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2023 Vol. 43, No. 11 Published: 2023-11-01

	3321	Introduction to Perovskite Quantum Dots and Metal-Organic Frameworks and the Development of Composites
		LAI Niu, HUANG Qi-qiang, ZHANG Qin-yang, ZHANG Bo-wen, WANG Juan, YANG Jie, WANG Chong, YANG Yu, WANG Rong-fei^*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3321-09
		All-inorganic perovskite quantum dots CsPbX₃ (QDs) (X=Cl, Br, I) have a luminescence that can cover the entire visible light region (400~700 nm). A single luminescence peak is narrow and has a high quantum efficiency. Their emission wavelength and band gap can be regulated by regulating the halogen atom X in CsPbX₃. CsPbX₃ is widely used in the display field due to its excellent optical properties. However, it hinders its further applications. Metallic-organic framing (MOFs) are porous frame structures that serve as a matrix carrier to improve material stability due to their unique porous structure and permanent porosity. MOFs restricting QDs inside the subject can not only protect them from the external environment, isolating them from each other without reunion, but also realize various new features and applications. Composites (CsPbX₃ QDs @ MOFs) that restrict QDs to MOFs have better optical properties than CsPbX₃ QDs and better stability to the surrounding environment. Composites (CsPbX₃ QDs@MOFs) have many applications in optoelectronic devices, sensors, encryption, and anti-counterfeiting. The review begins with the CsPbX₃ QDs correlation structures, preparation, optical properties, applications, and existing problems. For example, in toxicity, stability, anion exchange and the corresponding solution, the structure, preparation, characteristics, and application of MOFs and further, the application of CsPbX₃ QDs@MOFs, such as white light diodes (WLEDs), security and encryption, used as catalyst, remote white light transmitter, and realize the wide gamut application, verify these new composite applications in backlight display. This paper presents some problems and needs for improvement in the composite CsPbX₃ QDs@MOFs, provides some ideas on the next direction of the composite materials, and makes prospects for the research prospect.
		2023 Vol. 43 (11): 3321-3329 [Abstract] ( 131 ) RICH HTML PDF (6258 KB) ( 126 )

	3330	Adaptive Weighted Spectral Reconstruction Method Against Exposure Variation
		LIANG Jin-xing^{1, 2, 3}, XIN Lei¹, CHENG Jing-yao¹, ZHOU Jing¹, LUO Hang^{1, 3*}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3330-09
		The surface spectral reflectance of the object is regarded as the fingerprint of its color, and at the same time, it is also an important feature to characterize the physical and chemical properties of substances. Multispectral imaging technology that is based on spectral reconstruction can overcome the dependence of RGB images on imaging conditions. Meanwhile, it can effectively improve the spatial resolution and acquisition efficiency of multispectral images and reduce equipment costs. Different from the principle of multispectral cameras, multispectral imaging based on spectral reconstruction first capture the digital images of the object using a digital imaging system, and then the corresponding multispectral images are reconstructed using spectral reconstruction methods. However, due to the mechanism of current spectral reconstruction methods, for both machine learning and deep learning methods, they are sensitive to exposure change of the image in practice. This means the spectral reconstruction model established at one exposure level cannot be directly used at another exposure level, or the curve shape of the reconstructed spectral reflectance will deviate from the ground truth. The sensitivity to exposure changes of current spectral reconstruction methods has limited their application in open environments with variable illumination intensity and inhomogeneity. To deal with the problems of current methods, an adaptive weighted spectral reconstruction method based on polynomial root expansion is proposed in this paper. In the proposed method, the raw RGB response of samples is firstly expanded by the root polynomial, and then the spectral reconstruction model is established by the pseudo-inverse algorithm. It will ensure the proposed method will be against the exposure changes. After that, an adaptive weighting matrix is constructed in the spectral invariant feature space to improve the spectral reconstruction accuracy further. The proposed method is verified and compared with the existing method through theoretical experiments and three sample sets. Results show that the existing spectral reconstruction methods are all sensitive to exposure change, and the proposed method can effectively adapt to the exposure change. The spectral root-mean-square error (RMSE) and the color difference (ΔE^*_ab) are significantly lower than existing methods. In addition, results indicate that constructing the adaptive weighting matrix in spectrally invariant feature space is crucial to improve the spectral reconstruction accuracy of the proposed method. The research results are important for high-precision multispectral image acquisition in the open environment.
		2023 Vol. 43 (11): 3330-3338 [Abstract] ( 170 ) RICH HTML PDF (5114 KB) ( 237 )

	3339	Research on the Influence of Lamp Structure of the Combined LED Broadband Light Source on Differential Optical Absorption Spectrum Retrieval and Its Removing Method
		ZHENG Ni-na^{1, 2*}, XIE Pin-hua¹, QIN Min¹, DUAN Jun¹
		DOI: 10.3964/j.issn.1000-0593(2023)11-3339-08
		The narrow emission spectrum of light emitting diode（LED) limits differential optical absorption spectroscopy（DOAS）retrieval range, and it is not easy to realize simultaneous measurement of various gases. In this paper, two kinds of ultraviolet LEDs are combined to form a combined LED broadband light source, which is applied to DOAS to simultaneously detect atmospheric SO₂ and O₃. The spectral analysis shows that their spectrum is superimposed in 280~295 nm and has an obvious lamp structure in 275~301 nm. The structure enhances and drifts to the shortwave direction with the increase of the dual-peak light intensity ratio. During actual measurement, the LED spectrum will change independentlydue to the environmental conditions. Moreover, atmospheric extinction is different in their emission spectral bands. Therefore, the dual-peak light intensity ratio ofthe atmospheric spectrum will change continuously and is inconsistent with the lamp spectrum. It is not easy to offset the lamp structure by dividing the two.The spectrum retrieval results show that the combined lamp structure as a reference spectrum can not fit well with the interference structure. In order to remove the influence of independent LED spectrum changes on spectrum retrieval during measurement, it is proposed to use each LED lamp structure as a reference spectrum to participate in the fitting. The fitting residuals of SO₂ and O₃ are reduced from 1% and 6‰ to about 4‰, respectively, and the interference structure is well removed.Compared with evading interference structure, the retrieval range of SO₂ and O₃ is broadened, and the number of SO₂ and O₃ absorption peaks in the retrieval range is increased by 1.75 and 1 time, respectively. The average fit errors of SO₂ and O₃ are reduced by 67.5% and 37.3% respectively. The measurement accuracy is significantly improved. The measuremens are compared with the SO₂ and O₃ levels measured by the traditional xenon lamp long path DOAS system. The comparison shows excellent agreements with Pearson correlation coefficients (R) of SO₂ and O₃ measurements above 95%. The results demonstrate that the lamp structure of a combined LED broadband light source can be fitted by the independent lamp structure of each LED in DOAS retrieval.
		2023 Vol. 43 (11): 3339-3346 [Abstract] ( 112 ) RICH HTML PDF (5985 KB) ( 155 )

	3347	A New Interim Connection Space MLabPQR for Spectral Image Compression and Reconstruction
		LÜ Cong¹, LI Chang-jun¹, SUN Hong-yan¹, GAO Cheng^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3347-04
		Multispectral images can carry more data information to represent color than common three channel images, which causes problems in storage space and communication. In order to solve the above problems, researchers propose to use an interim connection space (ICS). Multispectral data is compressed into the ICS before storage and transmission, spectral data is reconstructed from the ICS when needed, and the interim connection space determines the effect of the transition. Derhak et al. [JIST, 50: 53-63, 2006] proposed a 6 dimension ICS called LabPQR. First three dimensions of this space for a given spectral reflectance r are the tristimulus values XYZ (denoted by a column vector t) under a specified viewing condition (represented by a weighting table matrix H). The rest three dimensions is the combination coefficients, denoted by a column vector t_PQR, for the metameric black r_b under the first three main unit and orthogonal basis vectors, denoted as a matrix B, for the metameric black space, funded using principal component analysis. Here, the spectral decomposition gives the metameric black r_b based on the compressed tristimulus value vector t, i. e., r_b=r-Mt，where the mapping matrix M is the well-known “R-matrix”. The metameric black space consists of all metameric black r_b from the spectral image or an independent training reflectance dataset. The reconstructed reflectance r_p is simply given by Mt+Bt_PQR。In this paper, a new ICS is proposed and is named MLabPQR. The difference between MLabPQR and LabPQR is the choice of the mapping matrix M. For the proposed MLabPQR, the matrix M was chosen as the “Wiener estimation matrix”. The “Wiener estimation matrix” does not only depend on the viewing condition matrix H but also depends on the training reflectance dataset. Therefore, the choice of the Wiener estimation matrix can keep the main spectral information for the spectral image, which, we hope, can improve the spectral and colorimetric accuracies for the reconstruction. The proposed ICS was tested using the NCS reflectance dataset and a spectral image, and compared with other ICSs such as LabPQR, LabRGB, XYZLMS and LabW2P in terms of spectral accuracy measures (root mean square error (RMSE) and goodness of fit coefficient (GFC)) and colorimetric accuracy measure (CIELAB colour difference). All ICSs were trained using an independent Munsell reflectance and test datasets. Comparison results showed that our proposed ICS out performed all other ICSs in terms of both spectral and colorimetric accuracy measures. Hence, the proposed ICS is expected to find applications in spectral image compression and cross media reproduction.
		2023 Vol. 43 (11): 3347-3350 [Abstract] ( 147 ) RICH HTML PDF (946 KB) ( 75 )

	3351	Detection of Benzene Concentration by Mid-Infrared Differential Absorption Lidar
		DUAN Ming-xuan¹, LI Shi-chun^{1, 2}, LIU Jia-hui¹, WANG Yi¹, XIN Wen-hui^{1, 2}, HUA Deng-xin^{1, 2}, GAO Fei^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3351-09
		Benzene is an important component of volatile organic compounds (VOCs), and its pollution of the atmosphere has attracted increasing attention. The mid-infrared band is usually the fundamental frequency fingerprint absorption region of molecules, so it has become an important band for detecting trace gas molecules. Moreover, the differential absorption lidar is an important means of detecting atmospheric trace gases. Therefore, aiming at the problem of real-time remote sensing of regional benzene concentration, an integral path differential absorption (IPDA) lidar for detecting atmospheric benzene concentration based on inter-band cascade lasers (ICLs) is proposed. Firstly, we construct the retrieval algorithm of IPDA lidar and its error analysis model based on analyzing the detection principle of IPDA lidar. Secondly, the absorption spectra of benzene and major interfering gases (such as HCl, CH₄ and H₂O) near the mid-infrared vicinity region of 3 100 cm^-1 from the HITRAN database are analyzed in detail. By considering comprehensively the influence of HCl, CH₄ and H₂O on the detection results, the measurement wavelength and reference wavelength of the IPDA lidar are selected to be 3 090.89 and 3 137.74 cm^-1 respectively. Thirdly, we designed an IPDA lidar for detecting atmospheric benzene concentration based on two continuous-wave ICLs. The output wavelengths of these ICLs can be tuned by controlling the temperature and driving curren, so that their wavelengths can be stabilized in the strong absorption spectrum region and the weak absorption spectrum region respectively. And then, a spectroscopic system with a mid-infrared diffraction grating as the core is designed to realize synchronous detection of dual-wavelength receiving signals. Finally, combined with the mid-latitude standard atmospheric model, the performance of lidar under the conditions of different visibilities, path lengths, and water vapor concentrations is analyzed and discussed. And then, we carry out test experiments by building a mid-infrared band detection gas cell to verify the feasibility of the IPDA lidar. These results from simulations and experiments show that the relative error of benzene concentration is less than 10% within the concentration-path length product (CL) range of 0.1～24 mg·m^-3·km, and the relative error of detection is better than 1%, while the CL of benzene is 5 mg·m^-3·km, under the condition of atmospheric visibility of 5 km, and the water vapor concentration of less than 0.4%; and that the linear correlation coefficient R² of differential absorption lidar detection in the mid-infrared band is about 98.7% by preliminary experiments.
		2023 Vol. 43 (11): 3351-3359 [Abstract] ( 135 ) RICH HTML PDF (4818 KB) ( 50 )

	3360	Porosity Measurement of Tablets Based on Continuous Terahertz Wave
		WU Jing-zhi^{1, 2}, ZHOU Si-cheng³, JI Bao-qing¹, WANG Yan-hong^{1, 2*}, LI Meng-wei^{2, 3}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3360-05
		The ratio of the pore volume in the drug tablet to the total volume of the tablet in the natural state is called porosity. In the production process of drug tablets, due to the physical and chemical properties of raw materials, human factors and environmental factors, the formation of pores is inevitable. Porosity is an important characteristic of drug tablets. The porosity size will affect the disintegration, dissolution and bioavailability of tablets. At present, the common methods for measuring the porosity of tablets, such as mercury intrusion method, helium specific gravity method, infrared spectroscopy, etc., can not achieve nondestructive and rapid detection of the porosity of tablets. For this reason, this paper proposes a method to detect the porosity of a single drug tablet by using continuous terahertz waves. Two standard planar drug tablets are used as research objects respectively. The signal transmitted through each tablet is measured in the frequency range of 500~750 GHz using vector network analyzer, and the packaging phase value of each tablet is extracted from the measured S parameters. Then phase unwrapping and correction are carried out to obtain the true phase value of the tablet, and the effective refractive index of the tablet is obtained by calculating the phase difference between the tablet and the air. At the same time, the theoretical model of zero porosity approximation (ZPA) is used to link tablets' effective refractive index and porosity. The relative errors between the calculated porosity of the two tablets measured by the vector network analyzer and the standard porosity measured by the gas displacement method are 7.3% and 5.3% respectively. The experimental results show that measuring the tablets' porosity using continuous terahertz wave is feasible. The THz wave method for measuring the porosity of tablets is simple, practical, non-destructive and fast, which lays a foundation for rapid, sensitive and non-destructive porosity measurement in the future pharmaceutical tablet manufacturing and production.
		2023 Vol. 43 (11): 3360-3364 [Abstract] ( 137 ) RICH HTML PDF (2234 KB) ( 42 )

