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2022 Vol. 42, No. 08 Published: 2022-08-01

	2325	Research Progress of Isotope Analysis Method Based on Optical Spectroscopy
		DONG Jun-hang^{1, 2}, ZHU Zhen-li^{1, 2*}, DING Han-qing^{1, 2}, XING Peng-ju¹, ZHOU Fei-yang^{1, 2}, ZHENG Hong-tao², LIU Xing¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2325-09
		Isotope analysis has attracted much attention in various industrial fields dominated by the nuclear industry, and it has promoted the development of geology, materials science, chemistry and other related disciplines. In recent years, the optical isotope analysis method has attracted increasing attention. Mass spectrometry methods, such as multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), thermal ionization mass spectrometry (TIMS) and isotope ratio mass spectrometry (IRMS), are the standard methods of isotopic analysis. However, they typically require complex sample pretreatment procedures and frequent instrumental maintenance. In this regard, optical isotope analysis methods possess their unique advantages.They can even meet the on-site real-time and rapid isotope analysis, which has already shined in nuclear industry isotope analysis and traditional stable isotope analysis. With the further development of key components of spectroscopy instruments and data processing methods, the performance of spectroscopy analysis, such as sensitivity, resolution and precision, has been greatly improved, so that optical isotope analysis methods have been developed rapidly and applied to the isotope analysis of environmental and geological samples. This article reviews the progress of the optical isotope analysis methods, classified into emission spectroscopy (atomic emission spectroscopy, molecular emission spectroscopy and Raman spectroscopy) and absorption spectroscopy (atomic absorption and molecular absorption) from the perspective of the principle of spectroscopic analysis. It mainly focuses on the basic principle, development history and important progress of these methods, and the advantages, and limitations compared with mass spectrometry are also briefly described. It also discussed the prospects of optical isotope analysis,especially the technical difficulties that still need to be broken through. This review will provide a reference for understanding the development of optical isotope analysis.
		2022 Vol. 42 (08): 2325-2333 [Abstract] ( 294 ) RICH HTML PDF (3867 KB) ( 261 )

	2334	Research on Optimization of Determination Conditions for Trace Gold Analysis by Graphite Furnace Atomic Absorption Spectrometry Based on RSM Model
		WANG Peng, MEN Qian-ni^*, GAN Li-ming, YANG Ke
		DOI: 10.3964/j.issn.1000-0593(2022)08-2334-06
		The analytical method of trace gold in geochemical samples by graphite furnace atomic absorption spectrometry(GFAAS) has been widely used. However, it is hard to figure out the appropriate matching parameters in practice in terms of the configuration of its determination conditions and temperature-rising program parameters. Due to this reason, it is of great practical significance to work out effective parameters quickly and accurately. The single factor test is carried out with lamp current, ashing temperature, and atomization temperature as independent variables and its parameters are set to be 6~8 mA, 300~500 ℃ and 2 200~2 400 ℃ respectively; The Box-Behnken test is done according to the Response Surface Methodology (RSM). The influence of three-factor and three-level surface design on the response value (absorbance) is analyzed; the significance level table is prepared, and the response surface test is completed; a prediction model of the quadratic polynomial regression equation is built analyze the significance. F=43.95, p<0.000 1 means that the model has high significance with its correlation coefficient of 0.985 1.Thefact that the correction coefficient of determination is 0.962 6 indicates that the model can explain more than 95% of the changes in response values; the response surface and contour map are drawn to regress and fit the test data. Judgement and analysis are made according to the shape of the response surface and the steepness of the contour. These optimal parameters as lamp current 7.12 mA, ashing temperature 412.32 ℃, and atomization temperature 2 311.61 ℃ are worked out. The results suggest that under an optimized condition, the average absorbance of national first-class reference material GBW07246a is 0.101 2, basically consistent with the predicted value of 0.108 0, and the relative error remains 6.30%; six national first-class reference materials, including GBW07243b are selected for 12 repeated tests, and the standard curve is drawn, and the results are read. The logarithmic deviation between the average value and the standard value of each reference material is less than 0.05, the relative standard deviation (RSD, n=12) is less than 10%, and both the accuracy and precision comply with GB/T 27417—2017 (Guide for confirmation and verification of chemical analysis for conformity assessment), indicating that the parameters of conditions for the determination of trace gold by graphite furnace atomic absorption spectrometry based on RSM model are accurate and reliable, which proves the correctness and feasibility of this model and achieves a good optimization result. This method is expected to be applied to determine and analyze other elements and in the method research of instrument analysis platform to find the optimal analysis and test conditions.
		2022 Vol. 42 (08): 2334-2339 [Abstract] ( 133 ) RICH HTML PDF (2257 KB) ( 192 )

	2340	Inversion of Object Materials and Their Proportions Based on Scattering Spectra
		SHI Jing^{1, 2}, TAN Yong¹, CHEN Gui-bo¹, LI Shuang¹, CAI Hong-xing^1*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2340-07
		The paper investigated the inverse method for the surface materials and the proportions of the space object from long distances based on the scattering spectra. The research results shall provide the data references for space debris detection and forecasting. Firstly, based on the long-distance object detection physical model of scattering spectra, we first constructed the object parameter inverse physical model based on scattering spectra and provided the inverse algorithm for object surface material and its proportion based on the least norm theory method. By combining with the lighting characteristics, object material surface optical reflection characteristics, incidence, reflection and detector angle data, etc., using the multimodal fusion model of the bidirectional reflectance distribution function (BRDF) and characterizing the optical reflection characteristics of the complex material surface. We took the corresponding area in the BRDF as the parameter to be inverted and obtained the inversion algorithm of the object surface materials and its proportion information. Secondly, we performed the experimental validation by building an indoor scattering spectrum detection and acquisition system to perform the scattering spectrum detection and data acquisition of single material and multiple materials with different scales. We intercepted the effective wavelength range of 400~800 nm by preprocessing the scattering spectrum data. Combined with theoretical analysis and inversion algorithm, we took the samples with 4 kinds of materials and performed the material and its proportion inversion with equal proportional and non-equal proportion combination for the samples. The minimum, maximum and average errors of the equal proportion inversion are 0.8%, 13.6% and 4.9%. Moreover, the minimum, maximum and average errors of the non-equal proportion inversion are 6%, 12% and 9.25%. Therefore, according to the above testing results, the maximum average error for inversion is 9.25%. Once considering the error influence of 2.89% from the incidence light source, the maximum average error for inversion will be lower than 6.36%. So we suggested the average inversion error will be less than 10%. Thereby the accuracy of the inverse method is verified. Finally, take one failed satellite as an example. The proposed method was used to invert the materials and proportions of its on-orbit scattering spectra. Its surface materials consist of solar arrays, insulation film and carbon fiber plates, which matches well with the real situation. In summary, this paper has provided a new technical approach for the inversion identifying of space object materials and their proportions from long distances.
		2022 Vol. 42 (08): 2340-2346 [Abstract] ( 112 ) RICH HTML PDF (3309 KB) ( 69 )

	2347	Study on the Spectral Prediction of Phosphor-Coated White LED Based on Partial Least Squares Regression
		ZHANG Yuan-zhe¹, LIU Yu-hao¹, LU Yu-jie¹, MA Chao-qun^{1, 2*}, CHEN Guo-qing^{1, 2}, WU Hui^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2347-06
		To predict the luminescence spectrum of phosphor-coated white LEDs more conveniently and efficiently, GaN Blue LED chip and YH-S525M green phosphor and YH-C640E red phosphor from Hangzhou Yinghe Optoelectronic Materials Co., Ltd. were selected for preparing experimental samples. The monochromatic fluorescence spectra were measured respectively. The emission peak wavelength of the blue-chip is 453 nm, the emission peak wavelength of red and green phosphor is 631 and 526 nm respectively. The red and green phosphors were mixed with AB glue and coated on the blue-chip. The mass ratio of red and green phosphors was set as 1∶3, 1.2∶3, 1.4∶3, 1.6∶3, 1.8∶3 and 2∶3. The concentration of red phosphors was set as 7%, 9%, 11%, 13%, 15% and 17%. 3~5 samples were prepared under each proportion and concentration, and the luminescence spectrum of each sample was measured by HAAS-2000 high-precision fast spectral radiometer of Hangzhou Yuanyuan chromatography Co., Ltd. A total of 36 groups of SPD (spectral power distribution) data were obtained by normalizing the relevant data. The white light spectrum was regarded as the linear superposition of blue, green and red monochromatic fluorescence spectra. The corresponding emission spectrum was directly selected for blue and red peak terms, while two Gauss linear equations were used for fitting the green peak term, and the intensity determined the coefficient. Therefore, a prediction model of the white light spectrum was established. The circular search algorithm calculated the optimal values of the model parameters under 36 groups of experimental conditions, and the model’s goodness of fit was tested. R² ranged from 99.33% to 99.88%. Then, the partial least squares regression method was used to establish the regression equation between the mass ratio, concentration of phosphors and the model parameters. Finally, a new method that can accurately predict white LEDs’ emission spectrum coated with red and green phosphors was obtained. The SPD of a group of newly prepared samples was used to test the prediction effect. The goodness of fit of the predicted spectrum is 99.62%, which proves that the prediction effect of this method is good. Based on the physical mechanism of phosphor-coated LEDs, the mathematical relationship between the mass ratio, concentration of phosphors and the white light spectrum is established more simply and effectively. Meanwhile, the interaction between the two phosphors was analyzed, and the broadening effect of the green phosphor spectrum was introduced to the prediction model. There is good universality, and this method provides a new idea for optimising the light source parameters of the phosphor-coated LEDs.
		2022 Vol. 42 (08): 2347-2352 [Abstract] ( 124 ) RICH HTML PDF (3547 KB) ( 74 )

	2353	A Comparative Study of the COD Hyperspectral Inversion Models in Water Based on the Maching Learning
		WANG Chun-ling^{1, 2}, SHI Kai-yuan^{1, 2}, MING Xing^3*, CONG Mao-qin³, LIU Xin-yue³, GUO Wen-ji³
		DOI: 10.3964/j.issn.1000-0593(2022)08-2353-06
		Chemical oxygen demand (COD) is an important indicator of organic pollution in water. How to quickly and accurately test the COD content of water is particularly important. The application of machine learning in the field of water quality inversion is increasing, and more research results have been obtained. Hyperspectral remote sensing has the advantages of high spectral-spatial resolution and multiple imaging channels, so it has great potential in retrieving water’s COD. This study uses different hyperspectral pre-processing methods to process the original hyperspectral data. It uses the hyperspectral data before and after processing to compare the inversion performance of different machine learning models and different hyperspectral pre-processing methods on the COD content of water. Firstly, 1 548 groups of COD content and corresponding hyperspectral data (400~1 000 nm) samples were collected by ZK-UVIR-I in-situ spectral water quality on-line monitor in Baodai River. In order to reduce the interference of spectral noise and eliminate the influence of spectral scattering, Savitzky-Golay (SG) smoothing, Multiplicative scatter correction (MSC) and SG smoothing combined with MSC methods were used to pre-process the original spectra. Secondly, the sample set is randomly divided into training set and test set, where the training set accounts for 80% and the test set accounts for 20%. A COD hyperspectral inversion model based on the four machine learning methods of linear regression, random forest (random forest), AdaBoost, and XGBoost was established for the pre-processed training set full-band spectrum. Moreover, three indexes of determination coefficient (R²), root mean square error (RMSE) and relative analysis error (RPD) were selected to evaluate the accuracy of the hyperspectral inversion model. The results show that random forest, AdaBoost and XGboost are all the better than linear regression. The prediction ability of the inversion model established by XGboost is the best whether the spectral data is processed or not, with R² of 0.92, RMSE of 7.1 mg·L^-1, and RPD of 3.4. Considering that the original spectrum may be redundant, the dimensionality reduction of the spectrum after SG smoothing and MSC processing is performed by principal component analysis (PCA), and the top ten principal components with a cumulative contribution rate of 95% are selected as the input variables of the model. XGBoost established the inversion model, and the results show that after PCA, the accuracy of the inversion model is improved, the RPD is 3.8, and the training time of the model is shortened from 72 seconds to 2.9 seconds. The above research can provide new methods and ideas for establishing hyperspectral inversion models of this water area and similar water areas.
		2022 Vol. 42 (08): 2353-2358 [Abstract] ( 142 ) RICH HTML PDF (3565 KB) ( 101 )

	2359	A Model for the Identification of Counterfeited and Adulterated Sika Deer Antler Cap Powder Based on Mid-Infrared Spectroscopy and Support Vector Machines
		YANG Cheng-en¹, WU Hai-wei^1*, YANG Yu², SU Ling², YUAN Yue-ming¹, LIU Hao¹, ZHANG Ai-wu³, SONG Zi-yang³
		DOI: 10.3964/j.issn.1000-0593(2022)08-2359-07
		Sika deer antler caps are of great medicinal and economic value. Because of its hard texture, the finished product is generally presented as powder. It is difficult for consumers to determine the authenticity of sika deer antler cap powder from its appearance, which leads to endless series of counterfeit and adulterated products. Therefore, this paper proposes a FTIR technology and machine learning method to identify counterfeited and adulterated sika deer antler cap powder. This method can identify counterfeited sika deer antler cap powder by horse stag deer antler cap powder, sika deer bone powder, and adulterated sika deer antler cap powder by beef bone powder. This research’s sika deer antler caps, stag deer antler caps and sika deer bones are from five regions of the three provinces of Heilongjiang, Jilin and Liaoning. The samples are divided into 360 portions, including 120 portions of sika deer antler caps, 120 portions of red deer antlers caps and 120 portions of sika deer bones. The beef bone powder is purchased in Changchun Nanguan District Farmers’ Market. Adulterate the beef bone powder into 120 portions of sika deer antlers powder with 5%, 10%, 20%, 30%, 40%, and 50% for every 20 portions. Sample spectral data were collected by mid-infrared spectroscopy, preprocessed by multiple scattering correction (MSC), and sampled by the K-S method. After the training and test sets were divided by 2∶1, Normalization and principal component analysis (PCA) dimension reduction was conducted on spectral data. According to the principle of cumulative contribution rate of the number of principal components≥85% and principal component characteristic value≥1, the first 7 principal components were selected to form the spectral data after dimensionality reduction. The recognition models of support vector machine (SVM), random forest (RF) and Extreme learning machine (ELM) were established by using full-spectrum (FS) data and PCA dimensional-reduction spectral data as model inputs. The results showed a difference between the authentic and counterfeit and adulterated products in the waveband of 1 300~1 800 and 2 800~3 600 cm^-1. The difference between the pure sika deer antler cap powder and sika deer antler cap powder of the adulteration rate ≥10% was the most obvious. FS-SVM, PCA-SVM and FS-RF models all have excellent recognition effects in identifying fake and adulterated sika deer antler hat powder. The recognition rate of the training and test set is 100%, and the recognition rate of other models is less than 98%. From the perspective of simplified models, the modeling time of FS-SVM and FS-RF is 4 859.36 and 1 818.96 s respectively, while the modeling time of PCA-SVM is only 19.91 s. Therefore, PCA-SVM has the best overall effect among the six recognition models. The research shows that the method based on mid-infrared spectroscopy combined with support vector machine modeling can be used as a fast, accurate and non-destructive identification method for counterfeiting and adulteration of sika deer antler cap powder.
		2022 Vol. 42 (08): 2359-2365 [Abstract] ( 393 ) RICH HTML PDF (3667 KB) ( 85 )

