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2021 Vol. 41, No. 07 Published: 2021-07-01

	1993	Research Progress on Improving the Accuracy of Near Infrared Spectroscopy Detection of Human Blood and Other Complex Solution Components
		HAN Guang^{1, 3}, WANG Xiao-yan¹, CHEN Si-qi¹, WANG Hui-quan^{1, 3}, WANG Jin-hai^{1, 3}, ZHAO Zhe^{2, 3*}
		DOI: 10.3964/j.issn.1000-0593(2021)07-1993-05
		Near-infrared spectroscopy has rich structure and composition information and is often used to measure hydrogen-containing organic substances’physical and chemical parameters. In recent years, it has been widely used in the quantitative analysis of complex solutions. However, the near-infrared spectroscopy analysis of complex solutions such as human blood, noise interference and redundant variables caused by strong background information seriously affect the spectral measurement and analysis of the sample itself, and affect the efficiency and accuracy of the analysis. Therefore, eliminating the interference of background noise to improve the accuracy of analysis has attracted great attention. In recent years, scholars at home and abroad have proposed many related methods based on chemometric methods. This article focuses on spectral preprocessing, variable optimization and modeling analysis. On the one hand, starting from traditional chemometric methods, we summarize and analyze the application and respective characteristics of these methods in the near-infrared spectroscopy quantitative analysis of complex solutions such as human blood, and provide a reference for research on improving the accuracy of quantitative spectral analysis.
		2021 Vol. 41 (07): 1993-1997 [Abstract] ( 331 ) RICH HTML PDF (890 KB) ( 170 )

	1998	Application Progress of Artificial Neural Network in Laser-Induced Breakdown Spectral Data Analysis
		ZHAO Wen-ya^{1, 2}, MIN Hong², LIU Shu^2, AN Ya-rui^1, YU Jin³
		DOI: 10.3964/j.issn.1000-0593(2021)07-1998-07
		Laser-induced breakdown spectroscopy (LIBS) has the advantages of real-time, rapid, and multi-element simultaneous detection. It has attracted more and more attention in recent years and played an essential role in online industrial analysis. However, based on the emission spectrum characteristics, LIBS has spectral noise, baseline drift, self-absorption, and overlapping peaks, etc. In addition, spectral stability and reproducibility are poor due to environmental changes, laser energy fluctuations, matrix effects, and samples’ surface topography. These result in the nonlinear relationship between spectral information and qualitative and quantitative analysis, limiting the analysis’s sensitivity and accuracy. With the gradual improvement of LIBS devices’ stability, LIBS spectral data analysis methods are also changing with each new day. Artificial neural networks (ANN) can track and identify nonlinear characteristics, adaptive learning of LIBS spectral characteristics, screening out interference information, and its application in LIBS data analysis has been rapidly developed. This paper introduces the principle, instrument structure, and working process of LIBS and common neural network model in the field of LIBS spectrum analysis, summed up the LIBS in 2015—2020 in combination with the common ANN model in geological, alloy and organic polymer, coal, soil and biological areas such as the specific application. It pointed out that ANN’s super ability in the field of data analysis can effectively improve the LIBS analysis accuracy and improve the utilization rate of spectrum data, reducing the spectrum collection and environmental requirements. Given the technical difficulties that still required broken through, ANN’s development prospect in LIBS spectral depth information mining, portable special equipment development, technology combination, and other aspects has prospected. LIBS is becoming more and more mature, but data analysis of this technology still has a broad space for development. This review can provide a reference for the application of machine learning in LIBS data analysis.
		2021 Vol. 41 (07): 1998-2004 [Abstract] ( 333 ) RICH HTML PDF (2210 KB) ( 115 )

	2005	Study on the Optimization Method of Maize Seed Moisture Quantification Model Based on THz-ATR Spectroscopy
		WU Jing-zhu¹, LI Xiao-qi¹, SUN Li-juan², LIU Cui-ling¹, SUN Xiao-rong¹, SUN Mei¹, YU Le¹
		DOI: 10.3964/j.issn.1000-0593(2021)07-2005-07
		Characteristic Terahertz(THz) bands of maize seed moisture were screened using the Terahertz time-domain spectroscopy technique combined with the interval partial least squares method. The support vector machine was used to construct a rapid quantitative analysis model of seed moisture based onthe characteristic spectral region against nonlinear interference. Take Zhengdan 958(Corn variety), for example, in this experiment, 40 sets of seed powder samples (3 samples from each set) with moisture content ranging from 9.58% to 12.71% were prepared. Terahertz time-domain spectra of 120 samples were collected by Terapluse 4 000 terahertz time-domain system with Attenuated Total Reflection (ATR) module. According to the SPXY method, 90 training set samples and 30 test set samples were obtained. Given the strong absorption of terahertz waves by seed moisture, the moving interval (mwPLS), independent interval (iPLS), backward interval (biPLS) and synergy interval (siPLS) methods based on partial least squares linear regression were firstly used to screen the optimal combination of the characteristic spectral regions. In view of the inevitable nonlinear interference of environmental moisture, other seed components and systematic noise on the terahertz spectrum of seed moisture, a nonlinear model for rapid quantitative analysis of seed moisture with optimal prediction performance was further constructed using support vector machine and grid search method based on RBF kernel function on the above spectral feature intervals. The optimal SVR model was obtained with a lower root mean square error of the training set (RMSEC) of 0.021 2, a lower root mean square error of the prediction (RMSEP) of 0.069 7 and a higher residual predictive deviation (RPD) of 12.345 7.The model performance was significantly improved compared with the traditional partial least squares linear regression model. Seed moisture content is an important factor in seed storage safety and seed vigour.The experimental results show that THz time-domain spectroscopy combined with the chemometric method can effectively be used to screen the characteristic absorption spectral region of seed moisture and establish an interference-resistant and high-precision model for rapid quantitative analysis of seed moisture, which is expected to be a Promising complementary technology in the field of rapid seed quality determination.
		2021 Vol. 41 (07): 2005-2011 [Abstract] ( 200 ) RICH HTML PDF (3056 KB) ( 118 )

	2012	Experimental Measurement and Theoretical Simulation on Terahertz Spectra of Crystal Acetamiprid
		ZHANG Tong-jun, LI De-hua, CAO Qiu-hong, LIN Hong-mei, HAO Jian-jun
		DOI: 10.3964/j.issn.1000-0593(2021)07-2012-06
		Acetamiprid is a chloropyridine neonicotinoid insecticide that is one of the most commonly used pesticides due to its binding selectivity toward the nicotinic acetylcholine receptor of insects. To understand the relationship between the fingerprint feature and the corresponding structural information in the terahertz region, experimental and theoretical investigations of the terahertz absorption spectrum of acetamiprid crystal were carried out using the terahertz time-domain spectroscopy (THz-TDS) and density functional theory(DFT). The terahertz absorption spectrum of acetamiprid was measured in 0.3~3.3 THz frequency range at room temperature by the THz-TDS system. A number of characteristic absorption peaks in this range were observed at 1.08，1.38，1.97，2.54 and 2.89 THz, respectively, which can be the THz fingerprint spectrum for detecting acetamiprid. To better understand the experimental absorption spectrum’s theoretical mechanism, calculations based on density functional theory were performed to analyze the isolated molecule and unit cell of acetamiprid. The geometry optimization and frequency calculation of the isolated molecule model was performed using DFT with periodic boundary conditions employing the B3LYP hybrid functional with the 6-311G(d,p) basis set. Some differences were observed between the simulation results and the experimental data, which means that the isolated molecule simulation has some limitations. The theoretical calculations of the unit cell were performed based on the solid-state DFT using the CASTEP program, a part of Materials Studio 8.0 from Accelrys. The calculations were performed on the crystalline state within the generalized gradient approximation (GGA) at PW91, PBE, PBEsol and WC correlation functions. The calculated structural data of bond lengths and bond angles for acetamiprid molecule and unit cell were compared with -X-ray diffraction values(XRD). Among these calculations, PBE simulations provide a significantly similar tendency with the XRD experimental values. And the calculated THz spectrum of the PBE provides better agreements with observed THz spectral characters. Consequently, the observed spectral features were assigned according to the results of the PBE simulation. The study indicates that acetamiprid’s characterized features primarily originated from intermolecular collective vibrational modes, which were dominated by hydrogen bonds such as C—H…N.
		2021 Vol. 41 (07): 2012-2017 [Abstract] ( 161 ) RICH HTML PDF (3333 KB) ( 194 )

	2018	Terahertz Transmission Characteristics of Electrolyte Solution
		QIAN Kun, BAI Zhi-chen, WU Rui, WANG Jia-hui, SU Bo^*, WEN Yi-wei, ZHANG Cun-lin
		DOI: 10.3964/j.issn.1000-0593(2021)07-2018-05
		The vibration and rotational energy levels of many biomacromolecules are in the terahertz band, and the terahertz wave has low photon energy and high peak power. Therefore, detection with terahertz technology can ensure the biological molecules are not destroyed to a large extent. However, most of the biomolecules can maintain their biological activity only in an aqueous solution, and water is a polar molecule, which has strong absorption of terahertz wave, so it is not easy to use conventional terahertz technology to detect the characteristics of biological samples in an aqueous solution. In this paper, a terahertz microfluidic chip with a sandwich structure is designed, including substrate, cover and microchannel layers. The substrate and cover are made of COC and PMMA. COC material has high transparency to terahertz wave and transparent to visible light. It is ideal for making a terahertz microfluidic chips, but it is expensive and hard to obtain. To reduce the amount of COC, the COC is embedded in the PMMA of the substrate and the cover to ensure that the terahertz wave can pass through the COC. The diameter of COC is 5 mm, the thickness is the same as that of PMMA material, both of which are 2 mm, aligned with the center of the microchannel. A strong adhesive double-sided adhesive with a thickness of 50 μm is selected as the microchannel layer, and the center of the double-sided adhesive is hollowed out as the microchannel, with a length of 3 cm and a width of 4 mm. The THz microfluidic chip is composed of substrate, cover and microchannel. The THz detection area is 4 mm in diameter. The combination of microfluidic technology and terahertz technology reduces the consumption of samples, shortens the distance between terahertz wave and samples, and provides the possibility of detecting liquid samples. It is found that the strong absorption of THz wave by water is mainly due to the hydrogen bond in water, while the electrolyte solution will affect the hydrogen bond in water. In this paper, the electrolyte solutions were prepared with different potassium chloride concentrations, potassium sulfate, copper chloride and copper sulfate solutions, and their terahertz transmission spectra were studied by terahertz microfluidic technology. The results show that THz’s transmission intensity of THz of the four electrolyte solutions is lower than that of pure deionized water, but the experimental phenomena are different. The transmission intensity of THz of copper chloride solution increases with the increase of concentration, while potassium chloride, potassium sulfate and copper sulfate solution decreases with the increase of concentration.
		2021 Vol. 41 (07): 2018-2022 [Abstract] ( 217 ) RICH HTML PDF (2158 KB) ( 81 )

	2023	Research on Near Infrared and Color Visible Fusion Based on PCNN in Transform Domain
		SHEN Yu, YUAN Yu-bin^*, PENG Jing
		DOI: 10.3964/j.issn.1000-0593(2021)07-2023-05
		Aiming at the problems of low contrast, loss of detail and color distortion after fusion of near-infrared and color visible images, a new fusion algorithm of infrared and color visible images based on multi-scale transformation and adaptive pulse coupled neural network (PCNN) is proposed. Firstly, the visible color image is transformed into HSI (Hue Saturation Intensity) space. HSI color space contains three components: brightness, chroma and saturation, and these three components are not correlated with each other. Therefore, using this feature, the three components can be processed separately. The brightness component and the near-infrared image are transformed by multi-scale transformation, respectively. Tetrolet transform is chosen as the transformation method. After transformation, the low-frequency and high-frequency components are obtained, respectively. For the low-frequency components of the image, a fusion rule with the highest expectation is proposed. For the high-frequency components of the image, the threshold of the PCNN model is adjusted by the Gauss difference operator, and an adaptive PCNN model is proposed as the fusion rule. The fused image of the processed high and low frequency components through Tetrolet inverse transformation is used as a new brightness image. Then, the new brightness image and the original chromaticity and saturation components are mapped to RGB space, and the fused color image is obtained. In order to solve the problem of image smoothing and uneven illumination of the original image, a color and sharpness correction mechanism (CSC) is introduced to improve the quality of the fused image. In order to verify the effectiveness of the proposed method, five groups of near-infrared and color visible images with the resolution of 1 024×680 were selected for experiments and compared with four current efficient fusion methods and the method without color correction. The experimental results show that, compared with other image fusion algorithms, this method can retain the most details and textures with or without CSC color, and the visibility is greatly improved. At the same time, the results of this method have more details and textures under weak illumination conditions and have better contrast and good quality. Good color reproduction. It has great advantages in information retention, color restoration, image contrast and structural similarity.
		2021 Vol. 41 (07): 2023-2027 [Abstract] ( 213 ) RICH HTML PDF (3015 KB) ( 56 )

	2028	Near-Infrared Spectral Feature Selection of Water-Bearing Rocks Based on Mutual Information
		ZHANG Xiu-lian^{1, 2}, ZHANG Fang^{1, 2*}, ZHOU Nuan^{1, 2}, ZHANG Jing-jie^1,2, LIU Wen-fang³, ZHANG Shuai^{1, 2}, YANG Xiao-jie^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2021)07-2028-08
		The relationship between near-infrared spectroscopic measurements of rock and its water content does not follow simple linear correlations, preventing the direct use of classical correlation analysis. In the present paper, an experiment on the water migration in cliff conglomerates from the Mogao Grottoes was performed，and collected 51 pieces of near-infrared spectra from three different positions sample. These spectra cover the whole process of the conglomerate from the initial dry state to the saturated state; then we selected a combined N point smooth and baseline correction method (NPS+B-corr) to preprocess the original near-infrared spectrum. According to the spectral curve features at 1 450 and 1 950 nm of the strong absorption spectrum, six initial feature variables, namely Height, Full Width at Half Maximum (FWHM), Area, Left Half Width (LHW), Right Half Width (RHW), and (LHW/RHW), were extracted to establish the initial feature set. Simultaneously, the extracted spectral characteristic variables were normalized, and the curve of each spectral characteristic parameter and the change of water content were drawn according to the result of the processing, determine the water content level. Then，the correlation among the feature variables of the initial feature set should be screened to remove redundant features. The initial feature set is simplified to three characteristic variables: Height, LHW, RHW. Finally, based on mutual information, the Best Individual Feature and Maximal Information Coefficient methods were used to evaluate the relationship between samples’ spectral characteristic parameters and water content. We found that: (1) at wavelengths between approximately 1 450 and 1 930 nm, the near-infrared spectrum of the conglomerate has obvious absorption peaks, and the absorption peaks show a strong correlation with the change of water content, which indicates that spectral reflectance was significantly correlated with water content; (2) the relationship of primary spectral characteristic parameters with total water content can be described by an S-shaped function，water content can be divided into three states of dry, water-absorbing, and saturated; (3) The near-infrared spectral characteristics selected by the two information methods are not completely consistent. Based on the BIF method, the correlation between the characteristic variable at 1 450 nm and the rock moisture content ranks from right to left as right shoulder width, peak height, and left shoulder width; at 1 900 nm, the peak height, right shoulder width, and left shoulder width. Based on the MIC method, the correlation between the characteristic variables at 1 450 and 1 900 nm and the rock water content level from the highest to the lowest in the left shoulder width, peak height, and right shoulder width; (4) Decision tree analysis suggests that the MIC method achieves higher accuracy in identifying water content level than the BIF method.
		2021 Vol. 41 (07): 2028-2035 [Abstract] ( 193 ) RICH HTML PDF (3738 KB) ( 77 )

