SPECTROSCOPY AND SPECTRAL ANALYSIS

News

Forgot your password?

Enter your e-mail address to receive an e-mail with your account information.

Journal Online

Archive

Links

	CAST

	CNCOS

	CNKI

	WanfangDATA

	CHEMSOC

	sinospectroscopy

	CPS

Quick Search

Adv Search

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

	1	Research on Spatial Resolution Adjustable USEDCARS Technology
		ZHANG Zhen-rong, LI Guo-hua, HU Zhi-yun, WANG Sheng, YE Jing-feng, TAO Bo, SHAO Jun, FANG Bo-lang
		DOI: 10.3964/j.issn.1000-0593(2021)01-0001-04
		Coherent anti-Stokes Raman scattering (CARS) is a very important combustion diagnostic technique, for its high accuracy and strong anti-interference ability in combustion field parameter measurement. But when there is a temperature gradient in the detected spatial region, spectra superposition of low and high temperature CARS signal result in spatial average effects which will cause CARS spectral distortion. As a result, it is difficult to analyze the CARS spectrum, and the combustion field parameters cannot be retrieved from the CARS spectrum. In Unstable-resonator Spatially Enhanced Detection CARS(USEDCARS), the lower spatial resolution has limited the application of CARS in flow with a high temperature gradient. Various factors affecting the spatial resolution of the technology were analyzed. The circular beam shaping of the pump laser was carried out by using the axicon group, and ring-shaped pump beam with different diameters was obtained by adjusting the distance between the axicons. Then a USEDCARS diagnostic system with the adjustable spatial resolution was established, and the spatial resolution of the USEDCARS was measured, the distribution of CARS signal strength with the spatial position was obtained. Taking spatial region over 95% of the total CARS intensity to represent the longitudinal spatial resolution, the spatial resolution of the system can be adjusted continuously from 1.7 to 6.5 mm. At the highest spatial resolution state, the spatial resolution achieves the level of BOXCARS technology. The temperature of an alcohol/air premixed flame was measured using the established USEDCARS system. Single-pulse CARS spectra with different longitudinal resolutions were obtained. The high-quality CAR spectrum was achieved when the measured spatial resolution was 1.7 mm, and the flame temperature was indicated by the spectral fitting. When the spatial measurement resolution was 4.9 or 6.5 mm, the CARS signal intensity was very strong, but the CARS spectra were distorted. The results show, the spatial resolution has a large impact on the intensity and spatial average effects of CARS signals, the spatial averaging effect can be reduced by improving the spatial resolution, the continuously adjustable spatial resolution enables the USEDCARS system to better adapt to various combustion flow fields.
		2021 Vol. 41 (01): 1-4 [Abstract] ( 237 ) RICH HTML PDF (2011 KB) ( 173 )

	5	Temperature in the Near Space From the Emission Spectra of Oxygen A Band
		YANG Xiao-jun^{1, 2, 3, 4}，WANG Hou-mao^{1, 2, 3*}, LI Ye-fei⁵, WANG Yong-mei^{1, 2, 3, 4}，HU Xiu-qing⁶
		DOI: 10.3964/j.issn.1000-0593(2021)01-0005-06
		Based on the simulation data of the edge radiation intensity of the oxygen A-band, the atmospheric temperature inversion of the adjacent space height (60~110 km) is studied and analyzed. Firstly, based on the forward model, the limb radiation intensity under noiseless and noise-added conditions are simulated respectively to derive the temperature profile, and the inversion results of all spectral lines were analyzed. The inversion results were analyzed, and the variation law of the weight function of each line in the oxygen A-band was determined as the basis for the judgment of temperature observation. Temperature influences the radiation intensity through line strength and self-absorption, and the influence of temperature on them is opposite. The weight function is used to express the influence of temperature on radiation intensity, and the difference of inversion results can be obtained from the weight function. In an ideal situation, when the effect of temperature on self-absorption is less than the influence on line strength, the weight function does not flip, the temperature inversion accuracy is higher, and the average inversion deviation is 4.1 K; when the effect of temperature on self-absorption is greater than the influence on line strength (mainly located at the height of 60~80 km), the weight function reverses because self-absorption reduces the sensitivity of radiation intensity to temperature, and the average inversion deviation reaches 34.9 K. In addition, the strong line has stronger anti-interference ability than the weak line in the presence of noise, and the inversion precision is higher, and it is more suitable for temperature inversion. In the practical observations, the line strength is also another line selection. Based on the weak line, this paper further analyzes the effect of radiation intensity on the inversion accuracy by improving the signal-to-noise ratio. The results show that the stronger the radiation, the larger the signal-to-noise ratio, the higher the accuracy of temperature inversion, and vice versa. When the line strength of the airglow spectrum reaches 10^-26, it can be used for temperature inversion above 80 km and obtain better inversion results, with the inversion accuracy <5 K.
		2021 Vol. 41 (01): 5-10 [Abstract] ( 236 ) RICH HTML PDF (3262 KB) ( 141 )

	11	Study of the Urban NO₂ Distribution and Emission Assessment Based on Mobile MAX-DOAS Observations
		LIU Hao-ran¹, HU Qi-hou^2*, TAN Wei², SU Wen-jing³, CHEN Yu-jia², ZHU Yi-zhi², LIU Jian-guo²
		DOI: 10.3964/j.issn.1000-0593(2021)01-0011-09
		Nitrogen dioxide (NO₂) plays a vital role in atmospheric photochemistry. It participates in the catalytic formation of tropospheric ozone (O₃) and also contributes to the formation of secondary aerosols. As an important emission product in transportation and industrial processes, NO₂ is usually regarded as a proper indicator of the intensity of the anthropogenic emission. Therefore, research on urban NO₂ distribution and emissions is very important for urban air pollution control. During January and February 2018, we conducted 4 times mobile measurements based on Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) in the outer route of urban Hengshui. Furthermore, the spatial distribution of the tropospheric NO₂ vertical columndensity (VCD) ranges from 0.89×10¹⁵~56.33×10¹⁵ molecule·cm^-2, besides the mean value of each measurement is from 22.42×10¹⁵ to 30.20×10¹⁵ molecule·cm^-2. It can be found the NO₂ pollution sources in Hengshui are mainly near the external urban factory cluster in the southeast direction and the overpass section in the east of the outer route. The western and northern areas in Hengshui are relatively clean. If the wind comes from this area, it will play a certain cleaning effect on the pollution source area, which can reduce the NO₂ concentration in the source area by more than 20%. During the mobile measurements, we also conducted stationary MAX-DOAS observations. Combined with both measurement results, we evaluated the relative contribution of pollution sources in eastern Hengshui. Combined with both measurement results, we evaluated the relative contribution of NO₂ pollution sources area (eastern part of Hengshui), which is 30.1%~61.9% higher than the clean area (western part) and contributes more than 7.89×10¹⁵~13.32×10¹⁵ molecule·cm^-2. With the supplementary of meteorological information simulated by the WRF model, we can calculate the NO₂ emission flux in urban Hengshui, which is 0.86×10²⁴ molecules·s^-1. This result is relatively lower than other cities in previous studies, which might be caused by two factors: one is the pollution source of Hengshui is not concentrated in the urban area; the other one is due to the research area is only 50 km² in this study, which is much smaller than the urban area of other studies. For the measured total flux of Hengshui, we found 96.16% is from transportation, and 3.84% is caused by city emission, which indicates that the main pollution source of NO₂ in Hengshui City is not located in the inner city. Through the mean value of the OMI tropospheric NO₂ with the backward trajectories of air mass during this campaign, we found that Hengshui was also affected by pollution transmission from northern regions (such as Baoding, Langfang) and northwest regions (such as Shijiazhuang). In general, the mobile MAX-DOAS has a good application prospect for urban pollution control, such as for finding the pollution source location, estimating the contribution, and calculating the emission flux of urban areas.
		2021 Vol. 41 (01): 11-19 [Abstract] ( 235 ) RICH HTML PDF (5459 KB) ( 100 )