	3365	Room Temperature Synthesis of Polychromatic Tunable Luminescent Carbon Dots and Its Application in Sensitive Detection of Hemoglobin
		HE Yan-ping, WANG Xin, LI Hao-yang, LI Dong, CHEN Jin-quan, XU Jian-hua^*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3365-07
		Fluorescent carbon dots (CDs) with long wavelength emission have attracted increasing attention due to their promising application prospects in biological fields. CDs with long wavelength emission have been synthesized mainly with high temperature and high pressure, while their synthesis at room temperature is relatively rarely studied. In this paper, blue-green luminescent tunable CDs were prepared by alkaline catalysis, and only two steps were required. The fructose and sodium hydroxide solutions were mixed firstly, followed by dialyzing without any additional energy input or external heating. The synthesized CDs were studied by transmission electron microscopy, steady-state fluorescence, and UV-Vis absorption spectroscopy. Moreover, circular dichroic spectrophotometer and picosecond time-correlated single photon counting system was used to analyze the interaction mechanism between CDs and bovine hemoglobin (BHb). Although there have been some studies about the detection of BHb using carbon dots, previous studies mainly focus on the detection of BHb rather than the fluorescence quenching mechanism of carbon dots. Stern-Volmer imagesof the interaction and the influence of BHb on the fluorescence lifetime of CDs have been measured, implying the contribution of static fluorescence quenching. In this process, the content of the α-helical structure of BHb has decreasedby about 3%, demonstrating that the secondary structures of BHb were changed after interacting with CDs. With the addition of BHb, the complexes of BHb and CDs were formed so the fluorescence of CDs decreased. Moreover, the addition of interference samples in the experiment of BHb detection confirmed that the proposed CDs were highly selective, and the variation of reaction times further revealed that the CDs had high stability. It is noticed that, when mixed with BHb, the fluorescence intensity of CDs decreased gradually, and the proportion of decline was linearly related to the concentrations of BHb. Therefore, the biosensors based on CD fluorescence quenching could be established for the specific detection of trace BHb with a linear concentration range from 0 to 5 μmol·L^-1 and a detection limit of 243 nmol·L^-1 (S/N=3). The CDs were also excited with different wavelengths at 370 and 425 nm, and the results proved that the fluorescence intensities of CDs had a good linear relationship with the concentrations of BHb for both excitation wavelengths, which might provide more application values for the detection of BHb. Due to its convenient synthesis, simple operation and easy availability, the proposed CD probe is of great significance in life sciences and criminal investigation.
		2023 Vol. 43 (11): 3365-3371 [Abstract] ( 141 ) RICH HTML PDF (5216 KB) ( 87 )

	3372	Geographic Origin Discrimination of Wood Using NIR Spectroscopy Combined With Machine Learning Techniques
		LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin^*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3372-08
		The illegal logging of valuable tree species is mainly motivated by the global market that consumes logs, lumber, veneers, and furniture. Rapid and reliable identification of the country of origin of protected timbers is one of the measures for combating illegal logging. There is a global need to create a wood origin identification system to ensure the integrity of wood supply and control the trade, exploitation, and smuggling of these products. Near-infrared spectroscopy (NIRS) is a promising technique for calibration-based and rapid species identification. In the present work, Near-Infrared Spectroscopy combined with machine learning techniques were used to discriminate six wood species (Pinus massoniana, Paulownia fortunei, Zelkova schneideriana, Tectona grandis, Tilia amurensis, Ailanthus altissima) originating from two regions. The initial step was to create a spectral dataset of tree origins by collecting spectral data on these six wood species from two distinct origins, each constituting a dataset. Then, reduce feature dimensionality to two dimensions to investigate the data distribution across datasets. Secondly, the high-dimensional spectral data were dimensionally reduced using principal component analysis and linear discriminant analysis, respectively, to improve the model's generalization and to compare the effects of the two techniques on the model's accuracy. Finally, six different machine learning, namely, Support vector machine, Logistic regression, K-Nearest neighbors, Naïve Bayes, Random Forest, and Artificial neural network, were used to train these wood samples' spectra and assess their discrimination performance. The results showed that the highest accuracies of Pinus massoniana, Paulownia fortunei, Zelkova schneideriana, Tectona grandis, Tilia amurensis, Ailanthus altissimaare 98.3%, 100%, 100%, 100%, 100%, 98.3%, and the fastest operation speed are 0.183, 0.182, 0.181, 0.182, 11.424 and 12.969 s respectively. We evaluated and compared the performance of six models based on different machine learning algorithms to predict the geographic origin of the wood. Compared to the other five models, the best results were obtained by the Artificial neural network approach, but its running time is more than other algorithms, and requires a higher number of tuned and optimized parameters. Moreover, both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying NIRS assisted by machine learning technique is suitable for the rapid identification and discrimination of wood origin and can be an essential tool for tracing the origins of wood, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.
		2023 Vol. 43 (11): 3372-3379 [Abstract] ( 162 ) RICH HTML PDF (3068 KB) ( 243 )

	3380	Accurate Semi-Empirical Potential Energy Function, Ro-Vibrational Spectrum and the Effect of Temperature and Pressure for ¹²C¹⁶O
		CHEN Heng-jie, FANG Wang, ZHANG Jia-wei
		DOI: 10.3964/j.issn.1000-0593(2023)11-3380-09
		With the rapid development of high-supersonic aircraft, non-contact diagnosis technology, etc，more molecular bands are excited, and the demand for rovibrational spectrum data under high temperature and high pressure has increased dramatically. In addition, with the rapid improvement of the cavity ring-down spectroscopy (CRDS), and the tunable semiconductor laser absorption spectrum technology (TDLAS) with high sensitivity, the research on the corresponding spectrum are promoted further accordingly. CO, an important product of high-temperature combustion, is the first to be investigated. In this paper, firstly, the local discrete potential energy points near the molecular equilibrium internuclear separation were obtained employing the Rydberg-Klein-Rees (RKR) method, with the use of spectral parameters of the ¹²C¹⁶O on the ground state (vibrational quantum number ν<41) determined by the experimental. Then it is fitted to more than ten common analytical potential functions, and it is shown that the SPF and Morse functions have good fitting accuracy, but they are still unreasonable on the long-range part. Because of this, the dissociation energy from the experiment is adopted for revising the long-range part, and a new, semi-empirical, global potential function named Revised-Morse was constructed, which not only could accurately reproduce the known vibrational levels but reasonably predict the unknown high vibrational levels with accurate dissociation limit. The multi-reference configuration interaction method (MRCI) was used confirmed its rationality. The levels with high ν calculated in this paper agree with the result from the literature. Secondly, the electronic dipole moment surfaces (DMs) of ¹²C¹⁶O on the ground state at vibrational quantum number ν<63 under three kinds of electric fields were obtained using the multi-reference averaged coupled-pair functional (ACPF) theory combined with differential technology. Based on the above Revised-Morse potential function and DMs, the vibrational and transition levels up to the dissociation limit, and the transition moment, line strength, Einstein coefficient and intensity at room temperature with ν<63 were obtained by solving the one-dimensional Schrodinger equation, meanwhile, the spectral constants such as radiation lifetime and centrifugal distortion were also obtained. The calculated values are almost completely consistent with the results from the HITRAN. This paper not only reproduces the known spectral bands perfectly but also predicts hundreds of thousands of new spectral lines and some new spectral parameters, which can provide a reference for spectral detection. To establish the temperature measurement model based on ¹²C¹⁶O, the partition function of temperature below 9 000 K was further investigated, and the rovibrational spectra at different temperatures were simulated. The variation of the spectral line with temperature was illustrated by the line spectrogram below 20 000 cm^-1 (logarithmic coordinate) and ν_0-1 band (linear coordinate). Several possible temperature measurement model schemes are proposed. Finally, the influence of pressure on the rovibrational line is discussed.
		2023 Vol. 43 (11): 3380-3388 [Abstract] ( 108 ) RICH HTML PDF (4357 KB) ( 29 )

	3389	*Traceability of Geographical Origin of Jasmine* Based on Near Infrared Diffuse Reflectance Spectroscopy**
		ZHANG Shu-fang¹, LEI Lei², LEI Shun-xin², TAN Xue-cai¹, LIU Shao-gang¹, YAN Jun^1*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3389-07
		The quality of jasmine flowers in terms of flavour, medicinal and nutritional uses is influenced by the factors of its origin. Hence, the origin traceability of jasmine flowers is of great significance in protecting the rights and interests of consumers and promoting the healthy development of the jasmine industry. In order to discriminate the geographical origin of Jasmine, a hundred Jasmine samples from four main producing districts, including Hengzhou of Guangxi, Qianwei of Sichuan, Fuzhou of Fujian and Yuanjiang of Yunnan, were collected. Near-infrared spectra, (900~1 700 nm) of those samples were acquired using integrating sphere and fibre-optics probes. Savitzky-Golay (SG) spectral smoothing and multivariate scatter Correction (MSC) were used for spectral pre-processing. After the spectral pre-processing, a jasmine origin discriminant model was developed using PCA combined with linear discriminant analysis (LDA) and k-nearest neighbor (KNN). In the modelling process, 68 samples were used as the training set and 32 samples were used as the test set, and the model parameters were optimised by interaction tests. The results show that the discriminant models based on both PCA-LDA and PCA-KNN have good prediction ability, in which the prediction accuracy of both methods reaches 100% for the spectral data obtained by integrating sphere sampling, and the prediction accuracy of PCA-LDA and PCA-KNN for the spectral data obtained by fiber optic probe sampling is 100% and 93.75% respectively. Finally, a comparative analysis of the chromatographic fingerprint profiles of jasmine flowers from different origins further elucidated the material basis for identifying jasmine origins based on NIR spectroscopy. Thus, this work provides a fast, environmentally friendly, and accurate method to trace the geographical origin of Jasmine, which is meant for the protection of the place of origin for Jasmine.
		2023 Vol. 43 (11): 3389-3395 [Abstract] ( 134 ) RICH HTML PDF (4386 KB) ( 50 )

	3396	Non-Destructive Monitoring Model of Functional Nitrogen Content in Citrus Leaves Based on Visible-Near Infrared Spectroscopy
		YANG Qun^{1, 2}, LING Qi-han¹, WEI Yong¹, NING Qiang^{1, 2}, KONG Fa-ming¹, ZHOU Yi-fan^{1, 2}, ZHANG Hai-lin¹, WANG Jie^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3396-08
		Citrus is the largest kind of fruit in China. Nitrogen is very important for the growth and development of citrus. Real-time and non-destructive monitoring of the nitrogen status of citrus is of great significance for accurate management of nitrogen nutrients. Nitrogen in plants can be divided into assimilable nitrogen, structural nitrogen and functional nitrogen. The content of each component of different forms of nitrogen in citrus leaves has a certain indicative effect on the physiological and biochemical reactions of leaves. Among them, the content of functional nitrogen is an important indicator of nitrogen nutrition status in citrus. “Chunjian” orange was used as the experimental material in this study. The reflectance spectra of citrus leaves under different nitrogen treatments were measured by the visible-near infrared spectrometer at the fruit swelling period and fruit coloring period, and the functional nitrogen content in leaves was determined by chemical analysis. The correlation between the original spectrum, first-order differential spectrum and the functional nitrogen content of leaves at the fruit swelling and fruit coloring periods of citrus was analyzed, and the sensitive bands were selected. The non-destructive monitoring model of the functional nitrogen content of leaves at the fruit swelling period and fruit coloring period of citrus was constructed by using the full-band and sensitive bands, combined with the spectral vegetation index method, spectral chemical measurement method and machine learning method, and the effects of various spectral variants and spectral preprocessing methods on the accuracy of the model were compared and analyzed. The results showed that the non-destructive monitoring model of functional nitrogen content in citrus leaves constructed by standard normal variate transformation pretreatment of the full-band original spectrum combined with the backpropagation neural network had high accuracy during thefruit swelling period. The calibration set determination coefficient R²_c and validation set determination coefficient R²_v were all 0.78, and the RMSEC and RMSEV of the modeling set were all 0.82 g·kg^-1. The model accuracy based on the original spectrum of the sensitive band combined with the random forest was also high, with R²_c and RMSEC were 0.84 and 0.67 g·kg^-1, R²_v and RMSEV were 0.74 and 0.83 g·kg^-1, respectively. In the fruit coloring period of citrus, the full-band original spectrum was preprocessed by standard normal variate transformation. The accuracy of the non-destructive monitoring model of functional nitrogen content in citrus leaves constructed by BPNN was high, with R²_c and RMSEC was 0.77 and 1.04 g·kg^-1, R²_v and RMSEV were 0.76 and 1.13 g·kg^-1, respectively. The study has shown that visible-near infrared spectroscopy can achieve non-destructive monitoring of functional nitrogen content in citrus leaves.
		2023 Vol. 43 (11): 3396-3403 [Abstract] ( 110 ) RICH HTML PDF (3643 KB) ( 198 )

