise ratio. A hardware circuit system is designed based on a DMD miniature near-infrared spectrometer. The system adopts the architecture of Field Programmable Gate Array (FPGA) and ARM and innovatively realizes the generation and decoding process of odd-even Hadamard template at the bottom of the embedded system, which accelerates the speed of spectral analysis and improves the signal-to-noise ratio. By comparing with the DMD spectrometer on the market, the research results show that the spectrometer developed in this paper only takes 214 ms to complete the acquisition time of a single spectrum, which is 4 times higher than that of the commercial DMD spectrometer. In the same 3 s acquisition time, the signal-to-noise ratio of the spectrometer developed in this paper is 4 600, which is 1.5 times higher than that of the commercial DMD spectrometer. Furthermore, the spectral scanning of rapeseed samples was carried out by the spectrometer developed in this paper. The contents of fat, protein, and moisture in rapeseed were analyzed, and the corresponding models were established by partial least squares regression (PLSR). The correction correlation coefficient of rapeseed fat content was 0.986 5, and the prediction correlation coefficient was 0.967 2. The protein content correction correlation coefficient was 0.985 4, and the prediction correlation coefficient was 0.963 6. The correction correlation coefficient of moisture content was 0.987 5, and the prediction correlation coefficient was 0.961 4. The model evaluation results show that the spectrometer can meet the needs of rapeseed component detection and verify that the spectrometer has important application value in the commercial field..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 133 (2025)

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Research on Traditional Chinese Medicine Detection Based on Fluorescence Spectroscopy and Raman Spectroscopy Techniques

LU Mei-hong, ZHANG Fan, BAO Ya-ting, WANG Zhi-jun..., LEI Hai-ying, WANG Xiang-yu and GAO Peng-hui|Show fewer author(s)

Traditional Chinese medicine is a treasure of Chinese culture and the crystallization of Chinese civilization, playing an important role in the health of the whole nation. Due to the limited growth environment of traditional Chinese medicine and the increasing market demand, numerous substandard and falsified tradition Traditional Chinese medicine is a treasure of Chinese culture and the crystallization of Chinese civilization, playing an important role in the health of the whole nation. Due to the limited growth environment of traditional Chinese medicine and the increasing market demand, numerous substandard and falsified traditional Chinese medicines have emerged in the market, which is extremely detrimental to the development and quality assurance of traditional Chinese medicine. Therefore, it is of utmost importance to detect and identify traditional Chinese medicine. Four medicinal herbs, angelica dahurica, Sappanwood, yam, and sanguinaria officinalis, were selected for classification and detection research to explore traditional Chinese medicine's fluorescent and Raman spectral characteristics. On the one hand, in this paper, we analyzed the possibility of angelica dahurica and sappanwood, traditional Chinese medicinal herbs, to exhibit fluorescence from the perspectives of chemical composition and structure. An F-4600 fluorescence spectrophotometer (200~750 nm) was used to measure the fluorescence spectra of aqueous extracts of angelica dahurica and sappanwood at different excitation wavelengths and concentrations, investigating the fluorescence spectral characteristics of aqueous extracts of angelica dahurica and sappanwood, and discussing the relationship between fluorescence intensity and different concentrations and excitation wavelengths. On the other hand, a confocal micro-Raman spectrometer (100~4 000 cm^-1) was used to conduct Raman spectroscopy tests on herbal slices of yam from four different regions and sanguisorba officinalis from two different regions, and then the Raman spectra were obtained. The results showed that the aqueous extract of angelica dahurica exhibited strong fluorescence under excitation wavelengths ranging from 260 to 350 nm. The optimal excitation wavelength is 340 nm, and the peak fluorescence wavelength is 420nm. The fluorescence intensity increased with the concentration increase, following a linear relationship at lower concentrations. The aqueous extract of sappanwood exhibited strong fluorescence under excitation wavelengths ranging from 200 to 290 nm, with the optimal excitation wavelength at 220 nm and the peak fluorescence wavelength at 345nm. With the increase inconcentration, the fluorescence intensity first increased and then decreased, reaching its maximum at 0.175 mg·mL^-1. Moreover, the fluorescence intensity of angelica dahurica and sappanwood aqueous extracts followed a Gaussian distribution about the excitation wavelength, followed by the fluorescence law. After analyzing the Raman spectra of yam and sanguisorba officinalis, it was found that the Raman characteristics of different producing areas were the same. The Raman characteristic peaks of yam were mainly concentrated at 477, 862, 939, 1 080, 1 258, 1 337, and 1 457 cm^-1. The Raman characteristic peaks of sanguisorba officinalis were mainly concentrated at 862, 1 337 cm^-1, consistent with the existing research results on chemical composition. Differences in Raman activity at certain characteristic peaks can be used to distinguish the origin of different traditional Chinese medicines. The research results provide experimental data and method references for applying fluorescence spectroscopy in the identification and quality analysis of traditional Chinese medicine, and they also lay the foundation for the rapid and accurate detection and origin attribution of traditional Chinese medicine by using Raman spectroscopy techniques..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 139 (2025)

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Online and Multi-Component Analysis of Converter Flue Gas by Raman Spectroscopy

ZHENG Chuan-xin, LIU Xiao-meng, LI Quan, MENG Zheng..., WANG En-ning, SI Xing-yu, WANG Jian-nian, LI Zhen-lin and WANG Hong-qiu|Show fewer author(s)