	20	Temporal and Spatial Characteristics of Nitrous Oxide Concentration in China
		MA Peng-fei^{1, 2}, XIONG Xiao-zhen³, CHEN Liang-fu⁴, TAO Ming-hui⁵, CHEN Hui^{1, 2}, ZHANG Yu-huan^{1, 2}, ZHANG Li-juan^{1, 2}, LI Qing^{1, 2}, ZHOU Chun-yan^{1, 2}, CHEN Cui-hong^{1, 2}, ZHANG Lian-hua^{1, 2}, WENG Guo-qing^{1, 2}, WANG Zhong-ting^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2021)01-0020-05
		Retrieves nitrous oxide profiles from the thermal infrared satellite data, the results will be affected by the impact of atmospheric parameters such as atmospheric temperature and humidity profiles, as well as surface parameters such as surface temperature and surface emissivity and the like. N₂O own changes to a lesser extent, so when using the optimal estimation inversion, to get a priori profile and to select the inversion channel are the keys. Therefore, the inversion of nitrous oxide is rarely seen in China. Study and analyze the absorption characteristics of nitrous oxide and other interfering gases in the thermal infrared region for channel selection, based on this, to retrieve and analyze the temporal and spatial variation characteristics of nitrous oxide concentrationis of great practical significance for the study of global climate change in China. In this paper, under the inversion framework of the optimal estimation method, an optimal sensitive profile channel selection method is used to retrieve the nitrous oxide concentration using the AIRS data. Compared with the Canadian station in the TCCON observation network, the results show that the retrieval results are in good agreement with the ground observations, and the correlation coefficient r is 0.73. This algorithm can be extended to the thermal infrared hyperspectral data such as IASI and CrIS, and increase the observation data of nitrous oxide to more than 20 years. This long-time series product is an effective supplement to the current ground observation. The annual and monthly mean changes of nitrous oxide and its spatial distribution characteristics in China are analyzed for the first time. The results show that the change of thenitrous oxide concentration is obvious with time and latitude, among which the annual change of the nitrous oxide concentration is small, and the monthly and seasonal meanvalue changes greatly. From January of each year, the concentration of nitrous oxide increased month by month, reached the maximum in July and August, and then began to decline month by month. The seasonal variation was the highest in summer (June-gust), spring and autumn are the second, while winter is the lowest. According to the annual average, the concentration of nitrous oxide in China changes obviously with latitude, and the higher the latitude is, the lower the concentration is. High-value regions are mainly concentrated in South China, especially in summer. The low-value regions are north China and southwest China. In the west of China, the concentration of nitrous oxide is higher in summer. In addition to local emissions, this seasonal or monthly mean change is also affected by air convection and transport.
		2021 Vol. 41 (01): 20-24 [Abstract] ( 275 ) RICH HTML PDF (4145 KB) ( 155 )

	25	*Release of Phosphorus to Promote Biomineralization of Uranium by Saccharomyces Cerevisiae* Based on Spectroscopy Analysis**
		ZHANG Wei^{1, 2}, DONG Fa-qin^3*, HE Xiao-chun³, SONG Huai-qing⁴, QIN Yi-lin⁵, XIONG Xin⁵, TANG Zi-han⁵
		DOI: 10.3964/j.issn.1000-0593(2021)01-0025-07
		Phosphorus, as one of the necessary chemical elements for organisms, plays an important role in the process of uranium mineralization by microorganisms. The adsorption characteristics of U(Ⅵ) on Saccharomyces cerevisiae were investigated in this study. The relationship among the pH value of the adsorption solution, the concentration of U(Ⅵ) and phosphorus released in the process of biosorption were investigated by ICP-OES and ICP-MS. The mechanism of biomineralization of uranium by S. cerevisiae is speculated combined with spectroscopy and mesoscopic analysis. The results show that S. cerevisiae could effectively remove U(Ⅵ) from wastewater, and the phosphorus released by cells in the biosorption effectively promotes the biomineralization of uranium. The removal efficiency of U(Ⅵ) by S. cerevisiae is best at the initial solution pH is 3.0. The H⁺ in solution and phosphorus released by cells were involved in the removal of U(Ⅵ) by S. cerevisiae. The adsorption process was independent of temperature. Combined with FTIR, SEM, XPS and XRD analysis, it is speculated that the mechanism of biomineralization of uranium by S. cerevisiae as follows: firstly, U(Ⅵ) was rapidly adsorbed on the cell surface of S. cerevisiae by electrostatic attraction, then it was complex with phosphorus groups, hydroxyl groups, amide and other functional groups on the cell surface. Hydrogen ions in the solution and inorganic phosphorus released by S. cerevisiae could be combined with uranium as precipitation ligands, and continue to mineralize to form crystal on the extracellular surface of the cell. During this process, a part of hexavalent uranium was reduced to tetravalent uranium and then settled. In conclusion, phosphorus is the main functional element that caused the biomineralization of uranium by S. cerevisiae. It is of great significance to study the biomineralization mechanism of uranium in which phosphorus is involved in the bioremediation of uranium pollution and to understand the activation and immobilization ofuranium in nature.
		2021 Vol. 41 (01): 25-31 [Abstract] ( 209 ) RICH HTML PDF (5126 KB) ( 81 )

	32	Determination of Surface Plasmon Resonance Wavelength by Combination of Radiation-Based Spectral Correction With Self-Adaptive Fitting
		YIN Tao^{1, 2}, LIU Zi-wei^{1, 2}, CAI Chen¹, QI Zhi-mei^1,2*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0032-07
		Surface Plasmon Resonance (SPR) sensing technology has been widely used in biomedical diagnosis, chemical detection, food safety, environmental monitoring and other fields due to its high sensitivity, in situ and label-free and real-time detection capability. For a wavelength interrogated SPR sensor, its resonance wavelength is measured for obtaining the analyte concentration. Because the optical spectrum analyzer (OSA) used in the SPR sensor has different photoelectric responses at different wavelengths, the measured resonance wavelength will deviate from the actual value in some extent, resulting in the measurement error. A radiation-based relative reflectance correction method is proposed in this work, which consists of the following three steps: (1) establishing the instrument response function (IRF) of the OSA used; (2) using the IRF to convert the reflected intensity spectrum of the SPR sensor directly measured with the OSA into the reflected power spectrum; (3) using the reflected power spectrum to determine the reflectance spectrum of the SPR sensor. The obtained reflectance spectrum is not affected by the SPR sensor system. Compared with the conventional reflectance correction method that needs to measure the resonance and non-resonance spectra of the SPR sensor, our method makes the resonance peak in the reflectance spectrum have a narrower FWHM and a more symmetrical shape and thus allows for more accurately determining the resonance wavelength. Based on the goal of minimizing the fitting error, an order-adaptive polynomial fitting algorithm is performed for the resonance region to determine the resonance wavelength. Experimentally, the SPR spectra at different incident angles are measured, and the FWHMs of the reflectance spectrum obtained by our method are maintained at (100±10) nm, indicating a universal shape advantage of this correction method. Then 4 000 spectra are continuously collected, and their resonance wavelengths are calculated by the method, the results give a relative standard deviation of 0.007 8% and a processing speed of 12 ms per spectrum. This indicates that the method has good robustness against noise fluctuations and real-time processing of spectral data. Lastly, the SPR resonance spectra of NaCl solutions at different concentrations are measured, it shows a linear correlation coefficient of 0.998 5 between the resonance wavelength and refractive index of the solutions, and a Figure of Merit (FOM) two times higher than that of the conventional method. This indicates that the method has good reliability and improves the sensing performance from the level of the spectral processing algorithm. While the conventional method for improving FOM is from preparation technology, ours is easier to handle, and the effect is prominent. All the experimental results show that our SPR resonance wavelength determining the method that combines the radiation-based relative reflectance correction and the order-adaptive fitting algorithm has the characteristics of good reliability, fast calculation, high resolution, anti-noise and high FOM which can effectively improve the data processing and sensing performance of SPR sensor.
		2021 Vol. 41 (01): 32-38 [Abstract] ( 214 ) RICH HTML PDF (4113 KB) ( 62 )