	2036	Quantitative Analysis of Total Phenol Content in Cabernet Sauvignon Grape Based on Near-Infrared Spectroscopy
		LUO Yi-jia¹, ZHU He¹, LI Xiao-han¹, DONG Juan¹, TIAN Hao¹, SHI Xue-wei¹, WANG Wen-xia², SUN Jing-tao^1*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2036-07
		The contents of total phenol in wine grape are an important indicator of grape quality and also a key factor of wine quality directly. To detect the total phenol contents of the cabernet sauvignon grape quickly and accurately, this paper used near-infrared spectroscopy and GA-ELM prediction model to predict the total phenol content of Cabernet Sauvignon grapes. In the experiment, Cabernet Sauvignon grapes were collected in 5 harvest periods (40 bunches were collected in each harvest period, and 10 grapes were acquired in each cluster), and near-infrared spectra information in the range of 12 500~4 000 cm^-1 was collected for 200 groups of grapes. The total phenol content of Cabernet Sauvignon grapes was determined based on the principle of Folin-Ciocalteus colorimetry, SPXY algorithm was used to divide the samples into correction sets and prediction sets at a ratio of 3∶1, with a total of 150 correction sets and 50 prediction sets. Multiplicative Scatter Correction (MSC),Standard Normalized Variate (SNV), Mean Centering (MC), Moving Average (MA), and the First Derivative +SG was used to preprocess the raw spectra, MSC was compared as the best pretreatment method.And then, competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), successive projections algorithm (SPA) and synergy interval partial least squares (si-PLS) were extracted the characteristic wavelengths, respectively. The comparative analysis found that the 69 characteristic wavelength variables extracted by CARS could effectively improve the model’s stability and prediction ability. Based on the MSC and different variable optimization methods, the extreme learning machine (ELM) algorithm was introduced to establish the total phenol content prediction model. In predicting total phenol content, a genetic algorithm (GA) was used to optimize the ELM model and the influence of different kernel functions and the number of hidden layer neurons on the prediction ability of the GA-ELM model investigated. The optimal kernel function was Sigmoidal, and the optimal number of neurons was 50. Finally, the prediction capabilities of the ELM and GA-ELM models were compared. The results showed that GA-ELM models were more accurate in predicting than the ELM models, and the MSC+CARS+GA-ELM model was the best with a correlation coefficient of calibration (R_c) of 0.901 7, the correlation coefficient of prediction of 0.901 3, the root mean square error of calibration (RMSEC) of 2.112 4, the root mean square error of prediction (RMSEP) of 1.686 8 and residual prediction deviation (RPD) of 2.308 0. The combination of variable optimization methods and the GA-ELM model was an effective method, which provided a theoretical basis for detecting Cabernet Sauvignon grapes’ quality.
		2021 Vol. 41 (07): 2036-2042 [Abstract] ( 168 ) RICH HTML PDF (3238 KB) ( 124 )

	2043	Infrared Mid-Wave and Long-Wave Image Fusion Based on FABEMD and Improved Local Energy Window
		CUI Xiao-rong, SHEN Tao^*, HUANG Jian-lu, SUN Bin-bin
		DOI: 10.3964/j.issn.1000-0593(2021)07-2043-07
		Aiming the scene contrast is low in the fusion process of the mid-wave infrared image with a detection band of 3.7 to 4.8 μm and the long-wave infrared image with a detection band of 8 to 14 μm, the saliency target is not enough to protrude, and the artifacts introduce serious problems. In this paper, Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is used to the multi-scale decomposition of infrared medium-wave and long-wave images to obtain two-dimensional intrinsic mode functions (BIMFs) and residual components (Residual). For each layer of bidimensional intrinsic mode function, this paper’s improved local energy window fusion rule is selected. First, a weighting operator is configured to increase the central pixel’s energy proportion for the regional window. In this paper, different weighting operators are selected, which have been verified to effectively highlight the energy characteristic information of medium-wave and long-wave images by experiments, and secondly, the phase information of BIMFs is fully used, when the phases are opposite, the energy weighted average method is used to solve the problem that the polarity sign of the fusion coefficient is difficult to determine. When the phases are the same, the energy gap is judged, and the set fusion rule is selected according to the size of the gap based on the grayscale difference characteristics of the infrared medium wave and long wave images. Using the infrared mid-wave image and improved the regional energy window of Saliency detection of maximum symmetric surround weight map guides the fusion of base layer coefficients for the residual components. The adaptive local surround window makes full use of the low-frequency saliency information and has a very good suppression effect on the useless background. It can highlight the saliency objects in the complex background image, and finally obtain the guidance image with rich details and obvious contrast. Finally, the fusion image is obtained through the inverse reconstruction process of FABEMD, and subjective and objective performance evaluations are performed on five sets of infrared medium and long-wave images with different backgrounds and different sizes. The four sets of images are all taken from multi-band infrared acquisition systems and are strictly registration and comparative experiments with 7 related algorithms. In terms of subjective performance, salient objects is standing out and the clarity is high, the objective performance is excellent in two evaluation indicators of average gradient and spatial frequency, the effectiveness of this algorithm is verified.
		2021 Vol. 41 (07): 2043-2049 [Abstract] ( 172 ) RICH HTML PDF (2982 KB) ( 157 )

	2050	Investigation of Different Structures of Coals Through FTIR and Raman Techniques
		YU Chun-mei, ZHANG Nan, TENG Hai-peng
		DOI: 10.3964/j.issn.1000-0593(2021)07-2050-07
		In order to understand the structure and performance of coal further, the Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy was used to analyze the structure of five kinds of bituminous coals(HY，HJ，BL，DJ and HK) in detail. It was expected that the results obtained by infrared and Raman technology research would bring a deeper understanding of spectral characterization of coals and also provided reliable guide for the subsequent research of coal and coal-like materials. It could be seen that there are obvious absorption regions in the wavenumber range of 3 200~3 600 cm^-1 through the infrared spectroscopy, in which the sample HJ was more obvious than the other four coals, mainly due to the effect of the —OH functional group and some N—H vibrations. However, due to the influence of free water or crystal water on this band, the judgment error would not be discussed here. At the band of 2 923 cm^-1, it could be observed that the peak of CH₂ antisymmetric stretching vibration was significantly higher than its symmetric stretching vibration. The reason that resulted in this phenomenon was there existeda large amount of aliphatic CH₂ carbon chain structure, then the potential of hydrogen liquefaction and application in the subsequent research of coal was huge. In the range of 1 000 to 1 800 cm^-1, oxygen-containing functional groups mainly included hydroxyl groups (alcohol hydroxyl groups and phenolic hydroxyl groups), carboxyl groups, carbonyl groups, etc. In order to characterize the coal structure more clearly, the relationship between the apparent aromaticity f_a(FTIR)，(R/C)_u，H_al/H，A_ar/A_al and H/C atomic ratios of coal was studied. According to the calculation parameters, with the coal quality level increases, the aromatic hydrogen content in the coal increased, and the aliphatic hydrogen content decreased. In addition, comparing various structural parameters, found a linear relationship between f_a(FTIR)，(R/C)_u and H/C atomic ratios, which could more accurately characterize coal rank. Raman spectroscopy was fitted by Origin 2018, and the deconvolution method was used to divide the spectrum into ten peaks, namely G, G_R+V_L+V_R, D, S, G_L, S_L, S_R and R. The relationship between the ratio of the peak area in different bands and the half-height width of the main peak with H/C atomic ratio were studied. The comparison showed that with the increase of the H/C atomic ratio, the A_D/A_G value generally shows a downward trend. It was indicated that the number of aromatic rings of the unit core in the basic structure of the coal sample increased with coalification and the degree of graphitization, which was consistent with the result of infrared spectroscopy. The comparison of the above results could prove that infrared and Raman spectroscopy were reliable methods for structure research of coal. On the basis of the above, a simple coal molecular model was constructed from them, which could provide a reference for the construction of coal molecular for simulation calculation.
		2021 Vol. 41 (07): 2050-2056 [Abstract] ( 247 ) RICH HTML PDF (4202 KB) ( 128 )

	2057	Quantitative Detection of Mutton Hardness Based on Twice Iterative Monte Carlo Method
		BAI Xue-bing¹, LI Xin-xing¹, ZHANG Xiao-shuan², LUO Hai-ling³, FU Ze-tian^2*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2057-07
		Mutton, as a kind of meat with high protein content and low fat and cholesterol content, is becoming more and more popular with consumers. The demand for mutton is on the rise. According to the National Bureau of Statistics, China’s mutton production rose from 6.27% to 9.02% from 2012 to 2019. This study proposed a quantitative detection PLSR model of mutton hardness based on the twice iterative Monte Carlo (MC) method. In this study, the Image-λ-V10E-H camera of the GaiaSorter hyperspectral sorter was used to collect the hyperspectral data of mutton samples at 400~950 nm, and the Image-λ-N17E camera was used to collect the hyperspectral data of mutton samples at 900~1 650 nm. Firstly, the study compared and analyzed four spectral pretreatment methods (S-G smoothing, 2 derivations, MSC and SNV) in eliminating interference factors, such as noise and baseline drift. Then, in the first MC sampling, the samples were divided into normal samples, suspicious samples and abnormal samples according to the 2.5 and 3 times the means of the prediction error means and standard deviations of each sample. The second MC sampling was performed based on rejecting abnormal samples, retaining and labeling suspicious samples. The new abnormal samples were eliminated by 3 times of the means of the prediction error means and standard deviations of each sample. Finally, the PLSR model based on the full wavelengths and the characteristic wavelengths extracted by the regression coefficient method (RC) were established and analyzed. The experiment results show that the twice iterative Monte Carlo method proposed in the study could abnormal samples, optimize the sample set, and provide a good foundation for modeling. With MSC as the spectral preprocessing algorithm, the PLSR model based on 400~950 and 900~1 650 nm hyperspectral data was superior to the other three spectral preprocessing algorithms R²_P=0.947 2 and 0.978 3，RMSE_P=47.789 9 g and 30.590 1 g. And, the accuracy and stability of the PLSR model based on 900~1 650 nm were significantly better than that based on 400~950 nm. 14 characteristic wavelengths (410, 438, 450, 464, 539, 558, 612, 684, 701, 734, 778, 866, 884, 935 nm) and 10 characteristic wavelengths (915, 949, 1 085, 1 156, 1 206, 1 262, 1 318, 1 384, 1 542 and 1 580 nm) of mutton hardness were selected by RC algorithm from 900~1 650 and 400~950 nm. The PLSR model based on 900~1 650 nm was the optimal model for predicting the hardness of mutton with R²_P=0.985 0 and RMSE_P=24.397 0 g. In conclusion, the PLSR model based on the twice iterative MC algorithm can effectively predict the changing trend of mutton hardness during cold storage and provide a reference for related research on non-destructive detection of mutton quality.
		2021 Vol. 41 (07): 2057-2063 [Abstract] ( 166 ) RICH HTML PDF (3668 KB) ( 44 )

	2064	Research on Vis/NIR Detection of Apple’s SSC Based on Multi-Mode Adjustable Optical Mechanism
		LIU Yan-de, WANG Jun-zheng, JIANG Xiao-gang, LI Li-sha, HU Xuan, CUI Hui-zhen
		DOI: 10.3964/j.issn.1000-0593(2021)07-2064-07
		A multi-mode adjustable optical mechanism was used to collect the spectra of apples in three detection methods: diffuse transmittance, total transmittance and diffuse reflection. The spectrum characteristics of apples under different detection methods were studied and PLS established the apple soluble solid content prediction model. First, the system will collect the spectra of four points on the equator of each sample under the way of diffuse transmittance, total transmittance and diffuse reflection respectively, and then MSC (Multiplicative Scatter Correction), BOC (Baseline offset correction), Normalize, Gaussian filter smoothing and other methods will be applied to preprocess the 120 averaged spectra combined with the CARS method to filter the characteristic wavelength of the diffuse reflection spectrum. Finally, PLS will be used to establish a model for predicting apple’s SC , and another 30 apples will be purchased to verify the performance of the model. The results show that the spectra collected by the self-designed fruit quality detection system under the three detection methods have good result in predict SSC content in apples after 3-point Gaussian filtering smoothing pretreatment. The performance of diffuse transmittance model is R_cal=0.972, R_pre=0.967 and RMSEC=0.436%, RMSEP=0.507%. The performance of total transmittance model is R_cal=0.964, R_pre=0.957 and RMSEC=0.5%, RMSEP=0.574%. The performance of diffuse reflection model is R_cal=0.963, R_pre=0.949 and RMSEC=0.522%, RMSEP=0.536%. The fusion modeling performance of the three spectra after normalization pretreatment is R_cal=0.894, R_pre=0.857 and RMSEC=0.836%, RMSEP=0.966%. Further, the diffuse reflection spectrum is combined with the CARS algorithm to filter the characteristic wavelengths. The performance of the model established with 119 variables is R_cal=0.986, R_pre=0.977 and RMSEC=0.323%, RMSEP=0.362%. Finally, the model is imported into this new multi-mode adjustable fruit detection system, and 30 non-model apples are used to test the model to predict the performance of apple’s SSC. The results show that the correlation coefficient of the 30 external validation sets is 0.906, and the root means square error of validation is 0.707%. It further shows that the diffuse reflection spectrum collected by the multi-mode adjustable fruit internal quality detection system, which is combined with spectral pretreatment, CARS and PLS can establish a better model to predict the solid soluble content of apple. This research provides new technical support for Apple’s internal quality testing.
		2021 Vol. 41 (07): 2064-2070 [Abstract] ( 149 ) RICH HTML PDF (2566 KB) ( 52 )

	2071	Theoretical Study on the Structures and IR Spectra of Hydration of Arsenates and Iron Arsenates
		LI Hui-ji¹, SUN Hai-jie¹, LIU Na¹, PENG Zhi-kun^2*, LI Yong-yu¹, YAN Dan³
		DOI: 10.3964/j.issn.1000-0593(2021)07-2071-06
		The removal of arsenate in water is closely related to its hydration, but there are few reports on the hydration characteristics of different protonated arsenates and iron arsenates, and there is no correlation analysis on infrared spectra of hydration layers of protonated arsenates and iron arsenates. The hydration energies of different protonated arsenates [H_mAsO₄]^m-3(m=0～2) and iron arsenates [FeH_mAsO₄]^m+(m=0～2) were compared at B3LYP/6-311G(d, p) level. Reduced density gradient functions conducted graphical analyses for the intensities, types and locations of the interaction between water molecules with [H_mAsO₄]^m-3(m=0～2) and [FeH_mAsO₄]^m+(m=0～2). And, the characteristics of infrared spectra of the hydration layers of different protonated arsenates and iron arsenates were analyzed. The results show that the hydration of [H_mAsO₄]^m-3(m=0～2) gradually decreases with hydrogen protonation, while the protonation enhances the hydration of [FeH_mAsO₄]^m+(m=0～2). Hydrogen bonds tend to form when a water molecule hydrogen interacts with an oxygen of [H_mAsO₄]^m-3(m=0～2). However, when two hydrogens of water molecules simultaneously interact with two oxygens of [H_mAsO₄]^m-3(m=0～2), the interaction becomes weaker, and the van der Waals force appears. The hydrogen bond formed by water molecules through hydrogen with the oxygen of arsenates is stronger than the hydrogen bond formed by water molecules through oxygen with the hydrogen of protonated arsenates. The unprotonated O_N tends to form hydrogen bonds with 2~4 water molecules, while the protonated O_P forms hydrogen bonds with at most 2 water molecules, and the O_P…H_W hydrogen bond is weaker than the O_N…H_W hydrogen bond. In the infrared spectra, 2 954, 3 114, 3 179, 3 252 and 3 297 cm^-1 is the stretching vibration peaks of Ow—Hw in the first hydration shell of AsO^3-₄; 3 277, 3 324 and 3 376 cm^-1 is the stretching vibration peaks of Ow—Hw in the first hydration shell of HAsO^2-₄; 3 189, 3 277, 3 306 and 3 383 cm^-1 is the stretching vibration peaks of Ow—Hw in the first hydration shell of H₂AsO^-₄. The stretching vibration regions for Ow—Hw in the first hydration shell of [FeH_mAsO₄]^m+(m=0～2) are 2 500~3 060, 2 660~3 200, 2 900~3 360 cm^-1. Therefore, the stretching vibration regions for the first hydration waters of [H_mAsO₄]^m-3(m=0～2) and [FeH_mAsO₄]^m+(m=0～2) have blue shifts with protonation. Compared with [H_mAsO₄]^m-3(m=0～2), the water molecules in the first hydration layers of [FeH_mAsO₄]^m+(m=0～2) exhibit a significantly red shift of the bending vibration peaks and stretching vibration peaks in the infrared spectra. The hydrogen bond bridge Fe—Ow—Hw…Ow—Hw…O_N—As is formed in the first hydration shell of [FeH_mAsO₄]^m+(m=0～2). The Ow—Hw in this hydrogen bond bridge has a special absorption peak, such as the stretching vibration peak located in 2 195, 2 526 and 2 673 cm^-1, respectively. Its stretching vibration peak is significantly blue-shifted, but the peak strength is almost unchanged, while its bending vibration peak is red-shifted with the protonation and the strength is significantly reduced. The stretching vibration peak of independent O_P—H is not affected by the complexation of Fe, while the position of stretching vibration peak for O_P—H in O_P—H…Ow is significantly blue shifted, due to the complexation of Fe. This study is helpful to understand better the solubility of arsenates and iron arsenates in water at different pH.
		2021 Vol. 41 (07): 2071-2076 [Abstract] ( 214 ) RICH HTML PDF (4394 KB) ( 44 )

