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2024 Vol. 44, No. 01 Published: 2024-01-01

	1	Research Development of the Application of Photoacoustic Spectroscopy in Measurement of Trace Gas Concentration
		ZHENG Hong-quan, DAI Jing-min^*
		DOI: 10.3964/j.issn.1000-0593(2024)01-0001-14
		As an important gas sensing technology to achieve high-efficiency and high-precision measurement of trace gas concentrations, photoacoustic spectroscopy is widely used in atmospheric environment detection, power system fault diagnosis in medical and health diagnosis. This paper introduces the basic principle of trace gas concentration measurement by photoacoustic spectroscopy, and the mechanism of applying photoacoustic effect to realize trace gas concentration measurement is briefly described. At the same time, trace gases based on photoacoustic spectroscopy are introduced. Structure of the concentration measuring device. Secondly, starting from the theoretical analysis of photoacoustic signal intensity, the research hotspots of domestic and foreign scholars in photoacoustic spectroscopy trace gas concentration measurement are drawn. In order to realize the stronger anti-interference ability of the trace gas concentration measurement device based on photoacoustic spectroscopy technology, more compact structure, lower detection limit, and higher detection sensitivity, the research focus of domestic and foreign scholars can be summarized as the following three points: (1) Selection and design of radiation light source to achieve better output wavelength of radiation light source, Wider tuning range, higher output power of radiation source. (2) Better photoacoustic cell design to achieve more efficient acoustic energy accumulation, more compact structure and stronger anti-interference ability. (3) The design of the sound-sensitive detector to achieve higher sound sensitivity and signal-to-noise ratio. This paper introduces the three core components of the trace gas concentration measurement device based on photoacoustic spectroscopy in detail: radiation light source, mainly introduces the application research progress of coherent light source and incoherent light source; photoacoustic cell, mainly introduces the design principle of photoacoustic cell And the application research progress of non-resonant and resonant photoacoustic cells; Microphone, mainly introduces the application research progress of condenser microphone and piezoelectric microphone, and briefly introduces the recent research hot spot quartz-enhanced photoacoustic spectroscopy technology's introduction. While introducing the current research progress in the application of radiation light sources, photoacoustic cells and microphones, the advantages of each component and the problems to be solved are analyzed. Finally, the problems of low signal-to-noise ratio, complex structure, detection sensitivity and lower detection limit are easily affected by cross-interference between mixed gases in the application of photoacoustic spectroscopy technology in trace gas concentration measurement. The development trend of the three core elements in the paper: radiation light source, photoacoustic cell, and microphone is prospected.
		2024 Vol. 44 (01): 1-14 [Abstract] ( 148 ) RICH HTML PDF (17606 KB) ( 226 )

	15	Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review
		CHENG Jia-wei^{1, 2}，LIU Xin-xing^{1, 2*}，ZHANG Juan^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0015-07
		Infrared spectroscopy has characteristics of rapid, economical and highly efficient, and it is also a high-tech widely used in geological prospecting work in China and abroad in this field. By studying the differential absorption characteristics of minerals in the infrared band, infrared spectral mapping and spectral parameter analysis were carried out to determine the characteristic mineral assemblages in the mineralized area and the range of mineral spectral parameters that pointed to the center of the mineralized hydrothermal fluid. In recent years, with the development of spectral resolution and the optimization of unmixing algorithms, more and more bands have been used to extract rock and mineral information. The developed mature short-wave infrared technology（SWIR，1 100~2 500 nm）can identify the medium and hypothermia clay minerals such as kaolinite, alumite and chlorite effectively, it has established indicators in porphyry, epithermal, VMS and other deposits; Thermal infrared technology ( TIR, 6~15 μm) has better detection ability mainly for mega thermal minerals such as feldspar, quartz, garnet and pyroxene, and has an excellent performance well in skarn deposits in recent years; mid-infrared technology (MIR, 3~6 μn) is still in the research in the prospecting and exploration work in the field of geology, and amount of mineral information extraction methods has not been formed. In this study, with the development history of infrared spectroscopy technology, the causes of waveform changes in various bands, commonly used spectral parameters and their characterization meanings, four types of testing instruments, and analysis software. Chlorite, garnet and carbonate minerals are four mineral spectral features often used as prospecting indicators. It discusses the two core contents of infrared spectroscopy technology in mineral exploration work and the main problems. Finally, this paper puts forward some suggestions for the application of infrared spectroscopy technology in future geological prospecting: strengthening the verification of laws, establishing multi-band and multi-platform comprehensive spectral exploration model, and comforming a spectral database and parameter extraction standards for mining areas.
		2024 Vol. 44 (01): 15-21 [Abstract] ( 86 ) RICH HTML PDF (1158 KB) ( 218 )

	22	Application and Progress of Residual Magnetometry Based on Electron Paramagnetic Resonance Spectroscopy
		ZHANG Quan-zhe¹, ZOU Sheng¹, ZHANG Hong^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0022-07
		Electron paramagnetic resonance, which is similar to nuclear magnetic resonance, is a method based on the paramagnetism and Zeeman splitting of electron magnetic moments in an external magnetic field. It is associated with unpaired electrons inside matter and external magnetic fields. Extremely weak magnetic fields are generally defined as those on the order of nT or less. Currently, scientific research is often conducted in extremely weak magnetic environments, such as basic physics research, biomagnetism of the heart and brain, etc. Therefore, stable and reliable magnetic shielding is often used to create the required environment. Magnetic shielding includes passive magnetic shielding and active magnetic compensation. Passive magnetic shielding refers to using a magnetic shielding chamber to shield the external magnetic field, which often has a certain amount of residual magnetic field inside. To facilitate further active magnetic compensation, it is necessary to detect the residual magnetic field inside shielding. At present, the means of measuring the residual magnetic field in the shielding room are mainly divided into commercial magnetometer measurement and in-situ measurement. The commercial magnetometer measurement method is simple, with low accuracy and high noise, and it is not conducive to miniaturization. With low noise, in-situ measurements are the main method of study now. Since the outermost layer of the alkali metal atom contains an unpaired electron, it becomes an ideal sample for electron paramagnetic resonance experiments. With the development of laser and optical pumping, it has been realized to probe the electron paramagnetic resonance spectrum of alkali metal atoms by Faraday rotation. The in-situ measurement of residual magnetic fields by related electron paramagnetic resonance spectrum of alkali metal vapor is profitable and precise and has great promise for application. Usually, the electron paramagnetic resonance-based residual magnetometry system includes the residual magnetic environment and sample module, the optical probe module, the signal modulation module, the environmental monitoring module, and the data acquisition and procession module, with the core being the optical probe module, which determines the sensitivity of the magnetic field measurement. This paper briefly describes the principle of the electron paramagnetic resonance technique, introduces typical magnetometers based on this technique and current developments with a focus on optically-pumped magnetometers, and outlines the residual magnetometry system based on electron paramagnetic resonance spectroscopy, each component module and the current development of related technologies in recent years.
		2024 Vol. 44 (01): 22-28 [Abstract] ( 72 ) RICH HTML PDF (2126 KB) ( 185 )

	29	Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering
		LIU Zhen^1, LIU Li^2, FAN Shuo², ZHAO An-ran², LIU Si-lu²
		DOI: 10.3964/j.issn.1000-0593(2024)01-0029-07
		Developing an efficient training sample selection method is one of the goals of spectral reflectance reconstruction. In spectral reflectance reconstruction, the training set selection method and sample capacity are strongly related to the reconstruction accuracy. The accuracy of the spectral reconstruction is affected by the unstable clustering results due to the randomness of the starting value selection and the outliers. K-means clustering has low computing complexity and great computational efficiency. Based on this, this paper proposes an improved K-means clustering training sample selection method. Firstly, the geometric center of the training sample set is used as the initial value of the clustering center; secondly, the probability density function of the spatial distribution of the samples is constructed based on the Gaussian function, and the Euclidean (Euclidean) distance is used as the measure of other clustering centers; finally, the similarity between the spectral reflectance samples in the training sample set is measured based on the intra-cluster squared difference, and the sample with the closest distance to the center in each clustering subset is used as the training samples are used to verify the effectiveness of the method. The spectral reconstruction was performed by principal component analysis. The experimental results show that the proposed method has been improved significantly compared with the traditional method, the average root-mean-square error of the reconstructed spectra is less than 4%, and the CIEDE2000 color difference is less than 3.756 7. The improved training sample selection method of K-mean clustering proposed in this paper can improve the spectral reconstruction accuracy to some extent and meet the requirements of reproducing the reproduced images.
		2024 Vol. 44 (01): 29-35 [Abstract] ( 83 ) RICH HTML PDF (3685 KB) ( 122 )

	36	Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis
		YANG Chao-pu^{1, 2}, FANG Wen-qing^3*, WU Qing-feng³, LI Chun¹, LI Xiao-long¹
		DOI: 10.3964/j.issn.1000-0593(2024)01-0036-08
		Current research on photobiosafety focuses on artificial lighting with a constant spectral distribution. The influence of human transmittance with age is less considered. In the next 20 years, AMOLED will gradually become the mainstream display of smart-phones. This paper provides a theoretical reference for the personalized design and use of AMOLED from the perspective of blue light harm and rhythm effect. According to the function expression of the change of human eye transmittance with age given by the International Commission on Illumination (CIE) in 2012, the transmittance of the human eye was obtained for 11 different ages (1~100 years, with intervals of 10 years). We collected the original spectral distribution data of AMOLED at six different color temperatures (2 300, 2 700, 3 400, 4 100, 5 000, 6 500 K). We normalized the original spectral distribution data of AMOLED at different color temperatures. Then, we multiplied the normalized spectral distribution data of AMOLED at different color temperatures by the transmittance of human eyes at different ages and finally obtained the effective spectral distribution of AMOLED at different color temperatures on the retina of human eyes at different ages. We used the effective spectral distribution of the human retina to replace the original spectral distribution of the blue light hazard factor and rhythm factor in the calculation formula, and calculated the effective spectral distribution of the blue light hazard factor and rhythm factor of AMOLED in the human retina at different ages. Through the high-quality function fitting analysis, the blue light harm and rhythm effect of AMOLED with age change were studied. The research and analysis results show that: The blue light harm and rhythm effect of AMOLED decrease with the increase in the user's age. For AMOLED with a color temperature of 6 500 K, the user's age increases from 1 year to 100 years, and the blue light hazard factor and rhythm factor of the retinal effective spectrum decrease to 0.290 7 and 0.403 8 times respectively. When the age is more than 40 years old, the decrease of blue color hazard factors and rhythm factors of various temperatures with the increase of age is significantly accelerated. Under 6 different color temperatures, the average speed of blue light harm factor and rhythm factor decreased with age increase in 40~100 years old, which was 2.748 2 and 2.993 3 times as much as that in 1~40 years old. When the user's age increases from 1 year to 100 years old, the average blue light hazard factor and the average rhythm factor of the 6 color temperatures decrease to 0.305 6 and 0.452 0 times respectively. Based on the above results: AMOLED greatly harms blue light and rhythm effect on young people, especially young users under 40 years old, so they should reduce their use time and pay attention to the harm and rhythm effect of blue light. The harm of blue light is more affected by age than rhythm effect. These conclusions may provide some theoretical references for relevant research.
		2024 Vol. 44 (01): 36-43 [Abstract] ( 78 ) RICH HTML PDF (7188 KB) ( 73 )