	39	Raman, IR and DFT Studies of Moxifloxacin
		XU Di，XIN Min-si, LIU Chun-yu, CAI Hong-xing, FAN Ya^*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0039-06
		Moxifloxacin is widely used as a fourth-generation fluoroquinolone antibiotic, and it has drug residues in humans and livestock, which endangers everyone’s life and health. In order to avoid secondary intake, it is particularly important to be able to quickly detect the presence of Moxifloxacin residues in meat products. To this end, this paper uses vibrational spectroscopy combined with density functional theory to provide basic data for the vibrational spectroscopy detection and identification of Moxifloxacin and provides a reference for its application in the field of drug detection. The specific research contents and results are as follows: The first step is to construct the molecular structure of Moxifloxacin based on Density functional theory (DFT) and optimize the structure by using B3LYP/6-311+G(d) basis set. Calculate its theoretical Raman and infrared spectra. Theoretical calculations show that the Moxifloxacin molecule has obvious Raman and infrared activity in the range of 3 700~2 800 and 1 800~400 cm^-1. The former is mainly the vibration of the upper group of the functional group, and the latter is the fingerprint area. The vibration of the upper button. Due to the superiority of the complementary information of the two kinds of spectral information, firstly, by comparing the theoretical Raman spectrum and the infrared spectrum, the vibration peak frequency of two or only one vibration activity is marked, and the Gaussian view is combined with each of the Moxifloxacin molecules. The vibration frequency corresponding to each key is fully attributed, and the spatial structure parameters such as the bond length, bond angle and dihedral angle of the Moxifloxacin molecule are given. In the second step, the natural Raman spectroscopy (NRS) and infrared spectroscopy (IR) of Moxifloxacin (MXF) were measured experimentally. The theoretical calculation result error is corrected by the frequency correction factor of 0.973 and compared with the experimental data. In the fingerprint area, the Raman and infrared characteristic peak wave number matching degree are good. The peak wave number difference is mostly in the range of 0~10 cm^-1. The calculation results are basically consistent with the experimental data. The results provide basic data for the vibrational spectrum detection and identification of Moxifloxacin and provide a reference for its application in the field of drug detection.
		2021 Vol. 41 (01): 39-44 [Abstract] ( 238 ) RICH HTML PDF (1937 KB) ( 120 )

	45	Spectroscopic Characterization of Carbon Structure in High Sulfur Fat Coal
		GE Tao^{1, 2}, LI Yang¹, Wang Meng², CHEN Ping³, MIN Fan-fei¹, ZHANG Ming-xu¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0045-07
		Coal structure is an important research content of coal chemistry. High-quality fat coal is a scarce coking coal type in China. Carbon is the basic skeleton of the coal structure. Carbon is the basic skeleton of coal structure. It is the main element that forms organic matter in coal and forms coke. Study the carbon structure in high sulfur fat coal is of great significance for understanding the structure and properties of fertile coal, improving the utilization efficiency of low-quality coking coal. Collected and prepared the coal samples from Shandong Dongtan (DT), Shanxi Shuihu (SY), Huozhou (HZ), and Gaoyang (GY) mining areas. The carbon structure in coal was characterized and analyzed by XRD, FTIR and XPS. Combined with coal quality analysis results to calculate the carbon structure parameters of different coal samples. The research results show that the fa-XRD of SY, HZ, GY, DT increases sequentially. Lcand La decrease in order. The aromatic carbon structure of Shanxi coalsis stronger than Dongtan coal in arrangement regularity and aromatic ring condensation. The aromatic hydrocarbon structures in DT and GY coal exist mainly in the form of benzene ring penta-substitution, benzene ring tetra-substitution and benzene ring tri-substitution. The aromatic structure in SY and HZ coal is dominated by benzene ring disubstitution and benzene ring tetra substitution. DT and GY coals contain more branches and higher degrees of aromatic ring condensation. The fat structure of the four kinds of fat coal is dominated by methylene. The proportions of methylene groups in DT, SY, HZ, GY coal to the fat structure were 46.27%, 48.89%, 44.21%, 41.85% respectively. Coal contains more alkyl side chains. GY and DT coal have slightly higher methyl content than methine, while SY and HZ coal have the opposite. It is mainly related to the degree of fracture of the long aliphatic structure of different samples during hydrocarbon generation. The aromatic carbon ratios of SY, HZ, GY, DT coal are 0.83, 0.81, 0.74, 0.68 respectively and aromatic hydrogen ratios are 0.51, 0.43, 0.34, 0.29 respectively. The degree of aromatization in coal decreases in turn. The aromatic ring condensation degree increases in order. The oxidation degree of DT and HZ coal is high. DT coal contains C—O structure, and it is judged that there is more inactive oxygen in coal which is not easily decomposed by heat or chemical reaction.
		2021 Vol. 41 (01): 45-51 [Abstract] ( 211 ) RICH HTML PDF (3845 KB) ( 76 )

	52	Progress in Research on Rapid and Non-Destructive Detection of Seed Quality Based on Spectroscopy and Imaging Technology
		WANG Dong^1,3, WANG Kun², WU Jing-zhu^2, HAN Ping^1,3
		DOI: 10.3964/j.issn.1000-0593(2021)01-0052-08
		Seed is an important means of production in the process of agricultural production. The quality evaluation, vigor and aging detection, purity and authenticity identification, classification and traceability are common problems in seed quality detection. Seed quality mainly includes the content of moisture, protein, fatty acid, starch, and so forth, which is the important indices of seed quality classification. Moreover, seed quality is related to the safety of storage. Seed vigor is the sum of seed germination and emergence rate, seedling growth potential, plant stress resistance and production potential. High vigor seeds are of obvious growth advantage and production potential. Seed aging refers to the natural decline of seed vigor, which is manifested by seed discoloration, low germination rate, poor growth potential and crop yield reduction. The purity and authenticity of seeds will affect crop yield and agricultural product quality. Seed classification and traceability is an important method to ensure the purity and identify the authenticity of seeds, by which, crop yield and product quality will be guaranteed. For seed quality analysis, it usually need to do irreversible destructive analysis on samples according to the traditional methods, which is time-consuming with complex procedures. It is obvious that traditional methods are difficult to meet the needs of modern agriculture for seed production. Therefore, it is an urgent problem to carry out the research on non-destructive and rapid detection technology of seed quality. In recent years, with the development of chemometrics and the progress of computer technology, near-infrared spectroscopy, with the advantages of fast, non-destructive and high efficiency, has been widely applied in the non-destructive and rapid analysis of agricultural products, food, agricultural inputs, and so on. In addition, combined with spectral and imaging technology, hyperspectral imaging technology is rising in recent years. Compared with the traditional spectral technology, hyperspectral imaging technology can acquire not only the spectral information of the sample but also the spatial distribution information and image characteristics of it. In this paper, based on the near-infrared spectroscopy and hyperspectral imaging technology, the literature of seed quality non-destructive detection from the aspects of seed quality evaluation, vigor and aging detection, purity and authenticity identification, classification and traceability research were reviewed. Based on the analysis of the characteristics of different detection technologies, the problems of seed quality detection are sorted out, respectively. Furthermore, the technical characteristics of non-destructive and rapid detection of seed quality are summarized and prospected.
		2021 Vol. 41 (01): 52-59 [Abstract] ( 230 ) RICH HTML PDF (867 KB) ( 145 )

	60	A Multi-Spectral Pyrometer for Measuring Cathode Temperature Field of Vacuum Arc Plasma Discharge
		YANG Zong-ju¹, DAI Jing-min^1*, YANG Lin², WANG Zheng-tao¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0060-05
		The cathode surface temperature is an important parameter in the vacuum arc plasma discharge process, which has an important influence on the formation of vacuum arc plasma, electrode corrosion prediction, heat conduction and ion source lifetime. The cathode of the vacuum arc ion source has the characteristics of the small target and fast discharge process, and its temperature measurement requires high time resolution and spatial resolution. The lack of measurement techniques for the surface temperature of the cathode makes it difficult to verify the results obtained by theoretical analysis alone. Moreover, the measuring instrument is highly susceptible to arcing during plasma discharge. How to avoid plasma radiation during discharge is also a problem to be considered when measuring the surface temperature of the cathode by radiation. This undoubtedly brings difficulties to the testing of its temperature field. It is important to carry out the cathode surface temperature test experiment for pulsed vacuum arc plasma. In this paper, the vacuum arc plasma discharge characteristics and background radiation characteristics and the practical requirements of plasma discharge cathode temperature measurement are analyzed. A novel multi-spectral pyrometer is developed based on the high-speed CCD camera. In order to realize multi-spectral radiation temperature measurement using a monochrome CCD camera, an optical system of a pyrometer is designed, and the optical system adopts a 4-aperture spectroscopic system. For the first time, four different wavelength filters are embedded in one filter. The pyrometer designed in this paper can be used for plasma temperature measurement from 2 000 to 6 000 K. The field test was carried out at the Institute of Electronic Engineering of China Institute of Physics and Engineering. The pyrometer to be developed during the test was tracked and shot by the external trigger form. The pyrometer completely captured the plasma discharge process. The pyrometer was verified by the measured data of the vacuum arc plasma metal electrode cathode discharge. The experimental results show that the designed multi-spectral pyrometer can be used to measure the cathode temperature field information during vacuum arc plasma discharge. The measured temperature value is lower than the boiling point temperature of the discharge electrode, which is consistent with the gasification phenomenon during plasma discharge. It is stated that the pyrometer measures the temperature of the plasma discharge cathode.
		2021 Vol. 41 (01): 60-64 [Abstract] ( 202 ) RICH HTML PDF (2355 KB) ( 100 )