	2077	Development of a Mid-Infrared Cavity Enhanced Formaldehyde Detection System
		HE Qi-xin, LI Jia-kun, FENG Qi-bo^*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2077-05
		A mid-infrared formaldehyde gas detection system was developed based on the coaxial mode-locked cavity enhanced absorption spectroscopy technology. In order to realize the detection of formaldehyde, an interband cascade laser with a center emission wavelength of 3.6 μm was used as the light source, and a high-precision F-P resonator was used as the gas cell. The laser frequency was tuned by an electro-optic modulator and was locked to the cavity resonance frequency by the Pound-Drever-Hall (PDH) technology. In order to suppress the interference caused by the external environment and improve the accuracy and anti-interference of the system, a dynamic PDH locking technique was adopted. In this technique, the cavity length was modulated by a low-frequency saw-tooth signal, to realize the modulation of the cavity resonant frequency near the gas absorption peak. An appropriate scanning range should be selected to ensure the laser and the cavity keep locking during scanning. The formaldehyde concentration can be calculated by the amplitude of the cavity transmission signal. Experiments were carried out to evaluate the performance of the system. The absorption spectrum measurement experiment verified the effectiveness of the system. The system calibration experiment results show that the amplitude of the cavity transmission signal exhibits a good linear relationship with the formaldehyde concentration in the range of 0~10 mL·L^-1. The Allan analysis of variance shows that the system minimum detection limit is 52.8 nL·L^-1 when the integration time is 1 s which can be reduced to 3.3 nL·L^-1 at an integration time of 14 s. In addition, the system sensitivity can be further improved by increasing the effective absorption path of the resonant cavity. The system has high sensitivity, fast response speed, good anti-interference and long-term stability, making it have broad application prospects in the detection of trace formaldehyde.
		2021 Vol. 41 (07): 2077-2081 [Abstract] ( 166 ) RICH HTML PDF (2700 KB) ( 60 )

	2082	Effect of Degree of Doneness on Conformation of Myofibrillar Proteins by Two-Dimensional Infrared Correlation Spectroscopy
		WAN Hong-bing, LI Hai-peng, LEI Yuan-hua, XIE Peng, ZHANG Song-shan, FENG Yong-hong, LIU Xuan, WANG Huan, SUN Bao-zhong^*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2082-05
		Degree of doneness（DOD）is an important factor affecting the palatability and consumer satisfaction of steak. Myofibrillar proteins are important structural proteins, which are closely related to meat quality. Heating is the key technology of meat processing. In recent years, there were lots of articles that reported the effects of thermal treatment on the structural properties of myofibrillar proteins, but few reports on the effects of DOD on its structure. Based on modern infrared spectroscopy, the attenuated total reflectance Fourier transform infrared spectroscopy （ATR-FTIR）was combined with two-dimensional correlation spectroscopy, using infrared spectroscopy, second derivative spectroscopy and two-dimensional correlation spectroscopy to track the cooking process of myofibrillar proteins dynamically. The main changes in the protein cooking process were explained by analyzing the trend and regularity of characteristic peaks. The results showed that in the range of 1 700~1 500 cm^-1, DOD had a significant effect on myofibrillar proteins’ characteristic absorption peak. As the DOD increased, the intensity change of myofibrillar proteins characteristic peak was divided into three stages: the first stage was the initial stage of heating, and the intensity decreased from control to rare; the second stage was the middle stage of heating, from rare to medium. There was no significant change in peak intensity; the third stage was the late stage, from medium to over-cooked, and the peak intensity decreased significantly. The synchronous spectrum analysis results showed four autopeaks near 1 650, 1 640, 1 556, 1 540 cm^-1, and the cross peaks between the two autopeaks were all positive. The autopeaks intensity analysis results showed that the medium was the turning point of the change of the protein’s temperature sensitive region. When proteins were cooked from control to medium, the amide II band was the sensitive region of myofibrillar proteins, while the sensitive region was the α-helix of amide I banded when proteins were cooked from medium to over-cooked. The information of the dynamic changes of myofibrillar proteins molecular structure caused by cooking provided a theoretical basis for the control and optimization of Western steak cooking.
		2021 Vol. 41 (07): 2082-2086 [Abstract] ( 179 ) RICH HTML PDF (3427 KB) ( 49 )

	2087	High Pressure Raman Spectrum Study of Na₂CO₃
		XU Chao-wen^{1, 2, 3}, GAO Jing^4, LI Ying^1, QIN Fei⁵, LIU Hong¹, YI Li¹, CUI Yue-ju¹, SUN Feng-xia¹, FANG Lei-ming⁶
		DOI: 10.3964/j.issn.1000-0593(2021)07-2087-05
		Carbonate is one of the important carriers of carbon in the earth’s interior. Therefore, its crystal chemistry under the condition of temperature and pressure corresponding to the mantle is the key to understand the carbon occurrence state and cycle process of deep earth, but structural stability and phase transition are the basic research contents of crystal chemistry. Na₂CO₃ is a common alkaline carbonate, which enters the earth’s interior by subduction of oceanic crust. The existence of sodium carbonate in the subducted slab can significantly reduce the melting temperature of peridotite, promote partial melting and induce mantle heterogeneity. The inclusions of sodium carbonates have been found in the diamonds from the mantle transition zone and the lower mantle, providing direct mineralogical evidence that sodium carbonate can deeply subductin to the deep mantle. The lattice vibration modes of Na₂CO₃ at ambient condition were reported previously by Raman spectroscopy, but its stability and structural changes under high pressure are poorly reported. In this study, we used silicone oil as pressure transmitting medium and the Raman spectrum of Na₂CO₃ powder have been carefully ascertained in the pressure range of 0.001～27.53 GPa and the wavelength range of 600～1 200 cm^-1 using diamond anvil cell combined with advanced confocal Raman spectroscopy. This experiment focused on the analysis of the behavior of [CO₃]^2- group vibration mode in the process of compression and decompression. The results showed that splitting of vibration peaks respectively appeared in symmetric stretching vibration γ₁, antisymmetric stretching vibration γ₃ and the in-plane bending vibration γ₄ of the [CO₃]^2- at the pressure range of 0.001～11.88 GPa. With the increase of pressure, all peaks shift to high frequency, and the full width at half maximum (FWHM) increases gradually. The phase transition occurred at 13.40 GPa, accompanied by a new Raman peak at 690.08 cm^-1 and the intensity of the peak increases with the increase of pressure. At the same time, the intensity of antisymmetric stretching vibration and in-plane bending vibration continued to weaken, and the FWHM of Na₂CO₃ also continued to increase, indicating that the phase transition of Na₂CO₃ originates from the internal lattice vibration of [CO₃]^2-. When the pressure is decompressed to 4.18 GPa, it is found that the vibration mode of [CO₃]^2- is identical with that at ambient condition, and the new peak has disappeared, indicating that the phase transition is caused by the distortion of [CO₃]^2- group and is recoverable. The Raman peaks continued shifting to high frequencies when the pressure increased to 27.53 GPa, suggesting this new phase can remain stable in this pressure range. The intensity of Raman peaks at the antisymmetric stretching vibration γ₃ and in-plane bending vibration γ₄ decreased during compression. Meanwhile, the calculated dependence coefficient of relative pressure-shift of each Raman peak showed that the response of each vibration mode to pressure is different in [CO₃]^2-. This is probably related to the length of the C—O bond. Finally, by comparison, the intensity of symmetric stretching vibration γ₁ peak is higher than that of antisymmetric stretching vibration γ₃ and in-plane bending vibration γ₄. The pressure also has little effect on the typical Raman peak γ₁ of [CO₃]^2-, and therefore can be used to distinguish different kinds of carbonates.
		2021 Vol. 41 (07): 2087-2091 [Abstract] ( 251 ) RICH HTML PDF (1944 KB) ( 62 )

	2092	Preparation of Gold/Silver/Titanium Nitride Suface-Enhanced Raman Substrate and Its Effect on Nicotinic Acid Quantitative Detection
		LIU Yan-mei¹, PEI Yuan¹, LI Bo², LI Hui-yan³, WANG Xue-pei⁴, XIAN Hao-han¹, WEI Ying-na⁴, CHEN Ying⁵, DI Zhi-gang⁶, WU Zhen-gang^1, WEI Heng-yong^4
		DOI: 10.3964/j.issn.1000-0593(2021)07-2092-07
		The surface-enhanced Raman spectroscopy technology has specific recognition of molecules and rapid non-destructive detection capabilities, making it great potential in drug detection. In this study, drug detection research based on novel metal/titanium nitride composite substrate as SERS active substrate was carried out. Due to the synergy between the novel metal and titanium nitride, the composite substrate has high SERS performance, providing a drug detection method based on SERS technology. Electrochemical deposition and self-assembly methods were used to prepare novel metal/titanium nitride composite film. There are three kinds of phases: face-centered cubic crystal TiN, metal Au and Ag in the composite film. The electron microscopy showed that metal Au and Ag particles with a mean particle size of 90 and 50 nm were evenly distributed on TIN film’s surface. For this reason, the characteristic plasmon resonance absorption peaks of the novel metal gold and silver nanoparticles and TiN thin film appeared in the ultraviolet-visible absorption spectra of the thin film. Using the composite film as the SERS substrate, Raman detection was performed on the nicotinic acid solution. The results show that the novel metal/titanium nitride composite film has a significant SERS effect. The logarithm of the Raman signal intensity and nicotinic acid concentration at 1 033 cm^-1 indicated a certain linear relationship between the two, the linear correlation coefficient was 0.969, and the minimum detection concentration of nicotinic acid was 10^-5 mol·L^-1. This is due to the effect of charge transfer and the enhancement of the electromagnetic field caused by the surface plasmon resonance between the particles of TiN, Au and Ag. It was also found that nicotinic acid was vertically adsorbed on the novel metal/titanium nitride substrate’s surface through the COO^- group. When in an acidic environment, the protonation of nicotinic acid N atom mainly existed in cation N⁺H(Ⅰ). In an alkaline environment, it mainly existed in the form of anion COO^-(Ⅲ). In addition, for the illegal addition of nicotinic acid in the gypenoside solution, the lowest Raman detection concentration of the composite substrate is 10^-5 mol·L^-1, which provides a new way for the rapid detection of illegally added drugs on site.
		2021 Vol. 41 (07): 2092-2098 [Abstract] ( 219 ) RICH HTML PDF (5866 KB) ( 101 )

	2099	Study on Rapid Quantitative Analysis Method of Methanol Content in Methanol Gasoline by Raman Spectroscopy and Partial Least Squares
		LI Mao-gang¹, YAN Chun-hua², DU Yao¹, ZHANG Tian-long², LI Hua^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2021)07-2099-06
		Methanol gasoline is a new fuel to replace traditional gasoline, and its quality is greatly affected by methanol content. Therefore, the rapid analysis and detection of methanol content in methanol gasoline will have far-reaching significance for its quality control. A rapid quantitative analysis method of methanol content in methanol gasoline based on Raman spectroscopy and partial least squares (PLS) was established in this work. Raman spectra of 49 methanol gasoline samples were collected by laser Raman spectrometer, and spectral analysis was carried out. The effects of five spectral pretreatment methods on the raw Raman spectra of methanol gasoline were compared. In addition, variable importance in projection (VIP) was used to extract the Raman spectra’s feature variables preprocessed by wavelet transform (WT). The number of latent variables (LVs) and VIP threshold of the PLS calibration model was optimized by 5-flod cross-validation (CV). Under the optimal input variables and model parameters, PLS models based on different input variables were constructed. The results show that compared with RAW-PLS and WT-PLS, VIP-PLS can achieve better analysis performance, with the determination of the prediction set (R²_p) of 0.960 4 and root mean square error of prediction set (RMSEP) of 0.034 1. Therefore, Raman spectroscopy combined with PLS is a fast and accurate method for analysing methanol content in methanol gasoline.
		2021 Vol. 41 (07): 2099-2104 [Abstract] ( 133 ) RICH HTML PDF (2963 KB) ( 79 )

	2105	Nondestructive Identification of Mineral Inclusions by Raman Mapping: Micro-Magnetite Inclusions in Iridescent Scapolite as Example
		YE Xu¹, QIU Zhi-li^{1, 2*}, CHEN Chao-yang³, ZHANG Yue-feng¹
		DOI: 10.3964/j.issn.1000-0593(2021)07-2105-05
		Using Raman spectroscopy to nondestructively identify tiny inclusions in minerals is a significant problem in mineralogy and gemology. Iridescent scapolite is a kind of scapolite with special inclusions. Its inclusions present various spectral color under reflected light. In this research, magnetite inclusion in iridescent scapolite was nondestructively identified by hyperfield microscope, EPMA, Micro-Raman Spectrometer and XRD. Raman spectroscopy mapping technology was innovatively used. The microscopic characteristics indicate that the inclusions may be related to solid exsolution. The tiny inclusions grow parallelly and form a structure that is similar to the reflection grating, leading to iridescence under reflected light. The results of EPMA show that the end-member composition of iridescent scapolite is Ma_68.2—69.7Me_30.3—31.8, belonging to the dipyre subgroup. According to Raman spectra of some inclusions, there is a weak peak at 661 cm^-1. This peak, which does not appear in all testing locations, is easy to be ignored due to the low signal-to-noise ratio. In order to further study the assignment on this peak, the Raman mapping testwas carried out, and the mapping image was made based on the relative intensity of the peak range at 630～680 cm^-1. The result confirmed that the weak Raman peak at 661 cm^-1 appeared in most of the inclusion positions. This Raman peak can be assigned to the vibration mode A_1g of magnetite. Then we confirmed that the needle-like inclusions with iridescent effect contained smaller magnetite inclusions. According to the results of XRD, a diffraction peak at 2.51 Å, which belongs to the (311) crystal surface of the magnetite. It was detected in the sample containing many inclusions. It also furtherpro of the Raman mapping results are credible. According to the above experiments, Raman mapping technology may be an effective auxiliary means to identify tiny inclusions in minerals and gemstones. This study innovatively proposes that if the Raman signal of inclusions in minerals is weak, the effectiveness of the signal can be judged by combining the Raman mapping results with the distribution characteristics of inclusions. At the same time, it provides a new research idea and method for the nondestructive identification of inclusions in minerals.
		2021 Vol. 41 (07): 2105-2109 [Abstract] ( 195 ) RICH HTML PDF (2479 KB) ( 97 )

	2110	A Method for Assimilating the Raman Lidar Detecting Temperature in WRF on Simulating the Short-Time Heavy Rainfall
		LI Bo^{1, 2}, PU Ya-zhou¹, WANG Nan³, WANG Yu-feng¹, DI Hui-ge¹, SONG Yue-hui¹, HUA Deng-xin^1*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2110-06
		In order to take full advantage of Raman lidar, a high-resolution detecting experiment was carried out at the periphery of the convective cloud, and a temperature with a vertical resolution of 4 m was inversed by using the synthetically multilevel quality analysis and control technique. Three groups of experiments were conducted, and a novel method for assimilating the lidar detecting temperature was especially proposed based on the coupling between the Multiquadric method and WRFDA (the Weather Research and Forecasting model Data Assimilation system). Firstly, a controlled experiment (CTRL) was carried out, and the suitable model parameters were obtained after debugging model. Secondly, based on the WRFDA module, the conventional observational data, including ground station and radiosonde station were integrated into the initial field. The results showed that TS increased by 0.07, and the dummy precipitation in southwest Shaanxi was successfully avoided. Thirdly, the Multiquadric method was used to assimilate Lidar data. TS increased by 0.12, and the Miss hit forecast rate was reduced by 0.09. When the lidar data was integrated into the WRF model, the positive simulation effects could be obtained according to quantitative and qualitative analysis. It was also discovered that the variation of simulation precipitation was because of the change of elements, including wind, water vapor, and temperature. Lidar could be well used to detect short-time heavy rainfall, according to this study.
		2021 Vol. 41 (07): 2110-2115 [Abstract] ( 162 ) RICH HTML PDF (3890 KB) ( 48 )