	44	Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods
		GAO Feng^{1, 2}, XING Ya-ge^{3, 4}, LUO Hua-ping^{1, 2}, ZHANG Yuan-hua^{3, 4}, GUO Ling^{3, 4*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0044-08
		Southern Xinjiang is the region with the largest apricot planting area in the country, with a wide variety of apricots. In the apricot fruit market, the quality and price of different varieties of apricots ware vary greatly, and the phenomenon of shoddy and uneven quality has seriously restricted the development of the apricot industry in Xinjiang. To investigate the feasibility of rapid detection of apricot varieties using visible/near-infrared spectroscopy, a non-destructive identification method for apricot varieties is set up based on the qualitative discriminant analysis of six varieties of apricots in the southern Xinjiang region by visible/near-infrared spectroscopy of samples with chemometrics methods. The spectral data of six apricot varieties (“Huang apricot”, “Ganlan apricot”, “Xiaobai apricot”, “Xiaomi apricot”, “Kumaiti” and “Xiaodiaogan apricot”) were collected in the range of 350~1 000 nm (VIS/NIR) and 1 000~2 500 nm (NIR) by the spectrometer. After deleting the obvious noise at the head of the original spectrum, the retained spectrum is processed using Savitzky-Golay (SG) convolution smoothing and multiple scatter correction (MSC) to eliminate the interference information in the spectrum. The original spectra are reduceddimension using principal component analysis (PCA), competitive adaptive re-weighted sampling (CARS), random frog (RF), successive projection algorithm (SPA), and linear discriminant analysis (LDA), naive Bayesian (NB), K-nearest neighbor (KNN), support vector machine (SVM) were combined with modeling the whole spectrum and the reduced spectrum. The results showed that the model based on full-spectral data has a comparatively accurate result, and the classification accuracy of the SVM model was 95.7% in the VIS/NIR range and 97.8% in the NIR range for the LDA model, which could achieve the discriminative analysis of different species of apricots. After the reduced-dimension of spectral data by PCA, CARS-SPA, RF-SPA and SPA, the model still maintained high classification accuracy, and the PCA-LDA model had 97.8% classification accuracy in the VIS/NIR range, and the RF-SPA-LDA model had 95.7% classification accuracy in the NIR range. The results of different models show that the classification effect of models in the VIS/NIR range was better than that in the NIR range; among the four dimensionality reduction methods, the PCA method has the best dimensionality reduction effect; among the four classifiers. The accuracy of LDA and SVM models is higher than that of NB and KNN models, which is more suitable for the identification of apricot varieties. The results show that the rapid and nondestructive identification of apricot varieties can be achieved based on the VIS/NIR range spectrum combined with principal component analysis and linear discriminant analysis method, which provides aninnovative way for online sorting and identifying apricot fruits.
		2024 Vol. 44 (01): 44-51 [Abstract] ( 74 ) RICH HTML PDF (5507 KB) ( 108 )

	52	Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers
		FAN Ping-ping，LI Xue-ying，QIU Hui-min，HOU Guang-li，LIU Yan^*
		DOI: 10.3964/j.issn.1000-0593(2024)01-0052-04
		Spectrometers are the core tool in spectral analysis, but it is still unclear how spectrometers influence the results of spectral analysis. Here, we studied the spectral analysis of organic carbon in sediments of the Yellow Sea and Bohai Sea using Agilent Cary 5000, ASD FieldSpec 4, and Ocean Optics QEPro and compared differences in the reflectance spectra of organic carbon and their spectral analysis. Cary 5000 is an indoor spectrometer, and FieldSpec 4 and QEPro are portable spectrometers. QEPro could only collect the reflectance between 200 and 1 000 nm, and the reflectance is the highest among the three spectrometers. Cary 5000 and FieldSpec 4 could collect the reflectance of the complete visible and near-infrared waveband (350~2 500 nm), and both spectral curves were almost identical, especially in the near-infrared bands. However, the reflectance collected by Cary 5000 is higher than that by FieldSpec 4. The abilities of spectral analysis of organic carbon concentrations in the Yellow and Bohai Sea were also different across the three spectrometers. Cary 5000 had the strongest ability to perform spectral analysis. The spectral models in Cary 5000 had a strong prediction ability of organic carbon concentrations in sediments. In Cary 5000, the r² of the calibration set was as high as 0.99, and the r² of the validation set was as high as 0.86; the root mean square error (RMSE) of the calibration set and validation set was 0.04 and 0.11, respectively; the relative prediction deviation (RPD) was as high as 2.6, showing a strong ability to predict sediment organic carbon. In FieldSpec 4, r² of the calibration set was as high as 0.98, but r² of the validation set was only 0.56; RMSE decreased from 0.06 to 0.19, and RPD was as low as 1.4, showing a low prediction ability of sediment organic carbon. In QEPro, r² of the calibration set and validation set were both low (0.75 and 0.59, respectively), RMSE were stable and as high as 0.18, and RPD was larger than 1.5 (1.6), showing a convincing prediction ability of sediment organic carbon. Results showed that the portable spectrometers were worse than indoor instruments in spectral analysis due to their lower technological performance. For the portable spectrometers, results in spectral analysis were not different between FieldSpec 4 and QEPro, and even the results of QEPro were more stable. Therefore, we think QEPro can be replace with FieldSpec 4 in rapidly determining sediment organic carbon by spectroscopy because QEPro is cost-effective. In this study, the differences among three types of spectrometers in the spectral analysis of the same sample were compared, which provided an effective reference for the spectral analysis and model transfer of different studies.
		2024 Vol. 44 (01): 52-55 [Abstract] ( 55 ) RICH HTML PDF (1950 KB) ( 99 )

	56	Ultrafast Dynamics of CdSe/ZnS Quantum Dots and Quantum Dot-Acceptor Molecular Complexes
		BAI Xi-lin^{1, 2}, PENG Yue^{1, 2}, ZHANG Xue-dong^{1, 2}, GE Jing^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0056-06
		QDs are a new class of semiconductor light-emitting nanomaterials, which have attracted much attention due to their unique optical properties such as adjustable luminous colorand size, wide excitation spectrum, narrow emission spectrum, etc. It is the ideal material for photovoltaic device applications in which nuclear/shell QD has better optical performance than single QD. For instance, type I nuclear/shell QD solar cell devices show higher stability and conversion efficiency in quantum dot-sensitized solar cells. Nevertheless, how the interfacial process and recombination kinetics affect the performance of devices have been the focus of attention, and the lack of related cognition has hindered the further development of quantum dot photovoltaic devices. A comprehensive study on carrier dynamics of the topical CdSe/ZnS QDs and QD-acceptor (1-chloroanthraquinone (1-CAQ), anthraquinone (AQ), and methyl viologen (MV²⁺)) complexes are performed employing the femtosecond time-resolved transient absorption (TA) spectroscopy and quantum chemical calculations. As indicated by the spectroscopic analysis, the fastest ET and AR processes occurred in QD-MV²⁺ complexes, and the ET rate was positively correlated with the AR rate. In addition, the bandgap of electron acceptor molecules and the driving force were demonstrated as crucial factors affecting the rate of the ET process according to Marcus's ET theory combined with density function calculation.This study will provide new insights into the selection of electron acceptor molecules, which will be essential in improving the design of photovoltaic devices.
		2024 Vol. 44 (01): 56-61 [Abstract] ( 74 ) RICH HTML PDF (3558 KB) ( 94 )

	62	*Fast Prediction of Flavone and Polysaccharide Contents in Aronia Melanocarpa* by FTIR and ELM**
		YANG Cheng-en^{1, 2}, LI Meng³, LU Qiu-yu², WANG Jin-ling⁴, LI Yu-ting^2, SU Ling^1
		DOI: 10.3964/j.issn.1000-0593(2024)01-0062-07
		Aronia melanocarpa is one kind of berrie richer in flavone than blueberry and has thus been approved as a new food resource largely used in the beverage industry. Flavone and polysaccharides have been revealed to be the main bioactive components in its fruit juice and pomace, affecting its quality. Therefore, their contents were predicted by infrared spectroscopy combined with chemometrics, which provided a basis for establishing a simple and rapid method for the quality detection of A. melanocarpa. A total of 750 infrared spectral data of A. melanocarpa from 15 production areas were collected, and their contents in flavone and polysaccharides were measured. The samples were divided into calibration set and validation set by K-S sample division method in the proportion of 4∶1. The spectral information after grouping was pretreated by multiple scattering correction (MSC), standard normalization (SNV), smoothing (SG), first derivative (FD), second derivative (SD) and other spectral preprocessing, and the best spectrum preprocessing method was determined. The competitive adaptive reweighting algorithm (CARS) and continuous projection algorithm (SPA) were used to select the characteristic spectral bands of flavone and polysaccharides in A. melanocarpa. The spectral data selected by the two wave methods were combined with partial least square regression (PLS), limit learning machine (ELM) and support vector machine (SVM) for modeling and comparison, and the algorithm model with the best prediction effect was selected. The results showed that, MSC had the best effect on the original spectrum among the seven spectral pretreatment methods. Under this treatment, the RPD value of the flavone content prediction model was 6.201 7, and 5.447 3 for the polysaccharide content prediction mode, with the error of the prediction model significantly decreased. After extracting the characteristic spectra by CARS and SPA, the modeling results revealed that the R_C, R_P, and RPD of the flavone content prediction model were respectively 0.997 2, 0.991 2 and 10.631 5, while they were 0.996 5, 0.986 7 and 8.664 7 respectively for the polysaccharide content prediction model. Therefore, infrared spectroscopy combined with chemometrics methods, especially the CARS-ELM model, can accurately predict the contents of flavone and polysaccharides in A. melanocarpa, and the development of this method provides a fast and simple method for its quality evaluation.
		2024 Vol. 44 (01): 62-68 [Abstract] ( 56 ) RICH HTML PDF (4630 KB) ( 147 )

	69	Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing
		XU Tian^{1, 2}, LI Jing^{1, 2}, LIU Zhen-hua^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0069-07
		Manganese in soil plays an important role in plant growth, and high or low levels of soil manganese will have adverse effects on plants, so it is especially important to monitor soil manganese content quickly. At present, the studies related to the monitoring of soil Mn content by remote sensing technology mainly focus on the estimation of soil Mn content using soil spectra. At the same time, it is difficult to obtain soil spectra from satellite images in the southern region where vegetation covers all year round. Therefore, this paper introduces vegetation spectra to explore the rapid monitoring method of soil Mn elements in vegetation-covered areas. Firstly, 11 vegetation spectral indicators were extracted from Landsat 8 images, and the best vegetation spectral indicators were selected by Pearson correlation coefficient combined with Variance Inflation Factor (VIF); based on this, Partial Least Squares (PLS) regression was used. (PLSR), Multiple Stepwise Regression (MSR) and BP Neural Network (BPNN) algorithms were used to construct the best vegetation spectral indicators. Finally, spatial mapping of soil Mn content was carried out based on the best inversion model. Taking the Nanchuan District of Chongqing City as an example, the results showed that three vegetation spectral indicators (specific vegetation index, normalized vegetation index and visible atmospheric impedance vegetation index) were identified as the best spectral response indicators of soil Mn. The mapping accuracy of soil Mn content (R² was 0.69, RMSE was 567.64, and RPD was 1.30). The results showed that the inversion of soil Mn content by vegetation spectral indicators is feasible, and this study opens up new ideas for monitoring soil Mn content at the regional scale.
		2024 Vol. 44 (01): 69-75 [Abstract] ( 58 ) RICH HTML PDF (16129 KB) ( 58 )

	76	A DFT Method to Study the Structure and Raman Spectra of Lignin Monomer and Dimer
		WANG Fang-yuan^{1, 2}, HAN Sen^{1, 2}, YE Song^{1, 2}, YIN Shan^{1, 2}, LI Shu^{1, 2}, WANG Xin-qiang^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0076-06
		Lignin is the second most abundant natural polymer in nature. It widely exists in the xylem of various terrestrial plants. The rapid and nondestructive plant species identification using lignin Raman spectroscopy has great application prospects. However, due to the complex configuration of lignin macromolecules, it is difficult to study the Raman spectra of lignin macromolecules using first-principles calculations. In this paper, using the B3LYP density functional method included in Gaussian16W combined with the 6-311G(d,p) basis set, three lignin monomers and dimers composed of three homologous lignin monomers and three heterologous lignin monomers were constructed. A dimer composed of lignin monomers was proposed, and a method was proposed to use the three basic structural monomers of lignin and their dimers to simulate and analyze the Raman spectral characteristics of lignin macromolecules. Firstly, the geometric configurations, orbital energy levels and electronic spatial distributions of the three monomers are calculated, and the characteristics of Raman spectra and vibrational modes of the monomers are analyzed. Then, the spectral peak characteristics of β-O-4 dimers with different monomer combinationsare compared to explain the frequency shift and shape change of the characteristic peaks. The results showed that the 1 712 cm^-1 peak is the strongest Raman signal. The vibration is attributed to carbon-carbon double bond stretching vibration, and the characteristic peak caused by the vibration attribution can be used as the most important signal characteristics of lignin macromolecules. The 1 642 cm^-1 peak signal is only affected by the amount of aromatic ring methyl. The vibration is attributed to aromatic ring skeleton vibration, which can be used as a criterion for the content of three monomers in lignin. The characteristic peak near 1 352 cm^-1 belongs toHC═CH atomic chain sway vibration, which is affected by the type of monomer and the mode of polymerization, and a signal envelope may be formed in the Raman spectrum of lignin macromolecules, which can be regarded as an auxiliary criterion for the identification of lignin. The results provide a reference for the simulation of the spectral characteristics and physical mechanism of complex macromolecules and also provide theoretical guidance for the Raman spectral analysis of plant samples.
		2024 Vol. 44 (01): 76-81 [Abstract] ( 54 ) RICH HTML PDF (3631 KB) ( 65 )