	65	To Make a Good Infrared Spectrum in NaCl Aqueous Solution Where Lambert-Beer’s Law is Not to be Obeyed
		YAO Meng, WANG Hai-shui^*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0065-06
		If the solution concentration c and the spectral absorbance A is not obeyed Lambert-Beer’s Law，an individual solution sample should be prepared and acquired in order to obtain its infrared spectrum. The binary linear regression analysis and hybrid spectrum were applied to NaCl aqueous solution. The following conclusions have been reached: (1) The infrared spectra of NaCl aqueous solutions with concentrations of c₁, c₂ are A_c₁, A_c₂, respectively. Then the regression equation A_hc(c₁, c₂)=âA_c₁+A_c₂ is set up, where the relationship among c₁, c₂ and c is c₁＜c＜c₂. Hybrid spectrum A_hc(c₁, c₂) can be very similar to real spectrum A_c if â and values are selected properly. (2) The smaller difference between c₂ and c₁, the higher similarity between A_hc(c₁, c₂) and A_c is. (3) The regression equation A_hc(2%, 30%)=âA_2%+A_30% can be applied to the concentration range 2%～30%. (4) The relationships between â and c, and c are c=-3.592c+1.058 9 and =3.565c-0.055 15. If we select c, we can easily obtain â and then â and are substituted into regression equation A_hc(c₁, c₂)=âA_c₁+A_c₂. All hybrid spectra are very similar to the real spectra and they can even replace the real spectra.
		2021 Vol. 41 (01): 65-70 [Abstract] ( 234 ) RICH HTML PDF (2740 KB) ( 61 )

	71	Study on the Structure/Energy-Level of Palladuim-Porphyrin Sensitizers on the Triplet-Triplet-Annihilation Upconversion Performance
		YE Chang-qing, YU Xue, CHEN Shuo-ran, LIANG Zuo-qin, ZHOU Yu-yang, WANG Xiao-mei^*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0071-09
		The photon upconversion (UC) can realize the conversion of low-frequency photons to high-frequency photons. UC based on triplet-triplet-annihilation (TTA) has the characteristics of low threshold excitability, high quantum yield, and wide spectral conversion range, which have drawn broad research interested in the world. In this paper, two kinds of porphyrin complexes (named as PdOEP and PdBrTPP, respectively) were chosen as the sensitizer doped with p-DHMPA (as the emitter) to set up the TTA-UC research models. The absorption, fluorescence and phosphorescence spectra, as well as the upconversion spectra, were measured respectively. The relationships between triplet properties and the upconversion emission were discussed. Also, the relationship between the singlet-triplet energy level difference (ΔE_ST) of the sensitizer and their intersystem cross-section is discussed. And lastly, the relationship between the triplet energy level difference (ΔE_TT) from sensitizer to the emitter and the triplet-triplet energy transfer (TTET) is discussed. The results show that PdBrTPP has longer triplet lifetime (173.13 μs) than PdOEP (109.21 μs) and larger molar absorption coefficient (10.8 cm^-1·mmol·L^-1) than PdOEP (3.0 cm^-1·mmol·L^-1). Meanwhile, the quenching constant (k_q) of PdOEP/p-DHMPA (1.64×10^-3 μmol·L^-1·s^-1) is larger than PdBrTPP/p-DHMPA (6.53×10^-4 μmol·L^-1·s^-1); moreover, the threshold excitation intensity (I_th) of PdOEP/p-DHMPA (22.40 mW·cm^-2) is smaller than PdBrTPP/p-DHMPA (29.78 mW·cm^-2). All of these results in the upconversion efficiency (Φ_UC) of PdOEP/p-DHMPA (28.3％) is larger than PdBrTPP/p-DHMPA (26.8％). Therefore, it has been proved that the most important influence on the Φ_UC value is depended on the triplet energy level (E_T1) of sensitizer. Those porphyrin palladium sensitizers with higher triplet energy-levels can obtain high upconversion efficiency, regardless of difference among the molar absorption coefficient and the triplet lifetime. However, when using the overall upconversion ability (η) to revalue the upconversion processes, PdBrTPP has higher molar absorptivity than PdOEP, which can result in more absorbance. The total upconversion capability (η) of PdBrTPP is 3.4 times larger than PdOEP. Therefore, increasing the molar absorption coefficient of the sensitizer could be a feasible strategy to enhance the TTA-UC performance.
		2021 Vol. 41 (01): 71-79 [Abstract] ( 233 ) RICH HTML PDF (5689 KB) ( 69 )

	80	Preparation, Microstructure and Optical Properties of Cr³⁺ Single-Doped and Eu³⁺/Cr³⁺ Co-Doped GdAlO₃ Near Infrared Long Persistent Luminescent Nanoparticles
		ZHAN Ying-fei, LIU Chun-guang^*, WANG Ming-wei, YANG Jian, ZHU Han-cheng, YAN Duan-ting, XU Chang-shan, LIU Yu-xue
		DOI: 10.3964/j.issn.1000-0593(2021)01-0080-08
		It is important to prepare new nanoparticles that can be simultaneously used as contrast agents for magnetic resonance imaging and optical probes for near-infrared afterglow optical imaging in the biomedical field. In this paper, single-phase GdAlO₃∶x%Cr³⁺ and GdAlO₃∶1%Cr³⁺,y%Eu³⁺ near-infrared persistent luminescent nanoparticles with different doping concentrations wereprepared by a self-propagating combustion method. Their microstructure and optical properties were studied by means of X-ray diffraction, scanning electron microscopy, excitation and emission spectra and luminescent kinetics analysis. It is found that, for Cr³⁺ single doped samples, Cr³⁺ ions replace the Al³⁺ sites and the average particle size is ~202 nm. From excitation spectra of GdAlO₃∶x%Cr³⁺ samples, it can be found that, the excitation peaks are attributed to transitions of Cr³⁺ and Gd³⁺ ions. Meanwhile, four emission peaks appear in the near-infrared range of 650～750 nm upon 583 nm excitation. Among them, the emission peak at 725 nm belongs to the zero phonon line (PZL) and those peaks at 700 and 750 nm can be attributed to the emissions from phonon sidebands (PS) of the ²E→⁴A₂ forbidden transitions, respectively. In the doping concentration range of 0.2%～2.0%, the intensities of these emission peaks show an initial increase and a subsequent decrease with the increase of Cr³⁺ doping concentration. When the doping concentration reaches 1%, the strongest intensity can be obtained. However, the intensity of the emission peak at 735 nm increases with the increase of Cr³⁺ concentration, which is attributed to the emission from Cr³⁺-Cr³⁺ pairs. It is found that long afterglow luminescence at 725 nm can be observed for Cr³⁺ single-doped nanoparticles. Among them, the afterglow time of GdAlO₃∶1%Cr³⁺ nanoparticles is the longest and exceeds 30 s. On the basis of the above optimal Cr³⁺ concentration (1%), single-phase Eu³⁺/Cr³⁺ co-doped GdAlO₃ nanoparticles were prepared by the replacement of Gd³⁺by Eu³⁺. It is found that several emission peaks dominated by the emission at 614 nm can be observed in the red region under 266 nm excitation. In particular, the near-infrared emission peak of Cr³⁺ at 725 nm appears under 266 nm excitation due to the presence of the energy transfer from Eu³⁺ to Cr³⁺. It is found that, compared to the Cr³⁺ single-doped sample, GdAlO₃∶1%Cr³⁺, 13%Eu³⁺ sample exhibits the stronger afterglow emission intensity of Cr³⁺ at 725 nm after stopping 275 nm UV light irradiation for 5 minutes, although its average particle size is reduced to ~167 nm. By the comparative analyses of the results of absorption and emission spectra and luminescence kinetics of single-doped and co-doped samples, it is proved that the existence of a persistent energy transfer from Eu³⁺ to Cr³⁺leads to the enhanced near-infrared afterglow intensity. Meanwhile, this study provides new strategies for designing new near-infrared long persistent luminescent nanomaterials.
		2021 Vol. 41 (01): 80-87 [Abstract] ( 235 ) RICH HTML PDF (4860 KB) ( 80 )