	2366	Establishment of Visible and NIR Spectral Reflectance Database of Plant Leaves and Principal Component Analysis
		JIANG Wan-li^{1, 2}, SHI Jun-sheng^{1, 2*}, JI Ming-jiang^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2366-08
		Visible and near-infrared spectral reflectance is the basic database for research and application in color science and technology and remote sensing object classification and recognition.The principal component analysis (PCA) is widely used in spectral data analysis, spectral reconstruction, hyperspectral data dimension reduction, and remote sensing image classification. In this paper, a database of spectral reflectance from visible light to near-infrared of 150 leaves of 48 plants, including Salix, Cinnamomum camphora (L.) Presl, Dracaena marginata, and Jacaranda mimosifolia, etc. Which are common in park greenery of Yunnan, isestablished. The wavelength range from 400 to 1 000 nm with 4 nm intervals. The PCA wascarried out on the visible and from visible to near-infrared wavebands respectively.The measurement results show that the spectral reflectance of different vegetation leaves according to the same hue of red, green and yellow are the same, For the same plant,in the visible waveband, the spectral reflectances are quite different because of the different content of chlorophyll, lutein, carotene and anthocyanin in the body.The spectral reflectance of all plant leaves in the near-infrared waveband is only different in amplitude, while the spectral reflectance of the same plant does not change with wavelength.The PCA shows that the cumulative contribution rates of the first three principal components in the visible and visible near-infrared wavebands reached 98.62% and 94.97% respectively.The database and results of PCA provide support for the spectral reconstruction of natural objects, the multispectral imaging technology and the classification and recognition of the target of remote sensing images.
		2022 Vol. 42 (08): 2366-2373 [Abstract] ( 132 ) RICH HTML PDF (10820 KB) ( 88 )

	2374	Terahertz Imaging Study of Dentin Caries
		LI Yan¹, LIU Qi-hang^{2, 3}, HUANG Wei¹, DUAN Tao¹, CHEN Zhao-xia¹, HE Ming-xia^{2, 3}, XIONG Yu^1*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2374-06
		Dental caries are closely related to pulp infection status and vitality, and the depth of caries determines the clinical treatment. However, the detection and diagnosis methods of dentin caries, such as visual examination and probe, are easily affected by subjective factors. X-ray assistive examination for caries has low sensitivity and some defects, and its reliability and effectiveness still need to be improved. Terahertz time-domain spectroscopy can image different physical parameters, which has great potential in the physical and nondestructive detection of dentin caries. This study aimed to explore terahertz spectral images of dentin caries. Experiment by transmission and reflection type scanning terahertz platform, to 15 containing dentine caries in vitro teeth grinding slice scanning, through the data of two-dimensional reconstruction, won the terahertz transmission and reflection of different parameters in the image. Terahertz images and laboratory studies of dental decay light image and X-ray image contrast, the proposed merger under light microscopy, THz image caries damage area is under evaluation.The results show that the terahertz image is consistent with the light mirror image and has higher sensitivity than the X-ray image.Under the reflection mode, the sample thickness, surface roughness and system noise greatly influence the experimental results due to the weak THz reflection signal. The image can only identify the contour of the sample but cannot be used to distinguish enamel, dentin and dental caries.In the transmission mode, both the frequency domain 1.4 THz phase difference imaging and the time domain imaging corresponding to the minimum signal can be used to distinguish enamel, dentin and dental caries, and the time imaging corresponding to the minimum signal can be used to distinguish the three, among which the time imaging corresponding to the minimum signal has the best effect.Measured under the light dentine caries damage area and terahertz time-domain signal of transmission mode for the minimum time corresponding image of dentine caries damage area matching samples Wilcoxon signed-rank and inspection. The results (p>0.05) still cannot think of two methods in the area of dentine caries loss difference, two methods of measuring the difference due to system error.Therefore, we can think that the diagnostic information such as the range and size of dentin caries can be obtained through the time-image corresponding to the minimum value of time-domain signal in the terahertz transmission mode.This study shows that terahertz imaging technology can provide a more accurate and effective diagnostic method without ionizing radiation for the early diagnosis of dentin caries and provide some morphological basis for clinical digitalization and minimally invasive caries removal.
		2022 Vol. 42 (08): 2374-2379 [Abstract] ( 113 ) RICH HTML PDF (3469 KB) ( 49 )

	2380	Research on Protein Powder Adulteration Detection Based on Hyperspectral Technology
		LI Bin, YIN Hai, ZHANG Feng, CUI Hui-zhen, OUYANG Ai-guo^*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2380-07
		Protein powder is an essential nutritional supplement for bodybuilders, and the market demand is increasing. Some unscrupulous businessmen are adding cheap powder to protein powder for sale to profit. The traditional protein powder adulteration detection method is time-consuming, laborious, complicated and expensive. Hyperspectral technology has the advantages of easy operation and rapid detection without damaging the experimental sample. Therefore, this paper proposes the use of hyperspectral technology to achieve protein powder adulteration detection. In the experiments, three types of adulterants (corn flour, rice flour and wheat flour) with 5%~60% mass percentages and 5% concentration interval were added to the protein powder, and the spectral information of all samples was collected. In the qualitative discrimination of the three types of adulterants (corn flour, rice flour and wheat flour) in the protein powder, the spectral data were firstly processed using the pre-processing methods of convolutional smoothing (SG), normalization (Normalize), multiple scattering correction (MSC), baseline correction (Baseline) and standard normal transformation (SNV), and then the spectral data were established based on principal component regression ( PCR), backpropagation neural network (BPNN), and random forest (RF) models, among which the RF model built under the MSC preprocessing method based on full-band spectra is the best, and its overall accuracy reaches 100%. Its corresponding R_P and RMSEP are 0.997 9 and 0.018 9, respectively. In the quantitative analysis of different adulterant concentrations in protein powder, the spectra of the three types of adulterated samples were pretreated with SG, Normalize, MSC, Baseline and SNV, respectively, and LSSVM models were established. The performance between the models under different pretreatment methods was compared. The best LSSVM prediction models were used for corn flour, rice flour and wheat flour adulterated in protein powder preprocessing methods were None, Baseline and Normalize, and then, the continuous projection algorithm (SPA) and competitive adaptive reweighting algorithm (CARS) were used to screen them and build LSSVM models. The RP corresponding to the SPA-LSSVM models for the three types of adulterated samples were 0.989 0, 0.986 0 and 0.997 9, and the R_P of the CARS- LSSVM model corresponds to R_P of 0.991 0, 0.994 6 and 0.999 1, so the CARS-LSSVM model for the three types of adulterated samples has a better prediction. Research shows that hyperspectral technology can achieve qualitative and quantitative detection of protein powder adulteration and simple operation, rapid and non-destructive detection.
		2022 Vol. 42 (08): 2380-2386 [Abstract] ( 138 ) RICH HTML PDF (2553 KB) ( 193 )

	2387	Quantitative Analysis of Soil Heavy Metal Elements Based on Cavity Confinement LIBS Combined With Machine Learning
		LIU Ye-kun, HAO Xiao-jian^*, YANG Yan-wei, HAO Wen-yuan, SUN Peng, PAN Bao-wu
		DOI: 10.3964/j.issn.1000-0593(2022)08-2387-05
		The detection and control of the content of heavy metal elements in the soil are of great significance to the restoration of agriculture and the ecological environment. This study used external cavity confinement combined with traditional laser-induced breakdown spectroscopy (LIBS) to obtain soil spectral data. Then machine learning was used to analyze the content of heavy metal elements Ni and Ba in the soil. During the experiment, the delay time was set to 0.5～5 μs, Ni Ⅱ 221.648 nm and Ba Ⅱ 495.709 nm were selected as the target characteristic spectrum to study, and calculated the influence of delay time on the signal-to-noise ratio (SNR), spectral intensity and enhancement factor under two LIBS conditions. Experimental results show that cavity confinement LIBS (CC-LIBS) can increase the target element’s spectral intensity and SNR. As the acquisition delay time increases, the number of plasmas decreases, and the spectral intensity and SNR gradually decrease, then become stable; when the delay time is set to 1 μs, the SNR of the characteristic spectrum of Ni and Ba elements reaches the best under CC-LIBS conditions, which is determined to be the optimal experimental condition for LIBS at this time. Obtain the spectral data of 9 soil samples containing Ni and Ba through optimal conditions. Since there were 12 248 data points for each set of collected spectral information, the principal component analysis algorithm (PCA) was used to reduce the dimensionality of the spectral data under CC-LIBS conditions. After retaining more than 95% of the original soil information, 9 principal components were selected as the quantitative analysis model’s input variables to improve the model’s calculation speed. The Lasso, AdaBoost and Random Forest models in machine learning were used to model and predict the spectral data after PCA dimensionality reduction to realize the quantitative analysis of soil heavy metal elements Ni and Ba. The experimental results show that the Random Forest model has the best prediction performance in the training and test sets compared with Lasso and AdaBoost models. Under the Random Forest model, the correlation coefficient R² of the Ni element in the test set is 0.937, and the root mean square error (RMSEP) is 3.037; the R² of the Ba element in the test set is 0.886, the RMSEP is 90.515. This paper is based on the research of cavity-confinement LIBS technology combined with machine learning to provide theoretical support and technical guidance for the high-precision detection of heavy metal elements.
		2022 Vol. 42 (08): 2387-2391 [Abstract] ( 135 ) RICH HTML PDF (3243 KB) ( 163 )

	2392	The Study of Digital Baseline Estimation in CVAFS
		YU Xin, ZHOU Wei^*, XIE Dong-cai, XIAO Feng, LI Xin-yu
		DOI: 10.3964/j.issn.1000-0593(2022)08-2392-05
		There are three forms of mercury in water: elemental mercury, inorganic mercury and organic mercury. Methylmercury is the main organic mercury form, and is much higher than that of elemental mercury and inorganic mercury. Cold vapor atomic fluorescence spectrometry(CVAFS) is the recommended method for measuring methylmercury in the water. CVAFS is an element analysis method developed from atomic emission and absorption spectrometry. After years of development and improvement, it has become one of the most commonly used technologies for element analysis. It is widely used in environmental protection, life science, geology and other fields with the characteristics of high sensitivity and low detection limit. However, affected by the background noise of the excitation light source, electronic components of the detection instrument and the separation effect of the chromatographic column, the signal of CVAFS will haveproblems such as baseline drift and signal tailing, which will seriously influence the peak area calculation of the CVAFS’s data and the quantitative analysis of trace methylmercury. Baseline drift is the most critical problem. At present, improving analogue device parameters and digital baseline estimation are two important ways to solve baseline drift. In terms of improving the parameters of the analogue device, there are hollow cathode mercury lamps and closed-loop controlled hot cathode low-pressure mercury lamps with disadvantages such as complex experimental equipment and high cost. The digital baseline estimation includesthe least square method, difference fitting method and so on, as all of them have weaknesses like unstable baseline estimation and inaccurate content calculation. Thus, a digital baseline estimation method based on wavelet transform was proposed. Firstly, by analyzing the microscopic signal of CVAFS and baseline drift of methylmercury, the mathematical model of the signal of CVAFS and baseline drift wasestablished. Secondly, according to the characteristics of the signal of the CVAFS model and wavelet transform, an appropriate mother wavelet model was established. The mother wavelet model was convoluted with the baseline drift model, and the convolution result was always zero. Theoretically, it indicated that the baseline drift wouldbe eliminated after wavelet transform. Thirdly, taking 100 pg standard sample methylmercury as an example, the experiments verified that wavelet transformation could eliminate baseline drift and solve the problem of signal tailing. Finally, under the condition that the relative standard deviation (RSD) of the instrument is 1.29%~3.40%, the experiments were repeated for 5 times for standard methylmercury solutions of 0, 10, 20, 50, 100, 500 and 1 000 pg, and the calibration curves of the average peak area before and after wavelet transform were established respectively. The calibration curve’s correlation coefficient (R²) is increased from 0.994 to 0.997 after the wavelet transform. The experimental results showed that this method could effectively eliminate the influence of baseline drift and signal tailing and improve the system’s measurement accuracy.
		2022 Vol. 42 (08): 2392-2396 [Abstract] ( 126 ) RICH HTML PDF (2354 KB) ( 51 )

	2397	Study on Detection Method of Leaves With Various Citrus Pests and Diseases by Hyperspectral Imaging
		WU Ye-lan¹, GUAN Hui-ning¹, LIAN Xiao-qin¹, YU Chong-chong¹, LIAO Yu², GAO Chao¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2397-06
		Numerous kinds of diseases and insect pests affect citrus’ growth, but most of the current detection methods are for a single condition. It is important for the accurate application of pesticides in orchards and the healthy development of the citrus industry that the development of a detection method based on hyperspectral imaging and machine learning to achieve rapid and accurate detection of multiple pests and diseases on citrus leaves. Naturally onset citrus leaves in orchards were used as research objects, including normal citrus leaves (50 pieces), ulcer disease leaves (50 pieces), soot disease leaves (103 pieces), nutrient deficiency disease leaves (60 pieces), and red spider leaves (56 pieces) and herbicide damage leaves (85 pieces), hyperspectral data in the 350～1 050 nm band were collected. First-order derivation (1^stDer), multivariate scattering correction (MSC) and median filtering (MF) were used to preprocess the original (Origin) hyperspectral data, principal component analysis (PCA) and competitive adaptive weighting (CARS) algorithms were used to extract characteristic wavelengths from the prepossessed hyperspectral data. Characteristic wavelengths obtained by CARS were 10, 5, 12 and 10 respectively, and the 4 sets of characteristic wavelengths obtained by PCA were all 7, ranging in the 700～760 nm band. The limit gradient boosting tree (XGBoost) was used for the full band (FS), and the support vector machine (SVM) was used for the characteristic wavelength to establish a multi-classification model of citrus diseased leaves. The classification models established by XGBoost are Origin-FS-XGBoost, 1^stDer-FS-XGBoost, MSC-FS-XGBoost and MF-FS-XGBoost, and the overall classification accuracy (OA) obtained from the detection of 6 kinds of diseases and insect pests leaves was 94.32%, 93.60%, 95.98% and 96.56% respectively; the classification models established by SVM are Origin-CARS-SVM, 1^stDer-CARS-SVM, MSC-CARS-SVM, MF-CARS-SVM, Origin-PCA-SVM, 1^stDer-PCA-SVM, MSC-PCA-SVM and MF-PCA-SVM, model OA was 93.63%, 90.26%, 87.90%, 91.95%, 87.53%, 90.82%, 83.50% and 90.98% respectively. The experimental results demonstrate that the recognition rate of the XGBoost model with FS as input was better than the SVM model with characteristic wavelength as input. The OA of the MF-FS-XGBoost model was 96.56%, the Recall was 95.91%, and the model training time was 63 s. The overall performance was the best; the CARS-SVM modeling effect was better than PCA-SVM. After pre-processing by all three methods, the recognition rate of the CARS-SVM model was above 87%, and the recognition rate of the PCA-SVM model was above 83%. The results show that hyperspectral imaging technology combined with machine learning methods can classify and identify multiple species of citrus pests and diseases, providing a theoretical basis for rapid and non-destructive detection and control of citrus pests and diseases.
		2022 Vol. 42 (08): 2397-2402 [Abstract] ( 146 ) RICH HTML PDF (3184 KB) ( 72 )