	82	Study on the Method of Detecting Phosphate Ions in Water Based on Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm
		ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei^*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang
		DOI: 10.3964/j.issn.1000-0593(2024)01-0082-06
		With the evaporation of water vapor in industrial boilers, a large amount of calcium and magnesium ions are left in the boiler water. If not treated, scale will form in the water-cooled tubes, causing tube explosion and boiler shutdown. In order to ensure the safe operation of the equipment and eliminate potential safety hazards, the calcium and magnesium scale in the boiler is removed by maintaining a certain amount of phosphate ions in the water.The traditional detection techniques for phosphate ions mainly include colorimetry, spectrophotometry, chromatography, potentiometry, etc. These methods have cumbersome and time-consuming preliminary processing steps. The spectroscopic method is an analytical method to quantify the concentration of a substance by measuring the absorption spectrum and establishing a mathematical model of the relationship between the concentration and the substance. A method for rapidly measuring phosphate ions based on ultraviolet absorption spectroscopy and the SPA-ELM algorithm was proposed. According to the water quality parameter requirements before entering the hot water boiler stipulated in “Industrial Boiler Water Quality GB/T 1576—2018”, 37phosphate ion solutions with the concentration range of 5~100 mg·L^-1 were prepared, and the UV absorption spectrum was collected using the established experimental equipment. The training and test sets were divided randomly according to the ratio of 7∶3 by SPXY. Data were preprocessed by Savitzky-Golay (S-G) filtering to improve the signal-to-noise ratio of the spectrum. The dimensionality of the spectrum was reduced by Successive Projection Algorithm(SPA). Five characteristic wavelengths strongly correlated with phosphate ionswere screened out. Finally, the Extreme Learning Machine(ELM) was used to fit the absorbance at the characteristic wavelength with the sample concentration, and the regression model of phosphate ions was established withR² and RMSE as the evaluation indexes of the model. TheR² and RMSE of the training set established by the method proposed in this paper are 0.997 2 and 1.301 5 mg·L^-1, and theR² and RMSE of the test set are 0.999 5 and 0.517 4 mg·L^-1. In order to verify the effect of the SPA-ELM prediction model proposed, four other prediction models, LASSO-ELM, PCA-ELM, SPA-PLS and SPA-SVR, were established for comparison. The experimental results show that theR² and RMSE of the prediction model established by SPA-ELM are better than those.It shows that both the feature selection and regression methods adopted in this paper are optimal. The modelling method adopted in this paper can accurately predict the water with phosphate concentration ranging from 5 to 100 mg·L^-1, which provides a new solution for detecting phosphate ions in water.
		2024 Vol. 44 (01): 82-87 [Abstract] ( 71 ) RICH HTML PDF (4415 KB) ( 140 )

	88	Calibration Method of Aerosol Absorption Coefficient Based on Photoacoustic Spectroscopy
		XU Qiu-yi^{1, 3, 4}, ZHU Wen-yue^{3, 4}, CHEN Jie^{2, 3, 4}, LIU Qiang^{3, 4 *}, ZHENG Jian-jie^{3, 4}, YANG Tao^{2, 3, 4}, YANG Teng-fei^{2, 3, 4}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0088-07
		Atmospheric aerosols modify the energy balance of the atmosphere and the Earth's surface, visibility, and climateby absorbing solar radiation. Photoacoustic spectroscopy (PAS) is the most direct and effective method to measure the absorption of aerosols due to the advantages of high sensitivity, no change of aerosol stateand simple structure. The calibration method is the key factor of measurement accuracy of aerosol optical absorption coefficient using a PAS working at the near-infrared band. In order to explore the differences between different calibration schemes, NO₂ (532 nm) and monodisperse nigrosin aerosol (532 and 1 064 nm) were selected to carry out calibration experiments of gas and aerosol separately. Based on Mie's theory, the absorption coefficients of the monodisperse nigrosin aerosol at wavelengths of 532 nm and 1064 nm were calculated using the concentration of aerosol particles, the complex refractive index and the particle diameter. The experimental results show that: the value of the calibration coefficient obtained by NO₂ is between the calibration coefficient obtained by nigrosin aerosol at 532 and 1 064 nm wavelength, moreover, which is close to the value of calibration coefficient of nigrosin monodisperse aerosol with particle diameter of 225 nm (λ=532 nm) and 125 nm (λ=1 064 nm) butnot the same in other particle sizes. Despite the similar continuous absorption properties at specific wavelengths of NO₂ and nigrosin aerosol, for the same calibration system, there are still uncertainties in the calibration process such as light-matter interaction, calibration medium loss, and measurement environment other than photoacoustic effect due to the different properties and states of calibration gas and aerosol. All these will lead to the difference of calibration results between these two calibration methods. The calibration coefficients of nigrosin aerosol obtained by using different wavelength light sources are significantly different, and the calibration coefficient values of the same particle size at 532 nm wavelength is about 1.5～2 times that at 1064 nm wavelength. There are two possible reasons for this result: one is that theobtained complex refractive index of nigrosine at 1 064 nm is not accurate, and the other is that the matching mode of the 532 nm wavelength beam and 1064 nm wavelength beam with the resonator is not consistent during the calibration process. There was an inverse relationship between the calibration coefficient and the particle size of monodisperse aerosol. With an increase of 50 nm in the particle size of nigrosin aerosol, the calibration coefficient decreased by 19% for the 532 nm wavelength light source and decreased by 12% for the 1 064 nm wavelength light source. The inverse relationship between calibration coefficient and particle size may be caused by the different losses of nigrosin aerosol particles with different particle sizes through the pipeline and the photoacoustic cell. It can be seen that the gas calibration method and the aerosol calibration method have their advantages and disadvantages, but the two calibration methods can obtain the corresponding calibration coefficients. It is necessary to consider various factors comprehensively in selecting the actual calibration method.
		2024 Vol. 44 (01): 88-94 [Abstract] ( 77 ) RICH HTML PDF (4031 KB) ( 144 )

	95	Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in Water
		XING Hai-bo¹, ZHENG Bo-wen¹, LI Xin-yue¹, HUANG Bo-tao², XIANG Xiao², HU Xiao-jun^1*
		DOI: 10.3964/j.issn.1000-0593(2024)01-0095-08
		Polycyclic aromatic hydrocarbons (PAHs) residues in the environment seriously harm the human body. In this paper, a colorimetric and SERS dual-channel sensing detection of pyrene in water based on AgNPs-AuNPs was designed. Firstly, single-mercapto β-cyclodextrin was modified onto the surface of gold and silver nanoparticles. When Ox-TMB was present in the system, the nanoparticles would self-assemble to form an AgNPs-Pyrene-AuNPs core-satellite structure triggered by pyrene. On the one hand, pyrene acted as a molecular bridge, causing a certain aggregation of nanoparticles. Moreover, the concentrations of pyrene directly affected the solution color by the core-satellite structures of AgNPs-pyrene-AuNPs. Therefore, the relationship between solution color and pyrene concentrations can be established by visual inspection. On the other hand, there were many “hot spots” in the core-satellite structures, and they exhibited strong surface-to-enhanced Raman spectroscopy (SERS) activity, which can realize the highly sensitive and specific detection of pyrene molecules by SERS. Based on this structure, the highly sensitive and specific detection of pyrene in water and soil can be realized by colorimetric and SERS method. This method could quickly complete the detection of trace pyrene in 5 minutes. The detection limit of pyrene by colorimetric method was, and that of the SERS method was. According to the principles above, SERS sensors based on AgNPs-PAHs-AuNPs core-satellite structure can be used to detect multiple PAHs in water samples. Therefore, this dual-channel sensing system provides a new method for detecting PAHs in water and provides a new idea for the development of a multiplexing sensing system.
		2024 Vol. 44 (01): 95-102 [Abstract] ( 49 ) RICH HTML PDF (9266 KB) ( 55 )

	103	Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy
		LI Yu¹, ZHANG Ke-can¹, PENG Li-juan^2, ZHU Zheng-liang¹, HE Liang^1
		DOI: 10.3964/j.issn.1000-0593(2024)01-0103-08
		Tobacco contains a variety of sugars, including glucose, fructose, maltose, sucrose, xylose, etc. These sugars account for 25% to 50% of the dry weight of tobacco and are an important component and a crucial criterion for measuring the intrinsic quality of tobacco. Different sugars produce different products during the combustion process of cigarettes, which directly or indirectly affect the aroma and taste of cigarettes. Therefore, establishing methods for determining various monosaccharides in tobacco is relevant for the quality control of tobacco products. Traditional detection methods such as Ferring's reagent method and continuous flow analysis can only determine the content of water-soluble sugars or total sugars in tobacco, and cannot quantify single sugar components. However, gas chromatography and liquid chromatography can detect different monosaccharides, they have limitations such as troublesome sample pre-treatment process and long detection time. In this work, based on the traditional phloroglucinol chromogenic method for determination of xylose, the method was improved based on the color development mechanism to solve the technical problem of poor color development of glucose under this detection system, and the precise determination of glucose and xylose content in tobacco and cigarette products can be achieved and efficiently by UV-visible spectroscopy. When the above two sugar fractions are measured simultaneously, the UV spectra can affect each other's measurement accuracy due to their similar peak positions. A multivariate correction model for spectral analysis was developed using partial least squares regression (PLS), aided by a modified phloroglucinol chromogenic method to simultaneously determine glucose and xylose contents in tobacco and its products. The results showed that the linearity of the modified phloroglucinol method was satisfactory for glucose and xylose solution at concentrations in the range of 0.05～0.4 and 0.10～0.80 mmol·L^-1, respectively, and the limits of detection and limits of quantification of the method were 0.001 7, 0.005 7 mmol·L^-1 and 0.007 2, 0.024 mmol·L^-1, respectively. The intra-batch coefficients of variation were in the range of 0.69%～3.03%, and the spiked recoveries were in the range of 96.72%～102.85%. The lignin content in the extracts did not interfere significantly with the determination results. For the mixed sugar solution composed of glucose and xylose, the external test was used to evaluate the model effect, and the correlation coefficient R²=0.994 7 between the predicted and theoretical values of the external test set was within 10% relative deviation. It can be seen that this method can quickly and accurately determine the content of glucose and xylose in tobacco and tobacco products and provide an effective method for monosaccharide analysis in the tobacco industry.
		2024 Vol. 44 (01): 103-110 [Abstract] ( 91 ) RICH HTML PDF (3387 KB) ( 107 )