	88	Enhanced Development of Footprints Using YVO₄∶Eu Luminescent Nanomaterials
		DING Han¹, WANG Meng^2*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0088-06
		Footprint development has long been considered as one of the essential technologies in forensic sciences, which is an important prerequisite for footprint analysis and footprint identification. On the basis of our previous research achievements in nanomaterial based development of latent fingerprints, in this paper, we put forward an enhanced development of footprints using YVO₄∶Eu luminescent nanomaterials (NMs) in order to improve the results of footprint development greatly. Firstly, YVO₄∶Eu luminescent NMs were synthesized via a typical hydrothermal method, using rare earth nitrate and sodium orthovanadate as the raw materials, and trisodium citrate as the modifier. Then, the micromorphology, crystal structure, ultraviolet absorption property, luminescent performance and surface functional groups of synthesized NMs were characterized by transmission electron microscopy, powder Xray diffractometer, ultravioletvisible spectrophotometer, fluorescence spectrophotometer and Fourier transform infrared spectrometer, respectively. The YVO₄∶Eu luminescent NMs were quasispherical in shape with an average diameter of 39.2 nm and had a tetragonal crystal structure. The maximum ultraviolet absorption wavelength of the NMs was 257 nm. Under a 254 nm ultraviolet excitation, they could give strong red emissions at a wavelength of 614 nm. The NMs were modified with citric acid molecules on the surface. Finally, the YVO₄∶Eu luminescent NMs were used for enhanced development of barefoot and footwear impressions. The mechanisms for two types of footprint development were also discussed in detail. The barefoot development results showed that the morphological features were sharp, the friction ridge was coherent, and the detailed features were clear, and the folds, exfoliation and coherent substance were obvious. The footwear development results showed that the footwear pattern features were intact and distinct. The developed footprint features could fully meet the main requirements in footprint examination and identification. In addition, the promotion effects of luminescent property, particle size and micromorphology of synthesized NMs on the contrast, sensitivity and selectivity in footprint development were also discussed. Our enhanced development of footprints based on YVO₄∶Eu luminescent NMs has a series of advantages including strong contrast, high sensitivity and good selectivity, which will not only expand the applications of rare earth luminescent NMs but also provide innovative ideas for traditional footprint developing methods.
		2021 Vol. 41 (01): 88-93 [Abstract] ( 200 ) RICH HTML PDF (3474 KB) ( 74 )

	94	Research of Terahertz Time-Domain Spectral Identification Based on Deep Learning
		HU Qi-feng, CAI Jian
		DOI: 10.3964/j.issn.1000-0593(2021)01-0094-06
		Terahertz time-domain spectroscopy (THz-TDS) is an important method for rapid and nondestructive material identification due to its spectral fingerprint properties, which has broad application exploitation in the nondestructive inspection of drugs and explosives. Spectral identification is one of the most important aspects of the applied research of THz-TDS. Most existing spectral identification methods are machine-learning based classification of manually selected features or thresholding classification of absorption spectral peak. Those methods are not adapt well to low signal-to-noise ratio, because some materials have few or no spectral absorption peaks features in the terahertz waveband and spectra are affected easily by concentrations of samples, air humidity and noises. Meanwhile computational cost increases with data quantity and category. In recent years, with the rise of deep learning technology, the methods represented by CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) have been widely applied to fields such as computer vision and natural language processing where they have been shown to produce better results than traditional machine learning methods. Due to the strong nonlinear classification capability of deep learning technology, two networks respectively were designed based on RNN and CNN for spectral identification in this paper: one-dimensional spectral line classifier based on RNN and two-dimensional spectral image classifier based on CNN. To simulate the practical application scenario, over 20 000 terahertz time-domain spectra of 12 materials were measured in a non-vacuum environment as training-set and test-set. After analyzing the effects of concentrations of samples and air humidity on spectra, S-G(Savitzky-Golay) filter was introduced to reduce noises of spectra. Experimental results show that S-G filter could improve the identification accuracy, because processed spectra have more obvious feature compared with the unprocessed spectra; the proposed methods based on RNN and CNN are more accurate and faster on the test-set, compared with traditional machine learning algorithm k-NN (k-Nearest Neighbor); CNN demonstrated better robustness to noises than RNN on spectral identification task. Therefore, deep learning technology could be utilized for quick and effective identification terahertz time-domain spectra, which provide a theoretical and experimental basis for new nondestructive safety inspection techniques.
		2021 Vol. 41 (01): 94-99 [Abstract] ( 334 ) RICH HTML PDF (3211 KB) ( 371 )

	100	Investigation on Terahertz Spectroscopy of Food Additives Based on Dispersion-Correction Functional Theory
		College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
		DOI: 10.3964/j.issn.1000-0593(2021)01-0100-05
		Illegal use or abuse of food additives candamage human health. Terahertz (THz) spectroscopy was widely used for food additives detection with fast and accurate etc. advantages, but lacked in-depth theoretical analysis. This paperexplores to improve traditional methods to produce higher quality simulation spectra, and evaluate different models. THz spectra of sorbic acid, benzoic acid and xylitol were obtained by terahertz time-domain spectroscopy. Simulations were performed using CRYSTAL14 software to calculate the periodic crystals. Considering that dispersion force cannot be ignored in hydrogen-bonded systems, the dispersion correction term was used to augment the traditional functionals to construct the B3LYP-D and PBE-D models. The average absolute error (AAE) of benzoic acid absorption peak positions was 0.073 for B3LYP-D model, 0.096 for PBE-D model. For sorbic acid, AAE was 0.039 and 0.047, xylitol 0.023 and 0.087. The values of AAE were decreased by 0.03~0.1 compared with primitive functionals. The computation time of B3LYP-D model was more than twice of PBE-D model. Results showed that dispersion-corrected models can produce higher quality simulation spectra for hydrogen-bonded systems. The B3LYP-D model holds higher accuracy but is more time-consuming; the PBE-D model provides comparable accuracy to B3LYP-D model with much higher simulation speed. The spectral features were as signed as primarily lattice translations and rotations with lesser intramolecular torsions. The dispersion-corrected model proposed in this paper has important reference value for the theoretical research of other similar systems.
		2021 Vol. 41 (01): 100-104 [Abstract] ( 207 ) RICH HTML PDF (2093 KB) ( 70 )