	2403	Study on Radical Characteristics of Methane Laminar Premixed Flame Based on Hyperspectral Technology
		WANG Yan^{1, 2, 3}, WANG Bao-rui^{1, 2, 3*}, WANG Yue^{1, 2, 3}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2403-08
		Hyperspectral technology provides spatial and spectral dimension information. Meanwhile, the experimental technology and calculation method based on the traditional blackbody model is not suitable for the radiation characteristics of methane flame. The Hyperspectral Information of free radicals in the flame reflects many aspects of combustion characteristics, such as flame structure and component concentration distribution, which can provide a basis for improving the combustion model. This paper studied the spatial and spectral characteristics of free radicals in premixed methane flames by Hyperspectral techniques at different equivalence ratios and flow rates. The study of different equivalence ratios shows that with the increase of equivalence ratios, the radiation intensity of CH^* and C^₂ radicals in the center of the flame increases first and then decreases. In contrast, the average radiation intensity of CH^ and C^₂ radicals in the combustion region increases all the time. The point in the center of flame can represent the local combustion state. While the average radiation intensity in the combustion region represents the overall combustion state, such as heat release rate, this paper gives the different trends of the two methods quantitatively. The radiation intensity of CH^ radical in the center of flame reaches the peak when the equivalence ratio is 1.01, while the radiation intensity of C^₂ radical reaches the peak when the equivalence ratio is 1.12. The radiation peak of the two radicals can be used as the intensity criterion and stability of the reaction in combustion. Equivalence ratio can be expressed by the C^₂ to CH^* radiation intensity ratio. This paper corrected the linear relationship between C^₂/CH^ and equivalence ratio. It is proposed that the ratio of average radiation intensity of C^₂ and CH^ in the combustion zone should be used. The quadratic relationship between the ratio and equivalence ratio is also proposed. The cloud image of C^₂/CH^ in the combustion area is generated by hyperspectral technology, and the detailed spatial information is obtained. When the equivalence ratio is greater than 1, an obvious transition zone is found near the flame surface for the first time, which shows the advantages of hyperspectral technology. The study of different flow rates with a constant equivalence ratio shows that the flame height increases with the flow rate increase, while the concentration distribution of free radicals at the top and center of the flame does not change. It reveals that the characteristic time of a flow is far less than that of chemical reaction under experimental conditions, so the chemical reaction process is not affected. In this paper, hyperspectral technology is used to identify a variety of free radicals in the flame. The radiation characteristics of free radicals in methane laminar premixed flame and its variation trend with different equivalence ratios and flow rates are studied, which is of great significance for applying hyperspectral technology to study methane combustion characteristics and verify the reaction mechanism of methane combustion phenomenon.
		2022 Vol. 42 (08): 2403-2410 [Abstract] ( 108 ) RICH HTML PDF (4947 KB) ( 56 )

	2411	A High Precision and Large Range Measuring Method for Broadband Light Interferometric Microscopy Based on Phase Unwrapping and Stitching Algorithm
		ZHAO Wen-hao^{1, 2}, LI Jun¹, DU Kai¹, XIONG Liang¹, YIN Shao-yun¹, HU Jian-ming³, WANG Jin-yu^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2411-07
		Broadband light interferometric microscopy is widely used for high precision profile measurement in the industry field. Vertical scanning interferometry (VSI) is usually used to measure the submicron to millimeter level features, and phase shift interferometry (PSI) to measure the nanoscale features. Among them, the precision of PSI is of nanoscale order, while its measurable range is limited because the phase changes corresponding to the height variations of the sample surface should be limited within the scope of 2π.A large amount of phase unwrapping algorithms are developed to extend the range of PSI. However, they are only suitable to smooth surfaces. When the height fluctuations exceed the limited range determined by the focal depth or the coherent length of the light source, the interference fringes will be blurred. Even the contrast will be lost. Thereafter, great measurement errors will be introduced to the calculated results. This paper proposesa high precision and large range broadband light interferometric microscopy measurement method based on the phase unwrapping and stitching algorithm. The fringe modulation value quantified the fringe quality at a given focal plane, the areas with the high modulation values generally correspond to the regions of interests （ROI） with high contrast and clear image. The ideal regions (IRs) are defined as the ROI with a modulation value greater than a given threshold within the current focal plane. Meanwhile, the problem regions (PRs) are defined as the ROI with a modulation value lower than the given threshold. Only the true phase distribution in IRs is calculated with the phase unwrapping algorithm. By vertically moving the focal plane of the objective with a translation stage at a reasonable step length, the IRs of the adjacent focal plane will be partially overlapped. According to the differences between the phase of the overlapped IRs of the adjacent focal plane, the corresponding unwrapped phase of the adjacent plane can be stitched together with high precision. Finally, the complete profile distribution of the sample is restored according to the stitched true phase with high precision. The proposed method for broadband light interferometric microscopy avoids the error caused by the phase unwrapping in the PRs. Through simulation and experiments results, we demonstrate that the proposed method maintains the nanoscale precision of PSI in broadband light interferometric microscopy and extends its range from hundreds of nanometers to several micrometers. Moreover, its accuracy does not depend on the displacement precision of the focal planes by translation stages. Theoretically, the range of our proposed method can be extended to the total working distance of the microscopic objective.
		2022 Vol. 42 (08): 2411-2417 [Abstract] ( 126 ) RICH HTML PDF (3610 KB) ( 38 )

	2418	Spectroscopic Properties of Carbon Quantum Dots Prepared From Persimmon Leaves and Fluorescent Probe to Fe³⁺ Ions
		XU Yi-fei, LIU Lu, SHI Shi-kao^*, WANG Yue, PAN Yu-jing, MA Xing-wei
		DOI: 10.3964/j.issn.1000-0593(2022)08-2418-05
		As a new member of carbon nanomaterials, carbon quantum dots (CDs) with many advantages such as high optical stability, low toxicity, superior water solubility, diverse raw materials and preparation approaches, have shown application prospects in the fields of analytical detection, biomarker, photocatalytic degradation and environmental monitoring widely. The investigation on CDs has attracted significant interest. In general, the exceeding content of Fe³⁺ ion in water would be harmful to daily drinking and industrial production. It is of great significance to determine the content of Fe³⁺ in water accurately and quickly. At present, some techniques are used for the detection of Fe³⁺ ions, which include voltammetry, fluorescence spectrum, electrochemical and flame atomic absorption spectrometry. The fluorescence spectrometry has shown the merits of fast response and facile process, which makes it much better than other ways. In this paper, the CDs with bluish-green emission were prepared by hydrothermal treatment of persimmon leaves. The X-ray diffraction, high resolution transmission electron microscopy, Fourier transform infrared spectroscopy, ultraviolet visible absorption spectroscopy and fluorescence spectroscopy were used to characterize CDs’ structure, morphology, and spectroscopic properties. The CDs exhibit uniform spherical particles with an average diameter of 5.9 nm and abundant oxygen-containing functional groups on the surface. The UV absorption at 277 nm should be attributed to then→π^* transition of theC═O group. CDs’ emission wavelength and intensity are closely dependent on the excitation wavelength. Excited with 410 nm long, the emission maxima are 498 nm and it shows the strongest intensity. The fluorescence lifetime is about 4.59 ns. Moreover, the as-prepared CDs show high selectivity for Fe³⁺ ion compared to other metal ions, which can be used as a fluorescent probe to detect the trace concentration of Fe³⁺ in water. The dependence of fluorescence quenching rate F₀/F with Fe³⁺ concentration has a good linear relationship (R²=0.992), and the quenching constant, and detection limit value is 8.84×10³ L·mol^-1 and 0.21 μmol·L^-1, respectively. The detection limit value of 0.21 μmol·L^-1 is smaller than those reported in recent literature. Consequently, this work provides a preparation process with natural raw materials, simple operation and low-cost, and develops a new pathway for the fluorescence detection of trace metal iron ions in water.
		2022 Vol. 42 (08): 2418-2422 [Abstract] ( 150 ) RICH HTML PDF (3288 KB) ( 54 )

	2423	Detection of Melamine and Cyromazine in Raw Milk by Aptamer-Facilitated Gold Nanoparticle-Based Logic Gates
		QIAN Cheng¹, WANG Bo¹, FEI Xue-lian¹, YIN Pan-cheng¹, HUANG Bo-tao², XING Hai-bo^1, HU Xiao-jun^１
		DOI: 10.3964/j.issn.1000-0593(2022)08-2423-09
		In this paper, we developed a simple design and a detection method for AND logic gates by using aptamers, Cetyltrimethyl Ammonium Bromide (CTAB), and melamine and cyromazine to control the aggregation and dispersion of gold nanoparticles (AuNPs). First, Based on the fact that aptamer T31 can specifically bind with melamine, and CTAB immediately resulted in the aggregation of AuNPs, an AND gate was fabricated to find whether there was melamine. It also has a detection limit of 0.24 mg·L^-1 by the naked eye to detect melamine, and the limit of detection (LOD) by a spectrophotometer is 85 μg·L^-1. Second, with the adsorption of aptamer Tcy1 on AuNPs and the strong coordination of Tcy1 with cyromazine, the addition of cyromazine and CTAB immediately resulted in the aggregation of AuNPs, giving rise to another AND gate. It also has a detection limit of 0.17 mg·L^-1 by the naked eye, and the limit of detection (LOD) is 9.0 μg·L^-1 by spectrophotometer. So, these logic gates can be used to detect melamine and cyromazine in raw milk.
		2022 Vol. 42 (08): 2423-2431 [Abstract] ( 122 ) RICH HTML PDF (5471 KB) ( 42 )

	2432	Spectral Characteristics of Dissolved Organic Matter (DOM) in Leachate Released From Agricultural Soil Irrigated With Reclaimed Water
		FAN Chun-hui^{1, 2}, XIN Yi-bei¹, YUAN Wen-jing¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2432-05
		Agricultural irrigation is effective for the reutilization of reclaimed water, and the natural advantages and operational feasibility have been generally approved. Agricultural irrigation with reclaimed water can achieve acceptable ecological, social and economic benefits, while organic matters in the soil might lose or change during the irrigation process, and the related issue has received insufficient attention. Dissolved organic matter (DOM) was selected as the analysis target, and the scientific issue was to reveal the variation mechanism of DOM spectral information in leachate released from the irrigated agricultural soil. Samples of reclaimed water (from wastewater treatment plant) and surface water (from the river, for contrast) were used, and a one-dimensional undisturbed soil column was applied to study the irrigation effect on soil and soil leachate. The electronic scanning microscopy (SEM), ultraviolet-visible spectroscopy (UV-Vis) and three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEMs) were involved in reveal the fundamental information of DOM released from soil irrigated with water samples. The results show: that soil aggregates appear lose structure and rough surface after irrigation, and small soil particles gather and stick together. The contents of organic matter decrease with the increased soil depth, and DOM content reaches maximal at 10~20 cm of the soil layer. The concentrations of organic matter (0~30 cm of soil layer) and DOM (0~20 cm of soil layer) increase after irrigation, suggesting the interception effect of water organic compounds by soil samples. The UV-Vis spectra of leachate are similar, and the unsaturated double-bond conjugate structures cause the absorption regions at 240~270 nm in DOM. The lower aromatization degree and more straightforward structure of DOM can be detected in soil leachate irrigated with reclaimed water, and the content of fulvic acid is higher than that of humic acid. The only fluorescence peak of DOM appears in theE_x/E_m=260～270/420～430 nm, regarded as a humus-like component. Compared to the previous results, it is deduced that amino acid-like and protein-like components might be adsorbed and retained in soil. Bio-source (endogenous) DOM plays a more important role than exogenous DOM in soil leachate. The achievements are significant to estimate further the circulation, transportation and ecological effect of DOM or organic matters in the reclaimed water-irrigated agricultural soil systems.
		2022 Vol. 42 (08): 2432-2436 [Abstract] ( 122 ) RICH HTML PDF (2186 KB) ( 143 )

	2437	Study on Strengthening and Fixation Mechanism of Chinese Fir Modified by Silica-Magnesium Gel
		ZHANG Yuan, BI Xiao-qian, LI Ping, LI Xian-jun, YUAN Guang-ming, ZUO Ying-feng^*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2437-07
		The silica-magnesium gel impregnated modified Chinese fir has improved physical and mechanical properties. Exploring the fixing performance and mechanism of silica-magnesium gel in Chinese fir has significance for subsequent research and innovation. It is uses silica-magnesium gel as the modifying agent and fast-growing Chinese fir as the base material.Modified Chinese fir was prepared after dipping and drying. FTIR and XPS were used to analyze the chemical composition and combination of naturalChinese fir and modified Chinesefir and discussed the distribution of silica-magnesium gel in wood and the permeability of modified Chinese fir. The experimental results showed that the physical and mechanical properties of the modified Chinese fir impregnated with silica-magnesium gel hadbeen significantly improved, and the density was above 0.5 g·cm^-3, the compressive strength and bending strength were increased by 99.73% and 58.48% compared with the natural Chinese fir.The end surface hardness was increased from 3 659 to 5 843 N, and it reached the performance index of medium wood. The results of EDS proved that the medicament was well filled in the cell cavity of the fir, and the main elements such as Si, O, Na, Mg and the impregnating medicament silica-magnesium gel were consistent and evenly distributed. Layered XPS tests were performed on the Chinese fir before and after impregnation. The O/C of the modified Chinese fir increased, and the content of each element at different depths was very close. The silica-magnesium gel was evenly distributed in the Chinese firand had good permeability.After leaching, the Si element content changed little, while the sodium element decreased, which may be caused by the dissolution and loss of some sodium salts, and the element content changes at different depths tend to be consistent. The resistance of the modified Chinese fir compared with sodium silicate, the loss rate of silica-magnesium gel was reduced to 10.8% in 96 h, and the effect was good. The FTIR test results of modified Chinese fir, natural Chinese fir, sodium silicate solution and leachate showed that the sodium silicate solution had the effect of destroying and leaching out the lignin and hemicellulose of Chinese fir, improving the permeability of Chinese fir and making it easier to interact with the medicament forms a chemical bond. The XPS spectrogram analysis of C, O, and Si elements showed that C(1s) shifted to low binding energy after modification, and the properties were more stable. The O—H binding was greatly reduced, and the Si—O binding increased. The silica-magnesium gel can form a stable in Chinese fir with a Si—O—C chemical structure, and the agent can achieve high-efficiency fixation on the cell wall. The research results provide new ideas for the loss detection and fixation research of modified wood and provide certain theoretical support for the follow-up study of modified Chinese fir impregnated with silica-magnesium gel.
		2022 Vol. 42 (08): 2437-2443 [Abstract] ( 120 ) RICH HTML PDF (3858 KB) ( 34 )