In the modern metallurgical industry, online analysis of converter flue gas is commonly conducted by laser absorption spectroscopy, which can only measure a single gas component by each sensor. Therefore, a combination of multiple sensors is usually needed to monitor multiple gases simultaneously, including CO，CO2，O2，H In the modern metallurgical industry, online analysis of converter flue gas is commonly conducted by laser absorption spectroscopy, which can only measure a single gas component by each sensor. Therefore, a combination of multiple sensors is usually needed to monitor multiple gases simultaneously, including CO，CO₂，O₂，H₂, etc. The whole analysis system is complicated and expensive, with a slow response speed. Daily calibration and maintenance are also time-consuming and labor-intensive. This manuscript established a novel Raman spectroscopy-based methodology for real-time and multi-component analysis of converter flue gas to simplify the online analysis system and improve the efficiency of online measurement and the steelmaking process. A multi-gas analysis system for fuel gas measurement was also developed for the first time. The content of five CO, CO₂, N₂, O₂, and H₂ components could be quantified simultaneously based on the characteristic peak intensities. With a detection range of 0.1%~100%, the RSD value of our method was ≤0.1%. In the analysis system, the sample gas processing module adopted a high-temperature resistant probe and a high-power suction pump, which can effectively and quickly remove moisture and dust from the sample gas entering the analyzer, ensuring stable testing conditions. The analysis module employed a narrow-linewidth 532 nm laser, a transmission grating spectrometer, and a free space light path design to improve the detection sensitivity for gas analysis. Compared with single-component analysis techniques, the multi-component analysis method proposed in this manuscript could provide quantitative results for five components simultaneously without being affected by gas pressure variation. Sample gas processing and system maintenance were simple, while the detection accuracy was high. When N₂ standard gas was measured, the intensity of the characteristic peak was stable, with fluctuation less than 0.48% within 24 hours. The detection error of our method was less than 0.3% when quantifying standard gas samples, meeting the requirements of real-time analysis of converter flue gas. The multi-component gas analysis method by Raman spectroscopy could be widely applied in both the metallurgical industry and other fields by simple model expansion..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 146 (2025)

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Mineralogical and Spectroscopic Characterisation of Multicoloured Pigments From Sanwei Moutain at Dunhuang

LI Lin-xiao, TAN Yu-chen, XIAN Yi-heng, TIE Fu-de..., SUN Man-li, AI Hao, LIANG Yun and SUN Feng|Show fewer author(s)

An ancient mine site for extracting multicolored pigments was discovered at the Flame Gorge of the SanWei Mountain, Dunhuang, which holds academic value for researching the sources of ancient mineral pigments in northwestern China. To determine the mineral species and spectral characteristics of pigments discovered the

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 152 (2025)

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The Influence of Optical Dilution Effect for Spaceborne Wind Measurement on O₂ Near Infrared Airglow

LI Hao-tian, LI Fa-quan, LI Juan, WANG Hou-mao..., WU Kui jun and HE Wei-wei|Show fewer author(s)

Measurement of the Doppler shift information of the O2(a1g) day glow using the satellite-borne spectral imaging interferometer is currently the state-of-the-art technological means to realize the detection of the atmospheric wind field in the global adjacent space. Observing the Doppler shift of the spectral line O19P1 Measurement of the Doppler shift information of the O₂(a¹Δ_g) day glow using the satellite-borne spectral imaging interferometer is currently the state-of-the-art technological means to realize the detection of the atmospheric wind field in the global adjacent space. Observing the Doppler shift of the spectral line O₁₉P₁₈ (7 772.030 cm^-1) allows high-precision and high-sensitivity wind speed measurements in the 40～80 km spatial region. However, the specific effect of atmospheric scattering on its detection precision is unknown. This paper aims to quantitatively assess wind measurement errors due to the optical dilution effect. First, the spectral properties of the O₂(a¹Δ_g) spectrum and the atmospheric scattering spectrum are introduced. The contributions of different reaction mechanisms of O₂(a¹Δ_g) were calculated using the latest HITRAN spectral parameters, photochemical reaction rate constants, and NRLMSIS 2020. The volume emission rate (VER) of O₂(a¹Δ_g) was calculated based on the photochemical reaction rate, and the effect of the solar zenith angle on the VER distribution was analyzed. Spectral radiation models of O₂(a¹Δ_g) at different temperatures and self-absorption effects were obtained based on the Einstein coefficient and spectral line intensity, respectively. The effects of different geographical and meteorological factors on the atmospheric scattering spectrum were also analyzed. Secondly, the principles of the measurement technique of the Doppler Asymmetric Spatial Heterodyne spectroscopy (DASH) for limb-viewing are introduced. Describes removing the atmospheric scattering component to produce a pure airglow interferogram. The forward process for acquiring interferometric images is explained based on the DASH instrument concept. Thirdly, the “onion peeling” algorithm was introduced for the retrieval problem. The contribution of the atmosphere above the target layer is eliminated while considering the influence of the self-absorption and optical dilution effects. The problem of extracting target layer information in interferometric images is solved. Finally, the atmospheric wind field detection precision profiles and their changing laws with the influence of geographical and meteorological factors are obtained by error analysis. It is demonstrated that the optical dilution effect reduces the interferogram visibility and increases the measurement noise, adversely affecting the limb-viewing weights and the effective signal-to-noise ratio. In the tangent altitude range of 45~80 km, the wind measurement precision is less affected by atmospheric scattering, with an error of about 2～3 m·s^-1. Below 45 km, the wind measurement precision is affected by the optical dilution effect that increases sharply with decreasing altitude and is significantly increased by the surface albedo, aerosol, and cloud. When the effects of all three factors are considered simultaneously, the minimum lower detection limit is about 40 km..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 160 (2025)