	105	The Application of Kamers-Kronig Relation in Time Domain Spectral Measurement of Reflection Terahertz
		CAI He^1，2, ZHANG Jing³, ZHENG Yan¹, SUN Jin-hai¹, ZHANG Xu-tao¹, LI Liang-sheng¹, LIU Yong-qiang¹, YIN Hong-cheng¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0105-06
		Terahertz timedomain spectroscopy (THzTDS) technology is extremely significant in measuring the dielectric parameters of materials in the Terahertz band, and it is also important for analyzation and identification of these materials. The THzTDS technology is based on coherent detection, which can measure the amplitude and phase of terahertz wave simultaneously. The electromagnetic parameters of materials can be retrieved from the complex transmittance or complex reflectivity which measured by transmission or reflection of the materials. In most practical application, the refractive index and extinction coefficient cannot be obtained when materials are hard to penetrated by THz wave, and the weak absorption approximation condition cannot be met in the measurement. Therefore, reflection measurement has more application value in this field. However, in the published research results, researchers still generally use the transmission measurement scheme and rarely use the reflection scheme to obtain the material parameters. The reason is that the position error of the sample in the reflection measurement is difficult to eliminate, so it is impossible to extract the reflection phase accurately. In this paper, the Kamers-Kronig relation, which is widely used in the field of optics, is applied to the reflection measurement of terahertz timedomain spectral system to solve the problem that the phase information cannot be obtained accurately and the dielectric parameters cannot be extracted correctly. In order to verify the accuracy of the Kamers-Kronig relationship, on the one hand, the complex transmittance and reflectivity of silicon materials are measured by transmission detection and reflection detection respectively, and then the material parameters are inversed. The results indicated that they have a good consistency. On the other hand, Kamers-Kronig relation and maximum entropy method(MEM) are used to inverse the material parameters of the silicon reflection measurement data, and these two methods can also agree with each other, which ensuring the reliability of the extracted data. Finally, the results obtained by the Kamers-Kronig relation and the maximum entropy method are compared and discussed in this paper, respectively. Compared with the maximum entropy method, The Kamers-Kronig relation is more applicable in the extraction of material parameters and absorption spectrum. Therefore, the Kamers-Kronig relation is not only suitable for the coherent measurement, but also for the incoherent measurement in which the phase information cannot obtain. However, the method needs the reflectivity amplitude information of the whole frequency band, the frequency which we can’t measure needs to be extrapolated, so it is more suitable for the matter whose reflectivity changes little with the frequency. This paper provides an effective method for obtaining the terahertz optical parameters of materials by using the reflection THzTDS system, which can solve the problem of extracting the reflection measurement parameters in the vast number of cases. It is of great significance to the application of Terahertz Technology.
		2021 Vol. 41 (01): 105-110 [Abstract] ( 212 ) RICH HTML PDF (2795 KB) ( 227 )

	111	Baseline Correction Algorithm for Raman Spectroscopy Based on Adaptive Window Spline Fitting
		LIU Long¹, FAN Xian-guang^{1, 2}, KANG Zhe-ming¹, WU Yi¹, WANG Xin^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2021)01-0111-05
		Raman spectroscopy is a non-destructive and rapid detection technology that can provide qualitative and quantitative information of the material. Therefore, it has been widely used in many fields such as medicine and chemical industry. However, the Raman spectrum suffers from the baseline drift due to the background fluorescence of the sample. Moreover, it has a serious impact on the identification of characteristic peaks of Raman spectra and the Raman imaging. At present, there are two methods to solve this problem, that is, improve the experimental methods and numerical processing. The improve the experimental methods include polarization modulation method and high frequency modulation method. However, they suffer from the disadvantages of complicated experimental equipment and difficult detection technology. The numerical processing includes polynomial fitting and wavelet transform. However, it is prone to suffer from the over and under-fitting. In order to solve this problem, we propose the baseline correction algorithm for Raman spectroscopy based on adaptive window spline fitting, which based on the existing equipment and the traditional baseline correction algorithm. Firstly, the optimal search interval of the trough value is obtained based on the peak recognition algorithm and the initial search step, and then the trough recognition algorithm is used to complete the fitting of the trough curve. Secondly, the peak position of the trough curve is obtained based on the optimal search interval and the peak recognition algorithm. Then, the adaptive rectangular window is symmetrically added at this position, in order to delete the peak, and fitting the trough curve. Thirdly, the fitting trough curve is compared with the original Raman spectrum, point by point, and taking the smaller value to fit a new trough curve. Finally, the operation above will continue until the width of the adaptive window is lower than the threshold. Afterwards, the baseline fitting of the Raman spectrum is completed. And then the baseline correction of the sample is obtained based on our algorithm and the traditional methods. It can be seen that our algorithm can effectively eliminate the baseline drift, and some weaker Raman characteristic peaks can be better remaining. Simultaneously, the over and under-fitting is avoided, and the result of baseline correction is good. Therefore, it provides reliable information on the further analysis of the Raman spectrum and the realization of the Raman imaging.
		2021 Vol. 41 (01): 111-115 [Abstract] ( 230 ) RICH HTML PDF (3416 KB) ( 109 )

	116	Study of Raman Spectroscopy on the Structure of NH₄Cl Aqueous Solution Under Strong Magnetic Field
		CHEN Shuai¹, WANG Xu-yang¹, LI Fei^{1, 2}, YUAN Jun-sheng^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2021)01-0116-06
		Numerous controversies are existed at domestic and abroad up to now about the effect of strong magnetic field treatment on the structure of the aqueous solution. However, few pieces of literature have studied the structure of salt solution with the same treatment. Raman spectroscopy was used in this paper to determine the scattering data of high-purity water and NH₄Cl aqueous solution with mass fractions of 1%, 5%, 10%, 20% and 28% at a different time under the external strengthened magnetic field of 1.8 T. It provided a feasible method for Raman spectroscopy to investigate the effect of strong magnetic fields on the structure of the aqueous solution, enriching the Raman spectroscopy research field. According to the experimental results, the value of stretching vibrations of hydrogen bonds in water molecules gradually increased with the increased magnetization time. It could reach saturation after a certain magnetization time. The saturation time of high-purity water and NH₄Cl aqueous solution with different mass fractions was different. The time of high-purity water and NH₄Cl aqueous solution with mass percentages of 1%, 5%, 10%, 20% and 28% was 150, 120, 120, 100, 80 and 80 min. The time to reach the saturation of magnetic effect showed a decreasing trend with the increased mass fraction of NH₄Cl aqueous solution. The demagnetization memory time of high-purity water and different mass fractions of NH₄Cl aqueous solution was measured after the magnetic field was removed. The demagnetization memory time of high-purity water and NH₄Cl aqueous solutions with mass fractions of 1%, 5%, 10%, 20%, and 28% was 30, 40, 50, 60, 80 and 80 min, respectively. The demagnetization memory time presents an increasing trend with the increased mass fraction of NH₄Cl aqueous solution. NH₄Cl aqueous solutions with different mass fractions after 2 h of magnetization was processed by deconvolution fitting. According to the deconvolution fitting result, NH₄Cl aqueous solution with the mass fraction of 20% increased a peak of N—H compared to NH₄Cl aqueous solution with the mass fraction of 10%. The signal peak gradually increased with the increased mass fraction of NH₄Cl aqueous solution. The structure of the hydrogen bond of DDAA-type decreased with the increased magnetization time, which had a destructive effect on the tetrahedral structure of water. The hydrogen bond of DDAA-type did not change any more when it reached the saturation magnetization time. The influence of 1.8 T magnetic field on the structure of NH₄Cl aqueous solution could be obtained through Raman spectroscopy, which provided a certain theoretical basis for the study of another inorganic salt aqueous solution under the condition of external strengthening magnetic field.
		2021 Vol. 41 (01): 116-121 [Abstract] ( 185 ) RICH HTML PDF (4104 KB) ( 67 )