	2444	PARAFAC and FRI Preferred 3D Fluorescence Extraction Time of Dissolved Organic Matter
		YI Jun¹, YANG Guang², PAN Hong-wei^2*, ZHAO Li-li¹, LEI Hong-jun², TONG Wen-bin², SHI Li-li²
		DOI: 10.3964/j.issn.1000-0593(2022)08-2444-08
		The judgment and assessment of preprocessing scheme for 3D fluorescence spectroscopy Optimized still depend on the total extraction volume. Since the release of the DOM during extraction with the discrepancy between components, the studies might be more reasonable if we had considered the extraction of DOM components. This paper aims to clarify the differences in the extraction of DOM components and total and whether it is necessary to set the separate extraction time. Two common extraction methods, oscillating and centrifuging, were used. The time was set as the upper limit of the high-speed centrifuge and the constant temperature shaking box. PARAFAC and FRI were used to characterize the DOM compositional characteristics at different extraction times. According to the correlation and principal component analysis, it was found that there were different interactions between the components, and the optimal preprocessing time was screened out on the condition that the regional integral value and Fmax reached the maximum. The humus-like components were strongly positively correlated with the total (p<0.05), and the protein-like components were less correlated with the total using FRI analysis. The humic acid-like components were strongly positively correlated with the total.The fulvic acid-like fractions were weakly correlated with the total amount using PARAFAC analysis (p<0.05). There were differences in the extraction of total and components, which required separate pre-treatment time settings. The choice of extraction time is premised on the economy and stability of the distribution, the reby choosing the maximum of fluorescence regional integration or fluorescence intensity score (F_max). The results showed that the optimal extraction times for entirety, humic acid-like, fulvic-like, complexine-like, tryptophan-like substances and microbial metabolites were 12, 21, 12, 12, 12, and12 h, respectively. PARAFAC analysis showed that the optimal extraction times for entirety, C1, C2, and C3 were 12, 21, 12, and 39 h, respectively. In summary, the two extraction methods have their advantages. The same extraction time can be used for the components and the overall time under the centrifugal treatment. The regional integral and Fmax values under the shaking treatment showed more oscillation than the centrifugation, and the extraction effect is relatively stable. The research results can provide a data basis and guidance for optimizing the preprocessing scheme when measuring DOM by three-dimensional fluorescence spectroscopy.
		2022 Vol. 42 (08): 2444-2451 [Abstract] ( 202 ) RICH HTML PDF (6516 KB) ( 46 )

	2452	*Distribution Characteristics of Mineral Elements in Different Types of Cistanche deserticola* Y. C. Ma Were Analyzed by ICP-MS**
		GUO Meng¹, HUANG Yong^1*, CHEN Xin¹, ZHANG Zhi-feng², ZHANG Hong-rui¹, ZHOU Yan¹, LI He-min¹, GUO Yu-hai³
		DOI: 10.3964/j.issn.1000-0593(2022)08-2452-04
		Mineral elements are one of the important indexes for quality evaluation of Traditional Chinese medicine（TCM）, which are closely related to the synthesis and play of the effective substances of Chinese medicinal materials. The content of mineral elements in TCM is affected by its germplasm, harvesting period, medicinal site, origin and other factors. Cistanche deserticola Y.C.Ma is one of the famous tonic herbs and medicine-food homology in China, and its mineral element content has attracted more and more attention. The contents of 4 major mineral elements and 8 trace mineral elements in different parts of three types of C. deserticola were analyzed by ICP-AES. The results show that: (1) K, Ca, Mg, Na, Fe, Mn, Cu, Zn, Cr, As, Pb and Cd were all contained in the three types of C. deserticola, but the contents of different types were significantly different. The mineral element content of yellow flower type is lower than that of white and purple flower types. The contents of K, Ca, Mg, Na and Cu in the white flower types were higher than those in the purple flower types, while the contents of Zn, Cr and Cd were lower than those in the purple flower types. (2) There were significant differences in the distribution characteristics of 12 mineral elements in C. deserticola, and K, Ca, Mg, Na and Cu were mainly distributed in the upper part. (3) The ratios of mineral elements in different parts of deserticola were significantly different. K/Na of three types of deserticola were lower > middle > upper. The Mg/Fe ratio was the highest in the middle of purple and white flowers and the highest in the upper part of yellow flowers. The Zn/Mn ratio was the highest in the middle of purple and yellow flowers but the highest in the lower part of white flowers. Cu/Cr ratio was highest in the upper part of white and yellow flower types and highest in the middle of purple flower types. This study will provide data support for the breeding and quality control of C. deserticola.
		2022 Vol. 42 (08): 2452-2455 [Abstract] ( 121 ) RICH HTML PDF (677 KB) ( 168 )

	2456	Quantitative Analysis of Monoborates (H₃BO₃ and B(OH)^-₄) in Aqueous Solution by Raman Spectroscopy
		PENG Jiao-yu^{1, 2*}, YANG Ke-li^{1, 2}, BIAN Shao-ju^{1, 3, 4}, CUI Rui-zhi^{1, 3}, DONG Ya-ping^{1, 2}, LI Wu^{1, 3}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2456-07
		Salt lake is a natural complex system coexisting with water and salts. Borate species in salt lakes and their distributions are complicated than the pure borate solution. Generally, polyborates can be formed in brine by polymerization during the concentration process. Thus, borates in concentrated brine have a severe supersaturation behavior, which cannot favour the salt lake resource separation between boron and other slats. Therefore, the study of the poly borates distributions in the salt lake brine and their transformation mechanisms is of great importance. Laser Raman spectroscopy is characteristic of in-situ, non-destructive and weak water interference and thus has been widely used to determine borate structure in aqueous solutions. Recently, the modern Quantitative Raman technology with Chemometrics has become an efficient method to accurately acquire the number of matters in a complex system. It shows great advantages in solving spectral problems such as spectral overlap, background interference and baseline drifting and has been widely and deeply applied in the analysis field. Based on the Chemometrics, this paper has studied the quantitative analysis of monoboartes in aqueous solutions by Raman technology, with the three regression models as internal standard, multi-linear regression and partial least squares regression. Also, it has evaluated the three models by using the external standard sample. It was found that both multi-linear regression and partial least squares models had a more accurate amount prediction of the sample, with a relative error of less than 1%. However, the former model shows better values at lower boron concentration. Furthermore, based on the multiple linear regression models, we also explored the borate species and its distribution in the oilfield brine in the west of Qaidam Basin by Raman spectroscopy. The results showed that only the boric acid peak at 875 cm^-1 was detected in the oilfield brine during the evaporation process. The amount of boric acid predicted by the multiple linear regression models agrees well with the boric acid concentration measured using the titration method. The relative error between them is less than 5%. It indicates that the major form of borate in the oilfield is boric acid, and other borate species can be ignored, which explains why the boric acid solid is the only borate saltthroughout the whole oilfield brine crystallization process. The results of this study could provide fundamental information and theoretical guidancefor the future exploration of the quantitative analysis of the borate speciation in the brine under dynamic environmental conditions.
		2022 Vol. 42 (08): 2456-2462 [Abstract] ( 109 ) RICH HTML PDF (2745 KB) ( 52 )

	2463	Study on Nitrogen and Phosphorus Distribution Characteristics by Spectrophotometry and Quantitative Source Analysis of Rivers With Different Land Use Types in Different Water Periods
		LI Tong-fei¹, ZHOU Ping-yan¹, DING Yun-chang¹, TANG Qi-ding¹, ZHOU Shan-shan¹, LIU Ying^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2463-08
		Nitrogen and phosphorus are essential nutrients for the growth and reproduction of aquatic organisms and affect the primary productivity of the water body. The eutrophication level of the water body is closely related to the fractions of nitrogen and phosphorus. With the change in the water environment, the sediments will release nitrogen and phosphorus into the water body, causing secondary pollution. At the same time, quantitative identification of the contribution of external nitrogen and phosphorus pollution sources can effectively manage and control the nitrogen and phosphorus pollution load in the water body. Pihe River and Shiting River are important tributaries of Tuojiang River and affect the water quality of the Mother River of the Yangtze River. In this paper, the distribution characteristics of total nitrogen (TN), total phosphorus (TP) and various fractions of nitrogen and phosphorus in the water body and surface sediments of Pihe River and Shiting River in the upper reaches of Tuojiang River in the dry season and wet season were studied by molybdenum blue/ascorbic acid spectrophotometry and continuous extraction method.The behavior characteristics and release risk of nitrogen and phosphorus in rivers with different land-use types were compared. The APCS-MLR receptor model was used to identify and quantify the sources of nitrogen and phosphorus pollution. The results showed that: ① nitrogen and phosphorus in the water and surface sediments in the study area were at different pollution levels. The main contributors of TP in the dry season were particulate inorganic phosphorus (PIP) and particulate organic phosphorus (POP), while in the wet season, it was particulate inorganic phosphorus (PIP) and dissolved inorganic phosphorus (DIP). The main contributors of TN in the two water periods were nitrate-nitrogen (NO^-₃-N) and organic nitrogen (ON). In surface sediments, the main contributor of TP was calcium-bound phosphorus (HCl-P), and the main contributor of TN was acidolysis nitrogen (HN). In dry season and wet season, the average ratio of bioavailable phosphorus (BAP) in TP of surface sediments of Pihe River (19.7% and 23.0%) was higher than that of Shiting River (11.0% and 12.5%), indicatinga a higher risk of phosphorus release. It was found that the nitrogen and phosphorus pollution degree in the dry season was higher than that in the wet season, and the nitrogen and phosphorus pollution degree in Shiting river was higher than that in Pihe River. ② APCS-MLR model extracted four pollution source factors in the Pihe River, including urban domestic sewage, leachate generated by domestic garbage accumulation, decomposition of animal and plant residues and aquaculture wastewater. Among them, urban domestic sewage contributed the most to nitrogen and phosphorus pollution in the Pihe River (50.9% in dry season and 54.8% in wet season). At the same time, wastewater generated in industrial production, degradation of animal and plant residues and the weathering of agricultural waste, agricultural wastewater from farmland drainage channels and an unreasonable application of pesticides and fertilizers were five pollution source factors, among them, the wastewater produced in industrial production contributed the most to the nitrogen and phosphorus pollution of Shiting river (58.7% in dry season and 55.8% in wet season). Therefore, the relevant local departments should strengthen the management and control of high contribution pollution sources to reduce the basin’s nitrogen and phosphorus pollution load.
		2022 Vol. 42 (08): 2463-2470 [Abstract] ( 152 ) RICH HTML PDF (5591 KB) ( 55 )

	2471	Study on Identification of Common Diseases in Potato Storage Period Based on Spectral Structure
		LI Hong-qiang¹, SUN Hong², LI Min-zan^2*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2471-06
		At present, the detection of dry rot and potato scab was completed by manual visual inspection, and the detection results were subjective. This experiment studied the spectral detection method for classification and recognition of normal, dry rot and scab of potato. 116 potato samples were collected in the experiment, and the spectrum collection range was 860~1 745 nm. After the first derivative (FD) processing, the principal component analysis (PCA) classification recognition effect was better, and FD was used as the spectral preprocessing method. The shape of the spectral curve was determined by the extreme points on the spectral curve, the midpoint between the extreme points and the slope line between the extreme points. The shape change of the spectral curve represented the change of the internal substance and had fingerprint characteristics. The mode eigenvector was composed of the spectrum corresponding to the key points or the line slope between the extreme points. The average spectra of the key points of the three samples were used to form the standard pattern feature vectors. By calculating the Mahalanobis distance between the feature vectors composed of the key points of the tested samples and the standard pattern feature vectors, the minimum Mahalanobis distance was used to determine the attribution of the samples, and the error recognition rate tested the recognition performance of the model. There were 13, 12 and 15 key points in normal, dry rot and scab samples, respectively. The pattern feature vector was composed of the reflectance corresponding to each key point, and the error recognition rate of the three types of samples was zero. By removing redundant key points and integrating them into a standard pattern feature vector, the error recognition rate of normal and scab samples was zero, that of dry rot samples was 14.3%, and all were scab samples. The feature vector data points increase the fit degree between disease samples and reduces the discrimination between two types of disease samples. Using the slope between two points at the wavelength of 911, 1 269 and 1 455 nm to form the pattern feature vector, the error recognition rate of normal and scab samples was zero, and the error recognition rate of dry rot samples was 2.4%. Linear discriminant analysis (LDA) and Bayesian classifier (BC) were used to build the classification model by using the scores of the first two principal components as the parameters. Different classification models were provided. The effectiveness of the classification model based on the pattern feature vector was compared and verified. The error recognition rate of the two recognition methods was zero. The experimental results show that the pattern feature vectors representing the structural features of spectral curves could be used as the classification parameters, and the distance method could be used for modeling, which had the same recognition accuracy as the standard recognition methods.
		2022 Vol. 42 (08): 2471-2476 [Abstract] ( 95 ) RICH HTML PDF (2769 KB) ( 63 )

	2477	A New Model for Quantitative Analysis of Waste Textiles Using Near-Infrared Spectroscopy
		HAN Song-chen, LIU Sheng^*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2477-05
		If the waste textiles are classified, recycled and disposed of according to their components, many textile raw materials can be saved. At present, the manual sorting method is often used in the recycling process of waste textiles. This method is costly and inefficient. Near-infrared spectroscopy analysis is one of the most rapidly developing technologies in the 21st century. It can quickly determine the components of the sample and the content of each component without destroying the sample. Using this technology to analyze the waste textiles and prejudge the components and contents of various components of waste textiles can be helpful for the large-scale fine classification and recycling of waste textiles. In the multi-model method, the final predicted value is obtained by a weighted average of the predicted values of each sub-model. The near-infrared spectroscopy analysis model established by this method generally has good stability. In this paper, taking the nylon content of waste textile samples as an example, a near-infrared spectral analysis model for predicting the nylon content is first established using the multi-model method. The method is as follows: The reflectance vectors are divided into 15 groups according to their wavelengths. A sub-model of near-infrared spectral analysis is established with each data group. The final predicted value of the nylon content is obtained by a weighted average of the predicted values of sub-models. Then, based on the multi-model method, according to the approximately linear relationship between the predicted values and the experimental values of the nylon content, by replacing constants with variables and by standardizing the variables, a new model for predicting the nylon content by near-infrared spectral analysis is presented, and the model is convenient for optimization. After optimization, the parameters of each sub-model are reduced by 6. This can prevent overfitting of the model.The above two models are compared with the common model established by the partial least squares method. The results of cross-validation show that: the average of the goodness of fit of the (optimized) new model is 0.820 7. The average goodness of fit of the model built using the multi-model method alone is 0.769 1. The average goodness of fit of the model built by the partial least squares method is 0.746 7. Therefore, the prediction effect of the model built by the multi-model method is better than that of the model built by the partial least squares method. The prediction effect of the new model is better than that of the other two models. The main innovation of this paper is the establishment and optimization of the new model. The modeling method in this paper is expected to predict the content of other components in waste textile samples.
		2022 Vol. 42 (08): 2477-2481 [Abstract] ( 127 ) RICH HTML PDF (1106 KB) ( 46 )

	2482	Analysis of Soil Salinity Based on Spectrum and RVIPSO-MELM
		SONG Ni-na¹, XIAO Dong^1*, LI Sen¹, GAO Yu-jie²
		DOI: 10.3964/j.issn.1000-0593(2022)08-2482-06
		Studying, the nature and composition of saline soil, are significant to the ecological environment. Most traditional methods for determining salt content are based on chemical analysis. Due to their high cost and low efficiency, the feasibility of applying them to large areas of land is greatly reduced. An extreme learning machine (ELM), as a machine learning system based on a feedforward neural network, has been successfully used as a spectral processing method in many studies. In order to improve the traditional salt content detection methods of saline-alkali soils, this paper uses spectroscopy combined with an improved extreme learning machine (ELM) model to study saline-alkali soils, further expanding the application scenarios of spectroscopy analysis methods. We obtain the corresponding spectral reflectance and salt content data according to the 62 surface samples collected in Zhenlai County and then propose the multi-layer extreme learning machine model optimized by improved particle swarm optimization (PSO) algorithm with improved particle swarm optimization (PSO) algorithm with random values(RVIPSO-MELM) model. Firstly, we use the principal component analysis(PCA) to extract the characteristics of the spectral data and then adopt the ELM algorithm to establish a classification model for the spectral data. Finally, to improve the accuracy and speed, an improved particle swarm optimization algorithm is applied. This model combines the advantages of both multi-layer ELM with random values (RV-MELM) and the multi-layer ELM model optimized by an improved PSO algorithm (IPSO-MELM), using the heuristic algorithm to search for the optimal value and also having randomness, which improves the speed of model optimization. The parameters are optimized and selected to improve the performance of the model. Moreover, the model can be extended to multiple layers, and the two methods of selecting parameters between hidden layers, calculated by empirical formulas or improved heuristic algorithm, are discussed about the model’s performance and optimize the time. The practical results show that it is a more realistic method to select the parameters of the first layer to use the improved particle swarm optimization algorithm and determine the parameters of the subsequent hidden layers by using the empirical formula calculate. Before the heuristic search for the optimal value, the Monte Carlo method is applied to determine a better initial value, enabling the model to maintain a high accuracy rate and further improving the optimization speed. Compared with traditional methods, this spectral analysis combined with the ELM model saves time and economic costs, giving it a certain promotion significance.
		2022 Vol. 42 (08): 2482-2487 [Abstract] ( 124 ) RICH HTML PDF (3176 KB) ( 113 )