	3404	*A C/N Ratio Estimation Model of Camellia Oleifera* Leaves Based on Canopy Hyperspectral Characteristics**
		FU Gen-shen¹, LÜ Hai-yan¹, YAN Li-peng¹, HUANG Qing-feng¹, CHENG Hai-feng², WANG Xin-wen³, QIAN Wen-qi¹, GAO Xiang⁴, TANG Xue-hai^1*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3404-08
		Leaf C/N ratio is an important indicator reflecting the individual nutrient utilization efficiency of Camellia oleifera. Estimating C/N ratio based on canopy hyperspectral characteristics can provide important theoretical basis for nutrient monitoring and precise fertilization of Camellia oleifera. There are very limited studies on non-timber product forests' physical and chemical properties -using hyperspectral data, especially for Camellia oleifera with the synchronous biological characteristics of flowers and fruits. In addition to the collinearity problem, its complex physical and chemical properties pose great challenges to the response of sensitive spectral characteristics and the construction of estimation models. In this study, the Changlin series of Camellia oleifera in the Huanagshan area of Anhui Province was taken as the research objects. The canopy spectra of 120 Camellia oleifera plants were collected in the field, and the hyperspectral characteristics of the 400~1 000 nm wavelength range in the visible and near-infrared spectral regions were selected for analysis. Original hyperspectral data were processed by using multiplicative scatter corrections (MSC) and first derivative (FD) transformations, and three types of two-band indices (i.e., difference index-DI, ratio index-RI, and normalized difference index-NDI) were constructed respectively. Correlation analysis was used to observe the changes inspectral response feature regions under different processing methods. Response variables were extracted by variable combination population analysis (VCPA), and an optimal feature variable subset was obtained by removing collinearity to construct three machine learning models (i.e., random forest-RF, support vector machine-SVM and BP neural network-BPNN). Finally, the effects of spectral parameters on model estimation accuracy under different treatments were compared, and the optimal estimation model of the C/N ratio of Camellia oleifera leaves was identified according to model evaluation indices. Results showed that: (1) The original spectrum after MSC or FD feature transformation combined with VCPA can mine more potential variables. (2) The combination of a two-band spectral index expands the response region of sensitive bands and further enhances the ability of VCPA to select characteristic variables. FD-RI and FD-NDI are with the best treatment effect. (3) The overall accuracy of the three machine learning models ranked indescending order were BPNN>RF>SVM. Among all models, the BPNN model constructed by FD-NDI spectral parameters has the best prediction ability performance. The determination coefficient (R²) of the training and test sets are 0.71 and 0.66, respectively, and the relative percent difference (RPD) is 1.74. This study established an optimal BPNN estimation model for the C/N ratio of Camellia oleifera leaves in the harvest period, which expands the application range of hyperspectral of Camellia oleifera leaves.
		2023 Vol. 43 (11): 3404-3411 [Abstract] ( 140 ) RICH HTML PDF (4948 KB) ( 42 )

	3412	Analysis on the Spectral Characteristics and Origin of “Binary” Formula of Jingdezhen Traditional Ceramic Body
		MO Yun-jie^{1, 2}, CAO Chun-e^1*, HAN Guang-da¹, ZHENG Nai-zhang¹
		DOI: 10.3964/j.issn.1000-0593(2023)11-3412-07
		Jingdezhen porcelain plays an important role in the development history of Chinese ceramics. According to the research, Jingdezhen ceramics were made of porcelain stone as raw material before the Song and Yuan Dynasties, namely the “single-unit” formula. After the Yuan Dynasty, a “binary” formula by adding kaolin to the “single-unit” formula was created. From the Five Dynasties to the Southern Song Dynasty, the “single-unit” formula has been widely used, and the technology level has made the quality of Jingdezhen white porcelain reach the standard of modern fine porcelain. Since this is the case, why was kaolin introduced into the blank formula in the Yuan Dynasty? Besides, it would lead to an increase in technical difficulty and cost. Is the reason for the emergence of the “binary” formula inevitable or accidental? In this paper, by combing a large number of literature, simulating experiments and modern test methods such as wavelength dispersive X-ray fluorescence spectrometer (XRF), X-ray diffraction (XRD) and scanning electron microscope (SEM), the advantages of “binary” formula and the historical necessity of introducing kaolin were discussed from the social, technical and economic vision at that time. The results were as follows: Socially, due to the Yuan Dynasty government's political diplomacy, military war, foreign trade management system, and living customs and religious beliefs of the Mongolian nationality and Muslims different from Han nationality, there was a new demand for big porcelain. However, the increasing size was easy for deformation and cracking, creating the conditions for finding a better raw material-kaolin. Technically, first of all, kaolin can be directly used by panning, which has great advantages as a raw material compared to the porcelain stone that needed to be pounded and panned many times. Secondly, the body of the “binary” formula can overcome the disadvantages of large temperature difference between the front and back positions of the kiln high and uncontrollable firing temperature. Finally, compared with the body of the “single-unit” formula, introducing kaolin into the porcelain body had some obvious merits including higher temperature and wider firing range, higher strength, less deformation because of the increase of aluminum content, the complexity of composition, fine particle size and fully developed mullite quantity. Those were beneficial for kiln workers controlling and mastering the production of ceramics. Economically, with the emergence of the “binary” formula, the production of Jingdezhen porcelain no longer used single porcelain stone, which not only broadened the range of raw materials but also extended the ceramic industry chain, thus increasing the market competitiveness of the products. To sum up, it can be concluded that the emergence of “binary” formula technology is due to the above three factors, and its emergence is inevitable in history.
		2023 Vol. 43 (11): 3412-3418 [Abstract] ( 155 ) RICH HTML PDF (5065 KB) ( 85 )

	3419	*Classification of Different Maturity Stages of Camellia Oleifera* Fruit Using Hyperspectral Imaging Technique**
		YUAN Wei-dong^{1, 2}, JU Hao², JIANG Hong-zhe^{1, 2}, LI Xing-peng², ZHOU Hong-ping^{1, 2*}, SUN Meng-meng^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3419-08
		Camellia oleifera fruit is widely planted in hilly and mountainous areas in southern China. The harvest time of Camellia oleifera fruit is currently decided by solar terms and experience, and the prematurity or too late picking will bring economic losses. This study aimed to explore the feasibility of hyperspectral imaging (HSI) technology to identify the maturity stages of Camellia oleifera fruit accurately. The HSI system with a spectral range of 400～1 000 nm was applied to collect hyperspectral images of 480 Camellia oleifera fruit samples at different maturity stages. PLS-DA and PSO-SVM models were individually developed based on spectra preprocessed with five different pretreatments including SNV, SNV-detrend, SG, first-order derivative and second-order derivative. The optimal preprocessing method was selected and further used in feature wavelength screening. Consequently, it was found that the simplified model built by feature wavelengths selected using CARS gave better performance compared to SPA. The classification accuracies of CARS-PLS-DA and CARS-PSO-SVM models in the prediction set were 92.5% and 89.2%, respectively, and the kappa coefficients were above 0.86. Furthermore, color features were extracted from the hyperspectral images by color moment approach, and PLS-DA and PSO-SVM models were built based on the combination of color features and feature wavelengths. Then, the performance of the models built by feature wavelengths screened by CARS was still found to be the best with classification accuracies of 94.2% and 93.3% for CARS+color-PLS-DA and CARS+color-PSO-SVM models in the prediction set, respectively. The models developed by combination features showed better classification results than models based on wavelengths alone, and the classification accuracies were improved by 1.7% and 4.1% in the prediction set, respectively. The optimal CARS+color-PLS-DA model gave the best predicted performance with its Kappa coefficient of 0.923 1. As a result, our work indicates that the application of HSI technology combined with chemometric methods can be used to identify the maturity stages of Camellia oleifera fruit, which provides a rapid, nondestructive and accurate way in Camellia oleifera fruit maturity detection.
		2023 Vol. 43 (11): 3419-3426 [Abstract] ( 150 ) RICH HTML PDF (4044 KB) ( 85 )

	3427	Background-Free Development of Latent Fingerprints on Fluorescent Substrates
		DING Han
		DOI: 10.3964/j.issn.1000-0593(2023)11-3427-09
		In order to significantly increase the signal-to-noise ratio of fingerprint development, in this paper, the background-free development of latent fingerprints based on NaYbF₄∶Ho upconversion luminescent powders (UCLPs) and SrAl₂O₄∶Eu, Dy afterglow luminescent powders (AGLPs) were proposed, and the contrast between developed fingerprint (signal) and background substrate (noise) was also quantified using spectral analysis. First, NaYbF₄∶Ho UCLPs and SrAl₂O₄∶Eu, Dy AGLPs were synthesized via solvothermal and combustion approaches , respectively. Then, the morphologies, crystal structures, absorption spectra, and luminescent properties of the above two powders were characterized. NaYbF₄∶Ho UCLPs were cylindrical shaped nanomaterials with hexagonal crystal structure, had a maximum near-infrared (NIR) absorption wavelength of 976 nm, and could emit green upconversion luminescence at the wavelength of 539 nm under 980 nm NIR excitation. While, SrAl₂O₄∶Eu, Dy AGLPs were polyhedral shaped micromaterials with monoclinic crystal structure, had a maximum ultraviolet (UV) absorption wavelength of 331 nm, and could emit green afterglow luminescence at the wavelength of 515 nm under 365 nm UV excitation. Finally, latent fingerprints on various fluorescent substrates were stained using NaYbF₄∶Ho and SrAl₂O₄∶Eu, Dy dry powders, followed by fluorescently enhanced via upconversion luminescence mode and afterglow luminescence mode, respectively, and thus the background-free development of fingerprints was achieved. In addition, the signal-to-noise ratio in fingerprint development was subjectively evaluated and objectively analyzed by vision effect and spectral analysis, respectively. Fingerprint development results showed that developed fingerprints could emit bright green luminescence in the dark field, producing enough color contrast between the fingerprint and the substrate. The aid of upconversion luminescence mode or afterglow luminescence mode could avoid the interference of background noise. Spectral analysis showed that the intensity contrast between the developing signal and the background noise was significant, resulting a high signal-to-noise ratio. Compared with the normal fluorescence mode based on traditional fluorescent powders, the upconversion luminescence mode and afterglow luminescence mode had an outstanding advantage of ultra-high signal-to-noise ratio in latent fingerprint development. Our proposed UCLP-based upconversion luminescence mode and AGLP-based afterglow luminescence mode have achieved the background-free development of latent fingerprints, which will not only expand the applications of rare earth luminescent powders but also broaden the innovative ideas for fingerprint development.
		2023 Vol. 43 (11): 3427-3435 [Abstract] ( 137 ) RICH HTML PDF (7308 KB) ( 31 )

	3436	Determination of Mn, Co, Ni in Ternary Cathode Materials With Homologous Correction EDXRF Analysis
		LIN Hong-jian¹, ZHAI Juan^1*, LAI Wan-chang¹, ZENG Chen-hao^{1, 2}, ZHAO Zi-qi¹, SHI Jie¹, ZHOU Jin-ge¹
		DOI: 10.3964/j.issn.1000-0593(2023)11-3436-09
		Lithium-ion batteries play an important role in developing and applying new energy. The composition ratio of ternary cathode materials has a major impact on the performance and quality of lithium-ion battery products, and production control requires timely and accurate control of composition changes in the mix. Energy dispersive X-ray fluorescence (EDXRF) technology has a good prospect for rapid analysis in this area, but the analytical accuracy of commercial instruments cannot currently meet production requirements. To solve the technical problem of high-precision EDXRF analysis of ternary cathode material composition Mn, Co, Ni, a self-calibrating formal EDXRF analysis technology based on homologous excitation is proposed in this paper, which uses a tungsten target X-ray tube (25 kV/400 μA) and two electrically cooled SDD detectors with the energy resolution of 135 eV (@5.9 keV) to form a two-channel synchronous X-ray fluorescence excitation and detection device. After splitting the primary X-rays emitted by the X-ray tube by the dual channel collimator, the calibration sample and the sample to be measured are excited. The two detectors simultaneously measure the fluorescence counts of the two samples and use the energy spectrum data of the standard sample to perform “normalization” processing to achieve synchronous correction of the energy spectrum data of the sample to be tested, thus reducing the influence of X-ray tube instability in the analytical instrument. The stability instrument was analyzed regarding the count decay rate, count variation, and overall effect through 140 repeatability tests in 8 hours and compared with that of the single optical path. The relative standard deviation and maximum relative deviation were used as evaluation indices to assess the stability instrument. The counting attenuation rate decreased from -0.049 3%·h^-1 of the single optical path to 0.001 0%. For 11 data points with large fluctuations, the relative standard deviation decreased from 0.151 4% to 0.032 6% of the single optical path, indicating that the self-calibrating formal EDXRF analysis technology of homologous excitation can effectively reduce the effects of counting attenuation and primary X-ray energy spectrum fluctuations. From the perspective of comprehensive effect, the relative standard deviation and maximum relative deviation of Mn, Co, and Ni are 0.076% and 0.170%, respectively, after the correction of synchronous data, which is twice as stable as that of the single optical path. The paper establishes a mathematical model for quantitative analysis based on dual optical path EDXRF analysis. Through experimental verification, the absolute errors of Mn (17.361%~20.016%), Co (12.991%~14.965%), Ni (29.653%~33.065%) in powder compacted samples are not more than -0.072%, -0.061%, 0.098%, respectively, and the analysis time of single sample is 200 s, indicating that the self-calibration formal EDXRF analysis technology with homologous excitation can effectively improve the instrument analysis accuracy, and achieve fast accurate testing requirements.
		2023 Vol. 43 (11): 3436-3444 [Abstract] ( 125 ) RICH HTML PDF (5232 KB) ( 59 )