	2116	Research on FFT+FT Spectrum Zooming Method for Differential Optical Absorption Spectroscopy
		JIA Gui-hong^{1, 2*}, ZHANG Jian-jun¹, ZHENG Hai-ming²
		DOI: 10.3964/j.issn.1000-0593(2021)07-2116-06
		Differential Optical Absorption Spectroscopy (DOAS) can be used to achieve online monitoring of polluted gas. To improve the monitoring accuracy, the Fourier Transform Filtering Method (FFT) is usually used to process differential absorption spectral data. However, due to the limitation of frequency resolution, its amplitude accuracy is affected, which leads to a large inversion error of gas concentration. A spectral data processing method that combining FFT and FT (FFT+FT) is proposed. First, the panoramic spectrum of differential absorption spectrum data is obtained by FFT filtering，then the spectrum near the peak point is refined by using the improved continuous FT, which can improve the resolution of the characteristic absorption frequency band, the amplitude error is compensated, and the accuracy of gas concentration on-line monitoring is improved. Many SO₂ and NO₂ gases with different concentrations were measured. When the zoom multiple is 15, the maximum measurement error of SO₂ and NO₂ gas is not greater than 3.68% and 3.17%. Compared with the FFT method, the average error decreases by 1.82% and 1.45%, respectively. Compared with the traditional polynomial fitting method, the average error is reduced by 14.9% and 1.80%, respectively. SO₂ and NO₂ gas with the same concentration were measured many times, which verified the stability of the algorithm. The influence of the zoom multiple on the measurement accuracy is analyzed. When the zoom multiple is less than 15, the concentration inversion error decreases with the increase of the zoom multiple. When the refinement multiple increases from 15 to 20, the error increases gradually. When the refining multiple is greater than 20, the errors fluctuate, and all of them are greater than the measurement errors when the multiple is 15. This is because the spectrum lines are too dense due to the large refinement multiples, and the probability of finding the maximum value of the spectrum sequence is reduced. Therefore, when the spectrum with noise is corrected by this method, the measurement accuracy will decrease with the increase of zoom multiple. To reduce the calculation amount of frequency spectrum zooming, the optimal zoom multiple is determined, which satisfies the requirements of measurement accuracy and meets the requirements of DOAS method for real-time online monitoring of pollution gas.
		2021 Vol. 41 (07): 2116-2121 [Abstract] ( 196 ) RICH HTML PDF (2729 KB) ( 63 )

	2122	Exploration of Digestion Method for Determination of Heavy Metal Elements in Soil by ICP-MS
		MENG Ru^2,4, DU Jin-hua^1,2*, LIU Yun-hua^1,2, LUO Lin-tao^1,3, HE Ke^1,2, LIU Min-wu^1,2, LIU Bo^1,3
		DOI: 10.3964/j.issn.1000-0593(2021)07-2122-07
		In order to improve the wet digestion of soil samples with high organic matter content, this paper by ICP-MS compared to analyze Copper (Cu), Chromium (Cr), Lead (Pb), Cadmium (Cd), Nickel (Ni) and Zinc (zinc) in national soil component analysis standard materials (GSS-1a—GSS-8a) by five different digestion systems, and they were the H₂O₂-HF-HNO₃, HCl-HNO₃-HF, HNO₃·3HCl-3HNO₃·HCl-HNO₃-HF, H₂O₂-HCl-HNO₃-HF and LOI-HNO₃-HF. ¹⁰³Rh was used as the internal standard element in the determination of ICP-MS，and the appropriate monitoring mode was selected for different determination elements. The results showed that the digestion effect of Cu, Cr, Pb, Cd, Ni and Zn was the best when setting the heating rate of 3 ℃·min^-1, calcining at 550 ℃ for 3 h, and then digesting with nitric acid and hydrofluoric acid after cooling. The results of standard addition recovery experiment shown that there was no significant difference between the measured value and the standard value of the standard substance. The recovery rate of standard addition is 91%~105%, which shown that the accuracy and precision of this method met the requirements. In the hydrogen peroxide-hydrofluoric acid-nitric acid system and hydrogen peroxide-hydrochloric acid-nitric acid-hydrofluoric acid system, the decomposition of hydrogen peroxide is severe when heated, and the digestion solution is easy to splash, resulting in low test results. The test results of hydrochloric acid-nitric acid-hydrofluoric acid system and aqua regia-aqua regia-nitric acid-hydrofluoric acid digestion system are not stable, which may be due to the interference of Cl^- on the mass spectrum of the instrument, and in the open system, CrO₂Cl₁₂ is easy to volatilize during the sample decomposition process, resulting in low Cr content. At the same time, the above methods are also applied to the comparative analysis of soil samples with high organic matter collected from the peat land of Lake Dajiuhu. The result shown that the digestion method of loss on ignition-nitric acid-hydrofluoric acid digestion system was more thorough, with less acid addition, less interference and stable test results, so this method was suitable for digestion of large quantities of samples with high organic matter content.
		2021 Vol. 41 (07): 2122-2128 [Abstract] ( 292 ) RICH HTML PDF (845 KB) ( 268 )

	2129	Research on Tea Cephaleuros Virescens Kunze Model Based on Chlorophyll Fluorescence Spectroscopy
		LIU Yan-de, LIN Xiao-dong, GAO Hai-gen, WANG Shun, GAO Xue
		DOI: 10.3964/j.issn.1000-0593(2021)07-2129-06
		Tea is an important cash crop in China. The early detection and diagnosis of tea diseases will help agricultural producers to take effective protective measures in time. In order to achieve accurate discrimination of tea diseases, the spectral characteristics of tea were studied using chlorophyll fluorescence spectrum. A total of 90 samples of healthy tea leaves, 90 samples of the early stage of Cephaleuros virescens Kunze leaf disease and 90 samples of the severe stage of Cephaleuros virescens Kunze leaf disease were collected in the experiment and were accordance with the Kennard-Stone algorithm divided into the training set and prediction set according to the proportion of 3∶1 for each kind. Adopt the chlorophyll fluorescence spectrum collection system to collect the spectrum of tea leaf spot disease and normal leaves and set the collection parameters: integration time 20 ms and laser power 40 mW. The spectral response characteristics of the diseased and normal leaves were analyzed separately. In general, there are differences in the absorption intensity of the three types of leaves, and the spectrum trends are the same. There is a chlorophyll fluorescence peak near 685 and 740 nm. The difference is mainly reflected in the difference in fluorescence peak intensity. Then the polynomial smoothing(Savitzky-Golay)method was carried out for smoothing and noise reduction on the original spectral, the establishment of partial least squares discriminant model (PLS-DA), in the PLS-DA modeling set model, the number of misjudged samples is 3, the false positive rate is 3%; in the PLS-DA prediction set model, the number of false positive samples is 5, and the false positive rate is 7.1%. Then the support vector machine model established by 4 different kernel functions is compared. RBF is used as the kernel function. The SVM model established by PCA has the lowest misjudgment rate, and the accuracy rate reaches 95.72%. Finally, the model established by principal component analysis (PCA) and linear discriminant analysis (LDA) has the best effect, and the accuracy rate reaches 98.9%. The selection of the optimal number of principal components is obtained by the leave-one-out verification method. When the first 10 principal components are selected for modeling, the cross-validation accuracy rate is the highest, reaching 98%. Through model comparison, the accuracy of the PLS-DA modeling set and prediction set is more than 90%. Among the support vector machine models built with four kernel functions, the radial basis kernel function model is the best, reaching 95.72%, the linear discriminant model (LDA) established after principal component analysis has the best effect, and the recognition rate is 98.9%. This study uses chlorophyll fluorescence spectroscopy combined with chemometrics to identify tea diseases, providing a new method for rapid and accurate prediction of tea diseases.
		2021 Vol. 41 (07): 2129-2134 [Abstract] ( 161 ) RICH HTML PDF (3314 KB) ( 53 )

	2135	Preliminary Study on Atom N in High-Enthalpy Flow Field
		LUO Jie，MA Hao-jun^*，WANG Guo-lin，XIAO Xue-ren
		DOI: 10.3964/j.issn.1000-0593(2021)07-2135-07
		When the hypersonic vehicle re-enters the atmosphere, it is subjected to the compression of the shock wave and the viscous blocking effect in the shock layer. The air temperature in the surrounding flow field is between 4 000~15 000 K, which causes oxygen and nitrogen molecules in the air to dissociate, resulting in high-temperature gas effect and the formation of high enthalpy chemical nonequilibrium flow. There are a large number of carbon elements in thermal protection materials on the surface of aircraft. Generally, the reaction between oxygen and carbon is the main reaction at high enthalpy. But when the enthalpy is greater than 18 mJ·kg^-1，the dimensionless mass ablation factor for the reaction of nitrogen atoms with carbon on the aircraft surface is BC_N＞0.172 5，and at the time, the dimensionless mass ablation factor of carbon in high enthalpy air is BC_air＞0.345; as a result, the nitriding ablation of carbon becomes very intense, which is equivalent to the oxidation ablation. Meanwhile, the dissociated nitrogen atoms also produce a large amount of heat in the catalytic reaction on the surface of the aircraft, which makes the aircraft surface withstand more thermodynamic impact. Therefore, the analysis of nitrogen atoms in high enthalpy chemical nonequilibrium flow field is of great practical significance. A high enthalpy chemical nonequilibrium flow field is established in ground simulation equipment, and nitrogen atoms can be well studied by measurement. Two-photon absorption laser-induced fluorescence (TALIF) technology, as a non-contact measurement, can directly obtain the concentration distribution without disturbing the flow field. Nitrogen atoms in the flow field are excited by a pulse laser, and two-dimensional nitrogen atom fluorescence signals are obtained through ICCD arranged outside a wind tunnel test section in a direction perpendicular to a plane formed by the flow field and the laser. In order to ensure the fluorescence image is clear and the field of view is appropriate, the Nikon f=50 mm F/1.4 lens is selected as the front stage light receiving device. Experimental imaging is the cumulative result of 50 exposures to eliminate the uncertainty caused by turbulence and laser energy jitter. By testing around the theoretical excitation wavelength, 206.717 nm is optimized as the best excitation wavelength in the formal experiment. At the condition of the optimal laser wavelength, the laser energy is adjusted from small to large, and the unsaturated linear region for the nitrogen atom in this environment is less than 1.8 mJ. In the formal experiment, the laser energy is 1.6 mJ, which is in the linear region. Based on the analysis of the fluorescence intensity extracted along the laser centerline obtained from the fluorescence image, it was found that both the subsonic flow and the supersonic flow presented a hump-shaped distribution along the radial direction. Compared with the previous work of oxygen atoms, it was found that the nitrogen molecules in the flow field had not been completely dissociated, which was consistent with the flow field characteristics of the experimental wind tunnel.
		2021 Vol. 41 (07): 2135-2141 [Abstract] ( 175 ) RICH HTML PDF (3195 KB) ( 44 )

	2142	Aqueous Fluorescence Fingerprint Characteristics and Discharge Source Identification of a River in Southern China
		LIU Chuan-yang^{1, 2}, CHAI Yi-di^{1, 2}, XU Xian-gen³, ZHOU Jun³, LU Sen-sen³, SHEN Jian^{1, 2}, HE Miao¹, WU Jing^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2021)07-2142-06
		Based on the speciality of the corresponded relationship between three-dimensional fluorescence spectrum and water bodies or pollution discharge sources, which were relied on the variation of the species and concentrations of fluorescent organic matters, the technique of aqueous fluorescence fingerprint can be applied in pollution discharge identification by the three-dimensional fluorescent characteristic of the water body. In this study, a case was carried out mainlyin River A of City C, southern China, in which the technique of aqueous fluorescence fingerprint was applied to identify the characteristics of the aqueous fluorescence finger print and pollution discharge. The aqueous fluorescence fingerprint of River A showed a prominent characteristic of textile wastewater pollution with three typical fluorescent peaks on [excitation, emission] wavelength of [280, 320], [235, 345] and [255, 460] nm and the similarities among up-, mid- and down-stream of River A were all over 99%. River J diverts into River A from the source of River A. The similarity between River J and A was lower than 60%, and the fluorescent intensity of River J was not more than 40% of River A, which indicated that River J had little influence on the formation process of the aqueous fluorescent fingerprint of River A, and the fluorescence intensity of River A is mainly contributed by its upstream region. River A’s pollution might come from the textile wastewater of the printing and dyeing textile industrial park in upstream area, with the aqueous fluorescence fingerprint similarity index of 94%. The linear correlation indexes between the fluorescent peak intensity and the permanganate index of river water were 0.956 4, 0.937 5 and 0.985 4, respectively, and the aqueous fluorescence fingerprint method showed higher sensitivity in pollution perception. Compared with the results of other three-dimensional fluorescence spectral similarity algorithms, the technique of aqueous fluorescence fingerprint for water quality is a reliable water environment monitoring technology and provided a useful tool for the further achievement of pollution source control and precise environmental supervision.
		2021 Vol. 41 (07): 2142-2147 [Abstract] ( 451 ) RICH HTML PDF (3288 KB) ( 89 )

	2153	Study on Measurement of Troposphereic NO₂ in Beijing by MAX-DOAS
		ZHU Peng-cheng¹, LIU Hao-ran^1*, JI Xiang-guang², LI Qi-hua¹, LIU Guo-hua¹, TIAN Yuan¹, XU Heng¹
		DOI: 10.3964/j.issn.1000-0593(2021)07-2153-06
		Due to the importance of nitrogen dioxide (NO₂) in the atmosphere’s physical and chemical processes and its impact on the environment, climate and human health, reasonable and effective monitoring and control of NO₂ concentration in the atmosphere has become a very important topic.The Differential optical absorption spectroscopy (MAX-DOAS) instrument is a passive DOAS instrument that uses the sun’s scattered light. Compared with the point type instrument which measures in a small range and the active DOAS instrument that uses the light source and reflection device, it has high time resolution, high sensitivity, wide measurement range and not restricted by the platform. In 2018, the annual continuous observation of tropospheric NO₂ based on ground-based MAX-DOAS was carried out at the China Academy of Meteorological Sciences (116.32°E, 39.95°N) in Beijing. The original absorption spectra were collected and retrieved using the spectral processing software QDOAS to obtain the NO₂ slant column concentration (SCD). Combined with the simpler geometric approximation method of atmospheric quality factor (AMF), the NO₂ SCD was converted into vertical column concentration (VCD), and based on this, the Beijing area was studied and analyzed. The characteristics of monthly and seasonal mean change of NO₂ VCD, seasonal daily mean change and daily mean change in a week. The results show that the tropospheric NO2 VCD changes obviously with seasons in Beijing, showing the highest in winter and the lowest in summer. The mean value in winter is 2.94×10¹⁶ molec·cm^-2, which is 1.6 times that in summer. The average daily change in different seasons generally shows the obvious difference in the afternoon, and the maximum difference is 2.17×10¹⁶ molec·cm^-2. There is a certain regularity in the daily concentration change in a week. The average concentration on Sunday is about 17% lower than that on other days, and there is a certain weekend effect. By comparing the observation results of MAX-DOAS on the ground with the state control station’s data on the ground, the change trend of the two has good consistency, and the correlation coefficient r can reach 0.81. The research shows that ground-based MAX-DOAS can provide an effective means for real-time and rapid monitoring of regional pollution gases and analysis of variation rules and can verify the data obtained from other sources.
		2021 Vol. 41 (07): 2153-2158 [Abstract] ( 150 ) RICH HTML PDF (2530 KB) ( 52 )

	2159	Prediction of Acetic Acid Concentration in Chinese Liquors Based on Fluorescence Spectrumand Simulated Annealing Algorithm
		XU Lei¹, ZHU Wei-hua^1, YAO Hong-bing^1, CHEN Guo-qing², QIAO Hua³, ZHU Feng^4，5, GENG Ying⁵, TANG Chun-mei¹, HE Xiang¹
		DOI: 10.3964/j.issn.1000-0593(2021)07-2159-07
		In recent years, the industry of vintage liquor market is not standardized. It is of deep significance and market value to study year liquor. The concentration of monomer in liquor will change with liquor age, so the detection of monomer concentration in liquor can be used to identify liquor quality and age. In this paper, based on the three-dimensional fluorescence spectrum of a certain domestic puree liquor brand, the concentration prediction model of acetic acid is studied. The main contents and innovations are as follows: Firstly, wavelet decomposition and derivative preprocessing are performed on the original spectrum. It is found that the first layer and the second layer of the wavelet mainly present the characteristics of noise, the concentration information is mainly distributed in the third and fourth layer signals. The intensity distribution of fluorescence emission spectra with different excitation wavelengths is different. At present, there is no unified method to select the appropriate excitation wavelength. According to wavelet decomposition signal, this article introduced effective signal strength and obtained the proper modeling excitation wavelength (200 nm). The derivative spectrum has more detailed features than the original spectrum, which can improve the spectral resolution. Secondly, the correlation between acetic acid concentration and fluorescence spectrum was studied. In general, the correlation between the original fluorescence spectrum and the concentration of acetic acid is not high. The correlation between the wavelet decomposition spectrum and derivative spectrum and the concentration is more than 0.8 and shows more discrete correlation peaks. Therefore, the wavelet decomposition spectrum and derivative spectrum contain more information about the acetic acid concentration, which has a wider distribution than the original spectrum’s. Finally, the partial least squares (PLS) multiple regression model of acetic acid concentration was studied based on fluorescence spectra and simulated annealing. The results show that the root means square error of the prediction set of acetic acid concentration in the original spectrum is as high as 70.03 mg·L^-1, so its model’s effect is poor. Wavelet decomposition spectrum and derivative spectrum have better prediction effect because the multiple correlations between the spectra is reduced, and the resolution is improved. The second derivative spectral modeling is the best. The root mean square error of the prediction set is 20.32 mg·L^-1, and the correlation coefficient is 0.9998. The spectral information density curve based on 1000 simulated annealing algorithms shows that the second derivative spectrum contains more acetic acid concentration information than the original spectrum. This study provides a simple optical method for predicting the concentration of substances in the year liquor. The research methods have a certain reference value for studying the concentration prediction of multi-component gradual change system.
		2021 Vol. 41 (07): 2159-2165 [Abstract] ( 165 ) RICH HTML PDF (5771 KB) ( 49 )