	2488	Scientific Investigation of Materials Used in the Wall Paintings From the Tashilhunpo Monastery, Tibet
		HUANG Ya-zhen¹, SONG Yan², GUO Ju-wen², WEI Shu-ya^1*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2488-06
		Murals are an important decorative element of temple architecture and an important component of Tibetan Buddhism art. Tashilhunpo Monastery was built in the 12th year of the reign of Ming Emperor Zhengtong (1447AD), which treasures unique and resplendent paintings that plays an important role in Tibet. It is the largest temple in later Tibet, belonging to the Gelug sect of Tibetan Buddhism. Tashilhunpo Monastery has been serving for spreading Buddhist culture since its establishment and has undergone frequent large-scale construction. Investigating the painting materials and techniques now becomes one important part of cultural heritage protection. A total of 8 samples were collected from the typical murals on the north wall of the fourth floor of Maitreya Hall and the west side of the South Hall of Exoteric Buddhist Seminary. Pigments, the ground layer and the inside structure of the painting were studied by three-dimensional video Microscopy, scanning electron microscopy in combination with energy dispersive X-ray microanalysis, polarizing microscope, X-Ray Diffraction, microscope and laser Raman technique. The results show that three layers of the wall painting cross-section correspond to a ground layer, a yellow preparation layer, and a paint layer. Natural and synthetic pigments are both used in the paintings, including cinnabar (HgS), orpiment (As₂O₃), charcoal (C), antlerite (Cu₃(SO₄)(OH₄)), C. I. Pigment Red 14 (C₂₄H₁₇ClN₄O₄), synthetic ultramarine blue (Na₈(Al₆Si₆O₂₄)Sn), Phthalocyanine green G (C₃₂H₃Cl₁₃CuN₈-C₃₂HCl₁₅CuN₈). Pigment red 14 and phthalocyanine green are organic synthetic pigments, while synthetic ultramarine blue is inorganic synthetic pigments. As a mineral pigment, antlerite has been used in easel paintings, murals, manuscripts and other artistic works in Europe, but the history of usage has not been found in China. This discovery expands the knowledge of green pigments.The study demonstrated that loess and aga soil was the base of the wall paintings and painted with kinds of color finally according to the religious ritual. Furthermore, the research findings show traditional materials for Tibetan murals and modern synthetic materials, indicating that several wall paintings have been repaired or repainted later. The results make up for the murals research vacancy of Tashilhunpo Monastery and provide important evidence for the complement and improvement of its repair history.
		2022 Vol. 42 (08): 2488-2493 [Abstract] ( 128 ) RICH HTML PDF (3765 KB) ( 49 )

	2494	Diffuse Reflectance Spectroscopy Study of Mottled Clay in the Coastal Area of Fujian and Guangdong Provinces and the Interpretation of Its Origin and Sedimentary Environment
		WANG Jing^{1, 2*}, CHEN Zhen³, GAO Quan-zhou¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2494-05
		Hematite and goethite, the two most common iron oxides in nature, are widely distributed in sediments. Their relative content relationship can reflect the sedimentary environment and provide provides a basis for origin discrimination. Due to the complex operation and low efficiency of traditional methods, it is difficult to quickly and accurately determine iron the species and content of iron oxide within the sediments. Recently, diffuse reflectance spectroscopy(DRS) based on ultraviolet-visible-near infrared spectrophotometer has been widely used in sediments because of its simple operation, fast test and low detection limit. A set of Last Glacial yellow silt, sometimes mixed with red and gray and known as “mottled clay”, is widely developed in the late Quaternary basins of Fujian and Guangdong Provinces in the coastal areas south China. This layer was often attributed to exposed weathering of the underwater sediments during the global low sea-level period. However, there is no transition between mottled clay and its underlying deposit, which is difficult to explain by weathering. Moreover, marine fossils rich in the underlying layer are not found in the mottled clay layer, indicating great differences in the sedimentary environment and provenance between these two layers. In order to further determine the sedimentary environment and origin of the mottled clay, four Quaternary drill cores in the Pearl River delta with the method of DRS are analyzed from the perspective of iron mineral characteristics in this study. The results show that the peak value of hematite within the mottled clay is higher than that of goethite, suggesting that the sample is rich in hematite and relatively low in goethite. This trend is opposite to that of the underlying sediments. Hematite is formed in a dry, warm and onshore oxidation environment, where as goethite is the product of long-term wet and underwater reduction conditions. Hence, the mottled clay had not undergone long-term hydration transformation and is therefore not formed by weathering of in-situ underwater deposition but constitutes a subaerial exotic dust accumulation. The small coefficient of variation of the two iron mineral peak values and the similar DRS first derivative curves from the top to bottom of the mottled clay layer in every drill coreindicate that the composition of the mottled clay in different depths is uniform, and the samples had suffered a sufficient mixing and sorting before accumulation. It gives new evidence for the determination of mottledaeolian clay. It can be seen that the DRS method provides not only technical support for iron oxide identification of sediment but also contributes new ideas for the determination of sedimentary environment and origin.
		2022 Vol. 42 (08): 2494-2498 [Abstract] ( 115 ) RICH HTML PDF (4238 KB) ( 42 )

	2499	Study on the Spectral Characteristics of Filled Amazonite
		WU Yan-han, CHEN Quan-li^*, LI Jun-qi, ZHAO An-di, LI Xuan, BAO Pei-jin
		DOI: 10.3964/j.issn.1000-0593(2022)08-2499-07
		Using conventional gemological methods, energy dispersive X-ray fluorescence spectrometer, laser Raman spectrometer, Fourier transform infrared spectrometer and fluorescence spectrometer to compare the spectral characteristics of natural and filled amazonite and to explore the effective and non-destructive identification method of filled amazonite. The results showed that the refractive index of filled amazonite is consistent with that of natural amazonite, which is 1.52～1.53. The luster of filledamazonite is weaker glass to wax, which is weaker than natural amazonite. Enlarged observation shows that the surface is concave in the partially filled amazonite samples, and the luster is different from the surrounding. There may be bubbles in the cracks. So the weak luster and magnified observation can help distinguish natural amazonite from filled amazonite. The main elements are the same either in nature or in filled amazonite, including Al, Si, K and Rb. No abnormal chemical elements belonging to the filling material have been detected. The infrared reflection spectrum in the fingerprint area is the absorption of the group vibration of amazonite. In the functional group area, natural amazonite does not absorb obviously, while the filled amazonite has two characteristic absorption peaks that 2 844 and 2 912 cm^-1, produced by the vibration of (—CH₂—). The laser Raman spectra of natural and filled amazonite are the same in the 100～1 500 cm^-1 band, which are all the Raman peaks produced by the group vibration of amazonite. The fluorescence background of filled amazonite in the 100～3 700 cm^-1 band is stronger than natural amazonite. When organic filling material in surface fissures is detected, the fluorescence background will be stronger, and the Raman peaks will appear different from natural amazonite. There is no typical difference between natural and filled amazonite, and natural amazonite different fluorescence characteristics by themselves. The three-dimensional fluorescence spectrum cannot distinguish natural amazonite from filled amazonite.
		2022 Vol. 42 (08): 2499-2505 [Abstract] ( 178 ) RICH HTML PDF (4597 KB) ( 122 )

	2506	Analysis of Sandstone in Leshan Giant Buhhda Based on Hand-Held X-Ray Fluorescence Spectrometer
		LU Hao¹, FU Wan-lu^{2, 3*}, CHAI Jun², LIU Shuang², SUN Zuo-yu²
		DOI: 10.3964/j.issn.1000-0593(2022)08-2506-07
		In order to explore the application of handheld X-ray fluorescence spectrometer in research and protection of grottoes, based on lithofacies analysis and former geological data, we apply handheld X-ray fluorescence spectrometer to carry out high-density XRF tests on sandstone with a total thickness of 42 m from the feet to the chest of Leshan Giant Buddha in Sichuan Province at an average interval of 0.5 m and makes a curve of element content and ratio change. The research results show that the main elements such as Si, Ca, Al and Fe in the handheld XRF test results agree with the lithofacies analysis results and natural layer division results, reflecting the content changes quartz, calcite and limonite and the sericitization of debris. Element ratio can reflect the difference in weathering resistance between thick and massive rock mass. Si/(Si+Fe+Al) reflects the variation of cement content and its dissolution. (K₂O+CaO)/Al₂O₃ indicates the change of chemical weathering resistance. The decoupling of S element content with Fe and Mn indicates the development of dissolved pores, which comprehensively reflects the development of cement composition, porosity and bedding of Leshan Giant Buhhda rocks. The two high values of Cl are highly consistent with the banded stagnant water area in the chest and the unconfined water area in the feet of the Leshan Giant Buddha. Therefore, the application of handheld X-ray fluorescence spectrometer in the analysis of stone cultural relics shows the following three advantages. (1) For large and immovable stone cultural relics, handheld X-ray fluorescence spectrometer provides an efficient and non-destructive analysis method of petrochemical composition. The major elements can be in good consistence with the lithofacies analysis results of rock strata on rock mass in grottoes and the natural layer division results, which is helpful for the division of rock strata of stone cultural relics and the comparison of stone cultural relics in different regions. (2) Handheld X-ray fluorescence spectrometer can satisfy the high-resolution XRF scanning with a test interval of less than 0.1 m for the stone cultural relics in the same thick-massive rock stratum. The fluctuation of element ratio and the change of coupling relationship between elements reflect the internal difference in the weathering resistance of massive rock mass in cement composition, porosity and dissolved pore development. (3) The elemental concentration variations of Cl accurately indicate the rock strata with high water content, useful for the evaluation of water stagnation and seepage situation and the key prevention areas. It supports the comparative study of the locations and mechanisms of the stone cultural relics damages under different climatic and hydrological conditions.
		2022 Vol. 42 (08): 2506-2512 [Abstract] ( 122 ) RICH HTML PDF (3021 KB) ( 60 )

	2513	Evaluation of Various Atmospheric Correction Methods in the Processing of Landsat8/OLI Data in Jiaozhou Bay
		LIU Xiao-yan^{1, 2, 3}, SHEN Chen³, CUI Wen-xi³, YANG Qian^{1, 3}, YU Ding-feng^{1, 3*}, GAO Hao^{1, 3}, YANG Lei^{1, 3}, ZHOU Yan^{1, 3}, ZHAO Xin-xing³
		DOI: 10.3964/j.issn.1000-0593(2022)08-2513-09
		In ocean color remote sensing research, it is the key to obtainingthe accurate remote sensing reflectance spectrum (R_rs(λ)) data to retrieve marine biogeophysical parameters from ocean optical satellite data.In practice, R_rs is calculated according to the radiance received by the remote sensing instrument after the correction of atmospheric absorption and scattering and the correction of solar distance and solar elevation angle.Therefore, the atmospheric correction of satellite data is one of the key factors for obtaining accurate water remote sensing reflectance spectral data, which is also an important problem in the research of ocean color remote sensing.Jiaozhou Bay is a semi-closed bay in the west of the Yellow Sea of China and an important representative of the northern temperate zone bay ecosystem. A large range of Marine ranching areas are planned in this sea area, and the water’s bio-optical properties are complex. Landsat is the Landsatellite program of NASA in the United States. It was initially developed to observe the land. However, its advantage of high spatial resolution (30 m) is outstanding in Marine remote sensing monitoring, which makes it become one of the data sources that can not be ignored for satellite remote sensing to monitor rivers, lakes, inland bays and other water bodies. Based on the Quality Assurance system-QA Score, we evaluate the results of five atmospheric correction algorithms in processingLandsat8/OLI data in Jiaozhou Bay.Those five atmospheric correction algorithms are NASA’s (National Aeronautics and Space Administration) standard near-infrared atmospheric correction algorithm (Seadas adopted it as the Default atmospheric correction algorithm, recorded as Seadas Default in this paper). Acolite default atmospheric correction algorithm-Dark Spectrum Fitting (recorded as Acolite DSF in this paper), and the Exponential extrapolation method of Acolite, which is recorded as Acolite SWIR，Acolite Red/NIR，Acolite NIR/SWIR respectively according to the different bands used in the Exponential extrapolation algorithm. The analysis results show that the probability (83.95%) of QA score of R_rs(λ) data obtained by Seadas Default atmospheric correction algorithm in Jiaozhou Bay is much higher than that of Acolite DSF(49.66%)，Acolite SWIR(4.13%)，Acolite Red/NIR (7.25%)，and Acolite NIR/SWIR (1.38%). The atmospheric correction algorithm of Acolite DSF is superior to that of Acolite SWIR, Acolite Red/ NIR and Acolite NIR/SWIR. Finally, MODIS/Aqua satellite data were used to compare and analyze the R_rs(λ) data at 443，483，561 and 655 nm obtained by Seadas Default and Acolite DSF atmospheric correction algorithm respectively. The results show that the atmospheric corrected R_rs(λ) results obtained by the Seadas Default algorithm are better than that obtained by the Acolite DSF algorithm at all the bands. Based on the results of this study, we suggested that the NASA standard near-infrared atmospheric correction algorithm would be the first choice when applying Landsat8/OLI data to do remote sensing application research in Jiaozhou Bay and its adjacent waters areas.
		2022 Vol. 42 (08): 2513-2521 [Abstract] ( 148 ) RICH HTML PDF (7565 KB) ( 59 )

	2522	To Reveal the Occurrence States and Enrichment Mechanisms of Metals in Modules From Clarion-Clipperton Zone in Eastern Pacific by High Resolution Spectroscopy
		DENG Xian-ze^{1, 2}, DENG Xi-guang^{1, 2*}, YANG Tian-bang^{1, 2}, CAI Zhao³, REN Jiang-bo^{1, 2}, ZHANG Li-min^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2522-06
		The Clarion-Clipperton Zone (CCZ) in the equatorial eastern Pacific is the most economically potential nodule metallogenic belt globally. There are huge amounts of Mn, Co, Ni, Cu, Zn and Li metal resources in the CCZ. Previous studies focus on chemical and mineralogical analysis, lacking high-resolution spectroscopy analysis of micro-layers and metal distributions, thus resulting in a weak understanding of the enrichment mechanism of metals. In this study, high resolution scanning electron microscopy (SEM), X-ray diffraction (XRD), micro-area X-ray fluorescence surface scan (u-XRF) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were used to analyze the micro-lamina of nodules. The result shows that the nodule consists of hydrogenic and diagenetic alternating rhythms. The hydrogenic layer comprises Fe-vernadite, yields low Mn/Fe ratio, Li, Ni, Cu, Zn contents, and high Co, Fe, Ti contents. The hydrogenation layer adsorbs high Co, Ti and V contents due to the coulomb adsorption of FeOOH and surface oxidation of the high valence phyllomanganate octahedral layer. The diagenetic layer is birnessite, showing a high Mn/Fe ratio, Li, Ni, Cu and Zn contents. Its absorptive capacity of metals increases with Mn/Fe ratio and reaches its peak when Mn/Fe>8.The author proposes that the relative Mn and Fe fluxes during nodule accretion control the nodule’s mineral type and chemical composition, and the metal flux may also affect the metal composition of the nodule.
		2022 Vol. 42 (08): 2522-2527 [Abstract] ( 116 ) RICH HTML PDF (6264 KB) ( 47 )