	111	Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to Water-Leaving Reflectance
		LIANG Ye-heng¹, DENG Ru-ru^{1, 2*}, LIANG Yu-jie¹, LIU Yong-ming³, WU Yi⁴, YUAN Yu-heng⁵, AI Xian-jun⁶
		DOI: 10.3964/j.issn.1000-0593(2024)01-0111-07
		Inversion of heavy metals in water by remote sensing is a difficult problem in remote sensing of the water environment. There are still quite a few fundamental problems to be solved. The contribution law of the bottom sediment in the shallow water area to the water-leaving reflectance is one of the important factors affecting the accuracy of the remote sensing inversion model in the future. Especially in the special background of heavy metal pollution, revealing its contribution rule plays an important role in improving the accuracy of the water heavy metal remote sensing model. Meanwhile, the measurement results have reference significance for studying the reflectance spectral characteristics of heavy metal tailings sediments and distinguishing common bottom sediments. Firstly, the reflectance of the Dabaoshan Mountain tailings bottom in Guangdong was obtained by spectrometer measurement. It has reflection peaks at wavelengths of 755, 1 280, 1 620 and 2 200 nm, and has obvious spectral characteristics. The reflectance of three types of sediments coarse sand, silt and stones, which are common in riverbeds, were compared and analyzed. The results show that, on the one hand, the reflectance of coarse sand and silt shows a slowly rising curve, which is different from the sediment in the mining area with multiple characteristic reflection peaks. On the other hand, the reflectance of the stone shows a broad but flat reflection peak in the wavelength range of 550~650 nm, then a trough at the wavelength of 675 nm, and then increases to a wavelength of 750 nm and then tends to be flat. Its characteristic wavelength is different from that of the sediment in the mining area. Therefore, the above characteristic wavelengths can be used as important distinguishing bands of sediment spectra in the background of heavy metal pollution. Then, measure the water-leaving reflectance water depths of 1, 10 cm and deep water. The water-scattered light and water-bottom reflected light were calculated at a water depth of 1 cm, and the contribution of the water-leaving reflectance was discussed. The measurement results of the water-leaving reflectance show that the sediment greatly influences the shallow water area. The contribution law of the water-scattered light and water-bottom reflected light to the water-leaving reflectance takes the wavelength of 515 nm as the dividing line: in the short-wave direction, the water-scattered light is the main contribution, while in the long-wave direction, change to the water bottom reflected light. The contribution is determined by the sediment's reflection ability and the water body's scattering ability. Finally, the accuracy of the remote sensing model after considering the water-bottom reflected light is evaluated. The comparison results of the water-leaving reflectance in the wavelength range of 350~950 nm calculated by the model and the measured value in the field show a significant linear correlation (R²=0.964 2). The relative error is less than 10% in the wavelength range of 560~830 nm and even less than 5% in some of them. The simulation accuracy of the model is good, which is far higher than that when the influence of the sediment is not considered. The model satisfies the requirements of remote sensing inversion of heavy metals in water in the future. The above research results provide important reference data and theoretical basis for processing sediment effects in remote sensing of water heavy metals inversion in the future, which will help promote further development in this field.
		2024 Vol. 44 (01): 111-117 [Abstract] ( 197 ) RICH HTML PDF (10643 KB) ( 51 )

	118	Three Dimensional Fluorescence Characteristics of Soluble Organic Matter From Different Straw Decomposition Products Treated With Calcium Containing Additives
		XIA Ming-ming^{1, 2}, LIU Jia³, WU Meng^{1, 2}, FAN Jian-bo^{1, 2}, LIU Xiao-li^{1, 2}, CHEN Ling^{1, 2}, MA Xin-ling^{1, 2}, LI Zhong-pei^{1, 2}, LIU Ming^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0118-07
		Straw is a high-quality organic resource. Clarifying the differences in the composition and properties of dissolved organic matter (DOM) of different straw decomposition products treated by calcium-containing additives can provide a theoretical basis and technical reference for the efficient utilization of organic materials.Straw decomposition experiments were carried out using peanut straw and rice straw as test materials by adding different calcium additives (CaC₂O₄, Ca(OH)₂) with different amounts (4%, 8%). The nutrient contents of decomposition products were determined.At the same time, changes in the DOM chemical composition in straw decomposition products were analyzed using three-dimensional fluorescence spectroscopy (3DEEM) and the parallel factor method (PARAFAC). The results showed that: (1) The total carbon content of straw decomposition products was significantly greater in the calcium oxalate treatment than in the calcium hydroxide treatment.The total carbon content of straw decomposition products was less in the high-amount additive treatment than in the low-amount treatment. The total nitrogen, phosphorus and potassium levels of peanut straw decomposition products were higher than those of rice straw decomposition products. (2)The DOM composition of decomposing products was analyzed by the 3DEEM-PARAFAC method. Three fluorescent components, such as humic acid (C1), humic acid (C2), and tryptophan (C3), were identified. The effect of different additive types, addition amounts, and straw types on fluorescence fractions was complex and had no clear pattern.In general, the proportion of C1 and C2 fractions in DOM from the calcium hydroxide treatment was greater than those from the calcium oxalate treatment, while the proportion of C3 fractions in DOM from the calcium oxalate treatment was significantly higher than that from the calcium hydroxide treatment.Fractions C1 and C2 were significantly negatively correlated with fractions C3. The C2 content of DOM was significantly negatively correlated with total carbon content. (3) The HIX value of the DOM in straw decomposition products treated with calcium hydroxide was significantly higher than that treated with calcium oxalate. The BIX value of DOM treated with calcium oxalate was significantly higher than that treated with calcium hydroxide. DOM of straw decomposition products treated with high amounts of calcium hydroxide was mainly from the transformation of microorganisms, while DOM ofother treatments came from the transformation of microorganisms and terrestrial plants.The fluorescence index of straw decomposition products did not change significantly under different addition treatments. HIX, BIX, and McKnight's index of DOMin peanut straw decomposition products were higher than in rice straw. There was a significant negative correlation between McKnight and HIX and an extremely significant positive correlation with BIX. Meanwhile, the contents of BIX and TC were significantly positively correlated. The above results showed that calcium hydroxide can promote straw decomposition and humification. A higher amount of calcium hydroxide was more effective in peanut straw decomposition.
		2024 Vol. 44 (01): 118-124 [Abstract] ( 56 ) RICH HTML PDF (3799 KB) ( 56 )

	125	Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy
		LI Jie, ZHOU Qu^*, JIA Lu-fen, CUI Xiao-sen
		DOI: 10.3964/j.issn.1000-0593(2024)01-0125-09
		Furfural content in transformer oil is one of the important indexes of insulation aging in power transformers. Infrared and Raman spectroscopy have the advantages of fast detection speed, fast analysis speed and non-destructive detection in effectively identifying substances. This paper compares furfural detection methods in transformer oil based on infrared and Raman spectroscopy. Based on the Fourier transform infrared spectrometer and laser confocal Raman spectroscopy instrument configuration of furfural transformer sample testing for the laboratory and its spectral data, using wavelet transform, the multinomial least square method and locally weighted regression fitting the collected infrared and Raman spectra is preprocessed, careful consideration of noise, resolution and effective information loss, etc., The polynomial least square method has the best preprocessing effect. The molecular model of furfural was established based on Gaussian software, and the attribution of the infrared and Raman absorption peaks of furfural was studied by density functional simulation calculation. The Raman and infrared detection characteristic peaks of furfural in transformer oil were determined to be 1 703 and 1 704 cm^-1, respectively, combined with the experimental spectra. The repeatability experiments of infrared and Raman detection were carried out, and the relative standard deviations of the two detection methods were 7.21% and 8.67%, respectively. The relationship between the infrared and Raman 3D in situ spectrographs was analysed by drawing furfural infrared and Raman characteristic peak areas and transformer oil with different furfural concentrations. The infrared and Raman quantitative analysis models for furfural detection in transformer oil were established based on the least square method, and the goodness of fit was 0.998 and 0.885, respectively. Compared to the two kinds of detection methods of quantitative analysis of the model prediction results, the results show that Raman spectroscopy detection lower limit is lower than the infrared spectrometry, pretreatment by the multinomial least square method of spectral data of transformer oil furfural quantitative analysis of infrared and Raman model can well predict furfural content in transformer oil, to provide technical reference for related research.
		2024 Vol. 44 (01): 125-133 [Abstract] ( 96 ) RICH HTML PDF (14938 KB) ( 64 )

	134	Influence of Hydrochemical Ions on Three-Dimensional Fluorescence Spectrum of Dissolved Organic Matter in the Water Environment and the Proposed Classification Pretreatment Method
		LEI Hong-jun¹, YANG Guang¹, PAN Hong-wei^1*, WANG Yi-fei¹, YI Jun², WANG Ke-ke², WANG Guo-hao², TONG Wen-bin¹, SHI Li-li¹
		DOI: 10.3964/j.issn.1000-0593(2024)01-0134-07
		Three-dimensional fluorescence spectroscopy has broad application prospects in the in-situ water environment monitoring. However, due to its simple pretreatment method, the interference of hydrochemical ions in the water environment cannot be completely excluded, which could reduce the accuracy of water quality identification results. Currently, no relevant studies have classified this interference. In this study, we set nine kinds of hydrochemical ions (Na⁺, Cl^-, NO^-₃, CO^2-₃, Ca²⁺, Mg²⁺, K²⁺, HCO^-₃, SO^2-₄) at the level of three ion concentrations (1～100 mg·L^-1) and explored the changes of fluorescence characteristic by fluorescence parameters. Based on the fluorescence characteristic, the interference was divided into three different degrees using systematic clustering analysis: the first type (10～100 mg·L^-1 CO^2-₃), significant increase; the second type (10～100 mg·L^-1 SO^2-₄ and 100 mg·L^-1 NO^-₃), significant reduction; the third type (1～10 mg·L^-1 NO^-₃, no added ions, 1 mg·L^-1 CO^2-₃, SO^2-₄ and 1～100 mg·L^-1 Ca²⁺, Mg²⁺, K²⁺, HCO^-₃, Na⁺, Cl^-), mild interference. In other cases, priority should be given to methods that can remove the interference of hydrochemical ions, such as ion cyclotron resonance mass spectrometry. In addition, the combination of three-dimensional fluorescence spectroscopy and other technologies is strengthened to obtain more comprehensive information while reducing the limitations of a single technology. This study provides a data basis for restoring the real fluorescence spectra and a scientific basis for the selection and combined application of three-dimensional fluorescence and other techniques.
		2024 Vol. 44 (01): 134-140 [Abstract] ( 70 ) RICH HTML PDF (22558 KB) ( 61 )

	141	Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy （LIBS）
		LIU Jia^{1, 2}, GUO Fei-fei², YU Lei², CUI Fei-peng², ZHAO Ying², HAN Bing², SHEN Xue-jing^{1, 2}, WANG Hai-zhou^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0141-07
		"Magneto" neodymium iron boron is the most excellent permanent magnet,and it is widely used in the industrial internet, New Energy, 5G communications and many other high-tech fields because of its excellent magnetic properties. Currently, the main analytical methods for elements in Nd-Fe-B are Inductively coupled plasma emission spectrometry (ICP-OES) and X-ray fluorescence spectrometry (XRF). ICP-OES is a wet analytical method with complex sample pretreatment and a long testing period, and the XRF method can be used for direct analysis, but it is challenging to meet the analysis demand of the main light element B in Nd-Fe-B because of its detection ability. Laser-induced breakdown spectroscopy(LIBS) technology has many advantages, such as high analysis efficiency, simple sample pretreatment, direct multi-element analysis, suitable for on-site analysis and on-line, etc., it shows its unique advantage in fast quantitative characterization. In this paper, the LIBS technology is used to study the direct and rapid method for quantitative characterization of multi-elements in Nd-Fe-B. At first, the screening of characteristic spectral lines is completed for nine elements (Nd, Co, B, Dy, Tb, Pr, Cu, Al, Ga) in Nd-Fe-B was characterized quantitatively based on the analysis of the influence of the system laser voltage, laser ablation mode on the spectral signal stability of Nd-Fe-B under different conditions. The analysis conditions are optimized and established, in the end, 720 V laser voltage and 15 pre-ablation pulses and 15 ablation pulses are selected as the analytical conditions of Nd-Fe-B samples. Eight Nd-Fe-B samples were determined by the ICP-OES method. The samples had the gradient difference characteristics of element composition and were used as the standard samples for establishing the analytical method. The calibration curves of Nd, Co, B, Dy, Tb, Pr, Cu, Al and Ga in Nd-Fe-B samples were established by standard curve method which was used to correlate the strength ratio with the concentration.At last, two sintered samples of Nd-Fe-B were selected to perform the quantitative analysis of nine elements with the established quantitative analysis method. The analysis time was less than 30 seconds, and the quantitative results of the LIBS and ICP-OES methods have a good consistency. In this paper, the direct, simultaneous and rapid quantitative characterization of multi-elements in Nd-Fe-B has been achieved by using the Laser-induced breakdown spectroscopy analysis technique. It provides a new technical idea and characterization method for the rapid quantitative characterization of Nd-Fe-B.
		2024 Vol. 44 (01): 141-147 [Abstract] ( 59 ) RICH HTML PDF (6901 KB) ( 74 )