	2166	Fast and Non-Destructive Determination on Fresh Degree of Wheat Kernels Based on Biophotons
		GONG Yue-hong¹, YANG Tie-jun^2*, LIANG Yi-tao^{1, 3}, GE Hong-yi^{1, 3}
		DOI: 10.3964/j.issn.1000-0593(2021)07-2166-05
		Wheat kernels, as a type of living organisms, will continue consuming the nutrients of themselves to maintain their vital activities during the normal storage period. With the increase of storage time, various enzymes inside wheat kernels decrease or lose their activities, the intensity of respiration decreasing gradually, the colloid structure of protoplasm getting relaxed, and then the physical and chemical states of wheat kernels have changed, which result in the deterioration of subsequent edible and processing quality. Therefore, it is of great economic value and social significance for our country to carry out accurate fresh degree detection to stored wheat and ensure the quantity and quality of wheat kernels. The identification methods commonly used for fresh wheat degree mainly include sensory determination method and various biochemical methods. The former method with poor repeatability, mainly depending on the operator’s subjective experiences, is easily disturbed by external factors, and has an obvious error in the determination results, which is always used as a sort of auxiliary testing method in the aspect of wheat quality detection. Although the latter’s accuracy is high, the whole detection process is time-consuming, and it is usually involved in a complex pretreatment for the tested samples. Meanwhile, various chemical reagents used in the detection process may cause certain pollution to the environment. Thus, it is urgent to establish a fast, accurate and green identification method for the fresh wheat degree. Special biophotonic instruments have tested biophoton signals of stored wheat kernels in five different years in this paper, and then combined the improved multiscale permutation entropy algorithm to analyze the features of wheat biophoton signals in four years from 2015 to 2018, finally, taking advantage of backpropagation neural network to classify the fresh wheat degree in four years. Experimental results show that there exist certain differences in the spontaneous biophoton number of wheat kernels stored in different years, among of which the biophoton numbers of wheat kernels in 2019 are much larger than the numbers in the other years, and the permutation entropy value of biophoton numbers of the rest wheat samples shows an increasing trend with the extension of storage time. It has been validated by simulation experiment that the improved algorithm greatly solves the problems of signal dither and mutation that existed in the MPE algorithm which can be used as an obvious feature to characterize the fresh degree of wheat kernels. After simulating by backpropagation neural network, the recognition accuracy rate of the novel classification model proposed in this paper can reach 95% and be able to precisely determine the fresh degree of wheat kernels.
		2021 Vol. 41 (07): 2166-2170 [Abstract] ( 169 ) RICH HTML PDF (2539 KB) ( 69 )

	2171	Hyperspectral Imaging for Detection of Leguminivora Glycinivorella Based on 3D Few-Shot Meta-Learning Model
		GUI Jiang-sheng¹, FEI Jing-yi¹, FU Xia-ping²
		DOI: 10.3964/j.issn.1000-0593(2021)07-2171-04
		In order to reduce the influence of leguminivora glycinivorella on soybean production and quality, and to realize the rapid detection of leguminivora glycinivorella, this paper proposed a leguminivora glycinivorella detection model based on 3D-Realtion Network (3D-RN) model. Firstly, collect the hyperspectral images of 20 soybeans that are attached to eggs, larvae, gnawed and normal soybeans, respectively, and extract the region of interest (ROI) to establish a 3D-RN model based on hyperspectral images. The accuracy of the final model reached 82%±2.50%. Compared to the Model-Agnostic Meta-Learning (MAML) and Matching Network (MN) models, the 3D-RN model can fully measure the distance between sample features, and the recognition effect is greatly improved. Thus, this research shows that the 3D-RN model based on the hyperspectral image can detect leguminivora glycinivorella in a small number of samples. The method of combining few-shot meta-learning with hyperspectral provides a new idea for pest detection.
		2021 Vol. 41 (07): 2171-2174 [Abstract] ( 197 ) RICH HTML PDF (1398 KB) ( 46 )

	2175	Overlapping Peak Analysis of Soil Heavy Metal X-Ray Fluorescence Spectra Based on Sparrow Search Algorithm
		CHEN Ying¹, LIU Zheng-ying¹, XIAO Chun-yan², ZHAO Xue-liang^{1, 3}, LI Kang³, PANG Li-li³, SHI Yan-xin³, LI Shao-hua⁴
		DOI: 10.3964/j.issn.1000-0593(2021)07-2175-06
		In recent years, with the aggravation of soil heavy metal pollution and the gradual improvement of people’s environmental awareness, the research on the rapid detection method of soil heavy metal content has been strengthened rapidly. At present, X-ray Fluorescence analysis (XRF) has been widely used to detect heavy metal pollution in soil. However, due to the limited energy resolution of the X-ray fluorescence spectrometer and the low fluorescence yield of some heavy metal elements, overlapping phenomena occurred in adjacent spectral peaks of some elements. In the cause of overlapping phenomenon often appears between adjacent peaks in X-ray Fluorescence analysis (XRF), a new overlapping peak analysis method based on Sparrow Search Algorithm (SSA) was proposed. Firstly, samples with different moisture content and heavy metal element content were prepared, and original spectral data were obtained by X-ray fluorescence spectrometer from the soil sampled of Baoding, Hebei. Then, the spectral data were preprocessed, the spectral clustering algorithm removed the abnormal spectral samples, the spectral denoising and background subtraction were completed by the Savitzky-Golay five-point quadratic denoising method and the linear background method. The random number method is used to generate a large number of simulated spectral data for the use of subsequent algorithms. After that, expectation-maximization (EM) was applied to analyze overlapping peaks preliminarily. Set the initial parameters of the EM algorithm, and put simulation spectra data into the EM algorithm. When it reached the maximum number of iterations, can preliminarily get parameters of the Gaussian Mixture Model (GMM), expectation, variance and weights of each Gaussian peaks. However, the EM algorithm is easily affected by the initial parameter and is prone to fall into the local optimum, leading to inaccurate results. Therefore, further optimization of the EM algorithm is needed. In this study, SSA was used for global optimization of parameters of the GMM. After setting the basic SSA algorithm parameters, 100 groups of parameters obtained by the EM algorithm were taken as the initial population of the algorithm, and then set appropriate fitness function. Finally, the optimal global parameters were obtained through iteration, and the decomposition of overlapping peaks was realized. Sparrow Search algorithm (SSA) is less affected by parameter setting. Compared with some traditional optimization algorithms, such as GA, ACO, PSO, etc. SSA has fast convergence speed and is not easy to fall into local optimal. Therefore, this algorithm can achieve better optimization results. The analysis of overlapping peaks shows that the algorithm can get more accurate results with fewer iterations and be widely used in energy spectrum overlapping peaks analysis.
		2021 Vol. 41 (07): 2175-2180 [Abstract] ( 213 ) RICH HTML PDF (2542 KB) ( 62 )

	2181	Detection of Rice Sheath Blight Disease Index Based on Split-Window Gram-Schmidt Transformation and PSO-SVR Algorithm
		XIAO Wen¹, CAO Ying-li^1,2*, FENG Shuai¹, LIU Ya-di¹, JIANG Kai-lun¹, YU Zheng-xin¹, YAN Li¹
		DOI: 10.3964/j.issn.1000-0593(2021)07-2181-07
		Sheath blight is one of the main diseases of rice, whose control is of great significance to ensure rice yield and quality. Hyperspectral detection of rice diseases has been widely adopted in recent years, and hyperspectral dimensionality reduction is an important part of spectral analysis. In this study, the hyperspectral data of low altitude remote sensing canopy and rice ground canopy were obtained in Shenyang Agricultural University rice proving ground in 2019, and were smoothed by Savitzky-Golay with a window width of 15 and order of 3, as well as spectral transformations (original reflection spectrum, first-order differential reflection spectrum and inverse-log reflection spectrum), were carried out. To reduce the dimension of hyperspectral data in these 3 spectra, the split-window Gram-Schmidt transform method was used to find the projection space and map the main substrate, in which the main base with significant probability was drawn, and its maximum and minimum value was the characteristic band. The principal component analysis and successive projections algorithm were also used for dimensionality reduction of three spectra. Dimension-reduced data and rice sheath blight disease index were modeled by support vector machine regression, which was used for particle swarm optimization and radial basis function as the kernel function. The effect of three-dimensionality reduction methods was compared and analyzed. The results showed that the modeling effect of the rice ground canopy scale was better than that of the low-altitude remote sensing scale; in the aspect of hyperspectral data processing, the inverse logarithm transformation effect of low-altitude canopy hyperspectral data was better, and the first-order differential transformation effect of ground canopy hyperspectral data was better; the split-window Gram-Schmidt transformation algorithm was better than principal component analysis and successive projections algorithm; particle swarm optimization could optimize the penalty coefficient and kernel function parameters in SVR, and improve the inversion accuracy; in the low-altitude remote sensing canopy scale, the hyperspectral spectrum was processed by using the inverse logarithm processing and the split-window Gram-Schmidt transform, whose sensitive bands were 427.3, 539.6, 749.5 and 825.4 nm respectively. The determination coefficient R² was 0.731 and RMSE was 0.151 by using the PSO-SVR model; in the ground canopy scale, the hyperspectral spectrum was processed by using the first order differential processing and the split-window Gram-Schmidt transform, whose sensitive bands were 552, 607, 702 and 730 nm respectively. The determination coefficient R² was 0.778 and RMSE was 0.147 by using the PSO-SVR model. In conclusion, rice sheath blight can be effectively detected by hyperspectral technology, and its disease index can be retrieved by canopy hyperspectral analysis. The split-window Gram-Schmidt transform has a good effect on the dimensionality reduction of hyperspectral data. PSO-SVR modeling can significantly improve the inversion of rice sheath blight disease index. The results can provide a theoretical basis and technical support for the detection of rice sheath blight and disease occurrence on the canopy scale.
		2021 Vol. 41 (07): 2181-2187 [Abstract] ( 155 ) RICH HTML PDF (2919 KB) ( 53 )

	2188	Study on Non-Destructive Detection Method of Kiwifruit Sugar Content Based on Hyperspectral Imaging Technology
		XU Li-jia¹, CHEN Ming¹, WANG Yu-chao¹, CHEN Xiao-yan^{2, 3}, LEI Xiao-long^1*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2188-08
		The sugar content of kiwifruit is an important measure of its internal quality. Traditional sugar content detection is time-consuming and destructive sampling，and it is of great significance to non-destructive detect the sugar content of kiwifruit effectively for its quality classification, storage and sales. The common non-destructive detection methods of fruit and vegetable quality based on hyperspectral imaging technology mostly use a single algorithm of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), principal component analysis (PCA) and iteratively retains informative variables (IRIV) to extract features. However, using these algorithms alone will lead to insufficient stability of prediction results. This study designs a non-destructive detection method for kiwifruit sugar content based on hyperspectral imaging technology. The “Red Sun” kiwifruit samples in Ya’an city of Sichuan province were numbered, their hyperspectral images in the wavelength range of 400～1 000 nm were collected, and the average spectrum of the region of interest was calculated as the effective spectral information of the samples. Then, three spectral data preprocessing methods including Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), and Direct Orthogonal Signal Correction (DOSC), were used to analyze the influence on the accuracy of the prediction models, respectively. The comparison results showed that DOSC had the best preprocess effect. Further, for the preprocessed spectrum, 7 dimensionality reduction methods including CARS, SPA and IRIV from one-time dimensional-reduction algorithms, CARS+SPA and CARS+IRIV from the first-order combined dimensional reduction algorithms, and (CARS+SPA)-SPA, (CARS+IRIV)-SPA from the second-order combined dimensional-reduction algorithms respectively, were used to extract characteristic spectral variables, and three models for predicting the sugar content of kiwifruit were constructed i. e. Support Vector Regression (SVR), Least Square Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM) models. Finally, the prediction accuracy of the three models based on different feature extraction methods was compared through experiments. This study shows that the ELM model has the best prediction performance, while the SVR model has the worst prediction performance. When the characteristic spectral variables extracted by (CARS+IRIV)-SPA were input into LSSVM and ELM models, respectively, the prediction results are better than those obtained by other methods. Then (CARS+IRIV)-SPA is verified to be effective in improving the prediction accuracy of the models. Comparing the prediction results of these methods, the prediction performance of (CARS+IRIV)-SPA-ELM is better than other methods, with the correlation coefficient R_C=0.945 1, R_P=0.839 0, RMSEC=0.450 3, RMSEP=0.598 3, and RPD=2.535 1, which will provide reliable theoretical basis and technical support for the non-destructive, precise and intelligent development of kiwifruit sugar content detection.
		2021 Vol. 41 (07): 2188-2195 [Abstract] ( 243 ) RICH HTML PDF (4139 KB) ( 120 )

	2196	*Fast Classification Method of Black Goji Berry (Lycium Ruthenicum* Murr.) Based on Hyperspectral and Ensemble Learning**
		LU Wei¹, CAI Miao-miao¹, ZHANG Qiang², LI Shan³
		DOI: 10.3964/j.issn.1000-0593(2021)07-2196-09
		Black goji berry has high nutrition and medical value. Different grades of black goji berry have different quality, and prices are also significantly different. However, due to the lack of effective detection and grading methods, the black goji berry market is chaotic, and the bad become mixed with the good, which affects the black goji berry market’s quality supervision. To achieve fast, non-destructive and high-precision classification of black goji berry, this paper proposes a fast non-destructive classification method of black goji berry based on hyperspectral and ensemble learning. First of all, for Nomhong 1st grade (NMH-grade1), Nomhong 2nd grade (NMH-grade2), Nomhong 3rd grade (NMH-grade3), Nomhong 4th grade (NMH-grade4), select 200 for each grade. Then, in two placement modes (carpopodium up and overall horizontal after removing the carpopodium), the spectral image cube with a spectral range of 391.6~2 528.1 nm is acquired using a GaiaSorter-Dual wide-band hyperspectral sorter. Through the mask processing, automatically extract single black goji berry ROI hyperspectral information with cell counting algorithm. The spectral information of black goji berry in the range of 500~2 400 nm is extracted. After FD(First Derivative), FFT(Fast Fourier Transform), HT(Hilbert Transform), SG(Savitzky Golay), Normalize, SNV(Standard Normal Variate) preprocessing, the spectral information of the characteristic wavelength is extracted by PCA(Principal Components Analysis), SPA(Successive Projection Algorithm), CARS(Competitive Adaptive Reweighted Sampling). Then build LIBSVM, LDA(Latent Dirichlet Allocation), KNN(k-Nearest Neighbor), RF(Random Forest), NB(Naive Bayes) detection models. The combination of sarcocarp-Normalize-SPA-LDA, sarcocarp-FD-CARS-RF and sarcocarp-SNV-CARS-LIBSVM is the best, with accuracy rates of 0.941 7, 0.941 7 and 0.937 5, respectively. At the same time, it can be found that in the pretreatment, FD, HT, Normalize, and SNV have better effects. In the dimensionality reduction method, the models of SPA and CARS have better effects. And in the models established by LIBSVM, LDA, KNN, RF, and NB, the number of test set accuracy rates of not less than 0.9 are 2, 7, 0, 4, and 1, respectively, so the three classifiers LDA, RF, and LIBSVM work best. To further improve the classification accuracy of black goji berry, LDA, RF and LIBSVM are used as meta-models to build a fast and non-destructive classification model of black goji berry Stacking ensemble learning. When the sarcocarp-FD-SPA-Stacking is combined, the accuracy can be improved from 0.941 7 to 0.983 3. A total of 17 characteristic wavelengths is extracted, respectively (in nm): 591.6, 609.1, 721.6, 989.1, 1 083.3, 1 111.3, 1 296.1, 1 564.9, 1 844.9, 1 934.5, 1 996.1, 2 046.5, 2 130.5, 2 292.9, 2 315.3, 2 320.9, 2 348.9. Among them, there are C-H frequency doubling peaks and absorption peaks near 721.6, 1 083.3, 1 111.3, 2 130.5, 2 292.9, 2 315.3, 2 320.9, 2 348.9, O—H frequency doubling peaks and absorption peaks near 721.6, 989.1, 1 934.5, 1 996.1, 2 292.9, and C—O absorption peaks near 2 130.5 and 2 292.9. Research has shown that fast and non-destructive classification of black goji berry based on hyperspectral combined with ensemble learning is feasible.
		2021 Vol. 41 (07): 2196-2204 [Abstract] ( 229 ) RICH HTML PDF (5740 KB) ( 73 )