	3445	A SERS-Aptsensor for Detection of Chloramphenicol Based on DNA Hybridization Indicator and Silver Nanorod Array Chip
		GUO He-yuanxi¹, LI Li-jun^1, FENG Jun^{1, 2}, LIN Xin¹, LI Rui¹
		DOI: 10.3964/j.issn.1000-0593(2023)11-3445-07
		Chloramphenicol (CAP) is a synthetic antibiotic that inhibits protein synthesis by binding to the ribosomes of bacteria to achieve the purpose of antibacterial. Long-term intake of residual CAP animal-derived food can lead to anemia and leukemia in the human body and can also cause the body to develop drug resistance, which will seriously endanger human health. Many countries have regulations prohibiting the detection of CAP in livestock products. Therefore, designing a more rapid, simple and highly specific CAP detection method is of great significance. In this paper, the thiolated aptamer of CAP (SH-Apt) was used to modify the silver nanorod array chip(chip)as the SERS substrate, and the DNA hybridization indicator methylene blue (MB) as the Raman reporter, A novel high-specificity CAP-SERS aptamer sensor was constructed by utilizing the competitive binding relationship between CAP, CAP aptamer complementary strand (cDNA) and CAP aptamer (Apt). The chip and CAP-SERS aptamer sensor were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and EDS spectroscopy. The results showed that a large amount of silver elements were uniformly distributed on the surface of the chip and the CAP-SERS aptamer sensor was successfully prepared. Detection of CAP standards at room temperature, the results of the sensor-related performance analysis suggest that, with an increase of CAP concentration (0.001～10 ng·mL^-1) caused the decreased SERS signal at 1 624 cm^-1(I_SERS) intensity (I_SERS=-971logc+1 983). A good negative correlation (R²=0.991) was achieved, and the limit of detection (LOD) was as low as 0.2 pg·mL^-1 (S/N=3), the Raman enhancement factor EF=1.01×10⁷. Further indicating that the substrate has good Raman enhancement. The sensor is used to detect individual CAP tablets and CAP in human and pig sera, spik experiments were performed, and the results were satisfactory. The recovery and relative standard deviations(RSD) were 91.2%～120.5% and 0.97%～8.1%, which proved that the sensor had good accuracy. The sensor is intended to be used for the rapid quantitative detection of CAP in real samples because of its simple manufacture, high sensitivity, strong selectivity, good reproducibility, good stability, and fast detection speed. It provides a new idea for detecting CAP.
		2023 Vol. 43 (11): 3445-3451 [Abstract] ( 137 ) RICH HTML PDF (5097 KB) ( 37 )

	3452	Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis
		HUANG Li, MA Rui-jun^, CHEN Yu^, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen
		DOI: 10.3964/j.issn.1000-0593(2023)11-3452-09
		In order to realize the qualitative identification and quantitative detection of multi-component organophosphorus pesticides in mixed systems, this paper combines ultraviolet-visible absorption spectra with Parallel Factor Analysis (PARAFAC) to analyze the mixed solution of multi-component organophosphorus pesticides in water rapidly. The absorption spectra of experimental samples of single-component, two-component and three-component pesticide solutions composed of chlorpyrifos, methyl-parathion and profenofos in pure water were obtained by UV-Vis spectrometer. These pure water-organophosphorus pesticide absorption spectrum data were constructed into different three-dimensional data matrices. Then the PARAFAC algorithm was used to decompose the three-dimensional data after the factor number was determined by the nuclear consensus diagnosis method. It was found that the spectrum obtained by the decomposition of two-component and three-component pesticides was very similar to the actual single-component spectrum, which shows the algorithm can realize the qualitative analysis of multi-component organophosphorus pesticides in water. A linear regression model was constructed using the score matrix obtained by the algorithm decomposition and the true concentration of each component to predict different data sets (including a spectral data set with farmland water as dilution background). The prediction results of the model show that the PARAFAC algorithm has a significant second-order advantage. Even when the spectral overlap is serious, and there is interference information in the prediction set that does not exist in the calibration set, the algorithm can still effectively detect the mixed system. Qualitative analysis and quantitative detection were achieved for all the two-component mixed solutions, with the model evaluation coefficient of R² greater than 0.9 and the RPD greater than 3. The qualitative analysis was achieved for chlorpyrifos, methyl-parathion, and propamocarb in the three-component mixed solutions, in which chlorpyrifos and methyl-parathion met the quantitative detection requirements, and only profenofos showed unsatisfactory quantitative detection results. It may be that the overall spectral intensity level of profenofos solution is significantly lower than that of chlorpyrifos and methyl-parathion solutions of the same concentration, and its spectral contribution is the smallest, so the algorithm has poor resolution of profenofos in its mixed system. PARAFAC algorithm achieves the effect of “mathematical separation” instead of “chemical separation” that can qualitatively identify and quantitatively detect multi-component organophosphorus pesticide mixtures with serious spectral overlap without complicated preprocessing. The method provides a theoretical basis for rapidly detecting and analysing organophosphorus pesticide residues in water.
		2023 Vol. 43 (11): 3452-3460 [Abstract] ( 172 ) RICH HTML PDF (5124 KB) ( 301 )

	3461	Measurement of Plastic Film Thickness Based on X-Ray Absorption Spectrometry
		FANG Zheng, WANG Han-bo
		DOI: 10.3964/j.issn.1000-0593(2023)11-3461-08
		Plastic film is a bulk type accounting for one-fifth of plastic products in China. One of the most important indicators in manufacturers' production is the thickness of plastic film. How to accurately, quickly and conveniently measure the thickness of plastic film is a research topic with great economic value. In this paper, in order to verify the feasibility of measuring the thickness of the plastic film by X-ray absorption spectroscopy, experimental samples of polyethylene plastic film with different thicknesses are made, and the 30 kV pipe voltage and 1 μA. The tube current of A excites X-rays, irradiates plastic film samples of different thicknesses, collects blank spectral data and original X-ray absorption spectral data of different samples with X-ray detector, and obtains photon intensity of each spectrum in 256 channels. In the process of data analysis, in order to achieve the effect of data dimension reduction, principal component analysis is selected to process the collected data; The new dataset with reduced dimension is analyzed two times, one for machine learning directly and the other for machine learning after normalization. In machine learning, 70% are used as training sets, and the remaining 30% are used as test sets. The input data is the X-ray absorption spectra of each group of samples, and the output data is the plastic film thickness predicted by the model. At the same time, to reduce the error caused by randomness, multiple trainings were conducted to evaluate the effect of thickness estimation with average accuracy. Finally, comparative analysis of experimental data concludes that when the error tolerance is set to 50 μm, the accuracy of measuring the thickness of the plastic film by using the machine-learned X-ray absorption spectroscopy after normalization can reach 98.4%. At the same time, as long as the number of samples of the original spectral data is increased and the sampling distribution of different thicknesses is effectively planned, the accuracy of this method can be greatly improved in theory and can be extended to the thickness measurement task of other materials. Compared with other thickness measurement methods on the market, X-ray absorption spectroscopy has the advantages of nondestructive testing, rapid testing and a wide application range, which has a good application prospect for enriching the plastic film thickness measurement technology of manufacturers' production lines and relevant regulatory departments, improving the thickness measurement efficiency and improving the measurement accuracy. It has a good application prospect to enrich the thickness measuring technology of plastic film of the production line and related supervision department, improve the thickness measuring efficiency and accuracy.
		2023 Vol. 43 (11): 3461-3468 [Abstract] ( 107 ) RICH HTML PDF (3567 KB) ( 48 )

	3469	Research on Materials and Technology of Pingyuan Princess Tomb of Liao Dynasty
		LIU Jia-ru¹, SHEN Gui-yun², HE Jian-bin², GUO Hong^1*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3469-06
		The tomb of the Pingyuan Princess is located in Fuxin County, Fuxin City, Liaoning Province. As an aristocratic tomb of the Liao dynasty, its murals are rich in content, which is of great value to the study of politics, economy, social culture and funeral customs of The Khitan nationality in the Liao Dynasty, as well as the development of the tomb murals in Liao Dynasty. There are abundant research materials on the shape, theme, artistic value and other aspects of the Liao dynasty murals, and a clear review of their development. However, there are few reports on analysing the materials and techniques of the Liao dynasty tomb murals and the scientific and technological exchanges between the Khidan people and the Central Plains. Due to the need for an exhibition in 2020, it is urgent to carry out conservation and restoration. A super depth of field microscopy, SEM-EDS, laser Raman spectroscopy, XRD, Py-GC/MS were employed to research the pigment, the base layer and the cementation from the mural of The Princess of Pingyuan. The analysis results show that the pigments used in the tomb murals of Princess Pingyuan are all mineral pigments, among which the red pigments are lead, black pigments are carbon black, yellow pigments are iron yellow, and animal glue is used as pigment cementing material. Calcite is the main component of the limestone lithophore, quartz, plagioclase, and albite are the main components of the grass-mud ground layer. In terms of production technology, the Khitan people not only learned from and absorbed the culture of the Han people but also fully learned the advanced science, technology and technology of the Central Plains and practiced in the production of the murals of the Liao Tomb. The research on the material and technological characteristics of the tomb murals of the pingyuan Princess can provide a reference for the later protection and restoration work and enrich the material materials of the tomb murals of Liao and Song dynasties, which have a certain academic significance.
		2023 Vol. 43 (11): 3469-3474 [Abstract] ( 135 ) RICH HTML PDF (5028 KB) ( 72 )

	3475	A Solar Spectral Doppler Redshift Velocity Measurement Method Based on Adaptive EMD-NDFT
		WANG Zhen-ni¹, KANG Zhi-wei^1*, LIU Jin², ZHANG Jie²
		DOI: 10.3964/j.issn.1000-0593(2023)11-3475-08
		As the only energy source in the solar system, the sun is a rich treasure of spectral information for having a very wide continuous spectrum and tens of thousands of absorption and emission lines. The energy of solar electromagnetic radiation is mainly concentrated in the visible and infrared regions, among which the solar infrared spectrum with Doppler redshift characteristics can be used as the information source for astronomical velocity measurement and navigation. As an important part of astronomical velocity measurement navigation, Solar spectral Doppler redshift velocity measurement can deduce the relative radial velocity between spacecraft and the sun by calculating the Doppler redshift of the received solar spectrum relative to the standard solar spectrum. However, the spectral distortion caused by such solar activities as sunspots, corona, or flares will lead to the instability of the solar spectrum, which will affect the velocity measurement accuracy of the solar spectrum and in turn, the navigation accuracy. In order to improve the navigation performance of solar spectral velocity measurement, based on the principle of solar spectral velocity measurement, the signal processing method of solar spectral Doppler redshift velocity measurement is explored in this paper. This paper proposes an adaptive EMD-NDFT Doppler redshift velocity measurement method for solar spectral velocity measurement navigation. By this method, the redshift is calculated according to the Doppler effect of the solar spectrum and the radial velocity of the spacecraft relative to the light source is derived. The method consists of EMD processing, NDFT and correlation matching. First, the non-stationary received solar spectral signals are stratified adaptively by using the EMD algorithm, and the adaptive threshold filtering and noise reduction are carried out according to each layer of intrinsic mode signal to obtain a stable reconstructed signal. Second, according to the characteristics of non-uniform sampling of the solar spectrum, the standard solar spectrum and the received spectrum respectively are transformed by NDFT to convert the spectrum from the time domain to the frequency domain. Thirdly, Taylor matching is performed on the low-frequency characteristic spectral lines of the two spectra and the phase difference to obtain the radial velocity of spacecraft relative to the Sun. This method combines time-domain denoising and frequency-domain sparsity to obtain radial velocity more quickly and accurately. This paper analyses the spectral changes of sunspot activity in different years within a cycle, and their doppler redshift velocities are calculated and analyzed. The simulation results show that the adaptive EMD-NDFT method can effectively improve the accuracy of velocity measurement and greatly reduce the computational complexity for the solar spectral data in different periods and under different noises.
		2023 Vol. 43 (11): 3475-3482 [Abstract] ( 93 ) RICH HTML PDF (4093 KB) ( 37 )