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Near Space Oxygen Spectral Sensitivity Analysis Based on SCIATRAN

YE Song, HU Shuang-han, XIONG Wei, LI Shu..., WANG Xin-qiang, WANG Fang-yuan and WANG Jie jun|Show fewer author(s)

The study of atmospheric temperature fields in the near space is of great scientific significance. According to the study of the radiation mechanism and distribution characteristics of the full-time atmospheric temperature tracer, oxygen, as one of the main components of the atmosphere, is widely distributed in the nea The study of atmospheric temperature fields in the near space is of great scientific significance. According to the study of the radiation mechanism and distribution characteristics of the full-time atmospheric temperature tracer, oxygen, as one of the main components of the atmosphere, is widely distributed in the near space with obvious radiation intensity, so oxygen detection is an important basis for the study of temperature change in the near space. When the short-wave infrared part of solar radiation passes through the atmosphere, it is absorbed by oxygen molecules to obtain the oxygen absorption spectral line, which carries important information such as oxygen content. The oxygen spectral form obtained by the detector is used for high-precision inversion to obtain the temperature and other information. Due to the effects of solar zenith Angle, azimuth Angle, edge cutting height, and aerosol parameters on oxygen during the detection process, this paper analyzes oxygen sensitivity to different parameters based on the SCIATRAN model. Based on the SCIATRAN model, this paper will study the impact of environmental factors in the near space on oxygen detection and analyze and calculate the sensitivity of oxygen to the changes in radiation brightness caused by detection methods and environmental factors. The results show that the radiation brightness of oxygen increases with the zenith Angle below 60°, but the change is opposite when the zenith Angle exceeds 60°. The influence of solar zenith Angle on the radiation brightness of oxygen in one day is less than 0.01 W/m²/nm/sr, and the difference is less than 7%. Oxygen radiance decreases with the increase of azimuth Angle; the oxygen radiation brightness decreases as the tangent height increases, and the magnitude of the decrease gradually decreases. The difference ratio of aerosol type to oxygen radiation brightness is less than 10%. The influence of aerosol season on oxygen radiation intensity is greater in spring and summer than in autumn and winter. Under different aerosol types, the difference ratio of aerosol optical thickness multiples on the oxygen radiation brightness is less than 30%. The optical thickness multiples are proportional to the radiation brightness under rural and Marine aerosols. Still, the opposite is true under urban aerosols. The study also compares the simulation data with the measured data to verify the validity and feasibility of the simulation data of the SCIATRAN model. The research results of this paper provide a theoretical basis for oxygen detection in near space and a reference for the inversion of near space and other related research fields..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 170 (2025)

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Characterization of Pyrolytic Properties of Clay Minerals Based on Terahertz-Thermogravimetric Spectroscopy

LI Xin-yu, LIU Cai-qin, HUANG Hao-chong, ZHENG Zhi-yuan..., ZHANG Zi-li and QIU Kun-feng|Show fewer author(s)

Clay minerals are essential components of clay rocks and soils, forming the primary constituents of various terrestrial surface coverings. Studying their structural attributes, particle dimensions, and moisture content variations is crucial for understanding environmental dynamics in clay mineral-rich regions and guidi Clay minerals are essential components of clay rocks and soils, forming the primary constituents of various terrestrial surface coverings. Studying their structural attributes, particle dimensions, and moisture content variations is crucial for understanding environmental dynamics in clay mineral-rich regions and guiding mineral industry applications. Due to diverse geological applications and practical requirements, traditional mineral characterization methods often have limited applicability for mining minerals with distinct physical properties. Terahertz spectroscopy technology, a novel non-contact coherent testing method, utilizes fingerprint spectra, wide spectra, and water sensitivity within this frequency band. This technology enables the non-destructive detection of clay minerals, providing optical information to differentiate their crystal structure and composition. This article primarily focuses on using terahertz time-domain spectroscopy, thermogravimetric analysis, and Fourier transform infrared spectroscopy to study the thermal decomposition characteristics of clay minerals. The composition, particle size, and calcination products of kaolin, a vital raw material significantly influence the quality of ceramics. Experimental results confirm substantial variations in the absorption coefficient and refractive index of different states of kaolin within the terahertz frequency range. Differences in the crystal structure of talc and vermiculite, belonging to the 2∶1 type layer silicate, result in significant disparities in their terahertz spectra, effectively indicating thermal decomposition byproducts and moisture content in conventional electrically neutral hydrous minerals. Contrary to conventional understanding, vermiculite exhibits peaks at 1.10 THz without chemical interventions. The appearance of this can facilitate substance characterization and advance optical devices while enhancing the understanding of minerals in terahertz spectroscopy. This offers a fresh research perspective for the interdisciplinary investigation of terahertz spectroscopy and geology..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 179 (2025)

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Study on Spectral Characteristics of Turquoise Imitations of Single and Multiple Component Mixtures

YANG Chi-yu, WANG Tian-tian, ZHANG Wei-xiao, and TANG Wei-xi

Raman spectra, infrared spectra, chemical composition analysis, and UV-Vis NIR spectra analysis were carried out for new turquoise imitations in gem wholesale markets. The main spectral characteristics were summarized. The results show that the imitations are mainly composed of a single component or multiple mixed comp

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 183 (2025)

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Characteristics of Dissolved Organic Matter Fluorescence Components in Wetland Surface Sediment and Their Correlation With Fungal Communities