	2205	Research in Crop Yield Estimation Models on Different Scales Based on Remote Sensing and Crop Growth Model
		YU Xin-hua¹, ZHAO Wei-qing^2*, ZHU Zai-chun², XU Bao-dong³, ZHAO Zhi-zhan⁴
		DOI: 10.3964/j.issn.1000-0593(2021)07-2205-07
		Food security is a guarantee for social harmony, political stability and sustainable development of the economy. However, current research on crop yield estimation is mostly regional and empirical, relying too much on ground-measured data. Scalable Crop Yield Mapping (SCYM） is a satellite data based framework for estimating crop yield.It can be quickly applied to the estimated yield of different spatial scales and different types of crops without relying on measured data. This framework provides an important theoretical basis for multi-scale crop yield estimation research. We took the winter wheat of Anhui Province from 2012 to 2018 as the study object. Firstly, the sensitive parameters determined by the predecessors and their fluctuation ranges in the study area are summarized. Combined with a large amount of site data, the parameters optimization of the WOFOST model was completed. Secondly, random forest models were established based on the simulated yield, simulated leaf area index (LAI) at different periods, and selected meteorological indicators. Finally, the MODIS-LAI under the best observation date combination replaced the simulated LAI for the corresponding time periods to estimate the winter wheat yield in Anhui Province. The main outcomes in this study are as follows: (1) The overall correlation between the estimated outputs and the measured data of the stations is 0.758 (R² is 0.575), and the RMSE is 790.92 kg·ha^-1. The sites with higher production accuracy are mainly distributed in the Huaibei Plain (<1%), while the areas with high errors are concentrated in the hilly areas of southern Anhui (>40%). (2) The winter wheat yield in Anhui Province from 2012 to 2018 was estimated by SCYM. According to the spatial distribution of the 7-year average yield estimation, the yield is gradually decreasing from north to south. The high-value areas are located in the Huaibei Plain in northern Anhui, and the low-value areas are distributed in central Anhui and southern Anhui. (3) The average measured yield from 2012 to 2018 is 6 058.00 kg·ha^-1, while the average yield of the SCYM is 5 984.95 kg·ha^-1. The correlation between them in the interannual time series is 0.822, and the RMSE is 189.96 kg·ha^-1. In seven years, the relative error each year does not exceed 6%. This study shows that the SCYM framework is feasible for estimating winter wheat yield in Anhui Province and has a good effect on yield forecast. This method can improve the regionality and empiricism of the previous crop yield estimation models to a certain extent. Meanwhile, it also solves the shortcomings of computationally intensive methods, which are costly and difficult to popularize. Thus, SCYM has great potential in applying of regional scales, and it will provide an extremely important theoretical basis and practical value for agricultural production in the future.
		2021 Vol. 41 (07): 2205-2211 [Abstract] ( 355 ) RICH HTML PDF (2678 KB) ( 173 )

	2212	Effect Analysis of Using Different Polarization Quantities in Aerosol Retrieval From Satellite Observation
		ZHENG Feng-xun¹, ZHU Jia-yi², HOU Wei-zhen³, LI Zheng-qiang^3*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2212-07
		Polarimetry is one of the most promising types of remote sensing for improved characterization of atmospheric aerosol. As an important polarization data source in the world currently, the Directional Polarimetric Camera (DPC) onboard Gaofen-5 satellite can provide measurements of the Stokes vector, the polarization radiance (L_p), and degree of linear polarization (DOLP). The calibration accuracy of these polarization quantities will affect the retrieval error of aerosol parameters, and the corresponding impacts need to be analyzed quantitatively. For this purpose, the degree of freedom for signal (DFS) and the posterior error for aerosol model parameters are calculated based on the optimal estimation inversion framework and information content analysis theory. The results show that the information contained in the Stokes vector is the highest, followed by DOLP and L_p. The corresponding total aerosol DFS are 7.5, 6.1, and 5.2, respectively. The imaginary part of the complex refractive index (m^f_ｉ) and the effective variance (v^f_eff) reduce significantly when using L_p in the retrieval. This indicates that the two parameters are sensitive to the polarization direction and measurement error. The average DFS of the fine-mode columnar volume concentration (V^f₀), the real part of the complex refractive index (m^f_r), and the effective radius (r^f_eff) are larger than 0.85, which can be well retrieved from DPC multiangle polarization observation. The inversion of coarse-mode aerosol parameters has high uncertainties, which is related to the aerosol type. Compared with adopting the Stokes vector in the retrieval, the posterior error of aerosol parameters increase 67.6% and 65.5% on average for adopting L_p and DOLP, respectively. The fine-mode columnar volume concentration (V^f₀) and effective radius (r^f_eff) are most affected. Evidently, the polarization direction has great value to improve the retrieval of aerosol. Among all the aerosol parameters, the posterior error of the real part of complex refractive index (m^f_r) is the smallest, and the imaginary part (m^f_i) has the highest inversion uncertainty.
		2021 Vol. 41 (07): 2212-2218 [Abstract] ( 198 ) RICH HTML PDF (3298 KB) ( 66 )

	2219	Restoration of High-Frequency Vibration Blurred Hyperspectral Image Based on Dynamic Chaos Disturbance Genetic Algorithm
		WANG Xiao-yan¹, LI Jie^2*, PENG Bang-ping¹, TU Yi-cheng³
		DOI: 10.3964/j.issn.1000-0593(2021)07-2219-07
		Hyperspectral images have a higher spectral and spatial resolution and thus can differentially diagnose the spectral characteristics of ground objects. However, when acquiring hyperspectral images, the vibration of the platform often distorts the spectral image, which seriously affects the accuracy and reliability of spectral images in applications. This paper proposes a dynamic chaos disturbance genetic algorithm capable of restoring vibration-blurred hyperspectral images. Compared with ordinary genetic algorithms, this algorithm does not converge prematurely and can recover images more accurately with improved spectral quality. Based on the degradation principle of vibration-blurred images, we start by generating the mapping relationship between the vibration blurred image and the clear image and the point spread function of the vibration blurred image. Based on the nonlinear and chaotic characteristics of the degradation of the vibration blurred image, tent mapping is used to generate the initial chaotic population, which enhances the global search ability of the genetic algorithm. Specifically, we use Chebyshev mapping to chaotically perturb the outstanding individuals, thus enhancing the genetic algorithm’s local search ability. The three-dimensional hyperspectral image is tiled into a two-dimensional image, and the image correlation of adjacent spectral channels is used to restore the three-dimensional hyperspectral data. To verify the performance of our method, we run two sets of image restoration simulations using data cubes provided by the Australian airborne Hymap imaging spectrometer. The method in this paper is compared with the data-of-art spectral image restoration algorithm, and genetic rehabilitation algorithm under multiple criteria, such as non-parametric evaluation method average gray gradient GMG and Laplace operator LS, parametric evaluation method signal-to-noise ratio SNR and incident signal-to-noise ratio PSNR, spectrum uses the spectral information divergence SID and the spectral gradient angle SGA evaluation methods, and it is found that all evaluation indicators can be improved. Compared with the latest spectral restoration algorithm, our method improved the SNR of the image by 60%, PSNR by 10%, GMG by 11%, LS by 11% and reduced the SID by 39%, SGA by 5%. Compared with the original genetic restoration algorithm, our method improved the SNR of the image by 51%, PSNR by 12%, GMG by 33%, LS by 43% and reduced the SID by 39%, SGA by 16%. These results show that our method is highly effective in restoring vibration and blur of spectral image data by significantly improving the clarity of a single band image, and the spectral quality of the spectral data cube.
		2021 Vol. 41 (07): 2219-2225 [Abstract] ( 138 ) RICH HTML PDF (3377 KB) ( 34 )

	2226	Analysis of Impact Factors and Applications by Using Spectral Absorption Depth for Quantitative Inversion of Carbonate Mineral
		ZHAI Wen-yu, CHEN Lei^*, XU Yi-xuan, KONG Xiang-yu
		DOI: 10.3964/j.issn.1000-0593(2021)07-2226-07
		Spectral absorption feature is an essential essential indicator indicator for mineral classification and quantitative inversion. This paper take calcite mineral to represent carbonate mineral, linear mixed spectral model and continuum removal method as basic algorithms and continuum removal band the depth (CRBD) as analysis object, to analyze the variation of CRBD mixed spectrum at 2.33 μm by mixing calcite and spectra of those three types under different spectral features, spectral abundance and spectral reflectivity. According to the spectral features near 2.33 μm, the spectra are divided into three types. By analyzing the mixed spectral feature of calcite and spectra of three different types, a new expression is proposed by using distribution scope to instead quantitative value to demonstrate mineral abundance estimation. The results show that the mixed endmember abundance has an obvious influence on the CRBD value, and the higher the calcite abundance is, the more obvious the absorption characteristics are and the higher the CRBD value is. Similarly, the spectral reflectivity and spectral feature of mixed endmember greatly influence the CRBD of the mixed spectrum. When the mixed endmember is characterized by a non-characteristic or reflection peak near 2.33 μm, the smaller the spectral reflectivity of the non-characteristic endmember is, the more prominent the CRBD is with the increase of carbonate abundance. The larger the spectral reflectivity of the reflection peak endmember is, the more concave the CRBD changes will be. When the mixed endmember with calcite has an absorption valley near 2.33 μm, the CRBD variation meets the linear change rule. Through cross-analysis and CRBD of mixed spectra by multi-endmember, CRBD of mixed spectra changes with carbonate mineral abundance is limited by a certain space. The upper fitting equation satisfies exponential function variation, and the lower fitting equation is similar to the cubic polynomial function. Both have high fitting accuracy, R² are higher than 0.99, and the RMSE is lower than 0.005. In order to achieve the accurate prediction of mineral content, a new method, the distribution range of carbonate mineral content, is solved according to those fitting equations to express the distribution of carbonate mineral abundance by using a range instead of quantitative value to realize the accurate range expression of carbonate mineral content. The new expression of carbonate mineral content and impact factors analysis can provide a new way for mineral monitoring and quantitative evaluation and provide a theoretical reference for establishing a universal ground object quantitative inversion model.
		2021 Vol. 41 (07): 2226-2232 [Abstract] ( 163 ) RICH HTML PDF (6689 KB) ( 53 )

	2233	Adaptability Analysis of Various Versions of GDPS Based on QA Score for GOCI Data Processing in the Yellow Sea
		LIU Xiao-yan, YANG Qian^*, LIU Qiao-jun
		DOI: 10.3964/j.issn.1000-0593(2021)07-2233-07
		Spectral remote-reflectance sensing (R_rs(λ)) is significant to the retrieval of ocean bio-optical properties from ocean color. R_rs(λ) is defined as the ratio of water-leaving radiance to the downward irradiance just above the water surface. About 90% of the total signal received by ocean color sensor is contributed by the atmosphere, while only less than 10% is contributed by ocean water. Therefore, the process of atmospheric correction is an essential part of ocean color remote sensing to get accurate remote-sensing reflectance. Based on a large number of high-quality on-site hyper-spectral remote-reflectance sensing data, a QA (Quality Assurance) evaluation system for R_rs data has been established to diagnose problematic or possibly wrong R_rs spectra by calculating R_rs QA scores. GOCI (Geostationary Ocean Color Imager) is the main sensor on the world’s first geostationary satellite COMS (Communication Ocean and Meteorological Satellite), launched by the Korea Ocean Satellite Center (KOSC). Its high observation frequency (8 observation survey Data/day) makes it possible to monitor daily changes in biogeochemical parameters. KOSC developed GDPS (GOCI Data Processing System) software for GOCI data processing, within which atmospheric correction algorithm is integrated. The versions GDPS1.1, GDPS1.2, GDPS1.3, GDPS1.4, GDPS1.4.1 and GDPS2.0 have been published and provided for free. In this paper, a QA Score evaluation system was applied to evaluate the quality of GOCI remote-sensing reflectance product processed by GDPS1.2, GDPS1.3, GDPS1.4.1 and GDPS2.0 in the China Yellow Sea. It showed that the amount of valid R_rs data from GDPS1.2 was significantly less than that either from GDPS1.3, GDPS1.4.1 or GDPS2.0. The QA score of R_rs data from GDPS2.0 was lower than that of GDPS1.2, GDPS1.3 or GDPS1.4.1. It makes sense that the QA score of R_rs from GDPS1.3 and GDPS1.4.1 are the same. Because compared to GDP1.3, only software modularization is optimized, and some minor problems are fixed in GDPS1.4. Based on our results, when applying GOCI R_rs data to the Yellow Sea, it is suggested that if R_rs ratio is the first-order parameter (i. e., retrieving chl-a concentration) and there is no requirement of valid data amount, atmospheric correction codes in GDPS 1.2 can be selected to used to get R_rs. If Rrs data at a certain wavelength is concerned, GDPS2.0 is more suitable for processing GOCI data.
		2021 Vol. 41 (07): 2233-2239 [Abstract] ( 199 ) RICH HTML PDF (5114 KB) ( 39 )

	2240	Hyperspectra Used to Recognize Black Goji Berry and Nitraria Tanggu
		ZHAO Fan, YAN Zhao-ru, SONG Hai-yan
		DOI: 10.3964/j.issn.1000-0593(2021)07-2240-05
		Black Goji berry contains various nutrients such as cyanidin, polysaccharides, trace elements and so on, and has extremely high economic and medical value, the similar Nitraria Tanggu impersonates in the market. The market price of Nitraria Tangguis low. Hyperspectral image technology combines image and spectrum in one, commonly used in food detection and recognition. This study combined with hyperspectral image technology to non-destructively identify Black Goji Berry and nitraria tanggu. Hyperspectral reflection spectra of Black Goji Berry (180) and nitraria Tanggu (180) in the range of 900~1 700 nm were collected respectively, a total of 254 bands. Removing the first 22 abnormal bands and using the last 232 bands as model inputs. Kennard-Stone method is used to divide samples, correction set∶prediction set=2∶1. The successive projections algorithm (SPA) method is used for spectral dimensionality reduction, setting the characteristic wavelength range to 0~30, which extracts 20 characteristic wavelengths. The full spectrum and 20 characteristic wavelengths extracted by SPA are used as model inputs to establish support vector machine (SVM) and extreme learning machine (ELM) models to identify Black Goji Berry and nitraria Tanggu. The results show that the recognition rates of the SVM model based on FS and SPA are both 100%, the recognition rates of the ELM model based on FS and SPA are both 100%, the SPA method can reduce model input without reducing the accuracy of model recognition. The input is only 8.62% of FS, which greatly reduces the number of model calculations. This study provides a theoretical basis for identifying Black Goji Berry and nitraria Tanggu.
		2021 Vol. 41 (07): 2240-2244 [Abstract] ( 186 ) RICH HTML PDF (3006 KB) ( 52 )

	2245	Study on Composition and Spectral Characteristics of Turquoise Treated by “Porcelain-Added”
		HUANG Li-ying, CHEN Quan-li^*, GAO Xin-xin, DU Yang, XU Feng-shun
		DOI: 10.3964/j.issn.1000-0593(2021)07-2245-06
		In recent years, a kind of turquoise treated with a new type of inorganic binder has appeared on the market. The turquoise treated by this method is very similar to the natural turquoise, which is generally characterized by a fine structure, glassy luster or greasy luster. It is called “porcelain-added” turquoise in markets. Conventional gemological testing instruments, infrared absorption spectrometers, UV-Vis spectrometers and energy dispersive X-ray fluorescence spectrometers were used to systematically study and analyze the gemological properties, vibration spectrum characteristics and chemical composition characteristics of “porcelain-added” turquoise. The results show that the density of turquoise samples treated with “porcelain” is mostly less than 2.2 g·cm^-3, and its density is related to the density of turquoise before treatment. The samples treated with “porcelain” are mainly turquoise with a lower density. The turquoise treated by “porcelain” is characterized by typical low density, delicate structural appearance and strong wax-glass luster combination, which is inconsistent with the characteristics of natural turquoise and can be used as an important auxiliary identification feature of turquoise treated by “porcelain”. The luminescence of turquoise treated by “porcelain” under long-wave and short-wave UV fluorescence is nearly consistent with natural turquoise. Under microscopic observation, the white melting matter often appears in the iron wire and crevasses, and hair-like crystals can be seen in the pores. The main chemical composition of “porcelain-added” turquoise is similar to the natural turquoise, with CuO, Al₂O₃ and P₂O₅ as the main components, and containing a certain amount of FeOT, ZnO, SiO₂, K₂O and CaO. Among them, the SiO₂ content of the “porcelain-added” turquoise sample is basically above 6.40%, which is higher than that of the natural turquoise (1.96%～6.25%), and its Al₂O₃ and P₂O₅ contents are lower than those of the natural turquoise, and the proportion of phosphorus and aluminum is basically the same as that of the natural turquoise, about 1.10. The high content of SiO₂ and surface feature in turquoise can be effectively distinguished from natural turquoise by “adding porcelain”. The infrared absorption spectrum of turquoise treated by “adding porcelain” is basically the same as that of natural turquoise, which is difficult to distinguish. The Ultraviolet absorption spectrum of turquoise treated by “porcelain addition” shows the absorption peak at 620～750 nm and the relatively sharp absorption peak near 425 nm. The peak positions are slightly shifted due to different colors, but the overall UV absorption spectrum characteristics are consistent with those of natural turquoise.
		2021 Vol. 41 (07): 2245-2250 [Abstract] ( 231 ) RICH HTML PDF (3050 KB) ( 307 )