	3483	A Rapid Method for Stripe Chromatic Aberration Correction in Landsat Images
		YAN Xing-guang, LI Jing^*, YAN Xiao-xiao, MA Tian-yue, SU Yi-ting, SHAO Jia-hao, ZHANG Rui
		DOI: 10.3964/j.issn.1000-0593(2023)11-3483-09
		Landsat satellite images have become the most widely used data source in large-scale ecological monitoring studies worldwide. In remote sensing application studies of large and medium scale areas, due to seasonal, lighting and climatic conditions and different satellite re-entry cycles and sensors, patchy effects and chromatic unevenness may exist after stitching the mosaic of multi-scene remote sensing images. With the rapid development of remote sensing cloud computing technology, exploring a fast and efficient method to repair Landsat chromatic stripes based on cloud platform is important. In this paper, we propose a histogram image homogenization method based on a random forest algorithm implemented on the Google Earth Engine (GEE) cloud platform, which homogenizes the Landsat Top of Atmosphere (TOA) and Surface Reflectance (SR) of Shanxi Province from 1986 to 2020 (Landsat 5 TM/7 ETM+/8 OLI) normalized vegetation index (NDVI) images after inversion were used as the study data, and MOD13Q1 (250 m resolution), MOD13A1 (500 m resolution) and MOD13A2 (1 km resolution) MODIS datasets were used as the validation data after 2000. The NDVI images of Shanxi Province from 1986 to 2020 before and after image restoration were compared separately, and the results of the study showed that (1) 20 years of the 35-year image analysis had strip color difference problems, and in 1994, for example, the restored Landsat TOA and Landsat SR images compared with those before restoration, the mean NDVI values of the restored areas increased by 32.6% and 29.03% respectively, and the profile analysis showed that the fit increased by 0.162 3 and 0.118 0 respectively; (2) The results of the trend analysis of the 1986—2020 one-dimensional linear regression showed that the fit of the restored images was high and the fluctuation of the year-by-year images was smaller after the long time series analysis. Among them, the slopes of the restored Landsat TOA and SR images decreased by 0.006 2 and 0.006 7, and theR² improved by 0.024 8 and 0.008 4 respectively; (3) Pearson correlation analysis of Landsat and MODIS images found that the correlation coefficients of the restored Landsat SR and TOA images improved by an average of 0.049 and 0.061 (p<0.05), where the correlation coefficients of restored Landsat SR and TOA images and MOD13Q1, MOD13A1, and MOD13A2 images increased by 0.050, 0.047, 0.049, 0.066, 0.060, and 0.059, respectively; (4) 2000—2020 Landsat and MODIS image time series analysis results show that the overall trend of the restored Landsat images is more similar to MODIS images, and the fit of the restored Landsat TOA and SR images is improved by 0.058 6 and 0.031 9, respectively. The proposed GEE cloud platform-based stochastic The proposed fast image restoration method based on the GEE cloud platform random forest algorithm achieves the accurate evaluation of NDVI inversion results of long time series remote sensing images, and the application of this method can quickly and efficiently solve the chromatic patch and banding effects caused by image mosaic.
		2023 Vol. 43 (11): 3483-3491 [Abstract] ( 409 ) RICH HTML PDF (8482 KB) ( 164 )

	3492	Different Types of Deposits in Porphyry Metallogenic System Identified by 2 200 nm Al—OH Group Vibration
		GUO Na^{1, 2}, WANG Xin-chen^3*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3492-05
		2 200 nm Al—OH group vibration is important for the exploration of the deposit in the porphyry metallogenic system. ASD portable spectrometer was used to measure 15 core samples form three different deposit types: Tiegelongnan High- sulfide epithermal deposit, Jiama porphyry deposit and Sinongduo low- sulfide epithermal deposit in Tibet. The results show that: (1) the overall spectral reflectance is 45%~70% in Tiegelongnan, 38%~58% in Jiama and 27%~56% in Sinongduo, the spectral difference is 5~15%; (2) high- sulfide epithermal deposit show a double peak at 2 200 nm, and the other two types show single peak; (3) the spectrum of low- sulfide epithermal deposit enhanced by the second derivative is negative at 2 200 nm, the spectral symmetry and absorption index are also lower than the others; (4) The gaussamp function takes excellent fitting on the single peak spectrum at 2 200 nm (R²=1), which can completely simulate white mica group minerals. The above analysis shows that the 2 200 nm Al—OH group vibration can obviously distinguish different deposits in the porphyry metallogenic system. On the one hand, the presence of illite-smectite in low-sulfide epithermal deposit lead to low reflectance, and the water in minerals result in low values of spectral symmetry and absorption index; On the other hand, the average spectral curve of high-sulfide epithermal deposit takes double peaks because of the kaolinite and dickite minerals, which is the mark for the identification of different epithermal deposits. The study can be applied to aerospace hyperspectral remote sensing exploration by using a 2 200 nm single band.
		2023 Vol. 43 (11): 3492-3496 [Abstract] ( 105 ) RICH HTML PDF (2447 KB) ( 36 )

	3497	*Ultrastructure and Mineral Composition of Bathymodiolus* Shell From Wocan-1 Hydrothermal Vent, Northwest Indian Ocean**
		WAN Huang-xu^{1, 2}, LIU Ji-qiang^1*, HAN Xi-qiu^{1, 2}, LIANG Jin-long², ZHOU Ya-dong¹, FAN Wei-jia¹, WANG Ye-jian¹, QIU Zhong-yan¹, MENG Fan-wei³
		DOI: 10.3964/j.issn.1000-0593(2023)11-3497-07
		The Mussel organisms from the sulfide hydrothermal field of the mid-ocean ridge can virtually record the ecological environment information around the region. However, the distribution characteristics, ultrastructure and genesis of the minerals of the shells are not well studied. A mount of mussels were first collected from the Wocan-1 hydrothermal field in the Northwest Indian Ocean, by the Manned deep-sea submersible (JIAOLONG) in 2017, which were ideal samples for investigating this scientific issue. The mussel, deep-sea Bathymodiolus of the Indian Ocean (Bathymodiolus marisindicus), is analysed by the Scanning electron microscope, Laser Raman spectroscopy, and Fourier transform infrared spectrum for their natural cross-section morphology, and mineral component. The results show that the longitudinal growth of the Bathymodiolus shell includes periostracum, prism layer,transition layer, aragonite slate layer and myostracum from the outer to the inner. In the fibrous prismatic prism layer of the shell, the c-axis cross-section of the prism is irregular, and the width of the calcite prism perpendicular to a-axis is about 818~960 nm, and it is nearly 45° oblique with the aragonite layer, and there are interlaced calcite prisms. The shape of the transition layer of the shell is extremely irregular, and it continues the growth orientation of the prismatic layer showing a trend of transition from the prismatic to the slate of aragonite. The aragonite layer has a lamellar structure, and it's about 205~1 260 nm in thickness. In the aragonite layer of the Bathymodiolus shell of the Wocan-1 hydrothermal vent, the thickness of the aragonite tablets of the same region is the same, but the thickness of the tablet of different regions is different. The myostracum has a simple prismatic ultrastructure, which is overlaid by both prismatic and nacre layers (aragonite lamellar layer). Spectral analysis shows that the minerals of the nacre and prism layers of the Bathymodiolus shell are inorganic aragonite with relatively high crystallinity and biogenic calcite,respectively. The morphology characteristics, mineral components, and genesis of the Bathymodiolus shell analyzed in the study can provide a potential example for studying the hydrothermal field's mollusk shell formation mechanism and bioinduced mineralization process.
		2023 Vol. 43 (11): 3497-3503 [Abstract] ( 136 ) RICH HTML PDF (2843 KB) ( 40 )

	3504	Chemical Constituents and Spectra Characterization of Monocrystal Rhodonite From Brazil
		ZHANG Yu-hui^{1, 2}, DING Yong-kang³, PEI Jing-cheng^{1, 2*}, GU Yi-lu^{1, 2}, YU Min-da^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3504-05
		Rhodonite is a characteristic pink single-chain silicate mineral. As the gem-quality transparent rhodonite is rare, rhodonite is often produced as dense massive aggregate, which is usually classified as common jade in the gem trade. Monocrystal rhodonite is represented by the Broken Hill mining area in Australia and the Minas Gerais mining area in Brazil. In this paper, 10 samples of monocrystal rhodonite from Brazil are used for the LA-ICP-MS test, Raman spectroscopy test, infrared absorption spectroscopy test and UV-Vis absorption spectroscopy test, aiming to explore the chemical composition and spectroscopic characterization of rhodonite and provide basic data for the identification, optimization and origin identification of rhodonite. According to the results of LA-ICP-MS, the average crystal structure chemical formula of the samples is (Mn_0.763Ca_0.106Fe_0.070 Mg_0.061)_1.00SiO₃. The main elements are rich Mn and Ca-Fe-Mg, similar to the monocrystal rhodonite composition produced in Minas Gerais, Brazil. The Raman shift of the sample is mainly composed of 666 cm^-1 strongest peak, 972, 997 cm^-1 double-peak, and several weak peaks. Which are related to the stretching and bending vibration of [SiO₄] tetrahedral groups and the stretching vibration of octahedral coordination cations. Infrared test results show that the absorption peak of rhodonite in the fingerprint region is mainly due to the stretching and bending vibration of Si—O. The structure of rhodonite determines that there are five absorption peaks in the band of 750～550 cm^-1, which distinguishespyroxenes and pyroxenoids minerals. There is an obvious absorption peak at 3 631 cm^-1 in the near-infrared region, which is a typical OH stretching vibration band indicating that the sample contains a small amount of structural water. UV-Vis absorption spectra show that rhodonite is a typical self-colored mineral, mainly attributed to the d—d electronic transition of octahedral coordination Mn²⁺. The absorption peak is located at the purple and yellow-green regions, which is the main reason for the orange-pink color of the samples.
		2023 Vol. 43 (11): 3504-3508 [Abstract] ( 126 ) RICH HTML PDF (2057 KB) ( 76 )

	3509	Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination
		ZHENG Shu-yuan^{1, 2}, HAI Yan^{1, 2}, HE Meng-qi^{1, 2}, WANG Jian-xiong^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3509-09
		Based on the analysis of the spectral characteristics of healthy green vegetation and the spectral characteristics of five typical ground objects in the visible light band of GF-6 image, a visible light vegetation index based on the red, green and blue bands, high discrimination red green blue vegetation index (HDRGBVI), is proposed, This index is used to compare the effect of extracting green vegetation cover in the study area with other eight common visible vegetation indexes, and the SVM based supervised classification method is used to quantitatively evaluate the accuracy of green vegetation extracted by different visible vegetation indexes in the study area and compare the accuracy. The results show that the vegetation index gray-scale image calculated by HDRGBVI can effectively extract the vegetation in the experimental area, and has an inhibitory effect on large-area water bodies, long and narrow water bodies, and small water bodies; Compared with the low differentiation between vegetation and other land types when other eight common visible vegetation indexes are applied to satellite images, HDRGBVI enhances the differentiation between vegetation and other land types, so that the visible vegetation index also has a better extraction effect when applied to satellite images; HDRGBVI and other eight common visible light vegetation indexes are used to extract the vegetation-coverage area of the Changqiao-Lake test area respectively. The overall accuracy of HDRGBVI is 90.23%, and the kappa coefficient is 0.804 5, which is higher than the other eight common visible light vegetation indexes, and can accurately extract the vegetation coverage area of the Changqiao-Lake test area; In order to verify whether HDRGBVI has good applicability and reliability, three study areas are selected from three high score six images for verification, and the HDRGBVI of the three study areas are calculated respectively, and the accuracy is verified by using the SVM based supervised classification results of the corresponding test areas. The results show that the extraction accuracy of green vegetation in the three study areas is maintained at about 90%, It can effectively extract the vegetation covered area, and the fluctuation of extraction accuracy affected by the distribution difference of different land types is small. It can better weaken the influence of shadow and other factors in the image. To sum up, the high classification red, green blue vegetation index HDRGBVI proposed in this paper can effectively, quickly, accurately and widely extract the green vegetation information in the GF-6 visible band image, and has good applicability, which provides a feasible method for the combination of visible vegetation index and satellite image.
		2023 Vol. 43 (11): 3509-3517 [Abstract] ( 103 ) RICH HTML PDF (6471 KB) ( 92 )

	3518	*Study on Traditional Chinese Medicine of Lonicera* L. Based on Infrared Spectroscopy and Cluster Analysis**
		XU Rong¹, AO Dong-mei^2*, LI Man-tian^{1, 2}, LIU Sai¹, GUO Kun¹, HU Ying², YANG Chun-mei², XU Chang-qing¹
		DOI: 10.3964/j.issn.1000-0593(2023)11-3518-06
		Lonicera Linn has a large variety of herbs, is produced in large quantities and has similar trait characteristics, making it difficult to distinguish between them. In this study, Fourier transforms infrared spectroscopy was used to determine four Chinese herbal medicines from Lonicera Linn, namely, Lonicera japonica (Fols Lonicerae), Lonicera hypoglauca, Lonicera macranthoides and Lonicera fulvotomentosa, and the infrared spectra were scanned in the range of 4 000~400 cm^-1 and the second-order derivatives in the range of 1 800~400 cm^-1. After obtaining these fingerprints, the correlation coefficients were calculated, and second order derivative spectrum was by using the fingerprint spectrum (1 800~400 cm^-1) with dense spectral bands, while combined with SMICA cluster analysis to classify the infrared fingerprint profiles of the herbs and compare the similarities and differences. The results showed that the overall peak shapes of the infrared spectra of the four species of Lonicera Linn were similar. The spectral peak positions and peak heights were relatively close, with absorptions near 1 629~1 635, 1 376~1 384, 1 265~1 282, 1 152~1 158, 1 050~1 051, 814~816, 611~614, 534~537 cm^-1, but the 1 731 cm^-1CO stretching vibration absorption peak is most pronounced in Lonicera japonica, only Lonicera macranthoides and Lonicera japonica showed a telescopic vibrational absorption peak of 1 105 and 1 103 cm^-1 C—O, the bending vibration absorption peak is most pronounced in Lonicera hypoglauca and Lonicera macranthoides 1 317 cm^-1 CH, the characteristic peak of the C—OH stretching vibration of the sugar alcohols is most evident at 1 075 cm^-1 in Lonicera fulvotomentosa. The correlation coefficient results showed some differences among the four Chinese herbal medicines in the Lonicera Linn, with the largest difference between Lonicera hypoglauca and Lonicera fulvotomentosa, with a correlation coefficient of 0.94. The four Lonicera Linn Chinese herbal medicines differed significantly in the bands 1 700~1 300 and 971~780 cm^-1 in the second-order derivative spectra. Cluster analysis using Assure ID software with Chinese herbal medicines absorption wave number as the variable showed that the class spacing between Lonicera hypoglauca and Lonicera fulvotomentosa was the largest at 6.86, further indicating that the difference between Lonicera hypoglauca and Lonicera fulvotomentosa was the largest. The identification and rejection rates of the four Lonicera Linn herbs in the clustering model were the highest point of 100% and the lowest point of 99%. The class model plots, in which the different proto species herbs are separated two by two, indicate that the infrared spectra combined with SIMCA cluster analysis can identify the four herbal species of the Lonicera Linn. The clustering analysis model was validated by taking known origins samples, and the recognition and rejection rates were also above 99%. Therefore, the combination of infrared spectroscopy and cluster analysis can identify four herbs of Lonicera Linn in a rapid and nondestructive manner, providing a scientific and effective method for the genetic identification of Lonicera Linn.
		2023 Vol. 43 (11): 3518-3523 [Abstract] ( 165 ) RICH HTML PDF (3208 KB) ( 98 )