SHI Chuan-qi, LI Yan, MENG Ling-bo, HU Yu, and JIN Liang

Dissolved organic matter (DOM) and microbial communities are closely related, DOM provides nutrients to microorganisms, and microbial metabolism can transform DOM. Their interactions affect the material cycle and energy flow of the ecosystem. In this study, surface (0~15 cm) sediment samples were collected from natural Dissolved organic matter (DOM) and microbial communities are closely related, DOM provides nutrients to microorganisms, and microbial metabolism can transform DOM. Their interactions affect the material cycle and energy flow of the ecosystem. In this study, surface (0~15 cm) sediment samples were collected from natural wetlands (river wetland, HL; lake wetland, HP) and artificial wetlands (paddy field, ST; fish pond, YT) in northern cold regions of China. Three-dimensional fluorescence spectroscopy and high-through put sequencing techniques were employed to reveal the DOM fluorescence spectra and fungal community characteristics of the different types of wetland surface sediment, and their correlation was further analyzed. The results indicated that four kinds of fluorescent components were identified from the surface sediment DOM fluorescence spectrum, including fulvic-like acid component(C1), humic-like acid component (C2), and protein-like component［tryptophan-like component (C3) and tyrosine-like component (C4)］. C1 and C2 were significantly positively correlated, while no significant correlations existed among them and the protein-like components. The relative concentration of C1 was higher than that of C2, while the relative concentrations of C3 and C4 were close.The concentration of DOM in YT was relatively higher than that in the other samples. At the same time, there were significant differences in the relative concentration of C2 and protein-like components in HL and HP and significant differences in the relative concentrations of C1, C2, and C3 in ST and YT. The dominant species of fungal communities in wetland surface sediment (excluding unclassified groups) were Ascomycota, Basidiomycota, and Rozellomycota. The Chao1 richness index and Shannon diversity index of fungal communities in YT were significantly lower, while the Simpson dominance index was significantly higher. The Shannon diversity index in HP was significantly higher than the other samples.C1 and C2 were significantly correlated with fungal community diversity, C2 was significantly correlated with fungal community composition, while protein-like components had no significant correlation with fungal community. C1 and C2 were significantly positively correlated with Ascomycota, C1 was significantly negatively correlated with Chyridiomycota and Monoblepharomycota, and C2 was significantly negatively correlated with Basidiomycota, Rozellomycota, Chytridiomycota, and Monoblepharomycota. Therefore, fungal communities significantly impacted the relative concentration of fluorescent components with relatively large molecular weights of DOM. This study analyzed the DOM fluorescence spectral characteristics and its correlation with fungal communities in the surface sediment of typical wetlands in the cold region of northern China, providing fundamental data for wetland environmental monitoring and evaluation and theoretical references for the rational utilization of wetlands..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 191 (2025)

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Using Spectroscopy to Predict Soil Properties on Coastal Wetlands Invaded by Spartina Alterniflora

CHEN Xu, CAO Si-heng, YANG Ren-min, CHEN Qiu-yu..., LI Jian-guo and XU Lu|Show fewer author(s)

This study aimed to effectively monitor the changes in soil properties after Spartina alterniflora invasion on coastal wetland ecosystems. The study area is a typical Spartina alterniflora wetland in the Yancheng Wetland Rare Birds National Nature Reserve of Jiangsu Province. A total of 15 sites were identified by a st This study aimed to effectively monitor the changes in soil properties after Spartina alterniflora invasion on coastal wetland ecosystems. The study area is a typical Spartina alterniflora wetland in the Yancheng Wetland Rare Birds National Nature Reserve of Jiangsu Province. A total of 15 sites were identified by a stratified-random sampling method, and 45 soil samples were collected from three depth intervals (0~30, 30～60, and 60~100 cm). The visible-near infrared spectral reflectance and 10 soil physicochemical properties were measured. The performance of partial least squares regression (PLSR) and random forest (RF) was studied, spectral transformation forms' influence on prediction accuracy was analyzed, and the potential of invasion years and soil depth as auxiliary predictors were discussed. The results show that: (1) the visible-near infrared spectral reflectance can be used to predict organic carbon, inorganic carbon, total nitrogen, water content, pH, bulk density, salinity, and clay contents in soils with reasonable accuracy; (2) the method of partial least squares generally outperform random forest algorithm, the R² of prediction models developed using the PLSR method was between 0.341 and 0.979, and the biggest R² of random forest models was 0.722; (3) Differential transformation and reciprocal transformation of spectral reflectance can substantially improve the model performance. The optimal prediction model of full nitrogen can be obtained based on the original spectra (R² is 0.769 and RMSE is 0.091 g·kg^-1). In contrast, the optimal models for other soil properties are mostly based on differential or reciprocal transformation of the original spectra. (4) In general, the model performance can be improved by adding variables of invasion years and soil depth, and the prediction accuracy of organic carbon, total nitrogen, salinity, pH and bulk density models are more sensitive to the two variables. The prediction model accuracy (R²) for estimating soil organic carbonincreased from 0.794 to 0.806, the accuracy (R²) of the pH model increased from 0.838 to 0.884, and the accuracy (R²) of the salt optimal model increased from 0.978 to 0.997. To sum up, visible-near infrared spectroscopy can be applied to predict key soil physicochemical properties in Spartina alterniflora wetlands, and soil change monitoring of invaded Spartina alterniflora wetlands can be achieved through appropriate spectral transformation, and variable selection and model selection..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 197 (2025)

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Hyperspectral Estimation of Soil Organic Matter Based on FOD-sCARS and Machine Learning Algorithm