	2251	Spectral Study on Natural Seleniumin Turquoise From Shiyan, Hubei Province
		KU Ya-lun¹, YANG Ming-xing^{1, 2*}, LIU Jia¹, XU Xing¹
		DOI: 10.3964/j.issn.1000-0593(2021)07-2251-07
		Recently, there is a kind of turquoise with gray black impurity in turquoise market of Hubei Province, which has been studied very little. This study selected a turquoise protolith from Shiyan, Hubei Province. Blue basal turquoise is covered with dark gray inclusions of different shapes and sizes. The magnified observation shows that dark gray inclusions minerals have a metallic luster. The dark gray inclusions have been measured by in situ micro-element analysis with LA-ICP-MS, phase observation with back scattering electron image, principal quantitative chemical composition analysis using EDS for semi-quantitative testing and EPMA for quantitative testing, and micro-laser Raman spectrometer testing. The test results show that: The LA-ICP-MS results of dark gray inclusion minerals are influenced by the diameter and depth of the laser ablation spot. The results show that the chemical composition is a mixture of dark gray inclusion minerals and a small amount of turquoise. Se in dark gray inclusion minerals content is 95 927～221 394 μg·g^-1, which is obviously higher than that in blue basal turquoise (146～212 μg·g^-1). The content of CuO in dark gray inclusion minerals is 7.47%～9.28%, the content of Al₂O₃ is 28.1%～35.7%, and the content of P₂O₅ is 30.1%～37.8%. In dark gray inclusion minerals CuO content is 7.47%～9.28%, Al₂O₃ content is 28.1%～35.7%, P₂O₅ content is 30.1%～37.8%, which are caused by a small amount of turquoise mixture. Backscatter electron images show that the crystalline particles of inclusion minerals are fine and mix with turquoise, and heteromorphic inclusion minerals are aggregates formed by multiple crystals. The results of EDS show that inclusion minerals mainly contain Al, P, Fe, Cu and Se. Quantitative analysis of principal chemical components by EPMA shows that inclusion minerals mainly contain Se, with a content of 79.34%～87.97%. In addition, due to the fine crystalline particles of inclusion minerals, in the aggregates, inclusion minerals are mixed with turquoise. Therefore, Al, P, Fe and Cu of turquoise can also be found in the quantitative determination of chemical composition. Al content is about 0.93%～4.13%, Cu content is about 1.30%～2.04%, P content is about 0.66%～2.40%, Fe content is about 0.31%. The micro-laser Raman spectra of inclusion minerals are sharp peaks at 144 and 235 cm^-1. The inclusion mineral is mainly natural selenium, according to the results of chemical composition spectroscopy. Selenium minerals is a newly discovered inclusion mineral in turquoise. The discovery of natural selenium, an inclusion mineral in turquoise, can provide an effective basis for the identification of turquoise by jewelry practitioners.
		2021 Vol. 41 (07): 2251-2257 [Abstract] ( 171 ) RICH HTML PDF (4322 KB) ( 110 )

	2258	Gemological and Spectroscopy Characteristics of Synthetic Blue-Green Beryl by Hydrothermal Method
		ZHANG Jia-lin, ZHANG Qian, PEI Jing-cheng^*, HUANG Wei-zhi
		DOI: 10.3964/j.issn.1000-0593(2021)07-2258-05
		In this paper, for a new blue-green hydrothermal synthetic beryl on the market, the systematic research is conducted by using LA-ICP-MS, IR spectrum, Raman spectrum, UV-Vis absorption spectrum to obtain the gemology and spectroscopic characteristics and to analyze the causes of colour, and provide reference data for testing institutions to identify such synthetic gems. The results show that the sample’s refractive index is 1.570~1.576, which is similar to natural beryl. All sample have a characteristic water ripple growth texture inside, which can be used as one of the main identification characteristics of this synthetic beryl. LA-ICP-MS analysis showed that the synthetic beryl’s chemical composition is relatively single, the main chromogenic elements are Cr and Ti, and also contains trace amounts of V, and the alkali metal content is extremely low. The UV-Vis spectrum mainly shows the absorption peak of Cr, combined with the LA-ICP-MS test results, it is believed that Cr and Ti mainly cause the blue-green tone. The Cr mainly causes the green tone, and trace V may also affect the green tone. Titanium causes purple, superimposed with green to form the blue-green hue of the sample. The specific color mechanism needs to be further studied. In the infrared spectrum of 2 000~4 000 cm^-1, the broad absorption band centered at 3 700 cm^-1absorbs strongly, which belongs to the fundamental frequency vibration and coupling of two channel water types. Peaks at 2 449, 2 615, 2 746, 2 813, 2 885, 2 983 cm^-1are caused by Cl^-；The strong absorption peaks of 3 108 and 3 299 cm^-1are caused by NH⁴⁺. The near-infrared absorption spectrum of 4 000~8 000 cm^-1, it is the combined frequency and frequency doubling vibration area of the channel water in synthetic beryl. Among them, the strongest absorption peaks at 5 275 cm^-1, accompanied by the stronger absorption peaks at 5 455 and 5 106 cm^-1caused by the combined frequency vibration of type I water, and the strong absorption peak at 7 143 cm^-1caused by frequency doubling vibration of type I water can be used as an important identification feature of hydrothermal synthetic beryl and is particularly important for the identification of thick faceted gemstones. The corresponding absorption peaks in natural beryl are weak or even absent. The Raman spectrum of the sample is the same as that of standard beryl. The half-height width of the 685 cm^-1 peak is 7.1～7.3 cm^-1, less than 8.5 cm^-1, which can be used as another identification feature of this hydrothermal synthetic beryl.
		2021 Vol. 41 (07): 2258-2262 [Abstract] ( 166 ) RICH HTML PDF (2060 KB) ( 88 )

	2263	Study on Spectroscopy of Sphene From Pakistan
		REN Qian-qian^1*, YUAN Yi-chai²
		DOI: 10.3964/j.issn.1000-0593(2021)07-2263-06
		As a precious gem, sphene features sound characteristics of gemology and outstanding brilliance, enjoying great popularity in Europe, United State, Japan and India. As the communication between domestic and overseas jewelry market has been increasing in recent years, some Chinese regions are also witnessing increased popularity of such a gem with a brilliant appearance. However, domestic research on gem-level sphene is quite a few, with relevant recognition being limited to refractive index, chromatic dispersion and other basic properties of mineralogy. There is a big gap in terms of the study on its spectroscopy feature and color formation reasons. On the basis of analyzing the constituent analysis of yellow-green sphene, this paper summarizes characteristics of its infrared spectroscopy, Raman spectrum and ultraviolet-visible spectrum. According to the LA-ICP-MS test on sphene samples, it finds that the major element content of samples of this batch is stable. Among them, the content of TiO₂ is 35.42 Wt%, and the content of MnO and Cr₂O₃ is quite low (~0.04 Wt% and 0.01 Wt% respectively). Therefore, this batch’s color of sphene sample is mainly related with Fe, the element of high content. The scope of fundamental frequency 400~1 200 cm^-1 of infrared vibration could witness the absorption peak resulting from Si—O and Ti—O vibration. Meanwhile, the absorption width that existed in Fe²⁺ could be found at 6 800 cm^-1. The test result of the Raman spectrum (45~1 500 cm^-1) is basically in line with previous studies. It is speculated that the weak Raman spectrum is related to Fe. On the basis of the comparison of Raman spectrum results of cutting sample GS-1 in 5 different directions, it finds that the location of Raman spectrum in different crystal faces keeps unchanged, but the relative strength of some vibrations varies. As shown by the ultraviolet-visible spectrum peak-differentiating and imitating, the absorption at 14 461 cm^-1 might be derived from the d—d forbidden transition of Fe²⁺ within octahedron, but the three absorption peaks of 15 887, 16 781 and 17 781 cm^-1 have resulted from the d—d forbidden transition of Fe³⁺ within octahedron. Innovations of this research can be summarized into two aspects as below: (1) it systematically summarizes characteristics of infrared spectroscopy, Raman spectrum and ultraviolet-visible spectrum of gem-grade sphene and makes analyzes based on the constituent test result;(2) Relevant software is applied to make peak-differentiating and imitating on the ultraviolet-visible spectrum of sphene samples. Absorption peaks are assigned in accordance with locations of spectrum peaks, relative strength, peak shape characteristics and principles summarized by previous researchers. In the end, it raises that Fe might be the cause for the color development of gem-grade yellow-green sphene.
		2021 Vol. 41 (07): 2263-2268 [Abstract] ( 183 ) RICH HTML PDF (2792 KB) ( 96 )

	2269	Characterization of Original Position Statistical Distribution of Composition in Train Wheel Steel by Laser-Induced Breakdown Spectrum
		LIU Jia¹, SHEN Xue-jing^{1, 2}, ZHANG Guan-zhen³, GUO Fei-fei², LI Dong-ling^{1, 2}, WANG Hai-zhou^1*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2269-06
		With the rapid expansion of the railway scale, the requirements for the reliability and durability of train operation are getting higher and higher. As the core component of the railway vehicle system, the friction between the wheel and the track must ensure safety and increase the speed. The performance of the wheel material directly affects the sensitivity of the wheel to wear and rolling contact fatigue damage, and its service performance is also highly concerned. Studies have shown that the composition and distribution of wheel steel materials can significantly impact the performance of its microstructure.Therefore, this paper aims to use the laser-induced breakdown spectroscopy technology to quickly analyze the high efficiency of multi-element, better spatial resolution, scanning analysis capabilities in a larger area and other technical advantages, combined with statistical distribution analysis method, to achieve rapid characterization of the composition and distribution of wheel steel materials. In this paper, the vertical surface of the wheel rim was selected as the analysis surface. The low time’s test showed that there were obvious thick dendrite structures in thearea away from the tread surface, and the organization structure had unevenness, and use this as a feature analysis area for sampling. 320 mesh alumina sandpaper was used for surface treatment, and the LIBSOPA system was used for component distribution analysis. First, under different ablation conditions, the spectral signal intensity and stability of each element’s characteristic spectral line were compared and analyzed, and 20 pre-ablation and 10 ablations were optimized as experimental conditions; second, using established the standard internal method to characterize the quantitative results of nine elements such as Si, Mn, P, S, Cr, Ni, Mo, Cu, V in wheel steel. The quantitative results and the results of direct-reading spectrum analysis have good consistency; In the end, the sample was scanned regionally, and the statistical distribution of each element’s composition distribution was statistically characterized. The statistical results of the composition distribution partition showed that the statistical segregation degree of all elements near the tread area was less than that away from the tread area. Based on the statistical segregation degree and the two-dimensional distribution map of the components, it can be seen that the distribution of the components of the test sample away from the tread area is uneven, and the results correspond well with the results observed by the low-times test method. In this paper, the LIBSOPA technology is used to realize the composition distribution characterization of multi-element in the train wheel steel material, which provides a new idea and characterization method for quickly determining the composition and distribution state of the wheel steel material.
		2021 Vol. 41 (07): 2269-2274 [Abstract] ( 153 ) RICH HTML PDF (4380 KB) ( 56 )

	2275	Trace Pesticide Measurement Method Based on Immunosensor and Laser-Induced Breakdown Spectroscopy
		CUI You-wei^1,2, ZHENG Pei-chao¹, WANG Xiao-fa¹, JIAO Lei-zi^2,3, DONG Da-ming^1,2, WU Jing^2
		DOI: 10.3964/j.issn.1000-0593(2021)07-2275-04
		Pesticides have played a significant role in crop diseases and insect pests, as well as high and stable yields of crops, but the long-term large-scale use of pesticides has caused great harm to ecology and human health. According to the relevant literature review, there are no related reports on the detection of pesticide residues based on the analysis methods of immunosensors and laser-induced breakdown spectroscopy. In this method, a laser-induced breakdown spectrum is proposed to detect the probe of the immunosensor capturing the target to be detected, there by indirectly calculating the concentration of the target to be measured. This article uses immunochromatographic test strips to detect trace pesticides. Although immunochromatographic test strips can measure trace pesticides, they can only be qualitative, and the detection range is narrow. In order to broaden the measurement range of trace pesticides by immunochromatographic test strips, laser-induced breakdown spectroscopy (Laser-induced Breakdown Spectroscopy, LIBS) was used to perform spectroscopic measurements on metal nanoparticles captured trace pesticides on immunochromatographic test strips. Construction of detection methods for immunochromatographic test strips and LIBS. In this paper, chlorpyrifos pesticides are used as the research object. Because pesticide residues are small molecule antigen detection, the immunochromatographic test strips use the competition method to detect chlorpyrifos. The color difference between the control line and the detection line of the immunochromatographic test strip with a low concentration of chlorpyrifos dripped is not obvious, and human eyes cannot distinguish whether chlorpyrifos is detected. The control line and detection line of the immunochromatographic test strip added with chlorpyrifos were selected respectively, and the spectrum at Au Ⅰ 242.733 nm was measured. The average spectral intensity of the control line minus the average spectral intensity of the detection line is the signal of chlorpyrifos. This method can detect signals not observed by immunochromatographic test strips, and avoid the problem of high detection limit of LIBS. As the chlorpyrifos concentration increased from 0 to 10⁶ ng·mL^-1, the difference in LIBS spectral data gradually increased. In order to eliminate the effect of random errors, a calibration curve was constructed using ΔLIBS intensity (measurement sample intensity minus blank sample intensity) and chlorpyrifos concentration to obtain Lg. ΔLIBS intensity was linearly correlated with chlorpyrifos concentration in the range of 10～10⁶ ng·mL^-1, Y=6.14X+31.85, R²=0.969, and the detection limit of chlorpyrifos was 0.39 ng·mL^-1. The results showed that the immunochromatographic strip combined with LIBS could effectively expand the detection range of chlorpyrifos. At the same time, the combination of immunochromatographic test strips and LIBS for the detection of other substances is also worthy of further research.
		2021 Vol. 41 (07): 2275-2278 [Abstract] ( 157 ) RICH HTML PDF (1707 KB) ( 38 )

	2279	Determination of Thallium and Its Compounds in Workplace Air by Ultrasonic Extraction-Inductively Coupled Plasma Mass Spectrometry Using No Gas Mode
		ZHANG Fei¹，HUA Xia²，YOU Fan¹，WANG Bin³，MAO Li^3*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2279-05
		Thallium (Tl), an extremely toxic metal element, was mainly used to manufacture semiconductors, electronic equipment, pesticide, and rodenticide. The determination of thallium and its compounds in workplace air was of great significance to ensure the health of the occupational people because they posed a potential threat to their physical health. Although trace amounts of thallium were more toxic than other toxic metals in the workplace air, little research was done. At present, atomic absorption spectrometry (AAS) was the main method for thallium assay in workplace air, but this method had some shortcomings. Therefore, we proposed a new method for the determination of thallium and its compounds in workplace air by ultrasonic extraction-inductively coupled plasma mass spectrometry (ICP-MS) using No Gas mode. At the sampling point, the short sampling workplace air was sampled by a microporous membrane with an aperture of 0.8 μm according to GBZ 159—2004 method. The effect of ultrasonic extraction conditions on the result was investigated, and the interference and elimination of mass spectrometry were analyzed. The optimized experimental conditions were 3% HNO₃ for ultrasonic extraction of the filter membrane samples for 10 min at room temperature and No Gas mode for ICP-MS analysis. Under the optimal conditions, good linearity was obtained in the range of 0.087 to 80 ng·mL^-1, with linear calibration curves of Y=0.009 2X-0.001 8 (R=0.999 9). The detection limit (LOD) was 0.026 ng·mL^-1. When the sampling volume was 75 L, the minimum detected concentration was 0.001 7 g·m^-3, and the minimum quantitative concentration was 0.005 7 g·m^-3. The precision and accuracy of the method were verified by the quality control samples (thallium quality control samples ZK147 and ZK148 in the filter membrane). The results showed no significant difference between the measured value and the reference value, and the relative standard deviation (RSDs) was 0.77% and 0.86%. Interference analysis of the new method was carried out by adding standard method (common interfering elements with 3 times the thallium content in membranes), and the recovery rate was between 97.2% and 106.7%, indicating that the method had strong anti-interference ability. Comparing with the national standard method of “Determination of toxic substances in workplace air-Part 25: Thallium and its compounds” GBZ/T 300.25—2017 solvent elution-graphite furnace atomic absorption spectrometry (GFAAS), the results of 10 samples were basically consistent. And the proposed method exhibited a simpler operation, lower detection limit, wider linear range and higher accuracy, which met the needs of accurate, rapid, trace and high-throughput determination of thallium and its compounds in samples. The new method is expected to be a new method for the determination of thallium and its compounds in workplace air and can provide reference and basis for health monitoring of specific occupational groups more effectively.
		2021 Vol. 41 (07): 2279-2283 [Abstract] ( 199 ) RICH HTML PDF (1261 KB) ( 439 )