	3524	Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing
		ZHU Zhi-cheng¹, WU Yong-feng^2, MA Jun-cheng², JI Lin², LIU Bin-hui^3, JIN Hai-liang^1*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3524-11
		In order to explore the characteristics of canopy reflectance of winter wheat responding to leaf chlorophyll changes in each growth period under water stress, a total of three water gradient treatments were set for 11 wheat varieties (divided into strong, general and weak drought resistant strains) in the growth season from 2020 to 2021, including two irrigation treatments (jointing and flowering), one irrigation treatment (winter, turning green, jointing, 7 days after jointing and 14 days after jointing) and no irrigation, The correlation between chlorophyll and reflectance was analyzed. The narrow band spectral indexes most sensitive to chlorophyll were selected by using the random combination of wavelengths (simple ratio (SRSI), simple difference (SDSI) and normalization (NDSI)) and linear fitting methods. The results showed that: (2) With the development process and the drought resistance of varieties weakened, the difference of canopy reflectance in near-infrared region between different treatments gradually increased. (3) The high value of the linear fitting determination coefficient of chlorophyll and narrow band spectral index is concentrated in the green (445～591 nm) and red edge (701～755 nm) bands. The SRSI index of drought resistant and drought-resistant strains had the highest precision of chlorophyll retrieval at the flowering stage, reaching 0.762 and 0.811 respectively; The NDSI index of general drought-resistant strains had the highest precision at the filling stage, which was 0.732. This study has a certain reference value for revealing the reflectance response of chlorophyll change under water stress in different key growth stages of winter wheat and differences among varieties. It can lay a foundation for efficient screening of drought-resistant wheat varieties based on unmanned aerial hyperspectral technology.
		2023 Vol. 43 (11): 3524-3534 [Abstract] ( 173 ) RICH HTML PDF (13559 KB) ( 54 )

	3535	Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near Infrared Spectroscopy Combined With Machine Learning Algorithms
		HUANG Zhao-di¹, CHEN Zai-liang², WANG Chen³, TIAN Peng², ZHANG Hai-liang², XIE Chao-yong^2, LIU Xue-mei^4
		DOI: 10.3964/j.issn.1000-0593(2023)11-3535-06
		Soil fertility is usually determined by the content of organic matter, total nitrogen, available phosphorus, and available potassium. The content of these substances is usually studied by visible/long-wave near-infrared spectroscopy (Visible/near-infrared spectroscopy, Vis/NIRS: 350～2 500 nm), and visible/shortwave near-infrared spectroscopy (Vis/NIRS: 325～1 075 nm) research is scarce, and the combination of visible/short-wave NIR spectroscopy with machine learning algorithms to measure soil nutrients has great potential. In this paper, four villages in Xinjian District, Nanchang City and Anfu County, Ji'an City, were selected as sample acquisition sites, and soil samples with a depth of 10～30 cm in the diagonal area were selected by the 2×2 grid method, including 120 paddy soils (paddy soil 1 and paddy soil 2), 60 parts of brown soil, and 60 parts of red soil. After grinding and air-drying, the samples were evenly divided into two parts by the quartering method, which was used to determine the samples' spectral and physicochemical information. The acquired spectral data were removed from the noise bands of 325～349 and 1 073～1 075 nm and then preprocessed by S-G convolution smoothing combined with the first derivative. Principal component analysis (PCA) was performed on the preprocessed spectral data, and the score map (PC1: 98.44%, PC2: 3.5%, PC3: 0.14%) obtained according to the principal component analysis showed that the samples had obvious clustering and were in two The samples can be separated from each other in the dimensional space, and the samples have obvious clustering phenomenon. PCA can reasonably explain the differences in the spectral characteristics of different soil samples to a certain extent. In addition, full-band principal component regression (PCR) and partial least squares regression (PLSR) models were established on the preprocessed spectral data, and PCA and PLSR dimensionally reduced the spectral data to extract three principal component factors (PCs) and 9 latent variables (LVs), build nonlinear back-propagation neural network (BPNN) and least squares support vector machine (LS-SVM) models. By comparing the prediction accuracy of Vis/SW-NIRS for OM, TN, P, K by PCR, PLSR, BPNN and LS-SVM methods, the following conclusions can be drawn: (1) The LS-SVM-LVs model has good performance in all soil properties All are better than PCR, PLSR, BPNN-PCs, BPNN-LVs and LS-SVM-PCs models; (2) LS-SVM-LVs model has the highest prediction accuracy for OM and N, which is characteristic of spectral response in the NIR region (3) Determination of soil mineral nutrients P and K by Vis/SW-NIRS has different accuracy, which is due to the co-variation of spectral active components. Based on the results obtained in this study, LS-SVM-LVs analysis is recommended as the best model approach for predicting soil properties (OM, TN, P, and K). However, further research is needed to deeply interpret measurements of soil properties that do not have direct spectral responses in the near-infrared region. The research results of this paper can provide theoretical and technical references for the development of local precision agriculture.
		2023 Vol. 43 (11): 3535-3540 [Abstract] ( 147 ) RICH HTML PDF (4277 KB) ( 164 )

	3541	Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM
		KANG Ming-yue^{1, 3}, WANG Cheng¹, SUN Hong-yan³, LI Zuo-lin², LUO Bin^1*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3541-10
		Based on near-infrared spectroscopy and statistical methods, a rapid and non-destructive testing method for the internal quality of cherry tomatoes was proposed. First, the near-infrared spectrum of the sample was collected, and five preprocessing methods, Multiplicative Scatter Correction, Savitzky-Golay convolution smoothing, Savitzky-Golay convolution first derivative, De-trending, Standard Normal Variate, and SNV were used to eliminate spectral interference and screen out the best preprocessing method; then use the Successive Projections Algorithm, Stability Competitive Adaptive Reweighted Sampling, Genetic Algorithm, and the introduction of automatic ordered predictor selection algorithm for Improved Genetic Algorithm Four characteristic wavelength extraction methods reduce variable redundancy and select the optimal characteristic wavelength extraction. Method; finally, combined regression method-combining von Neumann topology, roulette selection, tournament selection and adaptive weights with whale algorithm to improve the algorithm, using the Improved Whale Optimization Algorithm, and based on Particle Swarm Optimization-BP Neural Network was compared with the Whale Optimization Algorithm-Least Squares Support Vector Machine, and the prediction models for the internal quality content of cherry tomatoes were established respectively. The results showed that the De-trending-IGA-IWOA-LSSVM model was used for the best soluble solid content in the internal quality of cherry tomatoes, where the coefficient of determination of the calibration set and prediction set were 0.917 2 and 0.866 7, respectively, the corrected root mean square error and the predicted mean square The root error was 0.542 3 and 0.768 2, and the relative error of prediction reached 2.592 9; the SG-IGA-IWOA-LSSVM model was used to predict the Vitamins C content the most accurate, and the coefficient of determination of the calibration set and prediction set were 0.857 6 and 0.821 6, respectively, and the corrected root mean square The error and prediction root mean square error are 0.661 4 and 0.634 2, respectively, and the prediction relative error reaches 2.078 5. The above results show that the combination of near-infrared spectroscopy and statistical methods can achieve rapid and non-destructive prediction and analysis of the internal quality of cherry tomatoes.
		2023 Vol. 43 (11): 3541-3550 [Abstract] ( 163 ) RICH HTML PDF (7022 KB) ( 150 )

	3551	Research and Application of On-Line Analysis of CO₂ and H₂S in Natural Gas Feed Gas by Laser Raman Spectroscopy
		ZHU Hua-dong^{1, 2, 3}, ZHANG Si-qi^{1, 2, 3}, TANG Chun-jie^{1, 2, 3}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3551-08
		To address the lack of online analysis for CO₂ and H₂S in the purification of raw natural gas, a cutting-edge laser Raman analyzer and accompanying pre-treatment device have been developed. These advancements aim to tackle the highly corrosive properties of raw natural gas, including water content, oil pollution, particulate matter impurities, high sulfur content, and carbon dioxide. Additionally, a pioneering online analysis method utilizing laser Raman spectroscopy was established. The accuracy and repeatability of carbon dioxide and hydrogen sulfide measurements were thoroughly investigated, confirming the reliability of the online analysis methods. Calibration on these methods was also studied. On-site application experiments were conducted in natural gas purification plants to assess the effectiveness of the developed technology. The method performance testing experiment, demonstrated a deviation of less than 1.0% between the laser Raman test results of hydrogen sulfide and the standard gas indication value, with a relative standard deviation of less than 0.6% across seven measurement results. Similarly, the relative deviation between Raman test results of carbon dioxide and the standard gas indication value was less than 1.0%, with a relative standard deviation of less than 0.76% across seven measurement results. In the application experiments, the relative deviation between on-line Raman spectroscopy and offline iodimetry for H₂S test results ranged from 0.3% to 7.5% (with over 90% of the data falling below 3%). In comparison, the relative deviation between on-line Raman spectroscopy and offline gas chromatography for CO₂ test results ranged from 0.6% to 8.4% (with over 80% of the data falling below 3%). The calibration cycle was set at 3 days. The online Raman spectrometer operated smoothly and consistently, enabling real-time monitoring of composition dynamics, thus satisfying the on-line analysis needs of natural gas purification plants.
		2023 Vol. 43 (11): 3551-3558 [Abstract] ( 156 ) RICH HTML PDF (3754 KB) ( 328 )

	3559	Characteristics Research and Optimal Shaping of Brillouin Scattering Spectrum in Multimode Fiber
		LI Yong-qian^{1, 2, 3}, FAN Hai-jun^{1, 2, 3*}, ZHANG Li-xin^{1, 2, 3}, WANG Lei^{1, 2, 3}, WU Jia-qi^{1, 2, 3}, ZHAO Xu^{1, 2, 3}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3559-06
		The presence of multiple degrees of freedom in multimode fiber allows high-capacity communication and multi-parameter sensing. However, the presence of high-order modes in multimode fiber is not only unstable, easy to couple and radiate loss but also leads to Brillouin gain spectrum distortion, spectrum width broadening and Brillouin gain peak reduction, which seriously deteriorates the measurement accuracy and sensing reliability of the system. Therefore, studying the characteristics and shaping optimization of Brillouin gain spectrum in multimode fiber is particularly important. Firstly, the Brillouin frequency shift and the multimode fibre gain spectrum characteristics are investigated and compared with few-mode fiber and single-mode fiber. The results show that the Brillouin frequency shift of multimode fiber is related to the mode refractive index and Brillouin scattering angle. When the Brillouin scattering angle is constant, the Brillouin frequency shift is negatively correlated with the mode group number. When the mode group number is constant, the Brillouin frequency shift is positively correlated with the Brillouin scattering angle. Compared with single-mode fiber, few-mode fiber and multimode fiber have lower Brillouin gain peak and Brillouin frequency shift and wider Brillouin gain spectrum due to the influence of high-order modes. Multimode fibers have the most high-order modes, corresponding to the lowest Brillouin gain peak and Brillouin frequency shift and the widest Brillouin gain spectrum. Furthermore, two Brillouin gain spectrum shaping optimization methods of multimode fiber based on single-mode fiber are analyzed and designed. A frequency-shifted local heterodyne Brillouin optical time domain reflectometry system is constructed to evaluate the degree of shaping optimization by measuring the Brillouin scattering spectrum width and bend-tolerant capacity of two. The experimental results show that the proposed two shaping optimization methods reduce the Brillouin gain spectrum width of multimode fiber to varying degrees, and the obtained Brillouin gain spectrums have good Lorenz fitting degrees, which are 0.974 47 and 0.987 89, respectively. Using a single mode optical circulator combined with a single mode fiber alignment fusion to an multimode fiber has better shaping optimization effect and bending tolerance. The minimum bending radius and Brillouin gain spectrum width are 2.25 mm and 53.12 MHz, respectively.
		2023 Vol. 43 (11): 3559-3564 [Abstract] ( 117 ) RICH HTML PDF (3589 KB) ( 41 )