WU Meng-hong, DOU Sen, LIN Nan, JIANG Ran-zhe..., CHEN Si, LI Jia-xuan, FU Jia-wei and MEI Xian-jun|Show fewer author(s)

Soil organic matter (SOM) content is a key index of soil quality and plays an important role in the global carbon cycle system. Rapid and accurate estimation and spatial mapping of SOM content are significant for soil carbon pool estimation, crop growth monitoring, cultivated land planning, and management. It is time-c Soil organic matter (SOM) content is a key index of soil quality and plays an important role in the global carbon cycle system. Rapid and accurate estimation and spatial mapping of SOM content are significant for soil carbon pool estimation, crop growth monitoring, cultivated land planning, and management. It is time-consuming and difficult to use traditional methods to monitor regional SOM content, and it is a reasonable and effective method to establish an SOM estimation model based on hyperspectral remote sensing images. However, the SOM content estimation model for hyperspectral remote sensing images has some problems, such as spectral data redundancy, low feature extraction accuracy, and weak generalization ability of a small sample model. In this paper, a total of 67 soil samples were collected in Huangzhong County, Qinghai Province. The ZY1-02D hyperspectral remote sensing image was obtained and preprocessed to obtain pixel spectral data of the sample points. The fractional-order differential transform (FOD) method explored the sensitive bands with a response relationship with SOM content. With 0.2 as a step, the correlation threshold method was used to compare and analyze different order differential processing data mining capabilities. The stable competitive adaptive reweighted sampling algorithm (sCARS) removes hyperspectral redundant data to obtain the modeling feature bands. Random forest (RF), extreme gradient lifting tree, extreme learning machine, and ridge regression machine learning are selected as modeling algorithms. The SOM estimation model is constructed using the spectral data of the full band and the characteristic band as input variables. The results show that the FOD transform can greatly improve the correlation between the band and the SOM content compared with the integer order, and more subtle spectral bands with a response relationship with SOM content can be mined. The 0.8th-order differential transform has the best effect, and the maximum correlation coefficient is increased by 0.546. Compared with full-band spectral data, the estimation accuracy of the model constructed with the sCARS feature extraction method is greatly improved, indicating that sCARS can effectively improve the quality of modeling data and the model's prediction accuracy. In the modeling algorithm, RF performance is the best, with

R_{p}^{2}

(determination coefficient) reaching 0.766 and RPD reaching 1.86, which is about 7.58% higher than the

R_{p}^{2}

of the full-band modeling result. Regional SOM content estimation mapping was realized based on FOD-sCARS and RF. This study further verifies that space-borne hyperspectral remote sensing images are a reliable way to achieve regional SOM estimation mapping, and the research results can provide a new idea for estimating regional SOM content and provide data support for mapping spatial distribution map of SOM content using space-borne hyperspectral remote sensing images..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 204 (2025)

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An Evaluation Method of Quantitative Analysis Software for Near-Infrared Spectroscopy

LI Rong, HAO Lu, YUAN Hong-fu, HE Gui-mei..., DENG Tian-long, DU Biao, GONG Li and YUE Xin|Show fewer author(s)

The commonly used evaluation indexes of multivariate models lack the ability to evaluate many important predictive performance indicators of near-infrared quantitative analysis software. This has become a pain point in evaluating the predictive performance of near-infrared instrument selection and the applicability of The commonly used evaluation indexes of multivariate models lack the ability to evaluate many important predictive performance indicators of near-infrared quantitative analysis software. This has become a pain point in evaluating the predictive performance of near-infrared instrument selection and the applicability of models in practical near-infrared analysis applications. Therefore, this study aims to develop an evaluation method for the predictive performance of near-infrared quantitative analysis software. 192 national VI gasoline samples, including 92#, 95#, and 98#, were collected for determination of olefin concentration of gasoline using near-infrared spectroscopy; their near-infrared spectra collected and olefin concentrations were measured as a reference value according to GB/T 30519—2014, and two different multivariate software(one is partial least squares (PLS) modeling software, and the other is non-PLS software) were used to study. It has been found that compared to the reference value, the PLS model has a positive bias in predicting low-concentration samples and a negative bias in predicting high-concentration samples, which is known as the phenomenon of “averaging”. The commonly used performance evaluation indicators for model prediction cannot yet evaluate the degree of the averaging, nor can evaluate (1) the proportion of samples with deviation from the reference value greater than the limit value for the reproducibility of the reference method and (2) the model's generalization ability. In this paper, four new evaluation indicators are proposed to address the above issues, including Averaging Index (AE), Ratio of samples with prediction bias exceeding the limit value (Ratio), Deviation of Abnormal Sample (DAS), and Deviation of Isolated Sample (DIS). The comprehensive use of commonly used evaluation indicators and new ones (12 items) has practical significance in evaluating the predictive performance of near-infrared quantitative analysis software for instrument selection and the applicability of models in practical near-infrared analysis applications. It also has reference significance for academic research in near-infrared analysis..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 213 (2025)

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A High-Sensitivity Full-Spectrum Spectrometer Based on a Cylindrical Lens Focusing System