	148	Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm
		BAO Hao^{1, 2}，ZHANG Yan^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0148-10
		As one of the primary steps in NIR spectral analysis, effective feature band selection can improve modelling efficiency and model performance. Traditional Characteristic band selection algorithms suffer from long run times and redundant feature selection, making achieving the desired results in practical engineering applications difficult. The Harris Hawk Optimisation (HHO) algorithm has the advantages of simple principles and few parameters, but it also has the shortcomings of low convergence accuracy and easy to fall into local optimum. In this paper, we propose an NIR spectral feature band selection model based on the Improved Harris Hawk Optimisation (IHHO) algorithm based on the HHO algorithm. For the HHO algorithm can only be used to solve optimization problems in continuous space, a discretization strategy is used to modify the HHO algorithm so that it can solve the discrete form of the characteristic waveform selection problem. Considering the poor quality of the initial population of the HHO algorithm, the quality of the initial population is improved using chaotic mapping and opposition-based learning to enhance the global exploration capability of the algorithm; Due to the low convergence accuracy of the HHO algorithm in local search, a new prey energy decay model and jumping strategy are proposed further to enhance the algorithm's search capability in local search. The HHO algorithm is perturbed by borrowing the variational approach of genetic algorithm. Support vector machine (SVM) identification models and partial least squares regression (PLSR) models were developed using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), particle swarm optimization (PSO) algorithms, genetic algorithms (GA), HHO algorithms compared to IHHO algorithms, and four qualitative analysis NIR spectral datasets and two quantitative analysis NIR spectral datasets, respectively. In the qualitative analysis experiments, the average accuracy obtained by the IHHO algorithm improved by 0.83%, 9.55%, 17.65%, and 0%, respectively, concerning the full band, and the average number of characteristic bands was only 9.97%, 2.59%, 1.36%, and 0.59% of the full band. In the quantitative analysis experiments, the average coefficient of determination obtained by the IHHO algorithm was 10.57%, 1.47%, 4.41%, 3.66% and 3.06% higher than the full band, and the average root mean square error was 0.162, 1.266 3, 1.868, 1.869 4 and 0.408 4 lower than the full band, and the average number of characteristic bands was only 9.24%, 10.53% and 0% of the full band. The average number of characteristic bands was only 9.24%, 10.53%, 6.54%, 6.91% and 7.14% of the full band. The experimental results show that the IHHO algorithm can remove redundancy in the selection of feature bands and target the most important ones, and its performance is better than several other selection algorithms. Therefore, the IHHO algorithm has good application prospects.
		2024 Vol. 44 (01): 148-157 [Abstract] ( 73 ) RICH HTML PDF (20864 KB) ( 77 )

	158	*Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii* Based on SMOTE and Inception-CNN**
		LAN Yan¹，WANG Wu¹，XU Wen²，CHAI Qin-qin^1*，LI Yu-rong¹，ZHANG Xun²
		DOI: 10.3964/j.issn.1000-0593(2024)01-0158-06
		Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is one of the most precious Chinese medicine with extraordinary effects in medical treatment and health protection. Planting and tissue-cultured are two main cultivated methods of A. roxburghii. There are slight characteristic differences between Planting and tissue-cultured A. roxburghii, but they show significant differences in medicinal and market value. Therefore, the identification of cultivated methods plays an important role in effectively securing the medicinal efficacy of A. roxburghii and maintaining a good market order. However, due to the influence of composite differences such as different cultivars, different geographical origins and different times of cultivation, the difficulty and complexity of identification in cultivated methods increase heavily. This paper proposes an effective model to discriminative different cultivated methods of A. roxburghii based on improved 1D-inception-CNN. The experiments were conducted on two kinds of A. roxburghii, and their NIRS data were collected by a Fourier transform near-infrared spectrometer. Considering the unbalanced proportion of planting and tissue-cultured samples，the NIRS data was over sampled by using SMOTE first. Secondly, a one-dimensional convolutional neural network based on improved Inception was constructed to identify planting and tissue-cultured A. roxburghii though both include different varieties, different geographical origins and different cultivating times. Finally, Bayesian optimization was used to optimize the hyperparameters of the model. The final average identification accuracy, precision, recall, and F1-score of five-fold crossvalidation reached 97.95%, 96.16%, 100%, and 98.02%. The identification model proposed in this experiment provides a useful method to identify planting and tissue-cultured A. roxburghii effectively and rapidly and provides an idea for the identification of cultivation methods of other Chinese herbal medicines.
		2024 Vol. 44 (01): 158-163 [Abstract] ( 72 ) RICH HTML PDF (4506 KB) ( 53 )

	164	Study on Monomer Simulation of Cellulose Raman Spectrum
		WANG Xin-qiang^{1, 3}, CHU Pei-zhu^{1, 3}, XIONG Wei^{2, 4}, YE Song^{1, 3}, GAN Yong-ying^{1, 3}, ZHANG Wen-tao^{1, 3}, LI Shu^{1, 3}, WANG Fang-yuan^{1, 3*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0164-05
		Cellulose is a macromolecular polysaccharide composed of glucose, the most abundant, cheapest and easiest to obtain natural polymer in the world. Cellulose, as the oldest and most abundant natural polymer, has attracted much attention in research. The controllability of cellulose depends on its molecular weight, size and structure, and Raman spectroscopy has “fingerprint characteristics”, which can identify different cellulose fibers, as well as historical aging textile fiber materials. However, it is difficult to simulate cellulose as a macromolecular polysaccharide theoretically. In this study, we propose using the basic unit to simulate macromolecular spectrum, and use cellulose monomer to simulate Raman spectrum to analyze the spectral properties of cellulose macromolecules. In this paper, based on density functional theory, the Raman spectra of cellulose monomers and cellulose double links under different external electric fields (a. u.) were calculated under the basis set conditions of B3LYP/6-31g (d, p) using Gaussian 16 software. The study shows that the Raman spectrum of cellulose monomer has characteristic peaks at 449, 597, 842, 1 127, 1 361, 1 395 and 3 005 cm^-1 without the action of the external electric field. The vibration analysis shows that these Raman peaks are respectively composed of the ring (C6—C4—O20) stretching vibration, C—C—H twisting vibration, ring (C4—O20—C2) stretching vibration, glycosidic bond (C2—O1—C8) stretching vibration, CH₂ bending vibration The expansion vibration of CH₂ is generated. Compared with the experimental value, the simulated Raman spectrum of cellulose monomer is consistent with the measured Raman spectrum of natural cellulose, so the first principle calculation of cellulose monomer can reflect the Raman spectrum characteristics of cellulose macromolecules. When the external electric field changes from the negative direction to the positive direction and gradually increases, the amplitude of the C—C—H torsional vibration of the ring corresponding to 597 cm^-1 has no obvious change. With no electric field as the benchmark, when the external electric field is applied, the vibration energy reduces the spectral red shift; The stretching vibration amplitude of the glycoside bond (C2—O1—C8) corresponding to 1 127 cm^-1 decreased significantly, and the spectrum showed a blue shift; The characteristic peak spectrum of the bending vibration of CH₂ at 1 395 cm^-1 shows a blue shift, and the spectrum generated at 1 361 cm^-1 shows a blue shift first, then a red shift, and then a blue shift again. It is speculated that the bending vibration area is more susceptible to the influence of the skeleton vibration and environmental changes. Comparing the difference between the Raman spectra of cellulose monomer and double chain link, in addition to the obvious characteristic peaks of the monomer, the double chain link molecule has obvious characteristic peaks at 1 041 and 151 cm^-1, which are respectively produced by C—O stretching vibration of secondary alcohol and C—O—H bending vibration, which can supplement the Raman spectra of cellulose monomer. These conclusions provide a theoretical basis for studying the spectra of cellulose under the action of external electric field and a new method for studying and analyzing the Raman spectra of other macromolecular polymers.
		2024 Vol. 44 (01): 164-168 [Abstract] ( 73 ) RICH HTML PDF (3420 KB) ( 144 )

	169	A Spectroscopic Study of Secondary Minerals on the Epidermis of Hetian Jade Pebbles From Xinjiang, China
		LIU Shu-hong^{1, 2}, WANG Lu-si^3*, WANG Li-sheng³, KANG Zhi-juan^{1, 2}，WANG Lei^{1, 2}，XU Lin^{1, 2}，LIU Ai-qin^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0169-07
		Hetian jade pebbles are secondary ores of Hetian jade that have been washed, carried and deposited by rivers for a long time, and are popular among consumers for their unique epidermis colors. For some time, the rising price of Hetian jade pebbles has led to the emergence of many artificially produced counterfeits on the market. Identifying Hetian jade pebbles and counterfeits has become a difficult problem for consumers, practitioners and even identification laboratories. In this paper, a representative group of Xinjiang Hetian jade pebbles from reliable sources were selected for the study, and the samples were confirmed as Hetian jade pebbles based on their morphological and epidermis color distribution characteristics. The secondary minerals of the representative Hetian jade pebbles samples were investigated by gemological microscopy, Raman spectroscopy, infrared spectroscopy, X-ray diffraction spectroscopy, and UV-Vis diffuse reflectance spectroscopy. According to gemological microscopy, in addition to the red to-yellow epidermis and brownish-black dendritic mineral aggregates, there are parallel-arranged flaky mineral aggregates in the fissures, fibrous minerals and minerals with complete cleavage planes and uneven film-like distribution of secondary minerals in the pits of the epidermis of Hetian jade pebbles. Raman spectroscopy results show that the main mineral on the epidermis of Hetian jade pebbles is tremolite, as is in the interior. The fluorescence intensity of the Raman spectrum of the Hetian jade pebbles is related to the degree of weathering in the tested area. The fluorescence of the strong weathering products not only obscures the Raman peaks of the secondary minerals, but also weakens the minor peaks of the primary mineral tremolite. Infrared spectroscopy and X-ray powder crystal diffraction tests show that the parallel arranged flaky mineral aggregates in the fissures of the epidermis, the fibrous minerals and the minerals with sparkling eminent cleavage planes and the unevenly distributed film-like secondary minerals in the pits of the epidermis are all clinochlore. The first derivative of the UV-vis diffuse reflectance spectra of the red and yellow epidermis have 430 and 520～541 nm peaks, and some samples have spectral peaks near 575 nm, indicating that the chromogenic minerals are goethite and hematite. The spectroscopic study of the epidermal secondary minerals of the Hetian jade pebbles shows that they have undergone a series of epigenetic geological actions under the long-term effect of geological forces such as atmosphere and water, forming secondary minerals such as clinochlore and goethite, which provides a scientific basis for the study of the identification of Hetian jade pebbles and the distinction from counterfeits.
		2024 Vol. 44 (01): 169-175 [Abstract] ( 69 ) RICH HTML PDF (20116 KB) ( 68 )

	176	Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang
		LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei^*, PAN Shao-kui
		DOI: 10.3964/j.issn.1000-0593(2024)01-0176-05
		This paper studies a kind of seed material gemstone called “blue Hotan jade” with sparse output and high marketing price. Due to the deficiency of previous research and standardized naming, its business name is confused in the trading market. Hydrostatic weighing method determines The relative density of 3.85 g·cm^-3. Only the white mineral part shows orange fluorescence under the long-wave ultraviolet lamp. Through microscopic observation, straight a blue-white color zone, coniform blue-white color zone, irregular blue zone and many white vein minerals can be seen. Through observation by orthogonal polarization and differential interference, it is determined that the vein minerals are different from the substrate and formed after the substrate. It is determined that the infrared absorption peaks of blue substrate and white zone mineral are located at 812/817, 652/655 and 613/620 cm^-1, which conforms to the characteristic vibration of corundum; 3 019, 2 950, 2 129, 1 990, 1 121, 1 030, 799, 733, 648, 598 cm^-1 absorptions of vein minerals conform to the characteristic vibration of diaspore. The absorption spectra of different parts of the sample were measured by micro UV-VIS-NIR spectrophotometer, in which no obvious absorption was found in the white color zone; 429 and 453 nm absorption caused by Fe³⁺ d electron transition ⁶A₁→⁴E, ⁴A₁(⁴G) and 1 810, 2 030 and 2 235 nm absorption caused by OH vibration were found in the vein minerals; 421/418, 562/566, 702/709 and 868/889 nm absorption caused by Fe³⁺ d electron transition ⁶A₁→⁴E, ⁴A₁(⁴G), Fe²⁺-Ti⁴⁺ ion pair charge transfer and Fe²⁺-Fe³⁺ ion pair charge transferwere observed on the blue substrate. As the color deepens, the blue region 421 nm absorption occurs blue shift, 500～900 nm absorption occurs red shit, and the absorption peak integral area increases according to the quantitative analysis of the XRD pattern, sapphire accounts for about 66. 9%, and diaspore about 33.1%. According to the national standard named jewellry and jade (GB/T 16552—2017), it is recommended to be named Diaspore-Sapphire.
		2024 Vol. 44 (01): 176-180 [Abstract] ( 73 ) RICH HTML PDF (33434 KB) ( 79 )