	2528	Serum Metabolomics Study of CCI Model Rats Based on UHPLC-QE-MS
		BAI Feng-yuan^{1, 2}, ZHAO Dong-mei^{1, 2}, CAI Ren-jun^{1, 2}, SONG Su-fei^{1, 2}, LIU Tao^{1, 2}, XU Qiu-ling^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2528-04
		In this study, ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap mass spectrometry ( UHPLC-QE-MS ) non-targeted metabolomics method was used to observe the changes of endogenous metabolites in serum of CCI rats, screen out the serum differential metabolites of chronic sciatica rats, and analyze the effect of chronic pain on differential metabolites. Twelve SPF SD male rats were randomly divided into the normal control group and a chronic constriction injury ( CCI ) group, with 6 rats in each group. A chronic compression injury model of the left sciatic nerve was established in the CCI group. The normal control group had the same steps except no sciatic nerve ligation. After 14 days, abdominal aorta blood was collected, and serum was separated, and then the metabolites in rat serum were detected by metabolomics. The differential metabolites were screened by UHPLC-QE-MS combined with PCA ( principal component analysis ), and the enrichment analysis of differential metabolites was performed by Metabolic Analyst 5.0. The enrichment analysis results showed that compared with the normal control group, the serum organic acids, organic heterocyclic compounds, fatty acyl, carbohydrates, nucleic acids, organic nitrogen compounds, hydrocarbons and other nine metabolites of CCI model rats were statistically different. The serum metabolomics method based on UHPLC-QE-MS can effectively distinguish the normal group and the CCI group, and the screened differential metabolites are helpful in studying the mechanism of chronic pain and drug targets.
		2022 Vol. 42 (08): 2528-2531 [Abstract] ( 315 ) RICH HTML PDF (1518 KB) ( 51 )

	2532	*Study on Geographical Traceability of Artemisia argyi* by Employing the Fourier Transform Infrared Spectral Fingerprinting**
		LI Chao¹, LI Meng-zhi¹, LI Dan-xia¹, WEI Shi-bing¹, CUI Zhan-hu², XIANG Li-ling¹, HUANG Xian-zhang^1*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2532-06
		The geographical distribution of medicinal plants significantly affect the quality and safety of Chinese medicinal materials. From the biological point of view, Chinese medicinal materials are formed during the long-term ecological adaptation of species affected by a specific ecological environment. The climate, soil, hydrology, and other ecological factors required for the growth of medicinal materials are closely related to their growth and quality and have fingerprint characteristics of geographical information. In recent years, the rapid development of the Chinese medicine industry has brought about a surge in demand for Chinese medicine resources. However, at the same time, there are also many potential safety hazards. The difficulty in distinguishing and tracing the origin of Chinese medicinal materials has become one of the main bottlenecks restricting the development of traditional Chinese medicine. In this study, 75 A. argyi samples from 5 major producing areas of 4 provinces in China were analyzed by FTIR for characteristic analysis and data mining. Spectral signal preprocessing methods include Gaussian filtering, multivariate scattering correction, standard normal transformation, first/second derivative, etc. and pattern recognition techniques include BP neural network model, random forest, K-nearest neighbor, Bayesian algorithm, particle swarm optimization support vector machine, etc. were applied to explore the feasibility of traceability for A. argyi. The results indicate that the algorithms of K-nearest neighbor, Bayesian, and particle swarm optimization support vector machine show the ideal recognition effect, with an accuracy of 100%. Considering the comprehensive factors of running time, identification accuracy, and model stability, the algorithm of K-nearest neighbor is determined as the best method to trace the origin of A. argyi. In general, FTIR technology combined with appropriate chemometrics methods can be used to trace the origin of A. argyi successfully. The results of this study can provide technical support for the evaluation and quality control of A. argyi, and also contribute useful reference for the isotropic research of other medicinal materials.
		2022 Vol. 42 (08): 2532-2537 [Abstract] ( 171 ) RICH HTML PDF (1812 KB) ( 80 )

	2538	Study on the Microstructure Characteristics of Kidney Stones Based on Synchrotron Radiation MicroCT
		XIE Ying-xin¹, WANG Yi-wei², XUE Yan-ling^{3, 4}, DENG Biao^{3, 4}, PENG Guan-yun^{3, 4*}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2538-04
		Kidney stones are a common and frequently-occurring disease of the urinary system worldwide, and their recurrence rate is still high. It is generally believed that the supersaturation of salts in urine and the lack of substances to inhibit the formation of crystals in the urine are the main causes of kidney stones. The etiology of kidney stones is complex. At present, a variety of theories about the etiology of kidney stones have been put forward, mainly from the aspects of heredity, disease, metabolism and eating habits, to study the etiology of kidney stones to infer the causes of different kinds of stones, and then make targeted treatment plans. However, the clinical effect of internal medicine treatment of kidney stones is still limited. Actively exploring the growth mechanism of stones will undoubtedly be of positive significance to the scientific treatment of kidney stones. It is important to observe the internal structure of the stones and infer the formation and growth track of the stones according to these structural characteristics. It is difficult to obtain the internal structure of kidney stones by traditional research methods, so there are few reports on the microstructure of kidney stones. The appearance of high-resolution microCT, especially synchrotron X-ray microCT, can undoubtedly provide advanced detection means for this research. As the third generation of high-quality synchrotron radiation source, Shanghai synchrotron radiation facility (SSRF) has the advantages of high photon flux, collimation, polarization, coherence and wide spectrum. The synchrotron radiation X-ray detector can realize the fast and non-destructive detection of accurate and sensitive tissue structure information. It can reproduce the three-dimensional microstructure inside the sample on the premise of maintaining the integrity of the sample. Thus, this new detection method overcomes the limitations of traditional two-dimensional slicing technology, such as destroying the integrity of tissue structure and being, unable to accurately obtain the three-dimensional spatial information as well as matrix composition of tissue structure. In this study, the X-ray microCT technique at SSRF was used to analyze the microstructure of kidney stones from 32 patients. The research results showed that there were obvious differences in the internal structure of kidney stones, which could be divided into six types: Ⅰ the two-phase compact type; Ⅱ the crystalline type; Ⅲ the continuous multilayer deposition type; Ⅳ the discontinuous multilayer deposition type; Ⅴ the mosaic porous type; Ⅵ the composite type. The results of this study would contribute to reveal the growth mechanism of kidney stones further, and provide a new scientific perspective and basis for more accurate treatment of kidney stones.
		2022 Vol. 42 (08): 2538-2541 [Abstract] ( 98 ) RICH HTML PDF (1676 KB) ( 240 )

	2542	Effect of Different Particle Sizes on the Prediction of Soil Organic Matter Content by Visible-Near Infrared Spectroscopy
		ZHONG Xiang-jun^{1, 2}, YANG Li^{1, 2*}, ZHANG Dong-xing^{1, 2}, CUI Tao^{1, 2}, HE Xian-tao^{1, 2}, DU Zhao-hui^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2542-09
		Soil organic matter is an important indicator that characterizes soil fertility information, and realizing its rapid and accurate detection can provide effective data support for precision agriculture regional management. The particle size of the soil has a great influence on the spectrum prediction of SOM content and instrument development. To analyze the impact of different particle sizes on SOM prediction, five soil samples with the uniform particle size of 1~2, 0.5~1, 0.25~0.5, 0.1~0.25, <0.1 mm, and mixed particle sizes of <1 mm were prepared, and the visible-near infrared (300~2 500 nm) spectral data was collected. Monte Carlo cross-validation was used to eliminate abnormal samples of different particle sizes, and the spectral data were smoothed and de-noised by the Savitzky-Golay convolution smoothing method. The spectral reflectance differences of samples with different particle sizes were compared, and three spectral transformations were performed on the smoothed original spectrum R, including reciprocal IR, logarithmic LR, and first derivative FDR. The correlation between SOM content and the reflectance of different transformed spectra was analyzed. The characteristic wavelength of the FDR transformed spectral data was extracted based on the Competitive Adaptive Reweighted Sampling (CARS) algorithm. Moreover, combined with the partial least squares regression (PLSR) to establish the corresponding prediction models of SOM content. The results show that the average spectral reflectance and coefficient of variation of soil samples with different particle sizes gradually increase with the decrease of particle size, and the difference is obvious in the wavelength range greater than 540 nm. With the decrease in particle size, the correlation between SOM content, particle size, and spectral reflectance in the whole band range become more obvious. FDR transformation can significantly change the correlation between SOM content and spectral reflectance. The CARS algorithm was used to extract the characteristic wavelengths from the FDR transformed spectral data, and the number of characteristic wavelengths was screened out and reduced to 13.1% of the total number of bands, which reduced the overlap of spectral data and the interference of invalid information. Comparing the results of different SOM prediction models, the FDR transformed spectrum had good modeling accuracy. Especially when the particle size was less than 0.1 mm, the model’s R²_p, RMSEP and RPD value was 0.91, 2.20 g·kg^-1, and 3.33. Among the SOM content prediction models constructed based on CARS characteristic variables, the prediction model with particle size <0.1 mm has the best effect. Its R²_p reached 0.78, RMSEP was 3.00 g·kg^-1, and RPD was 2.00, which can achieve reliable prediction of SOM content, and there is still room for optimization of models under other particle sizes. This research can provide a reference for the rapid and accurate prediction of SOM content in the field environment and the design of instruments.
		2022 Vol. 42 (08): 2542-2550 [Abstract] ( 156 ) RICH HTML PDF (7023 KB) ( 64 )

	2551	Optimization of Corn Stalk Liquefaction Conditions Under Atmospheric Pressure and Analysis of Biofuel
		ZHANG Yan¹, WANG Hui-le¹, ZHAO Hui-fang¹, LI Jing¹, TONG Xin¹, LIU Zhong²
		DOI: 10.3964/j.issn.1000-0593(2022)08-2551-06
		With the decline in the availability of petrochemical resources, lignocellulosic biomass as a renewable resource has been getting more and more attention. The atmospheric liquefaction technology has been used widely, which is one of the effective ways of biomass components utilization. In this paper, to optimize the liquefaction conditions, a single-factor method was used to study the effects of liquefaction temperature, mixing ratio of a compound liquefying agent, liquid-solid ratio, catalyst dosage and reaction time on the liquefaction yield of corn stalk. The thermo-gravimetric analyzer (TGA), gas chromatography and mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) spectra were adopted to detect the volatile degradability and components of the biofuel. The results indicated that the optimum conditions were determined as liquefaction temperature 170 ℃, diethylene glycol (DEG)/1,2-propanediol (PG)=1∶2, a liquid to solid ratio of 5∶1, phosphoric acid dosage 10% and reaction time 45 min. Under this condition, the liquefaction yield was up to 99.50%. The results of TGA showed that the biofuel contained more than 80% of compounds with a carbon number less than 25, and the final carbon content after pyrolysis was about 15%. GC-MS presented that 39 kinds of organic compounds were tested in biofuel, among which alcohols were the most, phenols were the second, and their relative contents were 70.70% and 25.63%, respectively. There were also some organic acids (2.80%), ethers (0.64%), esters (0.10%) and ketones (0.13%). Its components were complicated, and high oxygen content, so its stability was limited. ¹H- and ¹³C-NMR explained that different chemical shifts δ corresponded to different types of protons and carbon atoms. The distribution of hydrogen and carbon in the biofuel was clarified, conducive to the further exploration of its molecular structure. Hence, theoretical foundation and technical support could be provided for the existing related liquefaction technology and then promote the efficient utilization of biomass resources and the development of biomass-based products.
		2022 Vol. 42 (08): 2551-2556 [Abstract] ( 113 ) RICH HTML PDF (1766 KB) ( 30 )

	2557	Study on the Hyperspectral Discrimination Method of Lettuce Leaf Greenness
		GUO Jing-jing¹, YU Hai-ye¹, LIU Shuang², XIAO Fei¹, ZHAO Xiao-man¹, YANG Ya-ping¹, TIAN Shao-nan¹, ZHANG Lei^1*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2557-08
		Lettuce leaf greenness is important in the physiological and sensory evaluation of crop quality. Based on the comparison of existing methods for greenness discrimination, combined with the application status and prospects of hyperspectral detection and analysis technology in the detection of plant physiological information, the research on the application method of hyperspectral technology in the greenness discrimination of lettuce leaves was carried out. The quantification of sensory evaluation of the vegetable quality and developing a multifunctional synchronous collection device for physiological information based on hyperspectral technology provide necessary theoretical support. Lettuce is the subject of study. Cultivation experiments were conducted under three different light environments, and relative chlorophyll content (SPAD) was used as a parameter to respond to greenness. Acquisition of dynamic hyperspectral and SPAD data throughout the life cycle of lettuce. Study of hyperspectral response characteristics to leaf greenness. The variation pattern of the hyperspectral curve was analyzed. Finally, a relationship model between hyperspectrum and SPAD was developed. The Savitzky-Golay convolution smoothing (SG) method was used to reduce the noise of the original hyperspectral data. The smoothed data was combined with the three preprocessing methods of multivariate scattering correction (MSC), standard normal variable transformation (SNV) and first derivative (FD), and finally adopted competitive adaptive reweighted sampling (CARS) and extraction effective vegetation index (VI) two methods for sensitive wavelength extraction. Combine the two methods of partial least squares (PLS) and least squares support vector machine (LSSVM) for modeling, and use the coefficient of determination (R²) and root mean square error (RMSE) as evaluation indicators to select the optimal greenness prediction model. The results showed that the hyperspectral curves of lettuce under different light environments showed a consistent overall trend but different reflectance values during the whole life cycle of lettuce at 10, 20 and 30 days. The lettuce reflectance values in the visible light range of 450～680 nm exhibited higher natural light exposure than the supplemental light treatment, while the hyperspectral response characteristics in the NIR range of 730～850 nm were exactly opposite to the visible light range. The combination of SG+FD pre-treatment and CARS sensitive wavelength extraction method based on SG+FD can achieve the most effective extraction of chlorophyll content feature information, and the extracted sensitive wavelengths accounted for 64.59% of the total wavelengths, which increased the number of extracted sensitive wavelengths by 63.34% compared with the original hyperspectrum (1.25%). The LSSVM method was identified as the optimal modeling method, and the model built based on the combined SG+FD+CARS+LSSVM method was the optimal lettuce greenness prediction model with the training set R²_c=0.920 7, RMSEC=1.161 0, and the prediction set R²_p=0.828 8, RMSEP=2.400 8, indicating that the model had high accuracy. The purpose of greenness judgment of lettuce leaves can be realized.
		2022 Vol. 42 (08): 2557-2564 [Abstract] ( 178 ) RICH HTML PDF (5873 KB) ( 58 )