CHEN Ji-wen, CHEN Zuo-er, and ZHAO Ying

Many elements' most sensitive spectral lines are mainly distributed in the ultraviolet band. Some important elements commonly used as detection targets, such as C, P, and S, even have spectral lines in the vacuum ultraviolet range below 200 nm. However, radiation in the vacuum ultraviolet band is easily absorbed by Many elements' most sensitive spectral lines are mainly distributed in the ultraviolet band. Some important elements commonly used as detection targets, such as C, P, and S, even have spectral lines in the vacuum ultraviolet range below 200 nm. However, radiation in the vacuum ultraviolet band is easily absorbed by air and cannot be transmitted through air. Therefore, to detect the spectral lines in the ultraviolet range, a short optical path, and a streamlined Rowland circle optical system are commonly used as the dispersion system of the spectrometer. During the detection and analysis, it is also necessary to establish a relative vacuum environment inside the equipment cavity to reduce the weakening effect of the experimental environment on the intensity of the spectral lines in the vacuum ultraviolet range. However, even under these conditions, the spectral sensitivity of elements P and S in the vacuum ultraviolet range remains relatively weak. This study designed a cylindrical mirror focusing system at 30 mm in front of the image plane to achieve high sensitivity within the vacuum ultraviolet range. The system utilizes a cylindrical mirror with a material of CaF₂, a central thickness of 5mm, and a sagittal radius of curvature of 22.06 mm as the core component. The optical system design was simulated and optimized using the simulation software Zemax. Furthermore, the simulated radiance results under non-sequential mode revealed that the cylindrical mirror focusing system significantly improved the imaging sensitivity for various wavelengths with minimal impact on the resolution. In the final experiment, a medium-low alloy steel detection sample was used, and the various spectral lines of elements in the ultraviolet range were excited using an electrical spark excitation light source to verify the spectrometer's performance. Mathematical models were established for the peak intensity of each element's spectrum and its actual content through linear fitting, reference fitting, and quadratic fitting, with fitting goodness offit (R²) values above 0.99. The cylindrical mirror focusing system significantly improved the sensitivity of the equipment in the vacuum ultraviolet range, with the response increased by more than 1 fold. The experimental results demonstrate that the design approach of the cylindrical mirror focusing system has a certain reference value for enhancing the sensitivity of the Rowland circle dispersion system..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 222 (2025)

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Experimental Study on the Void Fraction Determination of Gas-Oil Two-Phase Flow in Elbow Based on Spectral Analysis

CHE Shao-min, MA Shi-yi, LIU Xue-jing, YIN Xiong..., ZHOU Yan, XIONG Bing, LI Kun and LI Fei|Show fewer author(s)

When air is entrained into the engine lubricating oil system to form an oil and gas two-phase flow, it will seriously affect the normal operation of the lubrication system. Therefore, it is very important to realize the accurate and rapid measurement of the void fraction of the oil-gas two-phase flow in the engine lubr When air is entrained into the engine lubricating oil system to form an oil and gas two-phase flow, it will seriously affect the normal operation of the lubrication system. Therefore, it is very important to realize the accurate and rapid measurement of the void fraction of the oil-gas two-phase flow in the engine lubricating oil system. This paper determines the void fraction in the engine lubrication system at the elbow based on the spectral matching method. Firstly, the absorption data of oil-gas two-phase flow were obtained at two flow rates and five temperature conditions, covering a gas content range of 0.10% to 1.00% (with an interval of 0.06%). This was accomplished by utilizing near-infrared, visible, and ultraviolet spectrophotometers, followed by rigorous analysis. It was ascertained that the oil-gas two-phase flow demonstrates absorption across all three wavelengths, with the intensity of absorption being correlated to the gas content. Secondly, the data preprocessing method combined with spectral similarity measure is proposed and applied to the gas content spectral analysis of bent pipe, significantly reducing the maximum relative error of gas content prediction based on the original spectrum. Using data enhancement methods such as center and autoscaling combined with Spectral Angle Cosine methods in the near-infrared spectrum, the maximum relative error of the gas content of the new lubricating oil was reduced from 48% to 36% relative to the original spectrum. The method predicts the void fraction of gas-oil two-phase flow in three bands respectively. The experimental conditions include two flow rates and five temperatures, and the influence of the temperature and flow rate of the two-phase flow on the void fraction prediction is analyzed. At the temperature of 30.0 ℃ and the flow rate of 5.1 m·min^-1, the gas content information in the ultraviolet band (193.5～413.8 nm) is more closely related to the spectral feature of the direction or shape difference of the spectral vector and the maximum relative error of the gas content prediction is only 6%. In the near-infrared and visible bands, the maximum relative error decreases with the increase of temperature or velocity when the flow rate or temperature is constant. There is no specific effect of temperature on the prediction of gas content in the ultraviolet band. With the increase of the two-phase flow rate, the maximum relative error of gas content prediction tends to increase. The results show that for new lubricating oil with good light transmittance, the gas content data is collected by the ultraviolet spectrometer, and the maximum relative error of gas content prediction is minimum by using standardized pretreatment combined with spectral Angle cosine method..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 231 (2025)

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Infrared Spectroscopy Analysis of Sulphur Content in Train-Set Gearbox Lubricants

DU Jin-yao, HE Shi-zhong, YANG Zhi-hong, ZHANG Lin-ying, and ZHANG Jing-ru

In addressing the issue of rapid loss of sulfurin the lubricating oil in high-speed train traction system gearboxes, it is imperative to monitor the sulfur content in in-service gear oil to determine the optimal oil change timing. The attenuation mechanism of the characteristic absorption peaks of sulfur-containing add

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 239 (2025)

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Attention Mechanism Based Hyperspectral Image Dimensionality Reduction for Mold Spot Recognition in Paper Artifacts

TANG Bin, HE Yu-long, TANG Huan, LONG Zou-rong..., WANG Jian-xu, TAN Bo-wen, QIN Dan, LUO Xi-ling and ZHAO Ming-fu|Show fewer author(s)