	3565	Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN
		HUANG Meng-qiang¹, KUANG Wen-jian^{2, 3*}, LIU Xiang¹, HE Liang⁴
		DOI: 10.3964/j.issn.1000-0593(2023)11-3565-06
		Qualitative and quantitative analysis of fiber composition has always been a research hotspot in textile testing. However, conventional detection methods have problems such as long cycles, complicated processes, and being unfriendly to the environment. Therefore, proposing a fast, non-destructive, and accurate detection method for textile fiber content is particularly important. This study proposes a quantitative calibration model for textile fiber content, which can accurately predict the fiber content of cotton/polyester/wool in textiles. It solves the difficulty that traditional calibration models cannot consider both accurate and multiple fiber predictions. In this study, 645 wool/polyester, cotton/polyester, and wool/polyester/cotton blended samples were taken as the research objects, and an infrared spectrometer collected the near-infrared reflectance spectra of the samples. After the spectral data is preprocessed, the one-dimensional convolutional neural network (1D-CNN) model is used to predict multiple fiber contents simultaneously. The prediction results of three different machine learning algorithms are compared on the same training and test sample sets. The results show that the preprocessing method of linear function normalization and polynomial smoothing filter (SG smoothing, the sliding window is 9, fitting order is 7), combined with the proposed 1D-CNN model has the best effect, and Its model determines that the coefficient R-Squared can reach 0.998, the mean absolute error (MAE) of each content prediction is 0.62, and the root mean square error (RMSE) of prediction is 1.31. At the same time, 138 textile samples that did not participate in the modeling were used to verify the mode's generalization ability. The model's performance on the test set was excellent, with a coefficient of determination R-squared of 0.996, a mean absolute error (MAE) of each content prediction of 0.80, and a predicted mean square of the root error (RMSE) of 2.01. Using the model proposed in this paper, the fiber content in wool, cotton, and polyester blended textiles can be accurately predicted, providing a feasible method for rapid non-destructive testing of textiles and a new idea for the quantitative analysis of other blended fiber content.
		2023 Vol. 43 (11): 3565-3570 [Abstract] ( 152 ) RICH HTML PDF (4447 KB) ( 74 )

	3571	Absolute Radiometric Calibration of Aerial Multispectral Camera Based on Multi-Scale Tarps
		CUI Zhen-zhen^{1, 2}, MA Chao¹, ZHANG Hao^2*, ZHANG Hong-wei³, LIANG Hu-jun³, QIU Wen²
		DOI: 10.3964/j.issn.1000-0593(2023)11-3571-11
		Site-based absolute radiometric calibration is an important guarantee for quantitatively application of aerial remote sensing data. The key to aerial camera calibration is to reduce the influence of field measurement environment and various measurement errors and improve the stability and accuracy of calibration results. In this paper, five gray scale tarps with different reflectance were set up at the Pu'er test site in Yunnan Province from 26th to 28th December 2020, and a reflectance-based method, which requires synchronous measurements including the surface reflectance, atmospheric parameters and geometry information when the aircraft overpass the calibration test site, was used for absolute radiometric calibration of Lecia DMC Ⅲ airborne multispectral camera. These parameters were input into the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric radiative transfer model to obtain the spectral radiance at the entrance pupil at the aircraft height. Then, combined with the average DN of the selected area of the image, the different absolute radiometric calibration coefficients were derived for the DMC Ⅲ camera via three consecutive calibration experiments based on single-, dual- and multi-site methods. By systematically comparing the calibration results of single-, dual- and multi-site methods and analyzing various error sources, proposing a high-precision absolute radiometric calibration method based on multiple observations with multi-scale tarps. Moreover, the calibration uncertainty of each band is 7.24% (blue), 6.20% (green), 5.35% (red) and 4.68% (near infrared), respectively. In order to verify the radiometric calibration results, the reflectance inversion validation was adopted to prove the rationality of the three different calibration coefficients obtained by single-, dual- and multi-site methods. The different absolute calibration coefficients obtained by single-, dual- and multi-site methods were used to conduct atmospheric correction for various typical ground objects in the test site with Atmospheric/Topographic Correction for Airborne Imagery (ATCOR 4) atmospheric correction software, and the surface reflectance obtained by inversion was compared with the measured surface reflectance for verification. The results show that multiple experiments based on multi-scale tarps in a time are strongly critical for improving the calibration accuracy. The single-site method with 5%, 20% and 60% tarps and single multi-site for single experiment method have relatively poor calibration accuracy. The calibration errors of the single-site method with 40% tarp and dual-site method decrease obviously, while the multi-site for multiple experiments method has relatively high calibration accuracy. The average relative errors of the three methods are 10%, 5.43% and 3.18%, respectively. The calibration method of aerial multispectral camera based on multiple experiments with multi-scale tarps put forward in this paper reduces the calibration uncertainty of single-site method, dual-site method and single experiment, which has the high reference for the high-accuracy site-based calibration of aerial cameras and the quantitative application of aerial data in the future.
		2023 Vol. 43 (11): 3571-3581 [Abstract] ( 136 ) RICH HTML PDF (7058 KB) ( 150 )

	3582	Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View
		YANG Lei^{1, 2, 3}, ZHOU Jin-song^{1, 2, 3}, JING Juan-juan^{1, 2, 3}, NIE Bo-yang^{1, 3*}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3582-09
		Hyperspectral remote sensing can provide rich information on the earth's surface, so it has attracted extensive attention from scholars at home and abroad. Affected by the level of technology, a single hyperspectral imager cannot meet the application requirements of a large field of view and high resolution simultaneously. Splicing hyperspectral imager technology combines multiple hyperspectral imagers into one imaging system, effectively expands the field of view of hyperspectral imagers, and is widely used in precision agriculture, earth observation, environmental monitoring, etc. Due to the influence of detector pixel response, optical system, electronic system and other factors, the output of a single pixel of the detector will be inconsistent when the focal plane array of the hyperspectral imager is under the same uniform radiation source, which is the non-uniformity of the hyperspectral imager. The non-uniformity of splicing hyperspectral imager seriously affects the image quality and interpretation.Non-uniformity correction methods are divided into calibration-based and scene-based correction methods. In this paper, non-uniformity models of a single imager and multiple imagers are established based on the analysis of the non-uniformity of the splicing hyperspectral imager developed by Aerospace Information Research Institute. Based on non-uniformity models, a novel non-uniformity correction method based on overlapping fields of view is proposed. This method integrates the laboratory calibration and the real-time flight data. The laboratory radiometric calibration is used to correct non-uniformity of a single image, while the overlapping field of view and wavelet filter isused to correct non-uniformity of multiple imagers. This method only needs to calibrate the radiation of a single imager in the laboratory so that it eliminates the limitation of requiring a large-aperture integrating sphere. Several experiments are carried out to evaluate the quality of images processed by different methods. Images with non-uniformity of two different bands are selected as original images, and original images are processed by the proposed method. In order to quantitatively compare the correction effects of different methods, three evaluation indexes,Improvement Factor (IF), Non-uniformity (NU) and Spectral Angle (SA), are introduced. The results show that the proposed method can correct non-uniformity effectively and preserve the spectrum features simultaneously as much as possible.
		2023 Vol. 43 (11): 3582-3590 [Abstract] ( 117 ) RICH HTML PDF (14070 KB) ( 51 )

	3591	Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing Imagery in the Mountainous Area
		QIAN Rui¹, XU Wei-heng^{2, 3 , 4*}, HUANG Shao-dong², WANG Lei-guang^{2, 3, 4}, LU Ning², OU Guang-long¹
		DOI: 10.3964/j.issn.1000-0593(2023)11-3591-08
		To explore the extraction effect of the rich spectral information of hyperspectral images on tea plantations in mountainous regions and to promote the application of domestic satellite hyperspectral images in tea plantations distribution mapping and resource monitoring. Taking the typical distribution area of tea plantations in the southern mountainous region of Pu'er City as the study area, an algorithm for tea plantations extraction in subtropical mountainous regions based on the random forest (RF) classifier was constructed, and the main data sources, including the hyperspectral 5 AHSI (GF-5 AHSI) image, digital elevation model (DEM), and the field survey data. Firstly, the 250 bands of GF-5 AHSI image after removing the noise bandsused as spectral features (SF). Based on the spectral analysis of the main features (tea plantation, forest and cropland) in the study area and DEM data, 45 vegetation index features (VIF) and 3 topographic features (TRF) were constructed, respectively. Moreover, the RF was used to rank the feature importance of each feature, and the features were input into the RF classifier for tea plantations extraction in order of feature importance from highest to lowest. The feature dimension of the optimal feature space is determined when the F1-Score of the tea plantations reaches saturation and no longer increases significantly with the continuous input of features. Finally, 12 classification schemes were constructed based on 3 feature factors (SF, VIF and TRF). Moreover, the accuracy of tea plantations extraction was compared among the 12 schemes and the optimal scheme was finally determined. The results showed that the producer's accuracy (PA) and user's accuracy (UA) of the 6 classification schemes after feature selection (FS) were better than those of the 6 schemes before FS. The VIF+TRF scheme had the best extraction accuracy (PA: 89.72%, UA: 81.97%) among the 6 classification schemes before FS, while the best performance of tea plantations extraction accuracy after FS was the SF+VIF+TRE scheme (PA: 90.69%, UA: 83.09%). The F1-Score of tea plantations extraction with different feature combinations was ranked as SF+VIF+TRE+FS>TRF+VIF+FS>SF+TRF+FS>TRF+VIF>VIF+FS>SF+VIF+FS>SF+VIF+FS>SF+VIF+TRF>SF+FS>SF+TRF>VIF>SF+VIF>SF. Among the 6 classification schemes after FS, the bands that were selected twice in the 4 schemes in which SF was involved in classification were b4, b5, b6, b27, b133, b150 and b281; the indices that were selected four times in the 4 schemes in which VIF was involved in classification were REP, VOG2, SR2, SR3, WBI, TIP3 and TIP9 and all terrain factors were selected in the 4 classification schemes in which TRF participated. SF+TRF+VIF features combined with the RF algorithm after FS can effectively identify the distribution of subtropical tea plantations with good recognition accuracy and credibility. The GF-5 AHSI satellite data has good potential and prospects for application in tea plantations distribution mapping and resource monitoring.
		2023 Vol. 43 (11): 3591-3598 [Abstract] ( 159 ) RICH HTML PDF (5419 KB) ( 52 )

	3599	Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM
		XIE Peng, WANG Zheng-hai^*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin
		DOI: 10.3964/j.issn.1000-0593(2023)11-3599-08
		Selenium (Se) is part of the essential trace elements in the human body. People obtain selenium mainly through the consumption of agricultural products, and selenium in agricultural products mainly comes from the soil. Therefore, studying the content and distribution of selenium in the soil is very important to human health and crop production. However, the development of hypersensitive remote sensing technology has made it possible to estimate the content and distribution of selenium in soil in an efficient, low-cost and large-scale manner. However, the sensitivity of soil selenium content of spectra is weak, which seriously affects the accuracy of quantitative inversion of hypersecretion selenium content. In this study, 50 soil samples were systematically collected from the study area to analyse the selenium content of the soil samples, and the soil reflection spectral data were collected simultaneously; the Savitzky-Golay convolutional smoothing algorithm, multiple scattering corrections (MSC), first-order logarithmic differentiation (lg(R)-FD), standard normal variance correction (SNV), multiple scattering corrected first-order differentiation (MSC-FD) for raw spectra enhancement; application of the stable competitive adaptive benighted sampling (sCARS) algorithm combined with Pearson correlation analysis (PCC) for feature band selection; comparative analysis of partial least squares (PLS), support vector machine (SVM) and particle swarm optimisation support vector (PSO-SVM) models for the quantification of soil selenium content in hypersecretion The results showed that the sCARS algorithm was applied to the inversion. The results show that applying the sCARS algorithm to the spectrally enhanced regression model and combining Pearson correlation (PCC) to select the feature bands with greater sensitivity to soil selenium content can not only reduce the complexity of the hypersecretion prediction model for soil selenium content and effectively avoid the loss of a large amount of useful information, but also improve the inversion efficiency of the hypersecretion regression model; comparing the training and prediction sets of different regression models The comparison of the coefficient of determination R2 and root mean square error RMSE between the training and prediction sets of different regression models showed that the support vector (SVM) model had better prediction results and higher model stability than the partial least squares (PLSR) model, and the non-linear model was more suitable for the prediction of soil selenium content; the inversion accuracy and stability of the SVM model were improved by optimizing the kernel function and regularization parameters of the SVM through the particle swarm (PSO) algorithm; the MSC-PSO-SVM model (R²=0.53, RMSE=0.34) and MSC-FD model (R²=0.50, RMSE=0.04) had more outstanding prediction results. In summary: the hypersensitive quantitative inversion model of soil selenium content using sCARS combined with the PSO-SVM algorithm can provide a new way to estimate the soil selenium content in a large hypersensitive area.
		2023 Vol. 43 (11): 3599-3606 [Abstract] ( 124 ) RICH HTML PDF (4253 KB) ( 121 )

	3607	Specular Reflection Removal Method Based on Polarization Spectrum Fusion and Its Application in Vegetation Health Monitoring
		LI Si-yuan, JIAO Jian-nan, WANG Chi^*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3607-08
		Vegetation remote sensing monitoring has been widely used in various fields, such as crop disease and insect pest monitoring, forest coverage monitoring, and vegetation growth monitoring. Monitoring changes in plant chlorophyll content is of great significance for understanding plant growth, monitoring vegetation pests and diseases, and even monitoring vegetation feedback on global climate change. However, these monitoring are often disturbed by the specular reflection of leaves, which reduces the inversion accuracy of chlorophyll content. This paper aims to eliminate the specular reflection interference in remote sensing monitoring of plant health, a polarization multispectral imaging system was established, a specular reflection removal index (SRRI) was proposed. A fusion algorithm was proposed to detect plants based on the spectral and polarization characteristics of diffuse and specular reflection of vegetation. SRRI, degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to eliminate the interference of specular reflection and improve the accuracy of plant health status detection. In addition, a fusion algorithm based on SRRI, DoLP and AOP calculates a polarization fusion specular reflection removal index (PFSRRI). Correlation analysis was performed on relative chlorophyll content (SPAD), ratio vegetation index (SR), normalized vegetation index (NDVI), SRRI_SR, SRRI_NDVI, PFSRRI_SR and PFSRRI_NDVI to understand their ability to eliminate specular reflection interference. The results showed that SR and SPAD (R²=0.012 8) and NDVI and SPAD (R²=0.007 5) had the worst correlation, indicating that SR and NDVI had the highest sensitivity to mirror reflection. SRRI_SR and SPAD (R²=0.818), and SRRI_NDVI and SPAD (R²=0.889) had a good correlation. The correlation between PFSRRI_SR and SPAD (R²=0.955) and PFSRRI_NDVI and SPAD (R²=0.948) was the best, which highlighted the potential of PFSRRI in eliminating mirror reflection interference and detecting plant health status. PFSRRI_SR and PFSRRI_NDVI 3d scatter plots show good discrimination ability for different health degrees of plants, with high sensitivity and specificity. The variation trend and classification status of vegetation health state can be intuitively seen through the color and trend of the surfaces. Among them, the sensitivity and specificity of PFSRRI_SR to classify specular leaves from stress level-1 was 100% and 100%, and the sensitivity and specificity of PFSRRI_NDVI to classify specular leaves from stress level-1 was 98% and 100%, indicating the excellent detection performance of PFSRR_SR and PFSRRI_NDVI after removing specular interference. In summary, this method can effectively eliminate specular reflection interference and improve the detection accuracy of vegetation health status.
		2023 Vol. 43 (11): 3607-3614 [Abstract] ( 121 ) RICH HTML PDF (7580 KB) ( 40 )