	181	Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico
		GU Yi-lu^{1, 2}，PEI Jing-cheng^{1, 2*}，ZHANG Yu-hui^{1, 2}，YIN Xi-yan^{1, 2}，YU Min-da^{1, 2}, LAI Xiao-jing^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0181-07
		Durango City, Mexico, is one of the richest sources of gem-quality fluorapatite. In previous studies, Mexican apatite was mainly used as a reference material for geochronology and mineralogy, with few gemological and spectroscopic materials. As a kind of natural luminescent material, early scholars mainly used laser-induced photoluminescence spectra to study apatite from unknown origin, but lacked 3D fluorescence spectra. In this paper, nine apatite samples from Durango City, Mexico, were systematically examined by using basic gemological tests, LA-ICP-MS chemical analysis, FTIR spectroscopy, Raman spectroscopy, UV-Vis-NIR absorption spectroscopy and 3D fluorescence spectroscopy, aiming to enrich thedata of spectrum of Mexican apatite and to provide the scientific information for origin determination. Chemical research shows apatite is rich in rare earth elements (REE), with high contents of La, Ce, Pr, Nd, and Sm, with average contents of 3 956, 5 430, 472, 1 596, and 213 μg·g^-1, respectively, which shows the characteristics of obvious enrichment of light REE and deficit of heavy REE. The average value of δEu is 0.29, with significant Eu negative anomaly, and the Ca/P molar ratio is close to the standard value of 1.68 of magmatic apatite, which indicate that Mexican apatite is the product of magmatism and the forming magma is in a moderately reductive state. FTIR spectroscopy shows that the intensity of the absorption peaks at 606 and 575 cm^-1 in the fingerprint region has obvious directionalregularity, which can provide a basis for crystal orientation. The functional group region shows absorption peaks of 3 482, 3 538, and 3 556 cm^-1 of structure water. In the UV-Vis-NIR absorption spectrum, the single peak at 528 nm and the double peaks at 578 and 585 nm caused by Nd³⁺ generate the obvious transmission window in the yellow-green region. Therefore, it is speculated that Nd³⁺ causes the yellow-green color of apatite. In the UV region, the absorption peak at 298 nm is responsible for the absorption edge in the visible violet region, presumably caused by Ce³⁺. Studies on the 3D fluorescence spectrum showthe strongest fluorescence peak (λ_ex300 nm/λ_em356 nm), caused by the electron leap of Ce³⁺. In addition, the emission peaks at 603 and 647 nm in the red region are caused by the electron leap of Pr³⁺ and Sm³⁺, corresponding to the dark red fluorescence phenomenon observed under the UV lamp. Therefore, the dark red fluorescence is presumed to be caused by Pr³⁺ and Sm³⁺. The systematic spectroscopic features in this study enrich the spectroscopic data of apatite of Mexico and provide a scientific basis for origin determination.
		2024 Vol. 44 (01): 181-187 [Abstract] ( 85 ) RICH HTML PDF (9809 KB) ( 92 )

	188	Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum
		GUO Ya-fei¹, CAO Qiang¹, YE Lei-lei¹, ZHANG Cheng-yuan¹, KOU Ren-bo¹, WANG Jun-mei¹, GUO Mei^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0188-09
		Research on the quality control of Chinese medicinal materials is one of the main aspects of the modernization of traditional Chinese medicine. Rheum taanduticum (R. tanguticum) is one of the most widely used Chinese medicinal materials. The research on the quality control of R. tanguticum is total anthraquinones, anthraquinones, fingerprint and so on. There are few studies on quality standards based on anti-inflammatory effect in the study of R. tanguticum. Eighteen batches of R. tanguticum from different origins and processing conditions were collected to study by Fourier-transform infrared spectroscopy (FTIR). FTIR founded sixteen common peeks. There is high similarity (0.798~0.900) in 18 batches of R. tanguticum. There is a high similarity (the similarity greater than 0.900 is more than 80%) in the same origin (Gannan, Qinghai). The similarity of R. tanguticum in the same origin (Gannan, Qinghai) was higher (more than 80% if the similarity is greater than 0.900) than that between the two origins (only 69.44% if the similarity is greater than 0.900). There were variations in FTIR spectra of R. tanduticum in different origins, combining the result of cluster analysis. The origins maybe the main influence factor in the FTIR spectra. Furthermore, the sequence of double index analysis was established to distinguish different origins of medicinal materials. LPS stimulated RAW 264.7 cells to establish an inflammatory cell model. NO inhibition rate was used as an inflammatory index. Determination of NO inhibition rate of 18 batches of R. tanduticum. Establishing the PLSR model with common peaks (X) and NO inhibition rate (Y) by SMICA 14.1 software. There were 4 common peaks (X₁₄，X₁₅，X₁₁，X₂), which VIP＞1. It was found that X₂ in the FTIR spectrum was positively correlated with an anti-inflammatory effect, and X₁₄, X₁₅, X₁₁ were negatively correlated with anti-inflammatory effect. By further establishing the mathematical model with common peaks (X) and NO inhibition rate (Y), Y=237.618+2.992X₂-0.845X₄+2.979X₆-3.722X₇+0.433X₈-0.957X₁₂-0.759X₁₄-0.632X₁₅ (p=0.003＜0.01，R=0.935), it can be seen that X₂, X₆ have a positive correlation with the anti-inflammatory effect, and X₄, X₇ have a negative correlation with the anti-inflammatory effect. The in vitro anti-inflammatory effect can be predicted by the FTIR spectrum of R. tanguticum, with an accuracy of 93.50%. However, due to the small sample size, it can only be used to explain the data included in this study. X₆ is made of methyl β(C—H), which may be related to the content of anthraquinone components (such as Emodin, Rhein, Physcion, Aloe emodin). The results of this study can introduce the anti-inflammatory efficacy evaluation into the quality evaluation system of Rheum tanguticum, enrich the quality evaluation system of R. tanguticum, and provide new ideas for the healthy development of Rheum tanguticum industry.
		2024 Vol. 44 (01): 188-196 [Abstract] ( 66 ) RICH HTML PDF (3404 KB) ( 48 )

	197	Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images
		ZHU Wen-jing^{1, 2}，FENG Zhan-kang^{1, 2}，DAI Shi-yuan^{1, 2}，ZHANG Ping-ping³，JI Wen⁴，WANG Ai-chen^{1, 2}，WEI Xin-hua^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0197-10
		In order to explore the applicability of the multi-feature fusion method in the fast and accurate identification of crop lodging, this study used UAVs to obtain wheat field multispectral data with different lodging rates at multi field canopy scales. The original lodging image is preprocessed by image mosaic, radiometric correction, geometric correction, etc., and the normalized difference vegetation index and shadow index are used to remove the soil and shadow background respectively. The wheat lodging DSM model and vegetation index were extracted and fused with the multispectral image for principal component transformation of the multi feature image, respectively, to screen the texture features with greater difference. Support Vector Machine (SVM), Artificial Neural Network (ANN) and Maximum Likelihood (MLC) supervised classification models are used to classify multispectral and DSM fusion images, multispectral and normalized vegetation index (NDVI) fusion images, multispectral images and texture feature images. The overall accuracy (OA), Kappa coefficient and extraction error were used to comprehensively evaluate each supervision model's classification performance and lodging extraction accuracy. The classification results show that the modeling effect of each supervised classification method in different lodging areas is consistent, and the overall extraction accuracy of SVM and ANN is higher than that of MLC. In the high lodging areas, the SVM supervised model (OA: 92.63%, Kappa coefficient: 0.85, extraction error: 1.11%) of multispectral and NDVI fusion images has the best extraction effect; in the middle lodging area, the SVM supervision model (OA: 90.35%, Kappa coefficient: 0.79, extraction error: 9.34%) of multispectral and DSM fusion images has the best extraction effect; in the low lodging area, the ANN supervised model (OA: 91.05%, Kappa coefficient: 0.82, extraction error: 8.20%) of the mean texture feature image has a good extraction result. In this study, the DSM model, vegetation index, texture features and multi spectral images are fused and compared, and whether the multi feature fusion method can effectively extract wheat lodging information with high accuracy is explored. The results show that the UAV multi-spectral remote sensing method combined with feature fusion can effectively extract wheat lodging area, and the extraction effect is better than that of wheat lodging image with a single feature. The results of this study can provide a reference for the accurate acquisition method of the wheat lodging disaster survey data.
		2024 Vol. 44 (01): 197-206 [Abstract] ( 64 ) RICH HTML PDF (35414 KB) ( 71 )

	207	Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV Remote Sensing
		LI Hu¹, ZHONG Yun^{1, 2}, FENG Ya-ting¹, LIN Zhen¹, ZHU Shi-jiang^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0207-08
		Soil moisture is an important factor affecting agricultural production and plays a vital role in crop growth and final yield. Rapid and efficient estimation of soil water content has become a hot issue in agricultural and forestry water resources monitoring. It has been widely recognized and applied to calculate vegetation index and build soil water content inversion model by using the characteristic bands of hyperspectral reflectance. Because of the problem that the inversion of soil water content is greatly affected by vegetation coverage, we propose to use multi vegetation index combination to weaken the influence of vegetation coverage on the inversion of soil water content. Thirty groups of citrus trees were selected as samples in the Cangwubang test base of Yichang City. The soil was collected at the drip line of the fruit tree, and the soil mass moisture content was determined by the drying method. Four times of sampling, a total of 120 groups of soil moisture content. We use the ASD Field Spectral FR spectrometer (wavelength range: 325~1 075 nm) and the Dajiang Genie 4 multispectral UAV to obtain the spectral reflectance in the blue, green, red, red edge, near-infrared and short wave infrared bands of 120 groups of test areas. We pretreat the spectral data with the moving average method for noise reduction, compare and analyze 9 vegetation indices with gray correlation method, and screen out 4 vegetation indices that are highly significantly related to soil water content (p<0.01). The correlation between each index and soil water content from high to low is the bare soil index (BSI), normalized blue-green differential vegetation index (NGBDI), green normalized index (GNDVI) and normalized differential vegetation index (NDVI). The correlation between BSI and soil water content is the highest, and the correlation coefficient is -0.687. We use the linear stepwise regression method and nonlinear BP neural network method to build a soil water content inversion model based on multi vegetation index and take the determination coefficient (R²), root mean square error (RMSE) and relative error (ARE) as the evaluation indexes of the inversion accuracy of the model. The results show that the R² between the inversion value of soil water content and the measured value of the stepwise regression model and BP neural network model are 0.816 and 0.889 respectively, the RMSE is 2.54% and 1.53% respectively, and the ARE is 21.13% and 8.88% respectively. It shows that the nonlinear BP neural network algorithm based on multi vegetation index combination has higher accuracy in soil moisture inversion based on vegetation index modeling, and can overcome the influence of different vegetation coverage on the accuracy of soil moisture inversion to a certain extent. As an effective alternative method to measure soil moisture directly, it provides theoretical support for quantitative decision-making and scientific agricultural irrigation management.
		2024 Vol. 44 (01): 207-214 [Abstract] ( 79 ) RICH HTML PDF (3072 KB) ( 76 )