Paper cultural relics are important for heritage transmission as they record human history and humanities in different periods. However, they are highly susceptible to microorganisms such as mold during preservation. Mold can accelerate the degradation of cellulose, generating mold on the surface of paper. Scattered sp Paper cultural relics are important for heritage transmission as they record human history and humanities in different periods. However, they are highly susceptible to microorganisms such as mold during preservation. Mold can accelerate the degradation of cellulose, generating mold on the surface of paper. Scattered spores can spread widely with airflow, increasing the risk of mold on other paper cultural relics. Regular mold spot detection is crucial for understanding paper artifacts' status and restoration. Hyperspectral imaging technology is a non-contact and non-destructive detection method that simultaneously obtains spatial and spectral data. This technology can be combined with computer technology to enable large batches of real-time, non-destructive testing of paper cultural relics. This paper proposes a method for reducing the dimensionality of hyperspectral data for Aspergillus niger, a commonly occurring mold. The method is based on the attention mechanism and allows for adaptive preprocessing of hyperspectral redundant data. This paper reports on the collection of 20 samples of Aspergillus niger, mold spots on paper artifacts provided by the Chongqing China Three Gorges Museum. The average spectral curves of the infected and healthy areas are analyzed using ENVI software in the 413~855 and 855~1 021nm bands. The results showed a significant difference in average reflectance between the two areas. The paper compares the proposed method with traditional principal component analysis and independent component analysis preprocessing methods for processing original hyperspectral data. The results are then experimented on four semantic segmentation networks: classical U-Net, SegNet, DeepLabV3+, and PSPNet. The experimental results demonstrate that the preprocessed data produced by the algorithm presented in this paper exhibit significant advantages over the classical U-Net and SegNet networks. Furthermore, compared to the principal component analysis method and independent component analysis method, the accuracy of mold spot identification has improved significantly by 89.49% and 88.46%, respectively. These results confirm the effectiveness of the proposed algorithm and provide valuable support and new ideas for the field of cultural relics protection..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 246 (2025)

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Multispectral and Hyperspectral Prediction Models of REC, SPAD and MDA in Overwintered Tea Plant

XU Yang, MAO Yi-lin, LI He, WANG Yu..., WANG Shuang-shuang, QIAN Wen-jun, DING Zhao-tang and FAN Kai|Show fewer author(s)

Determining cold resistance physiological indicators is an important way to evaluate the cold resistance of tea plants. Traditionally, methods of evaluating the cold tolerance of tea trees are mainly through the determination of physicochemical parameters of tea trees under low-temperature stress. However, these method Determining cold resistance physiological indicators is an important way to evaluate the cold resistance of tea plants. Traditionally, methods of evaluating the cold tolerance of tea trees are mainly through the determination of physicochemical parameters of tea trees under low-temperature stress. However, these methods are not only time-consuming and labor-intensive but also destructive. This study established a prediction model for REC, SPAD, and MDA of tea tree cold resistance based on multispectral and hyperspectral imaging techniques. Firstly, multispectral and hyperspectral images of 32 breeding materials under low-temperature stress were collected, and the REC, SPAD, MDA, SP, and SS contents of the corresponding tea tree leaves were determined. Secondly, the hyperspectral image data among them were spectrally pre-processed using five methods, namely, MSC, SNV, S-G, 1-D, and 2-D, and the characteristic bands were screened using two methods, UVE and SPA. Finally, the REC, SPAD, and MDA prediction models of tea tree cold resistance were established using SVM, RF, and PLS algorithms for multispectral and hyperspectral data. The results showed that (1) the spectral curves were more stable, the peaks and valleys were more prominent, and the accuracy and reliability of the models were higher after the joint preprocessing of MSC, SNV, S-G, 1-D and 2-D; (2) the UVE algorithm screened the largest number of characteristic bands, while the SPA algorithm screened the smallest number of characteristic bands, which was more suitable for establishing regression models with hyperspectral data; (3) The RF model has the highest accuracy in predicting leaf REC (R_p=0.735 2，RMSEP=0.077 1), SPAD (R_p=0.502 9，RMSEP=6.681 8), and MDA (R_p=0.784 6，RMSEP=8.885 3) content under multispectral imaging techniques; the SPA-SVM model has the highest accuracy in predicting leaf SPAD (R_p=0.734 9，RMSEP=4.154 6) and MDA (R_p=0.685 8，RMSEP=8.548 8) under hyperspectral imaging techniques, and the SPA-PLS model has the highest accuracy in predicting REC (R_p=0.629 8，RMSEP=0.066 9). Therefore, the REC, SPAD, and MDA prediction models based on multispectral and hyperspectral imaging and machine learning algorithms provide an accurate, non-destructive, and efficient method, which is of great significance for evaluating tea tree cold resistance..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 256 (2025)

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Exploration of Spectral Characteristics of Crop Leaves Under Cu²⁺ Pollution