	122	Study on the Method of Identifying Waste Plastic Materials Based on Raman Spectroscopy
		ZHAO Ying^1,2，LIN Jun-feng³，LIU Jia²，XIE Tang-tang³，LI Xiao-peng²，CUI Fei-peng²，LI Xiao-jia^1,2*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0122-05
		As an indispensable material widely used in various important fields such as information, energy, industry, agriculture, transportation, and even aerospace and marine development, plastic particle raw materials have penetrated into all aspects of human food, clothing, housing and transportation. China is a large importer country of plastic raw materials. The existing test methods often cost too much time and barely achieve on-site testing. Therefore, the development of a discriminating model for waste plastic particles used in the field is of great significance for fast clearance and anti-smuggling in customs. Raman spectroscopy has the advantages of fast, non-destructive, small sample consumption, non-pre-treatment and strong adaptability, and has been widely used in rapid on site identification. Firstly, this research establishes a Raman spectroscopy reproducibility test method. On the basis of ensuring the real and effective Raman spectroscopy data, Raman spectroscopy combined with chemical discrimination method is applied to the identification of waste plastic materials. Two kinds of actual customs clearance plastic materials with similar composition were selected, each including 40 standard and wasted products. The Raman spectrum information of the samples was collected by NCS Smart 200 Raman spectrometer. A total of 640 samples of data of plastic raw materials were collected. The original Raman spectra of the two kinds of plastic materials were compared and analyzed. To further explore the composition changes of waste plastics, 1 001 cm^-1 was selected as the normalized reference peak position. The relative peak intensity changes of waste plastic raw materials and standard plastic raw materials were compared. The changes of relative peak intensity indicated that the waste plastic raw materials had chemical aging causes a change in its molecular structure and composition. Based on the principal component analysis (PCA), the original Raman spectroscopy and pre-processed Raman spectroscopy are subjected to dimensionality reduction. The first two principal component spaces of the original Raman spectroscopy have intertwined, which is difficult to completely separate. The spatial separation of the first two principal components of the pre-treatment Raman spectrum conducts well. Therefore, by performing background subtraction and smoothing pre-processing on the original Raman spectral data, the influence of the fluorescence background and noise on the discrimination can be reduced, and the accuracy of the discrimination can be improved. Half of the sample is divided into a calibration set for model building, half is divided into prediction sets for model verification, and partial least square discriminant analysis (PLS-DA) is used to build a waste plastic raw material identification model. The correctness rate is 100% for the modeling training set and 99.06% for the model verification set. The research shows that based on Raman spectroscopy technology, combined with test data pre-processing and partial least squares discriminant analysis method, it can effectively achieve the on-site, fast and accurate identification of plastic raw materials, and provide theoretical reference for the development of on-site testing equipment and methods.
		2021 Vol. 41 (01): 122-126 [Abstract] ( 217 ) RICH HTML PDF (2061 KB) ( 68 )

	127	Assignments on Raman Peaks of Red Coral Based on Experimental Raman Spectroscopy and Density Functional Theory Calculation
		CHEN Chao-yang¹, HUANG Wei-zhi¹, GAO Qiang², FAN Lu-wei³, Andy Hsitien Shen^1*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0127-04
		Red coral is a kind of precious organic gemstone material, and it has been popular with people since ancient times because of its beautiful red color and delicate texture. Natural red corals with beautiful colors are rare in production, so some red corals are dyed to improve their appearance. Raman spectroscopy is a powerful method to identify whether red coral has been dyed or not, so the assignments on Raman peaks of red coral have important theoretical significance for identification. However, the assignments have not been studied profoundly. Based on this, the Raman spectra of three red corals (Corallium rubrum) with different red color saturation were collected. At the same time, the theoretical Raman spectrum of pigment molecule canthaxanthin in red coral was calculated by Density Functional Theory (DFT) using quantum chemistry program Gaussian 16. We innovatively compared Raman spectrum of red coral and theoretical Raman spectrum of canthaxanthin to further analyze the assignments on Raman peaks of red coral. The results show that there are peaks at 1 514, 1 295, 1 177, 1 125, 1 086 and 1 016 cm^-1 in the Raman spectra of red corals and the Raman peak at 1 086 cm^-1 belongs to CO^2-₃ in calcite. The darker the red color of coral is, the stronger the Raman peaks at 1 514, 1 295, 1 177, 1 125 and 1 016 cm^-1 are. On the contrary, the lighter the red color of coral is, the weaker the Raman peaks are. The intensities of Raman peaks at 1 514, 1 295, 1 177, 1 125 and 1 016 cm^-1 are positively correlated with the saturation of red the color, so it is presumed that these Raman peaks are produced by pigments in red corals. The main Raman peaks in the theoretical Raman spectrum of canthaxanthin are located at 1 512, 1 269, 1 189, 1 159 and 999 cm^-1, which correspond well to the Raman peaks at 1 514, 1 295, 1 177, 1 125 and 1 016 cm^-1 of red coral. The results of vibration analysis show that the Raman peaks at 1 512, 1 269, 1 189, 1 159 and 999 cm^-1 of canthaxanthin belong to CC stretching, C—H rocking, C—C stretching, C—C stretching and methyl rocking respectively. Therefore, the peaks at 1 514, 1 295, 1 177, 1 125 and 1 016 cm^-1 in Raman spectrum of red coral are assigned to CC stretching, C—H rocking, C—C stretching, C—C stretching and methyl rocking. In this study, the calculation method of DFT is innovatively used to study the assignments on Raman peaks of red coral and interpret the Raman peaks of red coral, which provides a theoretical basis for using Raman spectroscopy to identify red coral. It also provides a new method to study assignments on Raman peaks of this kind of gem biomaterials.
		2021 Vol. 41 (01): 127-130 [Abstract] ( 268 ) RICH HTML PDF (1426 KB) ( 142 )

	131	Raman Spectroscopic Characteristics of Reservoir Bitumen and Its Constrain on Stages of Hydrocarbon Accumulation ——Take Yangshuiwu Buried Hill of Jizhong Depression as an Example
		TIAN Jian-zhang¹, CHEN Yong^2*, HOU Feng-xiang¹, TIAN Ran¹, ZHANG Hui², WANG Yuan-jie¹, LIU Ting-yu², FENG Yan-wei², ZHONG Sheng²
		DOI: 10.3964/j.issn.1000-0593(2021)01-0131-05
		The quick and accurate determination of the hydrocarbon accumulation time and period is of great significance to understanding the mechanism and distribution rules of this process. As a marker of the hydrocarbon accumulation, bitumen is an important factor in the analysis of hydrocarbon migration path and the exact time of its accumulation. This article was based on systematic research on the Raman spectroscopy of bitumen in the Yangshuiwu buried hill reservoir. The bitumen in this reservoir is mainly distributed in the later formed cracks, and few are filled in the pores. Bitumen almost has no fluoresce under ultraviolet light, so it can be catalogued to carbonaceous bitumen. The experimental results show that the bitumen in this area has relatively stable Raman spectrum characteristics, with obvious D-peak and G-peak that do not change from different test points in the same stratum. However, the background noises may be changed at a different point. The Raman spectrum goes up after 1 500 cm^-1. When processing the Raman spectrum data, the background value was subtracted from the original data firstly, and then the Lorentz peak-differentiation method was used to fitting the peaks, until the fitted spectrogram curve is basically consistent with the original shape. The peak height (H) of D-peak and G-peak, the full width at half maximum (FWHM), the distance between D and G, the lowest value between D-peak and G-peak and other parameters were obtained directly from the fitted curve. Saddle index can be calculated by these parameters, and the bitumen maturity was also calculated by the formula. The results of the calculation indicate that the reservoir bitumen is heterogeneous, which means the oil is multi-sourced. The bitumen maturity parameters are mainly distributed in two areas, with R_o=1.00～1.37 and R_o=1.44～1.94, respectively. Combined the bitumen maturity parameters with the burial history to determine the Yangshuiwu buried hill hydrocarbon accumulation time, two periods of accumulation were confirmed, condensate and moisture gas accumulation stage of Dongying period (35～25 Ma) and natural gas charging stage of Minghuazhen-present (5 Ma～0). Results are consistent with the hydrocarbon generation history of the source rocks in the study area. This study shows that Raman spectroscopy can perform micro-analysis of bitumen in different positions in the reservoir, obtain high-precision spectral data, and then calculate the maturity of bitumen accurately. Therefore, quantitative characterization of reservoir bitumen Raman spectrum and quantitative calculation of maturity, combined with simulation of basin burial and thermal history, the exact time and period of hydrocarbon accumulation can be determined. This method is fast, accurate and inexpensive, therefore has a promising application prospect.
		2021 Vol. 41 (01): 131-135 [Abstract] ( 170 ) RICH HTML PDF (2747 KB) ( 55 )