	2565	Estimation of Leaf Moisture Content in Cantaloupe Canopy Based on SiPLS-CARS and GA-ELM
		GUO Yang¹, GUO Jun-xian^1*, SHI Yong¹, LI Xue-lian¹, HUANG Hua², LIU Yan-cen¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2565-07
		To realize more precise irrigation management during the growing period of Hami Melon in the field. The traditional methods for measuring leaf moisture content are inefficient, complicated and destructive, which is not conducive to obtaining moisture content of Hami melon leaves in the field. In this study, the leaf samples of cantaloupe in four periods of growth (M₁), flowering (M₂), fruit (M₃) and maturity (M₄) were obtained by spectral technology, and the moisture content of the leaf samples was measured by drying method. The influence of the choice of kernel function and the number of hidden neurons on the precision of the ELM model is discussed. Then SiPLS and its combined algorithm with CARS, GA and SPA were used to extract the characteristic wavelengths with a high correlation with leaf moisture content. GA and PSO algorithms are used to optimize the connection weights (W) between the input layer and the hidden layer of the ELM model, and the threshold (B) of the hidden layer of the ELM model, the optimal and stable W and B values are obtained further to improve the stability and prediction accuracy of the model. Finally, four feature wavelength extraction algorithms are combined with ELM, GA-ELM and PSO-ELM to analyze the model, and the Correlation Coefficient between the correction set and the prediction set is taken as the evaluation index of the model. Through the comparison and analysis, the inversion estimation model of cantaloupe canopy leaf moisture content was optimized. The results show that the number of SiPLS and its combination with CARS, GA and SPA are 273, 20, 32 and 6 respectively, accounting for 15.6%, 1.2%, 1.9% and 0.03% of the total spectrum variables. Taking the selected characteristic wavelength as the independent variable and the moisture content of the leaves as the dependent variable, the prediction model of ELM is established, but the prediction accuracy is not very ideal. Therefore, GA and PSO are introduced to optimize the randomly generated W and B values in ELM. Finally, it is found that the precision of predicting water content of cantaloupe canopy leaves based on the ELM model optimized by GA and SiPLS-CARS is the best. Therefore, the optimal modeling method of leaf moisture content retrieval is SiPLS-CARS-GA-ELM, R_C value is 0.928 9, R_P value is 0.903 2, the precision of the model is high, which can be used to detect the leaf moisture content in cantaloupe canopy, the research provides the theoretical basis for the field irrigation management.
		2022 Vol. 42 (08): 2565-2571 [Abstract] ( 110 ) RICH HTML PDF (2551 KB) ( 48 )

	2572	Retrieval of Dust Retention Distribution in Beijing Urban Green Space Based on Spectral Characteristics
		WANG Ge¹, YU Qiang^1*, Yang Di², NIU Teng¹, LONG Qian-qian¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2572-07
		As the political center of China and a super large city of Beijing, Tianjin and Hebei, the urbanization process of Beijing has been rapid in the past 40 years, and the pollution problems of atmospheric particles and dust particles are prominent. It is of great practical significance to play the role of green space dust retention. This paper combines hyperspectral technology and remote sensing technology to retrieve the urban scale green space dust distribution. This study selected Euonymus japonicus, a common green space vegetation in Beijing, as the research object. The dust retention capacity, spectral reflectance and leaf area of leaf samples were obtained through outdoor sampling and indoor experiments. The original spectral curve and the first derivative of reflectance before and after dust retention were compared, and the effects of different dust retention on spectral reflectance were analyzed, To explore the band which is highly sensitive to dust retention of leaves. Using the spectral response function, the narrow band spectral reflectance data collected on the ground are transformed into the wide band spectral reflectance data of remote sensing satellite. The regression model of vegetation index ratio and dust retention capacity of corresponding satellite band is established. The regression model with the best fitting effect is selected as the dust retention inversion model. Combined with the green space range extracted from the GF-2 image, the dust retention distribution of Beijing urban green space was obtained using the dust retention inversion model. The spatial autocorrelation model is used to test the spatial aggregation characteristics. The results show that: in the 740~1 870 nm band, the spectral reflectance after dust retention is significantly lower than before dust retention. Dust retention has no obvious effect on the position of the red edge, yellow edge and blue edge but has pronounced effect on the “red edge amplitude” and “red edge area”. EVI index calculated by Sentinel-2 image has the highest correlation with dust retention. The coefficients of determination (R²) of the linear and quadratic regression models are 0.705 and 0.751, respectively. Based on the Sentinel-2 images on April 7, 2021, and June 3, 2021, the distribution trend of green space dust retention in the Beijing urban area is as follows: the city center is higher than the suburbs, the north is higher than the south, and the East is higher than the West. The central, northern and eastern parts of Beijing are prone to dust pollution. The pollution distribution is aggregated and not completely random.
		2022 Vol. 42 (08): 2572-2578 [Abstract] ( 95 ) RICH HTML PDF (3518 KB) ( 35 )

	2579	Analysis of Inverted Y-Shaped Arc Photoelectricity Characteristic of Flux-Cored Wire Pulsed TIG Additive Manufacturing
		HUANG Shi-cheng¹, HUANG Yi-ming^{1, 2}, YANG Li-jun^1, YUAN Jiong¹, LIN Zhi-xiong¹, ZHAO Xiao-yan¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2579-08
		In the process of flux-cored wire pulsed TIG arc additive manufacturing, the phenomenon of the arc riding on both sides of the formed part was found. The arc was called the inverted Y-shaped arc. The inverted Y-shaped arc had a heating effect on both sides of the forming part, and its deviation caused uneven healing on both sides of the forming part, which affected the stability of the cladding process. The electron density of the trailing part of the inverted Y-shaped arc was calculated using Stark broadening according to spectral data measured by the point matrix method. Under the experimental conditions of this study, some areas (about 2 mm outside sidewall, about 1.5 mm below 0 positions in Z direction) conformed to local thermodynamic equilibrium. The electron temperature was calculated using the Boltzmann diagram method of spectral diagnosis, and the complete arc temperature field was obtained by fitting the data of each point. The temperature field parallels to, and perpendicular to the moving direction of the welding torch in the deposition process was analyzed. The results showed that the maximum temperature of the inverted Y-shaped arc at the tungsten electrode tip was about 14 000~16 000 K, distributed in the range of 0.5~1.5 mm below the tungsten electrode the temperature of the trailing part of the arc was about 5 000~8 000 K. In the direction perpendicular to the moving direction of the welding torch, when the tungsten electrode axis coincided with the center of the deposited layer, the normal inverted Y-shaped arc and the temperature field were symmetrically distributed along the tungsten electrode axis. When the tungsten electrode axis shifted by 1 mm to the left of the center of the deposited layer, the inverted Y-shaped arc shifted to the left, and the temperature field also shifted to the left. The temperature on the left side of the deposited layer was significantly higher than that on the right. In the direction parallel to the moving direction of the welding torch, the temperature field distortion of the inverted Y-shaped arc was small. During the deposition process, the welding wire was fed in from the front (left) side of the tungsten electrode, which disturbed and absorbed the arc’s heat. As a result, the size and temperature of the arc’s front (left) side were smaller than those of the rear (right) side, and the arc contraction. By analyzing the electrical signals of the two cases where the tungsten electrode axis coincided with the deposited layer center and shifted by 1mm to the left of the deposited layer center, it was indicated that the mean voltage, the base voltage average and the peak voltage average of the former were less than those of the latter. Based on the analysis by combining the electrical signal and the Gaussian heat source model, it was found that the temperature and heat flux of the normal inverted Y-shaped arc were smaller than those of the offset inverted Y-shaped arc at the same position on the left side of the formed part. In contrast, the opposite results were obtained at the same position of the right side of the formed part, which was consistent with the temperature field distribution obtained by spectral diagnosis. The results of this study were of great significance for establishing a new heat source model and process monitoring in the arc additive manufacturing process.
		2022 Vol. 42 (08): 2579-2586 [Abstract] ( 111 ) RICH HTML PDF (4680 KB) ( 29 )

	2587	Intensity Distribution and Inversion Reconstruction of Spectrum of Hydroxyl Radicals in Spray Combustion of PODE Under Different Environmental Oxygen Concentrations
		ZHANG Xiao-teng, LIU Wei, LIU Hai-feng^*, ZHENG Zun-qing, MING Zhen-yang, CUI Yan-qing, WEN Ming-sheng, YAO Ming-fa
		DOI: 10.3964/j.issn.1000-0593(2022)08-2587-08
		Polymethoxy dimethyl ether (PODE) is a potential diesel alternative fuel. However, currently, most of the research on PODE is concentrated on the engine bench tests, and corresponding basic spray combustion research is few which restricts the improvement of its efficient and clean combustion performance in power plants. The property of hydroxyl groups is active, and the area where they exist in large quantities is usually considered a high-temperature reaction area. By measuring the hydroxyl spectral band, important parameters such as flame structure, combustion reaction location and heat release rate can be obtained. Environmental oxygen concentration has a great influence on flame structure, and it is also an important parameter in controlling combustion reaction rate and pollutant emission. Therefore, on an optical constant volume combustion bomb, firstly used the self-luminescence measurement of hydroxyl spectral band to research the effects of oxygen concentration (15%~80%) on the lift-off length of PODE spray flame, then the integral value of hydroxyl self-luminescence spectrum intensity was converted to the point value by using Abel inverse transformation method to research the effects of oxygen-enriched concentration (40%~80%) on the hydroxyl distribution of PODE spray flame. The results show that: as the oxygen concentration increases from 15% to 40%, the flame lift-off length of PODE decreases rapidly. But further increase to 80%, the flame lift-off length decreases gradually until it is unchanged; The flame lift-off length of PODE is significantly smaller than diesel under the same oxygen concentration. At the distribution feature surface of hydroxyl spectral after inversion, the high-intensity area of PODE hydroxyl spectral is mainly concentrated in the thin layer of the spray edge diffusion flame under oxygen-enriched conditions; Meanwhile, the significant increase in local temperature makes the hydroxyl spectral intensity reach the maximum near the downstream of the premixed reaction zone. With the increase of oxygen concentration, the high-intensity area of hydroxyl spectral gradually migrates to the upper and middle areas of the flame. Its distribution appears to be shorter in the axial direction and narrower in the radial direction. When the flame reaches a quasi-steady state, compared with 40% oxygen concentration, the spectral intensity of hydroxyl at 60% and 80% oxygen concentration is significantly weaker in the middle and lower reaches of the flame, which indicates that the high concentration area of fuel upstream of the spray is more quickly to participate in the intense combustion reaction.
		2022 Vol. 42 (08): 2587-2594 [Abstract] ( 107 ) RICH HTML PDF (4525 KB) ( 36 )

	2595	Analysis on Temporal and Spatial Changes of Vegetation Net Primary Productivity in Typical “Alpine-Oasis-Desert” Ecological Region
		QIAO Peng¹, SUN Cong-jian^1*, LI Ya-xin¹, ZHOU Si-jie¹, CHEN Ya-ning²
		DOI: 10.3964/j.issn.1000-0593(2022)08-2595-08
		“Alpine-oasis-desert” is a unique natural landscape in arid areas, its internally distinct ecosystems are prone to different fluctuations under global change. As an important indicator to evaluate the quality of the ecological environment, vegetation net primary productivity ( NPP ) is of great significance to the overall understanding of regional changes. The generation of remote sensing images allows large-scale and long-term regional NPP estimation. The maximum light energy utilization efficiency under different plant covers classified by land-use type data also improves the accuracy of NPP estimation. Therefore, this paper selects the Yarkand River Basin with a typical alpine-oasis-desert ecosystem as the study area, using remote sensing image data and meteorological data for many years, selecting the CASA model based on light utilization rate simulate and analyze the NPP status of each ecological area. The following conclusions were drawn: (1) The annual average value of NPP in the Yarkand River Basin showed a fluctuating upward trend after 2 000, and about 85.9 % of the regions showed an upward trend. In the water area and the residential location of the oasis area, the NPP decreased. (2) The variation of NPP in the basin strongly correlations with precipitation, and its spatial distribution characteristics have an opposite correlation with NPP and temperature. (3) NPP in the Yarkand River Basin showed the highest in the oasis, followed by the desert-oasis transition zone, and the lowest in the alpine and desert regions. The fluctuation of NPP in the regions with relatively more fragile ecosystems (desert and alpine) was more substantial than that in the oasis and desert-oasis transition zone. The research results will provide theoretical support for restoring regional ecological environment protection, the response to climate change, the coordinated development of human beings and nature, and the promotion of multi-ethnic common prosperity.
		2022 Vol. 42 (08): 2595-2602 [Abstract] ( 92 ) RICH HTML PDF (12928 KB) ( 91 )

	2603	Comparison of Polarized Spectral Characteristics Between Petroleum-Polluted Cropland Soil and Wetland Soil
		SONG Zi-hao, HAN Yang^*, WEI Chen-yang, CHEN Xin, GU Qian-yi, LIU Zi-ping, LIU Sha-sha
		DOI: 10.3964/j.issn.1000-0593(2022)08-2603-07
		Remote Sensing Technology is an important and useful method to monitor the Earth’s surface by receiving the electromagnetic waves reflected or radiated from the targets, and optical and thermal remote sensing is the most common remote sensing methods. Polarization is a ubiquitous optical phenomenon which has commercial and technological applications.After decades of development, polarization remote sensing has become a widely applied technology in Earth observation. Depending on intensity information, traditional optical remote sensing has many disadvantages on target identification and information extraction, but it could be modified by appending polarization information to it, so that, the features and characteristics of the targets could be obtained from polarization information. Soil is a complex substance that plays an important role in the ecosystem. Hence itself is helpful to apply remote sensing on the soil to monitor the environment and treat pollution. Ideally, the soil do not obviously reflect or absorb electromagnetic waves in any range of wavelength, while the moisture, organic matters and roughness usually influences the spectrum characteristic of soil.According to recent studies, it is significant that petroleum an influences the polarized spectrum characteristics of the soil. Especially in the red and infrared spectral regions, the spectral response of petroleum is so pretty obvious, that the pollution on the Earth’s surface could be detected by remote sensing of a wide range and multiple periods.In addition, it is reasonable for us to assume thatthe influence on viewing effect of petroleum might vary in different soil types. In this paper, the petroleum-polluted soils were respectively sampled from a wetland and a cropland in Zhenlai Petroleum Factory, located in Jilin Petroleum Field. By multiangle measurement, we managed to compare the polarized spectrum characteristics of each kind of soil sample and analyze the influences on themwith quantitative and qualitative methods. The results suggest it is obvious the polarized spectrum characteristics of wetland soil differ from another one, because of the difference in moisture and structure, together with the existence of petroleum, for which the measured polarized reflectance value has a significant shift from the predicted polarized reflectance value.
		2022 Vol. 42 (08): 2603-2609 [Abstract] ( 109 ) RICH HTML PDF (3940 KB) ( 37 )