ZHANG Chao, WU Xuan, YANG Ke-ming, QI Fan-yu, and XIA Tian

A maize pot experiment with different copper stress gradients was designed in an outdoor greenhouse to explore the sensitive leaf types and spectral ranges of crop pollution response under heavy metal stress. Taking maize leaves as the research object, the hyperspectral reflectance data and heavy metal content data of A maize pot experiment with different copper stress gradients was designed in an outdoor greenhouse to explore the sensitive leaf types and spectral ranges of crop pollution response under heavy metal stress. Taking maize leaves as the research object, the hyperspectral reflectance data and heavy metal content data of maize leaves during the heading period were measured using instruments, providing basic data for research. This paper designed the Leaf Spectral Detection Method (LSDM) from the frequency domain perspective, combined with time-frequency analysis, to obtain sensitive leaf shapes and spectral bands under heavy metal copper stress, providing technical support for heavy metal monitoring in crops. Based on the growth process of maize, this study explores the full spectrum and sub-spectrum of the old leaf (O), middle leaf (M), and new leaf (N) spectra from 350 to 1 300 nm. Firstly, the hyperspectral reflectance data of maize leaves under copper stress were subjected to double differentiation (SOD) and envelope removal (CR) and transformed into the frequency domain. The Daubechies wavelet 6-layer decomposition was performed using time-frequency analysis methods. Then, based on the signal anomaly points, wavelet high-value points, and SODCR curve high-value points, the spectral anomaly parameters SAP (Spectral Anomaly Parameters) of maize leaves are defined, namely: Abnormal Changes Reflectivity (ACR), which is the absolute value of the difference between the abnormal reflectance and the reflectance of the next adjacent band; Abnormal Wavelet Coefficients (AWC), which is the absolute value of the difference between the abnormal wavelet coefficients and the wavelet coefficients of the next adjacent band; Abnormal SODCR value (ASR), which is the absolute value of the difference between the abnormal point SODCR value and the SODCR value of the next adjacent band. Finally, by examining the correlation between spectral anomaly parameters and heavy metal content in maize leaves, we aim to explore the leaf types and spectral segments sensitive to copper pollution. The results showed that the leaf spectral detection method LSDM can efficiently enhance weak information in maize leaves and accurately locate the spectral anomaly caused by heavy metal copper stress, with the anomaly range concentrated within 350 to 800 nm; Spectral anomaly parameters can quantitatively measure the spectral anomalies of maize leaves under heavy metal copper stress; Under different copper stress gradients, maize new leaves (N) exhibit sensitive leaf types, with sensitive spectral segments including blue edges, green peaks, yellow edges, and red valleys. This paper can provide technical support for monitoring heavy metals in other cereal crops and their canopy scales..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 264 (2025)

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Applicability of Different Fractional Order Differential Forms in the Hyperspectral Inversion of Saline Soil Conductivity

ZHOU Feng-xi, TENG Xiang-shuai, HAO Jun-ming, and WANG Li-ye

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 272 (2025)

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Study on the Interaction Mechanism of Ellagic Acid and Urolisine A~D With HSA Based on Spectral Analysis

XIE Xing, CHENG Xin-peng, ZHANG Lu, LUO Jing..., WANG Le-huai, LIN Wen-jing, LU Fei-yan and TU Zong-cai|Show fewer author(s)

Spectroscopy and molecular simulation technologies investigated The interaction mechanism between ellagic acid (EA) and urolithin A~D (UA~D) with HSA, which helped explore its pharmacotoxicity and efficacy. The results indicated that EA and UA~D could bind with HSA at a molar ratio of 1∶1 and quench its fluorescence vi

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 282 (2025)

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Non-Destructive Detection of Multi-Indicator Chilled Mutton Freshness Based on Improved Artificial Neural Network

XU Zi-yang, JIANG Xin-hua, ZHAI Cheng-jun, MA Xue-lei, and LI Jing

The freshness of chilled mutton is influenced by various factors and can be comprehensively evaluated through multiple physical, chemical, and microbiological indicators. Traditional testing methods are complex and inefficient. Hyperspectral imaging technology, as a rapid and non-destructive detection technique, can ef The freshness of chilled mutton is influenced by various factors and can be comprehensively evaluated through multiple physical, chemical, and microbiological indicators. Traditional testing methods are complex and inefficient. Hyperspectral imaging technology, as a rapid and non-destructive detection technique, can effectively detect the changes in different components during the freshness variation of chilled mutton. To study the feasibility of using hyperspectral imaging technology for the multi-indicator evaluation of chilled mutton freshness, this paper proposes an improved artificial neural network (ANN) algorithm that enhances the correlation between labels by redefining the loss function and fully utilizes multiple freshness indicators to classify the freshness of chilled mutton. Experimental high-spectral images were collected for chilled mutton samples from 0 to 14 days in the 400 to 1 000 nm range. Laboratory methods were used to determine the values of total volatile basic nitrogen (TVB-N), pH value, total aerobic count (TAC), and an approximate number of coliforms (ANC) indicators. The original spectral data of chilled mutton samples were preprocessed using the S-G smoothing filter and multivariate scatter correction. The continuous projection algorithm (SPA) was used to select 18 feature bands of the spectral data as input data, and the proposed improved ANN algorithm was employed to establish a multi-indicator chilled mutton freshness grading model. The results showed that the improved ANN achieved a classification accuracy of 96% on the test set. The recognition rates for the three freshness levels of the samples were 100%, 89.28%, and 98.68%, respectively. The model was evaluated using four multi-label model evaluation metrics: Hamming loss, one-error, ranking loss, and coverage. The corresponding evaluation scores were 0.008, 0.002, 0.002 5, and 4.048, respectively. The accuracy and various model evaluation metrics of the improved ANN classification model were superior to those of traditional ANN, demonstrating the feasibility of using the improved ANN for non-destructive detection of multi-indicator chilled mutton freshness..

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 291 (2025)

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[in Chinese]

Spectroscopy and Spectral Analysis

Publication Date: Feb. 28, 2025
Vol. 45, Issue 1, 1 (2025)

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