	2284	Study on the Conditions of Hydrothermal Synthesis of Chinese Purple BaCuSi₂O₆ and the Analysis of Its Products
		SUN Feng^{1, 2}, YAN Qing-qing¹, WANG Lu¹, SUN Zhen-fei¹
		DOI: 10.3964/j.issn.1000-0593(2021)07-2284-04
		Chinese purple BaCuSi₂O₆ is a kind of the ancient Chinese artificial pigment named barium copper silicate, representing a unique civilization achievement and a high level of science and technology in ancient China. Hydrothermal synthesis is a relatively new method in recent years to dissolve substances insoluble or sparingly insoluble under atmospheric conditions by means of high temperature and pressure water vapor, recrystallize them for inorganic synthesis and material treatment. Based on previous studies, this paper adopted the hydrothermal synthesis method, selected BaCl₂·2H₂O, CuO, Na₂SiO₃·9H₂O as raw materials, and weighed them according to the stoichiometric ratio of the target products, the influence factors such as the pH environment of the solution, the synthesis temperature and the holding time of the solution were adjusted to prepare high-purity Chinese purple. XRD characterized the phase and purity of the product. The experimental results showed that Chinese purple with higher purity could be produced at 10 ≤ pH ≤ 12, and the purity of Chinese purple at 160 ℃ was higher than that at 180 ℃, and the purity of Chinese purple increased with the extension of the hydrothermal time. It is concluded that the optimal preparation condition for the hydrothermal preparation of Chinese purple is that pH 12, the temperature reaches 160 ℃, and the holding time lasts for 48 h. In addition, the discovery of intermediates BaSi₂O₅ and Ba₄Si₆O₁₆ demonstrates that Ba and Si first combined in different forms through O during the growth of barium copper silicate crystals, and Cu finally participated in the construction of barium copper silicate crystals. To sum up, this study provides a new method for the synthesis of pure Chinese purple, which can be used to protect and restore cultural relics. It can also provide a basis and clue for the synthesis mechanism of Chinese purple, which has a considerable role in promoting the research on the history of ancient Chinese science and technology.
		2021 Vol. 41 (07): 2284-2287 [Abstract] ( 147 ) RICH HTML PDF (2221 KB) ( 41 )

	2288	Classification of Coal Mine Water Sources by Improved BP Neural Network Algorithm
		YAN Peng-cheng^{1, 2}, SHANG Song-hang^2*, ZHANG Chao-yin², ZHANG Xiao-fei²
		DOI: 10.3964/j.issn.1000-0593(2021)07-2288-06
		Coal mine safety is very important to the healthy and sustainable development of the coal industry, and the coal mine flood is a major hidden danger of coal mine accidents. Therefore, coal mine water source data processing is of great significance to prevent mine water inrush accidents. In this experiment, the laser-induced fluorescence technology was used to obtain the data information of 7 water sources. The laser power was set at 100 mW, 405 nm laser was emitted to the measured water, and 210 groups of fluorescence spectrum data of experimental water samples were obtained. n order to eliminate the influence of fluorescence background, detector noise and power fluctuation, SG smoothing and multiplicative scatter correction (MSC) preprocessing is used to reduce the noise and improve the spectral specificity of the data. Due to a large amount of initial data operation, data compression, redundancy elimination and data noise elimination, principal components analysis (PCA) is used to analyze the seven water samples Row modeling and dimensionality reduction are used to obtain small data and keep the original data characteristics. In order to identify the water inrush type of coal mine water source, particle swarm optimization (PSO) is used to optimize BP neural network for dimension reduced data. PSO algorithm updates the position of individual extremum and population extremum by comparing the fitness value of new particle with that of individual extremum and population extremum, PSO algorithm updates the position of individual extremum and population extremum by comparing the fitness value of new particle with that of individual extremum and population extremum, and endows the optimal initial weight and threshold value to BP neural network, so as to predict and analyze the types of water samples to be measured. The common PSO optimized BP neural network is prone to premature convergence, so mutation factor is introduced into the improved PSO algorithm to improve the possibility of finding a better solution. Experimental results show that the SG algorithm performs well among SG, MSC, and original preprocessing methods and improves the correlation of models. On the premise of SG pretreatment, the determination coefficient R² of BP is 0.984 5, the mean relative error MRE is 7.39%, and the root mean square error is 7.25%; the determination coefficient R² of PSO-BP is 0.999 8, the mean relative error MRE is 0.17%, the root mean square error is 0.08%; the determination coefficient R² of IPSO-BP is 0.999 9, the MRE and RMSE are 0.01%. The results show that the spectral data preprocessed by SG is more accurate than that by MSC, and the improved particle swarm optimization algorithm is more suitable for mine water source classification in this experiment.
		2021 Vol. 41 (07): 2288-2293 [Abstract] ( 216 ) RICH HTML PDF (4581 KB) ( 56 )

	2294	Spectrum Signal Extraction Algorithm and Application Based on Saliency and Statistics
		WU Jiang-bo, JIA Yun-wei^*, YAO Cheng-bin, HAO Chen-xiang, WANG Kun
		DOI: 10.3964/j.issn.1000-0593(2021)07-2294-07
		Signal extraction will be affected by noise and baseline distortion in most kinds of the spectrum. If the influence of noise and baseline distortion is not considered in spectrum signal extraction, the accuracy of signal extraction will be seriously decreased. Therefore, it is necessary to eliminate the influence of noise and baseline distortion before signal extraction. However, most signal extraction algorithms’ procedure is to extract the whole baseline first and then extract the signal, which makes it difficult to guarantee the extraction accuracy of the baseline. A spectrum signal detection and extraction algorithm (SSD algorithm) based on saliency and statistical characteristics was proposed because the presence of signals always causes the statistical characteristics of the signal region to be different from the background. Firstly, the signal’s saliency at different scales is calculated, and the detected significant signal points are taken as candidate signal points. Secondly, the pseudo-signal points in the candidate signal points are removed based on the signal characteristic that the signal should satisfy. Finally, the quadratic polynomial is used to fit the candidate signal region’s baseline to remove the false signal areas and realize the final signal extraction. Many experiments were run to verify the performance of the SSD algorithm. Firstly, gaussian signal and rectangular signal were simulated under different baseline types and signal-to-noise ratio (SNR). Then different algorithms were compared, such as the AirPLS algorithm, Wavelet algorithm and DoG algorithm, on the extraction results. Simulation experiment results show that: SSD algorithm was better than compared algorithms.The signal extraction results of the SSD algorithm were not affected by the signal type and baseline distortion type and were not affected by SNR when SNR is greater than 40. Its accuracy, stability, and dispersion were good, while the other algorithms are only applicable to a certain type of baseline distortion. From the overall extraction results, the mean value of the absolute error of the SSD algorithm is only 8.71% of the AirPLS algorithm, 3.52% of the Wavelet algorithm, and 2.01% of the DoG algorithm; the root means square of the absolute error is also only 13.08% of the AirPLS algorithm, 5.45% of Wavelet algorithm, 3.11% of DoG algorithm. Therefore, the SSD algorithm proposed in this paper has good comprehensive performance in extracting signals and can accurately extract signals under different SNR and baseline distortion.
		2021 Vol. 41 (07): 2294-2300 [Abstract] ( 187 ) RICH HTML PDF (5952 KB) ( 48 )

	2301	Quantitative Determination of Water-Soluble P in Biochar Based on NELIBS Technology and EN-SVR Model
		GUO Mei^{1, 2}, ZHANG Ruo-yu^{2, 3}, ZHU Rong-guang^{2, 3}, DUAN Hong-wei^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2021)07-2301-06
		Biochar can provide many available phosphorus (P) that can be absorbed and utilized by plants. In this paper, laser-induced breakdown spectroscopy (LIBS) was used to detect water-soluble P in straw based biochar quantitatively. To reduce the serious “coffee ring effect” on the substrate’s surface after droplet drying, hydrophobic polyethylene plate was selected as the liquid-solid conversion substrate. To solve the problem of the low sensitivity of LIBS signal of water-soluble P element in biochar, the signal enhancement performance of three kinds of Au nanoparticles (AuNPs) on four analytical lines of P element was studied and discussed. The results show that Au nanoparticles with large particle size (73 and 105 nm) are more prone to the aggregation effect, and the spectral signal-to-noise ratio is large. Furthermore, the P element’s univariate calibration curve models enhanced by three kinds of particle size Au nanoparticles were compared and analyzed. The results show that the univariate calibration curve models with 45 nm Au nanoparticles signal enhancement are the best. Finally, the four enhanced spectral broadening bands enhanced by the Au nanoparticles were used to develop the ElasticNet-support vector regression (EN-SVR) model. The average error of the prediction set (ARP) and the relative standard deviation of the prediction set (RSDP) of the optimal model were 5.40% and 11.09%, respectively. The results show that nanoparticle enhanced laser induced breakdown spectroscopy (NELIBS) combined with the EN-SVR model can be used for the accurate quantitative determination of water-soluble P in biochar.
		2021 Vol. 41 (07): 2301-2306 [Abstract] ( 181 ) RICH HTML PDF (3280 KB) ( 45 )

	2307	Spectral and Laser-Induced Damage Characteristics of Atomic Layer Deposited SiO₂ Films on Fused Silica Glass
		CHENG Hai-peng^1,2, GENG Feng², LIU Min-cai², ZHANG Qing-hua², LI Ya-guo^1*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2307-07
		In this paper, single-layer SiO₂ films of homogeneous material were deposited on the surface of fused silica glasses by atomic layer deposition (ALD) technology. The physical and chemical properties of the optical films and the laser induced damage performance under laser irradiation were deeply researched. Bis-tert-butylaminosilane (BTBAS) and ozone (O₃) were chosen as reaction precursors in the experiment, and ALD prepared a series of film samples under different temperature conditions. Firstly, a study on the characteristics of ALD and the uniformity of the films was carried out. It was found that the film growth thickness and the number of deposition cycles conformed to the linear growth, which verified the atomic layer-by-layer growth characteristics of the ALD. The uniformity of the deposited film on the surface is fine, while the error does not exceed 2%. Then, for the SiO₂ films deposited at different temperatures, the roughness and various spectral characteristics have been tested. The comparison results show that the surface roughness of the sample is slightly decreased after coating. The ALD film samples have excellent transmittance in the range of 200 to 1 000 nm, both exceeding 90% and gradually approaching 93.3%, and their transmission spectrum is not significantly different from the spectrum measured on a bare fused silica substrate. The difference between fluorescence spectrum and Fourier transform infrared (FTIR) spectrum before and after coating confirms the existence of point defects (non-bridging oxygen, oxygen vacancies, hydroxyl, etc.) in the SiO₂ films deposited by ALD, which will affect the film damage resistance performance. Finally, ultraviolet laser-induced damage tests were performed on the substrate and film samples. Results of damage performance show that the laser induced damage threshold of the thin film deposited on the surface is reduced, and the zero-probability damage threshold is decreased from 31.8 J·cm^-2 to about 20 J·cm^-2, which is consistent with the characterization of spectral defects. The point defect in the film will absorb ultraviolet laser energy, causing the local temperature to rise, and then the threshold of laser damage resistance is reduced while the phenomenon of laser-induced damage occurs. Within the selected deposition temperature range, SiO₂ films deposited at higher temperature seem to have better damage performance. The deposition temperature conditions can be controlled to make the samples’ damage performance closer to the substrate itself. It is expected that the optimization of other reaction parameters will further improve the film damage performance.
		2021 Vol. 41 (07): 2307-2313 [Abstract] ( 165 ) RICH HTML PDF (4296 KB) ( 47 )

	2314	Influence of Assembly Conditions on Spectral Properties of SiO₂ Structural Color Coatings Prepared by Rapid Coating Method
		LI Xiu, PAN Jie, HUANG Min^*, XI Yong-hui, LIU Zi-han
		DOI: 10.3964/j.issn.1000-0593(2021)07-2314-07
		In order to realize the rapid preparation of structural colors on the paper surface, and study the effect of different assembly conditions on the color rendering effect of SiO₂ structural color coatings, a rapid coating method to prepare a large area of structural color coating on paper substrates was reported, which has the characteristics of changing color with different a angle. In this article, the effects of SiO₂ microsphere particle size, dispersion concentration and coating times on the optical properties of structural color coatings were discussed. By optimizing the self-assembly conditions and analyzing the type of periodic structure construction, the self-assembly process of the SiO₂ microspheres constructed by the rapid coating method on the laser marking paper and the mechanism of the structural color rendering was clarified. The digital cameras and 3D laser confocal topography measuring microscopes were used to measure the color appearance and microstructure of the samples. The X-Rite MA68Ⅱ multi-angle spectrophotometer and optical fiber spectrometer were used to measure the reflection spectrum, and then the optical properties of the prepared structural color coatings were analyzed with CIEL^A^B^* chromaticity values. The results showed that the particle size of the SiO₂ microspheres has a significant effect on the color tone of the samples. As the diameter of the microsphere increases, the center wavelength of the reflection spectrum red shifted. The coating films were angle dependent. When the incident angle was 45°, as the angle between the detection direction and the mirror reflection direction increased, the center wavelength red shifted. The concentration of the microsphere solution can adjust the half-height width and peak reflectance of the structural color coating, which in turn affects the brightness and saturation of the sample, but has no obvious effect on the position of the photonic band gap. When the concentration of microspheres was 4%, the surface of the sample showed the black color of the substrate. When the concentration of microspheres was 8%, the different structural colors of blue, green and yellow with lower chroma were adjusted by changing the diameters of SiO₂ from 200, 220 to 250 nm. When the concentration of microspheres was increased to 10%, the chroma of the structural color coating on the paper surface was improved, but the hue was unchanged. As the number of coatings increases, the half-height width of the reflection spectrum curve narrowed, and the reflection peak blue shifted. When the coating frequency reached 3 times, the peak wavelength of the reflectance was closest to the theoretical value calculated according to Bragg’s law. However, the increase in the number of coatings caused white unevenness on the surface of the structural color coating.
		2021 Vol. 41 (07): 2314-2320 [Abstract] ( 161 ) RICH HTML PDF (3673 KB) ( 43 )

	2321	Study on Dye-Sensitized Solar Cell by Screen Printing
		ZHANG Ao¹, ZHANG Chun-mei¹, WU Wei-xia¹, WANG Duan-yang², YAO Song-ye¹, MENG Tao^1*
		DOI: 10.3964/j.issn.1000-0593(2021)07-2321-04
		Presently, the study of dye-sensitized solar cells (DSSCs) has become one of the focus of solar cells research. Owing to low cost, easy fabrication process, and high power conversion efficiency (PCE), the DSSCs with the nano-crystalline porous TiO₂ film as photo-anode by the preparation of screen printing are widely concerned. It is very important to optimize the screen printing process. This method is very effective to study the effect of the screen printing process on the photovoltaic performance of DSSCs. The TiO₂ were prepared by sol-gel method, and the porous structure of TiO₂ thin film observed by SEM has a high specific surface area, which is conducive to the adsorption of dye molecules and improving the solar absorption rate. The screen printing TiO₂ film after high-temperature sintering shows a narrow diffraction peak of anatase structure, which implies that TiO₂ particles have been fully crystallized. The increase of mesh number from 100 to 300 leads to the decrease of diameter and the increase of TiO₂ film density, making the diffraction peak of XRD enhanced. However, the increases from 300 to 400 mesh decrease TiO₂ colloids passing through the mesh due to the small mesh size, which makes the decline of XRD diffraction peak. The photovoltaic performance of DSSCs was studied by using single-layer TiO₂ photoanode printed on different mesh number. It was found that the photovoltaic performances of DSSCs with 200 and 300 mesh were better than that of 400 mesh. The multilayer TiO₂ anode were printed using 100, 200, 300, and 400 mesh respectively, and the dye-sensitized solar cells were assembled. The results show that PCE for dye-sensitized solar cells has been significantly improved by the use of different mesh combination printing. It was the highest efficiency 6.9% under 300 mesh+200 mesh+100 mesh printing. The screen printing electrode preparation method without any chemical treatment，the printed film of high-mesh bottom layer is uniform and firm, the cell preparation has simple process, good repeatability, and the dye-sensitized solar cells have high PCE.
		2021 Vol. 41 (07): 2321-2324 [Abstract] ( 181 ) RICH HTML PDF (1489 KB) ( 54 )

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