	215	Convolutional Neural Network Combined With Improved Spectral Processing Method for Potato Disease Detection
		LI Xin-ting, ZHANG Feng, FENG Jie^*
		DOI: 10.3964/j.issn.1000-0593(2024)01-0215-10
		For potato early blight at different infection periods (DPP), the spectral data is interfered with by factors such as stray light, noise, etc. In addition, a large number of bands, a large amount of data and complex bands will adversely affect the quantitative and qualitative analysis of the spectrum. 9 kinds of spectral preprocessing methods are studied, combined with the experimental results, the preprocessing methods are arranged and combined, and the 16 spectral preprocessing methods are extended and improved to combine with the continuous projection algorithm, the competitive adaptive weighting algorithm and the genetic algorithm. The band extraction methods are combined to obtain 64 spectral processing methods to optimize the original spectral data. In the convolutional neural network (CNN) classification model, most of the classification accuracy after spectral processing is significantly improved compared to the unprocessed prediction classification accuracy of 86.67%, and the classification accuracy of 12 spectral processing methods is 100%, the ideal classification of potato early blight at different disease stages can be achieved. In order to further quantitatively analyze the different infection stages of potato early blight, the spectral data processed by the spectral processing method were quantitatively analyzed using the constructed CNN quantitative estimation model. The spectral information useful to the target variable in the analysis method will lead to the result that the R² and RMSE of the data processed by the spectral analysis method will decrease compared with the original spectral data. The fusion spectral processing method used in the study can further optimize the original spectral data. Improve the performance of the model. Among them, the CNN quantitative estimation model based on the spectral processing method combined with mean centering, multivariate scattering correction, and moving average smoothing has achieved the best results. The fitting degree of the value is 100%, and its RMSE is only 0.001 1, indicating that the deviation between the estimated value and the actual value of potato early blight at different disease stages is close to 0, indicating that the model can perfectly predict potato early blight in different disease stages. The results show that the proposed CNN can perform effective classification detection and quantitative analysis of different infection periods of potato early blight, and an effective combination of various preprocessing and feature band extraction methods according to the optimization purpose can effectively improve the modeling effect, Provide theoretical and technical support for non-destructive, accurate and intelligent detection of crop diseases.
		2024 Vol. 44 (01): 215-224 [Abstract] ( 62 ) RICH HTML PDF (12868 KB) ( 76 )

	225	Rapid Identification of Inorganic Elements in Understory Soils in Different Regions of Guizhou Province by X-Ray Fluorescence Spectrometry
		HAN Xue^{1, 2}, LIU Hai^{1, 2}, LIU Jia-wei³, WU Ming-kai^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2024)01-0225-05
		Inorganic elements have a crucial impact on the yield and quality of crops by changing the secondary metabolic pathways during crop growth. The environmental characteristics of Guizhou Province are complex, and the inorganic elements of the understory soil in different regions are very different. Therefore, a fast and effective method is highly required to identify the differences in the inorganic elements of understory soil in those regions to provide theories for the selection of understory crops and planting requirements. This study used X-ray fluorescence spectroscopy (XRF) to detect inorganic elements in 12 samples of understory soils from 7 different regions in Guizhou Province. Totally 11 inorganic elements, including SiO₂, Al₂O₃, Fe₂O₃, CaO, MgO, Ti, K₂O, Co, P, Zn, and Cl, which are closely related to plant growth and development, were used to evaluate the adaptability of soil for planting crops. The results showed 23 elements in the understory soil in different areas of Guizhou, among which the major elements such as Si, Al and Fe comprised approximately 70% of the soil, and even more than 90% in some areas. Cluster analysis showed a certain correlation between the amount of soil inorganic elements and the region, and those regions can be clustered into 4 categories when the Euclidean distance was 6. Principal component analysis suggested that the higher soil scores in Dafang County of Bijie City and Liping County of Qiandongnan Prefecture are beneficial for understory crop growth and thus could be the key areas for the development of understory planting industries. This study suggested that X-ray fluorescence spectrometry can easily and effectively identify the composition of various inorganic elements in soil, explore the differences in element characteristics of understory soil, and provide references for under-forest crop planting in Guizhou Province.
		2024 Vol. 44 (01): 225-229 [Abstract] ( 55 ) RICH HTML PDF (3206 KB) ( 120 )

	230	Distributed Design of Optical System for Multi-Spectral Temperature Pyrometer
		ZHANG Nan-nan^{1, 3}, CHEN Xi-ya¹，CHANG Xin-fang¹, XING Jian¹, GUO Jia-bo¹, CUI Shuang-long^1, LIU Yi-tong^2, LIU Zhi-jun¹
		DOI: 10.3964/j.issn.1000-0593(2024)01-0230-04
		Multi-spectral radiation temperature measurement technology is one of the most powerful tools in high-temperature measurement because of its advantages of no interference to the measured field, no upper limit of measurement and fast response speed. At present, the multi-spectral pyrometer mainly measures point temperature. The optical path radiates the point to be measured through the objective lens, diaphragm, prism and other optical paths and then enters the detector array to realize the multi-spectral information collection at a single point. As industrial intelligence degrees unceasing enhancement, more need to obtain real-time information, a large number of temperatures of special metal materials such as the smelting process, high-temperature alloy laser automatic welding process, the semiconductor crystal growth process, the rocket launch the tail jet flame temperature diagnosis in areas such as all need to obtain real-time and even the entire two-dimensional temperature on the surface of a line, to improve product performance and quality. Therefore, it is very important to measure the temperature distribution of a line on the surface by multi-spectral radiometry. However, when the aperture is changed into a slit to realize the radiation splitting on the surface of the line to be measured through the traditional optical path such as lens and prism, the spectrum of the slit will be severely bent due to off-axis transmission due to the spherical aberration of the optical system, which is not good for the complete reception of the rectangular photoelectric detector array. Therefore, a multi-spectral line temperature and light path system based on an orthogonal cylindrical lens group is proposed in this paper. The special function from the circle to ellipse and straight line can be realized by using the orthogonal cylindrical lens in different positions, which better solves the spherical aberration problem existing in the light path of traditional multi-spectral radiation pyrometer. Using ZEMAX optical design software, the reverse optical system is designed based on the size of the s4111-16Q photodetector array. The parameters of key optical devices such as slit, objective lens, prism and orthogonal cylindrical lenses are determined. Based on the actual optical devices processed by these parameters, the optical system of multi-spectral line temperature pyrometer is built. The results show that the image of the slit is obviously bent without the orthogonal cylindrical lens, and the image of the slit is obviously bent without the orthogonal cylindrical lens, and the image of the slit is straight when the orthogonal cylindrical lens is added. It provides technical support for the spectral radiation information of the whole line to be integrated into each detector array, thus providing powerful spectral radiation data information for subsequent line temperature measurement.
		2024 Vol. 44 (01): 230-233 [Abstract] ( 51 ) RICH HTML PDF (7265 KB) ( 53 )

	234	Evaluation of Freezing Injury Degree of Tea Plant Based on Deep Learning, Wavelet Transform and Visible Spectrum
		LI He¹, WANG Yu², FAN Kai², MAO Yi-lin², DING Shi-bo³, SONG Da-peng³, WANG Meng-qi³, DING Zhao-tang^1*
		DOI: 10.3964/j.issn.1000-0593(2024)01-0234-07
		Identifying the freezing injury of tea plants is the basis of evaluating the stress resistance of tea plants and guiding the overwintering management of tea plantations. The traditional method of tea plant freezing injury identification observes the number and degree of freezing injury of tea leaves manually, which has some disadvantages, such as low accuracy, low efficiency, strong subjectivity and so on. A framework for evaluating freezing injury of tea trees based on deep learning, wavelet transform, and visible spectrum is proposed. Firstly, we collected 1000 crown images of frozen tea trees, divided them into the training sets and test sets according to 4∶1, and labeled the frozen leaves in the training set images. Secondly, we use the Faster R-CNN network to identify and extract tea plants' frozen leaves, select three feature extractors: AlexNet, VGG19 and ResNet50 respectively, and select the feature extractor with the highest robustness as the backbone network. Then, the extracted image of frozen leaves of tea plants is enhanced by wavelet transform, and one low-frequency image and three high-frequency images are obtained. Then the images processed by wavelet transform and those not processed by wavelet transform are input into VGG16, SVM, AlexNet, ResNet50 and other networks to classify the frozen leaves, and the classification performance of the four networks is compared. Finally, according to the number of frozen damaged leaves, the degree of frozen damaged leaves and the weight coefficient of leaves with different degrees of frozen damage, the freezing degree of tea plants is scored to evaluate the overall freezing degree of tea plants. The results show that: (1) the Faster R-CNN model based on ResNet50 has the best performance in extracting frozen leaves of tea plants, with a precision rate of 93.33% and a recall rate of 92.57%, which is higher than the recognition performance of VGG19 and AlexNet as the backbone network, which can ensure that most frozen leaves can be extracted and provide a basis for further classification of the degree of freezing damage of leaves. (2) The overall accuracy of the VGG16 model in classifying leaves with different degrees of freezing injury is 89%, which is higher than that of other models (SVM, AlexNet, ResNet50), which shows that the vgg16 model has high robustness. (3) Compared with the frozen leaves without wavelet transform, the overall classification accuracy of the model can be improved by 2%~6%. It shows that wavelet transforms enhancement technology can improve the accuracy of the network. Therefore, this experimental framework can accurately and efficiently classify the frozen leaves of tea plants, which is of great value for evaluating the degree of freezing injury of tea plantations and provides technical support for the overwintering protection of tea plantations in the north.
		2024 Vol. 44 (01): 234-240 [Abstract] ( 62 ) RICH HTML PDF (34276 KB) ( 84 )

	241	Early Classification and Detection of Kiwifruit Soft Rot Based on Hyperspectral Image Band Fusion
		GAO Hong-sheng¹, GUO Zhi-qiang^1*, ZENG Yun-liu², DING Gang², WANG Xiao-yao², LI Li³
		DOI: 10.3964/j.issn.1000-0593(2024)01-0241-09
		Kiwifruit soft rot is the most serious fungal disease in the kiwifruit postharvest storage and sales process. It has a long incubation period, and it is difficult to classify it by manual screening when it does not show obvious symptoms in the early stage of infection. Therefore, hyperspectral imaging technology (470～900 nm) was used to study the early detection and identification of kiwifruit soft rot. In the experiment, 295 hyperspectral images of healthy kiwifruit and early and late kiwifruit infected with soft rot were collected, and the samples were divided into training set and test set samples according to 7: 3 by Kennard stone algorithm. Firstly, the region of interest of the samples was selected, and then the average spectrum of the region was taken as the original spectral curve of the sample. Principal component analysis(PCA), successive projections algorithm (SPA) and competitive adaptive reweighting sampling algorithm(CARS) were used to extract spectral features from original spectral curves. Secondly, non subsampled contourlet transform (NSCT) was used for band fusion of the 8 feature bands in the SPA solution process to obtain the fusion image, and then gray level co-occurrence matrix method (GLCM) was used to extract the texture features of the fusion image. Finally, the spectral features and texture features were fused, and the nearest neighbor algorithm (KNN), random forest (RF) and support vector machine(SVM) classification models were established respectively for the early classification and detection of kiwifruit soft rot. In addition, this paper also compares with the texture features extracted from principal component images or feature bands in other literatures. The main innovation of this paper is using NSCT to fuse the feature band images and then extract the texture features, which not only reduces the feature dimension and feature redundancy, but also integrates the complementary information of different band images to improve the classification accuracy. The experimental results show that SVM is the most suitable classifier for this study, and the classification results using spectral features or texture features alone are not satisfactory. However, the classification accuracy can reach 92.05% after the fusion of the two features. Most of the early samples of kiwifruit soft rot have been correctly identified, which indicated that the fusion of the two features obtained the different information of spectrum and image in hyperspectral images. It embodies the “spatial spectral unity” of hyperspectral images. In this study, a rapid and accurate non-destructive test was carried out on kiwifruit at the early stage of soft rot, which could provide some reference and guiding significance for the quality classification of kiwifruit after harvest.
		2024 Vol. 44 (01): 241-249 [Abstract] ( 61 ) RICH HTML PDF (14992 KB) ( 59 )