	3615	Effect of Interaction Between Catechin and Glycosylated Porcine Hemoglobin on Its Structural and Functional Properties
		GUO Jing-fang, LIU Li-li^*, CHENG Wei-wei, XU Bao-cheng, ZHANG Xiao-dan, YU Ying
		DOI: 10.3964/j.issn.1000-0593(2023)11-3615-07
		Porcine hemoglobin (PHb) can endow and improve food quality during preparation, processing and storage. However, it is limited to be used in food because of its high viscosity, unstable properties and heavy blood smell, which people do not accept. Therefore, modification is important to improve its economic benefits and bioavailability. This study investigated the effect of the interaction between catechin and glycosylated porcine hemoglobin (G-PHb) on the functional characteristics and structure of the complex of catechin and glycosylated porcine hemoglobin (CG-PHb). PHb and G-PHb were used as controls. The solubility, turbidity, emulsifying property, surface hydrophobicity and oxidation resistance of CG-PHb were studied.The structural changes of the CG-PHb were analyzed via UV-Vis, FS, FT-IR and SEM. The results show that the solubility of CG-PHB increased significantly (p<0.05), and the turbidity decreased significantly (p<0.05). The emulsifying activity and stability were increased by 38.36%, 21.31%, 16.08% and 3.69% respectively (p<0.05), compared with the control group. CG-PHB has the largest surface hydrophobicity among the PHb, G-PHb and CG-PHB. When the concentration of the solution was increased to 1.60 g·mL^-1, the oxidation resistance was enhanced to 93.60%. Compared with the PHb and G-PHb, the UV absorption peak of CG-PHb has a wider peak shape, a larger peak and a slight red shift. The fluorescence peak intensity is CG-PHb>PHb>G-PHb. The secondary structure of CG-PHb was changed. Its β-sheet content increased significantly (p<0.05), but the content of α-helix, β-turn and irregular curl decreased (p<0.05). In addition, the scanning electron microscope showed that the protein structure changed due to the embedding of glycosyl groups and the interaction between glycosyl groups and catechins. It leads to the increase of the surface pore structure of CG-PHB. It is conducive to the exertion of its functional characteristics. This study can provide new ideas for protein modification research and provide a theoretical basis and reference for the property changes of the compound during food processing.
		2023 Vol. 43 (11): 3615-3621 [Abstract] ( 94 ) RICH HTML PDF (3020 KB) ( 26 )

	3622	Activation of Epigallocatechin Gallate on Alcohol Dehydrogenase: Multispectroscopy and Molecular Docking Methods
		ZHANG Xiao-dan^{1, 2}, LIU Li-li^1*, YU Ying¹, CHENG Wei-wei¹, XU Bao-cheng¹, HE Jia-liang¹, CHEN Shu-xing^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2023)11-3622-07
		Alcohol dehydrogenase (ADH) plays a key role in the pathway of alcohol metabolism. By activating ADH activity, the absorption of alcohol can be promoted to relieve alcoholism and protect the liver. This paper studied the interaction between ADH and epigallocatechin gallate (EGCG). The binding mechanism of ADH and EGCG was investigated by UV-Vis spectrum, fluorescence spectrum, Fourier infrared spectrum and molecular docking method. The thermal denaturation temperatures of ADH and EGCG-ADH complex were measured by differential scanning calorimeter, and then the thermal stability changes of the complex EGCG were analyzed. EGCG was characterized by scanning electron microscope. The results showed that EGCG activated the catalytic activity of ADH, and the activation rate was 33.33%. The effect of EGCG on ADH caused its microenvironment and secondary structure changes, forming a complex with anumber of binding sites close to 1, and van der waals force and hydrogen bond played an important role in its stability. Compared with ADH, the α-helix content in the secondary structure of the complex decreased, and the β-sheet content increased. In addition, the molecular docking results further confirmed that the hydrogen bond between the hydroxyl group of the EGCG benzene ring and the surrounding amino acids is beneficial to maintain the stability of the complex. In addition, the van der waals force and n-alkyl between EGCG and ADH are the main reasons for the activation of ADH activity. The above results proved that EGCG can activate the catalytic activity of ADH by combining with ADH, which can provide theoretical guidance for the preparation of safer and more efficient alternatives to hangovers.
		2023 Vol. 43 (11): 3622-3628 [Abstract] ( 182 ) RICH HTML PDF (3552 KB) ( 30 )

	3629	Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging
		SHEN Ying, WU Pan, HUANG Feng^*, GUO Cui-xia
		DOI: 10.3964/j.issn.1000-0593(2023)11-3629-08
		Algal bloom, a water pollution caused by marine algae, may threaten the development of fisheries due to some toxic algal species. Rapid and accurate identification of red tide algal species and their cell concentrations is important for pollution control and management. Traditional detection methods such as microscope and gene sequencing have low timeliness, remote sensing is susceptible to environmental interference resulting in low accuracy, and fluorescence spectroscopy is too expensive for widespread use. Hyperspectral imaging (HSI) technology provides a rapid and non-destructive method for detecting red tide algae species. In this study, a HSI detection system was built to establish a large amount of hyperspectral sample libraries constituted of dinophyta (Amphidinium carterae), bacillariophyta (Skeletonema costatumand Phaeodactylum tricornutum) and raphidophyceae (Heterosigma akashiwo). Two classification methods and three regression methods were used to construct models for algal species identification and cell concentration measurement, respectively, and the effects of seven spectral pretreatment methods (Autoscaling, Normalization, Multiplicative Scatter Correction, Standard Normalized Variate, Savitzky-Golay Smoothing, First Derivative Based on Savitzky-Golay, and Second Derivative Based on Savitzky-Golay) and two band extraction methods (Genetic Algorithms and Successive Projections Algorithm) on the accuracy of modelling were investigated. The results showed that the Second Derivative Based on Savitzky-Golay (SG+2^nd) pretreatment method can improve the accuracy of band extraction and modelling, and that the feature bands selected by the genetic algorithm (GA) are more representative and effective. The feature bands (644.7, 547.8, 562.6, 829.4, 832 nm) extracted SG+2^nd-GA correspond to the absorption spectral bands of specific pigments in the selected algae, combined with Support Vector Machine (SVM) or Back Propagation Neural Network (BPNN) modellingrealized the effective identification of dinophyta, bacillariophyta and raphidophyceae using HSI technology. Compared to Multiple Linear Regression (MLR) and Partial Least Squares (PLS) algorithms, Support Vector Regression (SVR) modelling achieved higher accuracy incell concentration measurements. The coefficients of determination (R²) of the four algal SG+2^nd-GA-SVR cellconcentrations prediction models were all greater than 0.98. Among them, the predicted concentrations of A. carterae e and S. costatumranged from 1.05×10³~1.05×10⁴ and 1.13×10⁴~2.38×10⁵ cells·mL^-1, with the lowest measured concentrations reaching the benchmark concentrations for this algae species in the event of red tide. The predicted concentrations of P. tricornutum ranged from 1.06×10⁵~4.36×10⁶ cells·mL^-1, with the lowest measured concentrations being lower than those of existing spectroscopic techniques. This study provides a new method for rapid, accurate, non-destructive algal blooms detection.
		2023 Vol. 43 (11): 3629-3636 [Abstract] ( 109 ) RICH HTML PDF (3590 KB) ( 95 )

	3637	Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method
		SUN Lin¹, BI Wei-hong¹, LIU Tong¹, WU Jia-qing¹, ZHANG Bao-jun¹, FU Guang-wei¹, JIN Wa¹, WANG Bing², FU Xing-hu^1*
		DOI: 10.3964/j.issn.1000-0593(2023)11-3637-07
		The green tide is a kind of algal bloom phenomenon formed by the growth and aggregation of Marine macroalgae, which seriously affects the coastal ecological environment. Accurate monitoring of the coverage area of green tide is of great significance for preventing, monitoring and managing green tide disasters. The use of spectral methods for remote sensing monitoring has the advantages of non-contact, low cost and small loss, among which airborne hyperspectral remote sensing has a wide range of application prospects in the Marine field due to its advantages of high spectral and spatial resolution and more imaging channels. This study used DJI M300 RTK equipped with 410 Shark hyperspectral imaging system to collect data from the green algae outbreak area in The Dream of Bay, Qinhuangdao City. The collected spectral data were preprocessed to extract the spectral features of different ground objects. Based on the features, a spectral feature dataset with a capacity of 30 000 was constructed. The dataset was randomly divided into the training set and a test set, in which the training set accounted for 75% and the test set accounted for 25%. Five machine learning algorithms established the hyperspectral green tide inversion model, including Decision Tree, Random Forest, SVM, K-Nearest Neighbor and three-input voting classifier. The green algae coverage area in the green tide outbreak area was calculated by ground resolution cell (GRC) based on an airborne hyperspectral imaging system, and the classification accuracy of the inversion model was tested based on the in-dataset accuracy, Kappa coefficient and the preset standard area error verification method. The experimental results show that time can be saved by band selection first when the dichotomy of green algae pixels and other earth objects is performed on hyperspectral data and big data prediction is performed using the proposed classifier. The classification accuracy of the hyperspectral data can be effectively improved by logarithmic processing to enhance the differences between the spectra and then constructing the classifier model. The inversion accuracy of the hyperspectral green tide inversion model based on the three-input voting classifier of Random Forest, SVM and K-Nearest Neighbor is 98.95% in the dataset, and the Kappa coefficient is 0.978 9. The classification error obtained by the prespecified standard area error verification method is 6.06%. Applied through the experimental area of hyperspectral image prediction, it proved that the model in the prediction of big data still keeps high accuracy, and for the mixed pixels underwater algae pixels can also define, show that the method is feasible and superiorin the field of green tide remote sensing monitoring, in the field of green tide area monitoring has universality, has extensive application prospect in the field of marine monitoring.
		2023 Vol. 43 (11): 3637-3643 [Abstract] ( 151 ) RICH HTML PDF (3690 KB) ( 88 )

	3644	Flame Temperature and Emissivity Distribution Measurement MethodBased on Multispectral Imaging Technology
		WANG Wen-song¹, PEI Chen-xi², YANG Bin^1*, WANG Zhi-xin², QIANG Ke-jie², WANG Ying¹
		DOI: 10.3964/j.issn.1000-0593(2023)11-3644-09
		Flame combustion parameters can directly reflect the flame combustion state and diagnose, predict and optimize the combustion process. The accurate measurement of flame combustion temperature and emissivity is of great importance for building combustion models, optimizing combustion processes and controlling pollutant emissions. With the development of digital image technology and spectroscopy, multispectral imaging technology has been gradually applied to flame combustion temperature and emissivity measurement. For the problems of low spatial resolution of the spectrometer and low spectral resolution of RGB color camera, multispectral imaging technology can obtain flame spectral images with both spatial and spectral resolution and realize temperature and emissivity distribution measurement of flame, which has the advantages of high spatial and spectral resolution, fast response and wide temperature range. Here, the temperature and emissivity measurement method of flame based on multispectral imaging technology was proposed.The standard high-temperature blackbody radiation experimental measurement system was built to carry out the high-temperature blackbody radiation response coefficient calibration experiments for the 665～960 nm band of multispectral imaging camera. The 25 band spectral response calibration coefficient of multispectral imaging camera was obtained, and the relationship between the instrument response value and theoretical radiation intensity at each band of multispectral imaging camera was established by fourth-order polynomial fitting. The measurement-validated experiment of multispectral imaging technology was carried out. The relative deviation of temperature and emissivity measurement is less than 1% and 4%, respectively. The flame multispectral imaging measurement system was established with a candle flame as the research object. The multispectral radiation images of the candle flame were obtained, and the temperature and emissivity distribution measurement was realized based on Planck's radiation law parameter fitting method.The measurement results show that the temperature and emissivity in the vertical plane of the central area of flame are higher than those in the upper and bottom in the vertical plane of flame; the range of flame temperature measurement results is about 1 350～2 050 K and the highest temperature in the central area is approximately 2 050 K; the range of flame emissivity measurement results is about 0.04～0.36, and the highest emissivity in the central area is 0.36. The measurement results are consistent with the candle flame-burning process and the distribution pattern of radiation characteristics.
		2023 Vol. 43 (11): 3644-3652 [Abstract] ( 264 ) RICH HTML PDF (4609 KB) ( 196 )

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