	2610	Multispectral Structural Characterization of Humic Acid-Enhanced Urea
		JING Jian-yuan, YUAN Liang, ZHANG Shui-qin, LI Yan-ting, ZHAO Bing-qiang^*
		DOI: 10.3964/j.issn.1000-0593(2022)08-2610-06
		Humic acid-enhanced urea (HAU) can be produced by adding humic acid (HA) into melted urea during urea production. Field studies have proved that HAU showed a better urea hydrolysis rate, crop yield, and nitrogen use efficiency than normal urea (U). However, the main reaction between HA and U during the production of HAU has not been reported yet. In this study, HA, derived from weathered coal, was used to produce HAU, and the added amount of humic acid was 5%, 10%, and 20%, respectively (named HAU5, HAU10, HAU20). The paper collected and analyzed the infrared spectra and their second derivative infrared spectra of HAU5, HAU10, HAU20, and U. HAU20 and U were characterized using X-ray photoelectron spectroscopy (XPS), and oxygen 1s near-edge X-ray absorption fine structure (NEXAFS). The urea in HAU20 was removed by dissolving HAU20 with absolute ethanol, and FTIR and XPS characterized the residue(UHA). The result showed that: (1) FTIR spectra and the second derivative spectra showed that the vibration intensity of primary amine C—N in HAU was lower than that in U, and the vibration intensity decreased with the increase of the addition amount humic acid. There were more secondary amine nitrogen, and non-carbonyl oxygens in HAU20 were separated from the XPS N(1s) spectra and O(1s) NEXAFS spectra, respectively, and prominent amide characteristics were shown from the result of FTIR spectra for UHA, which indicated that HA reacted with urea during the HAU production. (2) the percentage of carboxyl carbon in HAU20 or UHA was lower than in HA. FTIR spectra showed that C—O—H in-plane bending vibration from carboxylic acid detected in HA did not exist in UHA, the C═O stretching vibration position from carboxyl groups in UHA was shifted, and the characteristics of primary amine nitrogen for UHA were obvious. The above indicated that the carboxyl groups of HA participated in the reaction of HA and urea. The structure for R—CO—NH—CO—NH₂ in HAU will be produced after the dehydration reaction between the carboxyl group of HA and the amide group of urea. Therefore, the results from the spectral analysis used in this study clarified the main reaction modes of humic acid and urea during the production of HAU, which will provide basic information for the reveal of the synergistic mechanism of HAU and the development of value-added urea.
		2022 Vol. 42 (08): 2610-2615 [Abstract] ( 100 ) RICH HTML PDF (2935 KB) ( 42 )

	2616	Raman Spectroscopic Characterization and Surface Graphitization Degree of Coal-Based Graphite With the Number of Aromatic Layers
		LI Huan-tong^{1, 2}, CAO Dai-yong³, ZOU Xiao-yan³, ZHU Zhi-rong¹, ZHANG Wei-guo¹, XIA Yan⁴
		DOI: 10.3964/j.issn.1000-0593(2022)08-2616-08
		Comparison of Raman spectra at multi-excitation wavelengths (325, 405, 514, 633 and 785 nm) for coal-based graphite, and evolution of the spectra at 514 nm with the number of aromatic layers were detail studied. Moreover, the Raman mapping test studied the surface defects distribution of coal-based graphite block. The results show disordered graphite has a smaller size and arbitrary orientation than graphite crystallites. With the increase of stacking degree and average stacking layers, the Raman spectrum characteristics of graphite microcrystal edge appear. When the disordered structure of coal-based graphite transforms to order, the defects gradually disappear, and the D3 and D4 peaks in the first-order gradually become invisible or disappear, but the overtone peaks appear weakly, especially as the intensity of the 2D1 peak increases. Further extending the meaning of I_D1/I_D2 parameter to defect type and average orientation, the I_D1/I_D2 ratio of anthracite is the largest. With the increase in crystallite size (d₀₀₂<0.344 nm), the I_D1/I_D2 of 3D ordered graphite was the smallest. The FWHM of the G peak always decreases with the decrease of disorder at different excitation wavelengths. D1 peak and 2D1 peak show a strong dispersion effect, and the intensity of each peak grows with the increase of excitation energy. Under UV excitation, the peak position difference of D1 and G peaks is significantly smaller than that under visible light excitation. With the increase of excitation wavelength, the D1 peak moves towards the low wavenumber direction, and the dispersion of the 2D1 peak is about twice the intensity of the D1 peak. During the graphitization process of high rank coal, the non-oriented aromatic carbon experienced a series of physical and chemical structure evolution to produce various intermediate phases, and the residual coal macerals (vitrinite and inertinite) and new graphite components (pyrolytic carbon, etc.) coexist. (I_G-I_D1)/(P_G-D1)≥0.3, I_D1/I_G<0.4, A_D1/A_(D1+G)<0.45 were used as the boundaries of graphite and semi-graphite. The surface uniformity of the sample was characterized by planar scanning area imaging. The confidence interval of the frequency distribution of 0.9 was used to comprehensively determine the surface graphitization degree of the sample, which was 84.16%~86.40%, and the average was 85.49%, which was similar to the estimated value of XRD parameters.
		2022 Vol. 42 (08): 2616-2623 [Abstract] ( 107 ) RICH HTML PDF (5318 KB) ( 94 )

	2624	Molecular Representations of Jurassic-Aged Vitrinite-Rich and Inertinite-Rich Coals in Northern Shannxi Province by FTIR, XPS and ¹³C NMR
		LI Huan-tong^{1, 2}, ZHU Zhi-rong^{1, 2}, QIAO Jun-wei^{1, 2}, LI Ning³, YAO Zheng³, HAN Wei^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2624-07
		Jurassic high-quality coal resources provide the abundant material basis for clean and efficient coal utilization to oil and gas. Microlithotype composition of Jurassic high-quality coal resources is characterized by enrichment of inertinite. The macromolecular structure of vitrinite and inertinite largely determines coal’s physical and chemical properties and process performance, and then determines comprehensive utilization efficiency and added value of coal resources. Thus, raw coal (XR), vitrinite-rich coal (XV, NV) and inertinite-rich coal (XI, NI) samples were collected and prepared from Xiaobaodang and Ningtiaota coal mining area in the Jurassic coalfield of northern Shaanxi Province. Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and solid nuclear magnetic resonance spectroscopy (¹³C NMR) were used to quantitatively characterize the differences in molecular structures of different maceral enrichments combined with the results of coal quality analysis. The results showed that the aromatic ring substitution degree of aromatic structure in XI and NI coals is low, mainly in the form of three adjacent hydrogen atoms and four to five adjacent hydrogen atoms. Other functional groups less replace hydrogen atoms on benzene ring. At the same time, the vibration of aromaticC═C skeleton in the structure is obvious, and stretching vibration intensity of methylene in aliphatic structure is reduced. Methyl content is slightly higher than that of vitrinite-rich coals, and the relative content of theC═O group is slightly higher, indicating that inertinite-rich coal has more aromatic structures connected by oxygen-containing bridge bonds. Aliphatic chain and aliphatic ring groups fall off, fracture and aromatic enrichment, and branched-chain is relatively small, and the length is short. In addition aromatic carbon rate, aromaticity, aromatic condensation degree and maturity are high. The relative content of “C—C, C—H” and “C—O” in the surface structure of XV and NV coals is higher than that of inertinite-rich coals, which reflects that the structure should contain more aliphatic side chains replaced by aromatic rings. The oxygen species in the surface structure of XI and NI coals are mainly “C—O”, and “C═O” and “COO—” are significantly higher than those of vitrinite-rich coals. The aromatic carbon ratios of XV and XI coals are 57.91 % and 66.02 %, respectively. XV and XI coals’ aliphatic methyl carbon ratios are 10.02 % and 7.84 %, respectively. The protonated aromatic carbon is twice as much as the non-protonated aromatic carbon. The relative content of carbonyl and carboxyl carbon of XV coal is high. The ratios of bridge carbon and per carbon of XV and XI coals are 0.25 and 0.40, respectively. The average number of condensation rings of aromatic nucleus structure is 2.68 and 3.03, and the average sizes are 0.448 nm and 0.676 nm, respectively. The aromatic nucleus in the XI coal structure is mainly naphthalene and anthracene, and the branched-chain degrees are 0.22 and 0.19, respectively. It is indicated that XV has more aliphatic side chains and saturated ring structures than XI coal and has great hydrocarbon generation potential.
		2022 Vol. 42 (08): 2624-2630 [Abstract] ( 151 ) RICH HTML PDF (4872 KB) ( 211 )

	2631	Stochastic Process Prediction of Clutch Remaining Life Based on Oil Spectral Data
		ZHANG Jiang¹, CUI Jun-jie¹, ZHENG Chang-song^2, LIU Yong^1, LIU Ya-jun³, SHEN Jian¹
		DOI: 10.3964/j.issn.1000-0593(2022)08-2631-06
		The residual life prediction of wet clutch based on oil spectrum data significantly impacts on the condition monitoring and reliability of integrated transmission device. Aiming at the problems of high randomness of oil spectral data and single performance index and large error of existing methods, the prediction of clutch remaining life is carried out using the advantages of real-time and accuracy of binary Wiener process. Firstly, combined with the wet clutch life test, the indicator elements Cu and Pb and the failure threshold of the remaining life prediction of the clutch are extracted through the oil supplement and change correction of the spectral data of the whole life cycle; Secondly, the correlation characteristics of indicator elements are analyzed by MATLAB copula function, and the correlation function of residual life is derived; Thirdly, according to the inverse Gaussian principle, the performance degradation mathematical models of the unary and binary Wiener processes of the above two indicator elements are established; Finally, the maximum likelihood estimation method is used to estimate the parameters, and the univariate and binary performance degradation mathematical models are used to predict the remaining life of the tested clutch. By comparing the predicted results with the experimental results, the deviation of residual life prediction of binary Wiener process is 6%～22% in the range of 150～240 h; Compared with the univariate Wiener process, the accuracy of residual life prediction is improved by more than 9%. The results show that the binary Wiener process model and its prediction method have the advantages of real-time solid prediction and high prediction accuracy. At the same time, this method can be extended to related fields such as on-line monitoring of equipment status and residual life prediction.
		2022 Vol. 42 (08): 2631-2636 [Abstract] ( 143 ) RICH HTML PDF (4319 KB) ( 44 )

	2637	Spectral Oil Condition Monitoring Data Selection Method for Mechanical Transmission Based on Information Entropy
		YAN Shu-fa, ZHU Yuan-chen, TAO Lei, ZHANG Yong-gang, HU Kai, REN Fu-chen
		DOI: 10.3964/j.issn.1000-0593(2022)08-2637-05
		In mechanical transmission, the wear debris produced from different friction couplings is uniformly mixed in lubrication oil, which is a slow degradation process that can be observed by oil spectral analysis. The wear debris in a sample can be categorized into 15 groups of concentration (e.g., Fe, Cu and Mo) in parts per thousand using MOA II (atomic emission spectroscopy) during the sampling epochs. Its level is one of the most common data types used to monitor and evaluate the underlying health state. However, not all the oil spectral data can show the same degradation pattern. Only parts of the spectral oil data can provide useful information for degradation degree characterization. Using all the spectral oil data for condition monitoring will result in unreasonable degradation modeling for condition monitoring and unscheduled maintenance afterwards. Therefore, this article proposes a selection of degradation data based on information entropy to determine the appropriate degradation data for degradation modeling and remaining useful life prediction. Compared with the experiential selection method, the proposed method can characterize the degradation information contained in the multiple spectral oil dataset, leading to a quantitatively selecting the degradation data. The proposed method was verified through a case study involving a degradation dataset of multiple spectral oil data sampled from a power-shift steering transmission (PSST). The result shows that the proposed method can better characterize the degradation degree, which leads to an accurate estimation of the failure time when the transmission no longer fulfills its function.
		2022 Vol. 42 (08): 2637-2641 [Abstract] ( 131 ) RICH HTML PDF (1516 KB) ( 43 )

	2642	Remote Sensing Retrieval of Chlorophyll-a Concentration in Lake Chaohu Based on Zhuhai-1 Hyperspectral Satellite
		FENG Tian-shi^{1, 2, 3}, PANG Zhi-guo^{1, 2, 3*}, JIANG Wei^{1, 2, 3}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2642-07
		Chlorophyll-a is a critical water quality parameter which can be used to evaluate the eutrophication degree of lakes. Remote sensing technology has the advantages of real-time, rapidity and wide monitoring range and has been widely used in inland lake water environment monitoring. Obtaining the chlorophyll-a concentration in lakes dynamically in real-time is significant for lake governance. The spectral characteristics of inland lakes are complex. So, it is difficult to accurately obtain the spectral characteristics of water bodies by multi-spectral remote sensing data. The Zhuhai-1 hyperspectral satellite has a broad application prospect in remote sensing monitoring of inland lakes because of its high spectral resolution and sufficient band. In this paper, the hyperspectral data of Zhuhai-1 is selected to retrieve the chlorophyll-a concentration in Chaohu Lake. First, extract the remote sensing reflectance curve at the measured point from the image, filter the bands with significant spectral characteristics according to the curve, and use the OIF index to measure the ability of different band combinations to obtain water body composition information. And finally build the band combination, which is highly correlated with the actual measurement chlorophyll-a concentration. The results show that the three-band model [R_rs(700 nm)^-1-R_rs(670 nm)^-1]×R_rs(746 nm) constructed by Zhuhai-1 in bands 14, 16 and 19 has achieved high accuracy in retrieving chlorophyll-a concentration in Lake Chaohu, the MRE is 19.97% and the RMSE is 10.85 mg·m^-3, which is superior to most previous spaceborne remote sensing data sources. Retrieving the spatial distribution map of chlorophyll-a concentration of Chaohu Lake on May 10, 2019, shows that the chlorophyll-a concentration is increasing from east to west. The concentration of chlorophyll-a in the south and northeast of the lake is low, and the chlorophyll-a concentration in the north of West Chaohu The concentration of a reaches the highest concentration. The overall concentration of chlorophyll-a in West Chao Lake is the highest, especially in its northern waters, where the water quality is poor, and a certain bloom area has appeared. The main reason is that the area is close to Hefei City and is more susceptible to strong solid activities and large discharges of sewage and wastewater. The Zhuhai-1 hyperspectral satellite has certain advantages for the water quality retrieval of inland lakes, but it also has limitations such as difficulty in data processing, low band utilization, and poor universality of inversion models. It is necessary to use the Zhuhai-1 hyperspectral data to carry out more lake remote sensing research, continue to propose new methods of hyperspectral remote sensing image processing, and improve the accuracy and universality of the inversion model, fully tap the potential of the data source.
		2022 Vol. 42 (08): 2642-2648 [Abstract] ( 133 ) RICH HTML PDF (2590 KB) ( 75 )

	2649	Beneficial Effects of Short-Wavelength Narrow Band Ultraviolet Irradiation Delivered Using an LED Device on Bone Metabolism in Rats
		LI Yun-qi¹, ZHANG Ning², CHEN De-fu³, QIU Hai-xia¹, ZENG Jing¹, WANG Na^5, GU Ying^{1, 3, 4}
		DOI: 10.3964/j.issn.1000-0593(2022)08-2649-08
		Background: Ultraviolet radiation is one of the key conditions to maintaining bone health, but it has not been paid enough attention to in the field of osteoporosis because of the wide traditional ultraviolet spectrum. With the development of LED technology, all kinds of narrowband LED spectra can be adjusted arbitrarily. Materials and methods: In this study, we established a newly designed LED device with a narrow band spectrum to investigate the effects of ultraviolet LED on bone metabolism, bone morphology and skin of osteoporotic rats. We have set up a total of unovariectomized rats (n=24) and ovariectomized rats (n=36). The unovariectomized rats were then divided into the sham operation group (Sham, n=12) and the detection model group (Sham, n=12). The ovariectomized rats were divided into either the LED irradiation group (LED, n=12), the no treatment group (OVX, n=12), or the detection model group (OVX, n=12). The LED Irradiation parameters (0.8 mW·cm^-2, 1 000 s, twice a week).Results: Compared with the sham group, the expression of vitamin D mRNA, serum bone ALP, serum 25(OH)D₃, and serum P1NP increased significantly, the serum PTH and serum TRAP decreased significantly. Conclusion: Our results show that irradiation with the new ultraviolet LED device can significantly increase the level of 25(OH)D₃ in blood, promote bone formation and inhibit bone resorption without adverse effects on rat skin.
		2022 Vol. 42 (08): 2649-2656 [Abstract] ( 124 ) RICH HTML PDF (3263 KB) ( 52 )

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