	250	Optical Design of Airborne Large Field of View Wide Band Polarization Spectral Imaging System Based on PSIM
		LI Xin-quan^{1, 2}，ZHANG Jun-qiang^{1, 3*}，WU Cong-jun¹，MA Jian^{1, 2}，LU Tian-jiao^{1, 2}，YANG Bin³
		DOI: 10.3964/j.issn.1000-0593(2024)01-0250-08
		According to the technical requirements of the large field of view and wide spectral band in polarization spectral imaging detection, a wide spectral band and large field of view polarization imaging spectrometer based on Polarimetric-spectral intensity modulation (PSIM) was designed. For the front telescope group, according to the achromatic analysis of existing domestic glass materials, the achromatic glass from visible to short-wave infrared is selected. By controlling the light angle of the PSIM module in the mirror group, the incident angle demand on the PSIM module in the large field of view is realized. Based on the results of the analysis, optical design software is used to optimize the design. The design results show that the front telescopic system can achieve high-quality imaging with a wavelength range of 400～1 700 nm, a field angle of 72°, a focal length of 20 mm, and an F-number of 4. The transfer function of the detector at the cut-off frequency in the full spectrum is better than 0.4, and the maximum incidence angle on the PSIM module is ±4.99°, effectively ensuring the consistency of polarization modulation in each field of view. The post-spectral spectroscopic system uses a convex grating based on the Offner structure. The optimization results show that the point array of each band is less than one pixel and the MTF of the central wavelength at the Nyquist frequency of the detector reaches 0.6, and all indicators meet the design requirements. This paper has important practical significance for the engineering of polarization spectral imaging instruments based on PSIM wide spectrum and also has certain guiding significance for the achromatic design of wide-spectrum optical systems.
		2024 Vol. 44 (01): 250-257 [Abstract] ( 66 ) RICH HTML PDF (11108 KB) ( 53 )

	258	A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion
		WANG Cai-ling¹，ZHANG Jing¹，WANG Hong-wei^2*, SONG Xiao-nan¹, JI Tong³
		DOI: 10.3964/j.issn.1000-0593(2024)01-0258-08
		Hyperspectral images contain abundant ground object information and are widely used in agriculture, industry, military and other fields. Therefore, its identification and classification is an important research topic. However, hyperspectral images have problems, such as high spectral dimension, large noise and limited labeled samples, so they have not achieved good classification results. This paper proposes a hyperspectral image classification model based on band clustering and multi-scale structural feature fusion (ASPS-MRTV). The method mainly includes the following steps. First, hyperspectral data is normalized, and the normalized 3D image is divided into n subspaces according to spectral dimension. Secondly, an adaptive subspace spectral feature extraction framework is constructed using the coarse and thin division strategy. Each spatial band is stretched into a one-dimensional vector, and then the similarity matrix of the band is constructed by using the information divergence. Then, the spectral band averages of each adaptive subspace were superimposed to form spectral features. Finally, the multi-scale relative total variation technique extracts structural features from the obtained spectral feature data. In order to enhance the linear separability of the samples, kernel principal component analysis was performed after data stacking to form the null spectral features. In the comparison experiment, SVM with penalty parameter C and kernel parameter of 24.5 were uniformly used for classification. After testing, the ASPS-MRTV network model achieves the overall classification accuracy of 97.06% and 98.98% on Indian Pines and University of Pavia datasets with 5% and 1% training samples, respectively. Experimental results show that compared with SVM, ASPS(ED), ASPS(ID), ASPS-LBP, ASPS-GLCM and ASPS-BF models, the proposed model achieves better classification performance and computational efficiency and effectively improves the classification accuracy of hyperspectral images under small samples.
		2024 Vol. 44 (01): 258-265 [Abstract] ( 57 ) RICH HTML PDF (13474 KB) ( 49 )

	266	Research on Band Selection of Visual Attention Mechanism for Object Detection
		YANG Guang¹, JIN Chun-bai¹, REN Chun-ying^2*, LIU Wen-jing¹, CHEN Qiang¹
		DOI: 10.3964/j.issn.1000-0593(2024)01-0266-09
		In recent years, band selection has been widely used in hyperspectral image dimensionality reduction processing. However, the commonly used data dimensionality reduction methods have not effectively utilized the information related to the human visual system. The research on target detection of hyperspectral images will undoubtedly have a considerable role in promoting. This paper proposes to apply the theory of visual attention mechanism to the study of band selection and constructs a band selection model of visual attention mechanism for target detection applications. By analyzing and calculating the identifiability of the target and background in the band map and quantifying the discrimination ability of the band to the ground object target and background, a band selection method based on the target visual identifiability is proposed. The LC saliency algorithm is used to analyze the visual saliency targets in the spatial domain, calculate the absolute value of the significance difference between the background and the target, and propose a band selection method based on the structural distribution of LC saliency targets. These two methods are combined with the improved subspace partition method proposed to establish a band selection model of visual attention mechanism for target detection. The model is verified by target detection experiments on hyperspectral remote sensing AVIRIS San Diego public dataset. The results show that the proposed band selection model based on the visual attention mechanism has a good detection effect for target detection applications and realizes data reduction and efficient computing processing.
		2024 Vol. 44 (01): 266-274 [Abstract] ( 61 ) RICH HTML PDF (27128 KB) ( 73 )

	275	The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf
		LIANG Shou-zhen¹, SUI Xue-yan¹, WANG Meng¹, WANG Fei¹, HAN Dong-rui¹, WANG Guo-liang¹, LI Hong-zhong², MA Wan-dong³
		DOI: 10.3964/j.issn.1000-0593(2024)01-0275-08
		Anthocyanin (Anth) is the third major group of leaf pigments. It can provide valuable information about plant physiology, and the information on the dynamics of their concentrations is a key to understand plants' the physiological reaction and resistance to different environmental stress factors brought about by episodic events or seasonal fluctuations. Traditionally, pigments are extracted from vegetation with spectrophotometry or high-pressure liquid chromatography, which are destructive and do not permit repeated measurements on the same samples. Optical methods monitoring plant physiological status through measuring leaf optical properties (absorbance or reflectance) possess several advantages over traditional destructive methods. However, there is a lack of research on the inversion of Anth through optical methods. Overlapping features in the specific absorption coefficient of pigments makes retrieving Anth content through remote sensing challenging. Understanding the relationship between Anth and leaf optical properties is necessary and helpful to estimate Anth content from leaves. This research used PROSPECT-D, a radiative transfer model including Anth parameter, to construct leaf spectral data in different parameter conditions. The global sensitivity analysis was conducted to quantify the influence of Anth on leaf optical properties through the modified Sobol method. we aimed to find sensitive Anth bands and calculate spectral indices relating to Anth content. Furthermore, inversing strategies based on hyperspectral narrow wavebands and spectral indices, including the Anthocyanin Reflectance Index (ARI) and modified Anthocyanin Reflectance Index (mARI), were discussed. The results showed that: (1) Anth can influence leaf optical properties in the 400~689 nm range, and leaf reflectance decreased in the visible band when Anth concentration increased. (2) The leaf reflectance in 467~589 nm was sensitive to the dynamics of Anth concentrations. There was the highest total sensitivity index of Anth at 509 nm. Chlorophyll and carotenoid had an impact on leaf reflectance in 467~589 nm. According to total sensitivity indices of chlorophyll and carotenoid, three spectral regions can be formed: 467~505 nm (influenced by carotenoids, chorophylls and Anth), 506~541 nm (influenced by Anth and carotenoids), 542~589 nm (influenced by chorophylls and Anth). (3) Anth concentration correlated best with leaf reflectance at 560 nm. Because of the pigments' overlapping absorption features, including chorophylls and carotenoids, Anth had better relationship with spectral indices than reflectance in individual narrow wavebands. Spectral indices partly removed other plant pigments' influence on plant leaf reflectance. Consequently, they can describe the dynamics of Anth concentrations accurately. This research will provide a theory method for remote sensing estimation of Anth content at the leaf scale.
		2024 Vol. 44 (01): 275-282 [Abstract] ( 63 ) RICH HTML PDF (4085 KB) ( 148 )

	283	A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery
		WU Hu-lin¹, DENG Xian-ming^1*, ZHANG Tian-cai¹, LI Zhong-sheng¹, CEN Yi², WANG Jia-hui¹, XIONG Jie¹, CHEN Zhi-hua¹, LIN Mu-chun¹
		DOI: 10.3964/j.issn.1000-0593(2024)01-0283-09
		Hyperspectral imagery cube Data can provide spatial information and diagnostic spectral characteristics, in the range of visible and near-infrared wavelength, about the attributes of materials in the scene, which contribute to the unique advantage of hyperspectral imagery for target detection. However, hyperspectral target detection has some shortcomings, such as the classical hyperspectral target detection algorithm only uses the information of spectral dimension to detect the target. The detection model either has insufficient accuracy for the construction of background high-dimensional f characteristics matrix or has high requirements for the completeness of background prior spectral characteristics, resulting in poor adaptability of the algorithm to different scenarios. Therefore, based on the classic multi-target detection algorithm-multiple target constrained energy minimization (MCEM), which has low computational complexity, fewer parameter requirements and better detection performance, this paper proposes a modified algorithm R-MCEM (revised MCEM) based on the separation model of target and background. First of all, this algorithm designs a pixel-by-pixel moving operation window that is similar to the shape and size of the target and sequentially calculates the spectral distances between each pixel and other pixels in the window, referred to as D1, and the spectral distances between the pixel and all targets,referred as D2. Next, all pixel values in the window are replaced with the pixel obtaining the minimum value of D1/D2. Then, move the window from left to right and from top to bottom and repeat the same calculation. Until the moving operation window traverses the entire hyperspectral image, all the interested targets in the hyperspectral image are eliminated as much as possible, and the accuracy of the background high-dimensional characteristics matrix is greatly improved. In this paper, the performance verification tests of the modified detection algorithm based on the true hyperspectral image data and the simulated image data are designed respectively, and the detection accuracy evaluation of the proposed algorithm is carried out by the three-dimensional receiver operation characteristics curve (3D ROC) combined with the Separation Degree Between Background and Target（SDBT）. The experimental results show that the proposed algorithm can effectively reduce the false alarm rate and improve the detection accuracy. The detection accuracy and SDBT based on the actual data are increased from 0.937 7 and 0.61 of the MCEM algorithm to 0.993 5 and 0.71 of R-MCEM, and the sub-pixel detection ability based on the simulated data is increased from 20% abundance of MCEM to 15% abundance of R-MCEM.
		2024 Vol. 44 (01): 283-291 [Abstract] ( 60 ) RICH HTML PDF (50424 KB) ( 126 )

	292	Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province
		WANG Lan-hua^{1, 2}, CHEN Yi-lin^1*, FU Xue-hai¹, JIAN Kuo³, YANG Tian-yu^{1, 2}, ZHANG Bo^{1, 4}, HONG Yong¹, WANG Wen-feng¹
		DOI: 10.3964/j.issn.1000-0593(2024)01-0292-09
		Jet, also known as coal jade, is a type of coal with scarce resources and high costs, and it can be used as a material for art and craft carvings. Six jet samples were collected from Fushun City, Liaoning Province and Jimsar County, Xinjiang Autonomous Region, to analyse their petrographic properties and quality (i. e., maceral composition, maximum huminite reflectance, proximate analysis, and ultimate analysis) as well as Raman spectroscopic characteristics. The results showed that the macerals of the six jet samples are composed of mainly huminite, followed by liptinite, and then inertinite with a shallow content; moreover, the maximum huminite reflectance (R_{o, max}) is observed to be 0.41%～0.55%. The liptinite in the Fushun jet is mainly composed of bituminite and sporinite, whereas the liptinite in the Jimsar jet is mainly composed of cutinite. The Raman spectra of the jet samples from these two regions are considerably different from each other because their Raman structural parameters are dependent on the maceral and coal quality. The intensity ratio (I_D₁/I_G) and area ratio (A_D₁/A_G) of D₁ and G peaks of the six jet samples increased substantially with increasing liptinite content and decreased significantly with increased huminite and inertinite contents. The I_D₁/I_G and A_D₁/A_G values of the Jimsar jet are considerably lower than those of the Fushun jet. It is because the Fushun jet contains a large number of bituminite and sporinite, and the aromatic ring growth exhibited by its molecular structure is poor. Additionally, the I_D₁/I_G and A_D₁/A_G values of the jet samples from the two regions showed a differential decreasing trend with an increase in the R_{o, max} value. The spatial arrangement of aromatic layers became more orderly with increased coalification degree. These results indicate that Raman spectroscopic parameters can effectively indicate the macromolecular structural difference observed in jet samples of different origins. Furthermore, this will provide a scientific basis for tracing the provenance of jet cultural relics in the future and provide insights into the development history of the handicraft industry and commodity circulation in ancient China.
		2024 Vol. 44 (01): 292-300 [Abstract] ( 140 ) RICH HTML PDF (32152 KB) ( 77 )

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