	136	Detection of COD UV Absorption Spectra Based on PSO-PLS Hybrid Algorithm
		ZHENG Pei-chao, ZHAO Wei-neng, WANG Jin-mei^*, LAI Chun-hong, WANG Xiao-fa, MAO Xue-feng
		DOI: 10.3964/j.issn.1000-0593(2021)01-0136-05
		Chemical oxygen demand (COD) is an important indicator of the degree of water pollution by organic matter. Ultraviolet absorption spectroscopy is the most widely used method for COD detection in water. It has the advantages of no pretreatment of samples, low cost, no pollution, and fast measurement speed. However, the original spectral data has high dimensions, and the spectral information contains a large number of redundant variables. Modeling the full spectral data has problems such as low accuracy and complicated calculations. Aiming at the low accuracy of UV absorption full-spectrum modeling and a large amount of collinearity in spectral data, this paper presents a method based on particle swarm optimization (PSO) and partial least squares (PLS) to select characteristic wavelengths to establish a prediction model. Improve the accuracy and applicability of the UV absorption spectrum prediction model and simplify the model. The UV spectrum data of 29 different concentrations of COD standard solutions were collected. Each standard solution was collected 5 times and averaged and smoothed to reduce the errors caused by the instrument and the environment. Taking into account the absorption of the standard solution in the spectral range of 200～310 nm, 246 wavelength points in this wavelength range were selected as modeling data, and the absorbance data at each wavelength point was used as a particle and numbered in order. PLS was used as the model Method, the correlation coefficient r and the root mean square error (RMSE) are used as evaluation indicators. The particle swarm algorithm fitness function f(x)=min (RMSE) is set. The initial population of particles is 20, the inertia weight w=0.6, and the self The learning factor c1=1.6, the group learning factor c2=1.6, the maximum number of iterations is 200, and the algorithm termination condition is to reach the maximum number of iterations. The output value of the optimal global variable of the algorithm is 168, 94, 181, 183, 175, 209, 106, 142. The correlation coefficient r and the predicted root mean square error RMSE of the PLS prediction model established by the eight wavelength points selected by the particle swarm optimization algorithm were 0.999 98 and 0.155 1, respectively. In order to verify the effectiveness of the prediction model established by PSO-PLS, three prediction models of PLS, iPLS and SVR were established for comparison. The verification results show that the correlation coefficient r and the root mean square error RMSE of the PSO-PLS model are better than those of the other three prediction models, which shows that the particle swarm algorithm can effectively extract the characteristic wavelengths used for PLS modeling and eliminate the common of sub-interval variables Linear, improving the accuracy of the prediction model. This method provides an effective way for real-time online monitoring of COD in water bodies.
		2021 Vol. 41 (01): 136-140 [Abstract] ( 217 ) RICH HTML PDF (2317 KB) ( 73 )

	141	Automatic Classification of Rock Spectral Features Based on Fusion Learning Model
		HE Jin-xin¹, REN Xiao-yu¹, CHEN Sheng-bo^2*, XIONG Yue¹, XIAO Zhi-qiang¹, ZHOU Hai¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0141-04
		The spectrum of rock is a comprehensive reflection of the physical chemistry properties, composition and structure of the rock. Rock spectral data have been applied to the study of rock classification. But unlike the mineral spectrum, the Rock spectrum has no standard database and is influenced by many disturbing factors, for example, mineral composition, structure, chemical composition, weathering strength, the error of measuring instrument, etc. The traditional rock spectrum classification model firstly preprocesses the rock spectrum to eliminate the interference. Then, some spectral features are analyzed by different methods to achieve the classification goal. However, the loss of spectral data features makes the classification of low accuracy and cumbersome operation process; efficiency is not high. Therefore, it is of great significance to establish a simple, fast and accurate automatic classification model of the rock spectrum. Machine learning can learn all the data obtained; there is no omission, greatly improving the classification accuracy. And is the direct operation to the original data, does not need the pretreatment, simplifies the process. Therefore, Xingcheng city of Liaoning Province, China was chosen as the study area, and several typical rock samples were collected. Based on the measured spectral data from the ASD Portable Spectrometer, 608 pieces of data were obtained. According to the spectral characteristics of rocks, the study is divided into three types. Firstly, the decision tree and the upgrade model of the decision tree are used as a the random forest, But when the data noise is large, random forest is easy to get into overfitting. Therefore, the knearest neighbor model, which is not sensitive to outlier is used. But KNN needs to consider every sample when the data is large, the computation will be very large, inefficient. So use Support vector machine to improve classification accuracy. The experimental results show that the order of accuracy of the four classification models is: SVM>KNN>Random Forest>Decision Tree. In order to further improve the automatic classification accuracy of rock spectral features. By fusing several different models. That is to vote on the classification results of different models, choose the most votes as the final classification results. Since hard voting can reduce the occurrence of over-fitting to a certain extent, it is more suitable for classification models. In this paper, we use a hard voting method to fuse three machine learning models: RF, KNN and SVM. The final classification accuracy can reach 99.17%. To sum up, it is feasible, accurate and efficient to classify rock spectral features automatically based on the fusion learning model.
		2021 Vol. 41 (01): 141-144 [Abstract] ( 173 ) RICH HTML PDF (3522 KB) ( 99 )

	145	Research of Dental Caries Lesion Based on the Visible-Near Infrared Spectrum Polarization Detection
		LIANG Tian-quan¹, DUAN Xiao-jie², TANG Qing-xin¹, YU Quan-zhou¹, ZHANG Bao-hua¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0145-05
		In view of the problem to effectively characterize the damage of dental caries, we explore a kind of polarization spectrum detection method which have nondestructive and low consumption characteristics, it’s a beneficial to supplement for the conventional detected methods such as chemical analysis, surface profilometry and microradiography. Based on the characteristic which polarization spectral sensitive to the observation sample surface microstructure, dental caries lesion due to the demineralization process, as result calcium and phosphate are dissolved from the enamel and dentin. The tooth surface microstructure has taken place different change; the structure present different degree of difference in light scattering properties and phase change. Considering different dental caries samples its surface microstructure changes strongly associated with the polarization information, we put forward a kind of method which can effectively characterize different teeth from caries by using polarization spectrum. Selected the 450, 550, 670 and 860 nm as four different observation research wavelengths, as well as selected six different dental caries samples, the degree of polarization as the parameter to describe different samples polarization characteristics. The experimental results show that the consistent observation waveband for different tooth samples is positively related to the degree of polarization parameters, as well as the same observation samples showed observation waveband is negatively related to the polarization characteristics. For further quantitative characterization, the relationship between polarization spectrum and tooth decay damage levels, the index correlation mathematical model which can interpret internal coupling was built. To effectively validate model robustness, it needed quantitative validation the model simulation results and measured data. We selected the quantitative evaluation factor such as the sum of squares due to error (SSE), root means squared error (RMSE), coefficient of determination (R-square). The results show that models R-square close to 1, as well as the SSE and RMSE values are small. The model characteristic of robustness and effectiveness was verified, which can effectively interpret different caries damage teeth samples from polarization coupling relation. This research is effectively extending the teeth caries detection method, as well as revealed polarization spectrum can be effective characterization and distinction to different dental caries samples. It is also developing a new kind of nondestructive and low-cost polarization spectrum detection technology.
		2021 Vol. 41 (01): 145-149 [Abstract] ( 196 ) RICH HTML PDF (2553 KB) ( 71 )

	150	A Method for Estimating Thick Oil Film on Sea Surface Based on Fluorescence Signal
		CUI Yong-qiang¹, KONG De-ming^2*, ZHANG Xiao-dan¹, KONG De-han³, YUAN Li¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0150-06
		Oil film thickness is an important indicator for the assessment and analysis of oil spill pollution on the sea surface. Laser-Induced Fluorescence (LIF) is one of the most effective technologies for oil spill detection at present, the oil film thickness inversion algorithm based on LIF is only suitable for thin oil film (≤10~20 μm), there is no effective method for the evaluation of thicker oil film (>20 μm). In view of this, an inversion algorithm based on LIF detection technology for evaluating thicker oil film is proposed, the algorithm uses oil film fluorescence signal to invert oil film thickness, deduces oil film thickness inversion formula, and gives oil film thickness evaluation method based on the inversion algorithm. First, Otsu algorithm is used to select the appropriate fluorescence spectrum band, and then the oil film thickness is retrieved according to the spectrum data of each wavelength in the selected band, finally, the average value of the retrieved oil film thickness is used as the oil film thickness evaluation result. The applicable range of the algorithm is studied, the relationship between the maximum value of the effective evaluation range of the algorithm and the relative measurement error is given, the maximum value of effective evaluation range of oil with different extinction coefficients under various measurement errors is given. The method in this paper is verified by experiments. The mixture of crude oil and mineral oil (1∶50) is selected as the experimental oil and the laser with a wavelength of 405 nm is used as the excitation source. The collection wavelength range is 420～750 nm. The background fluorescence and Raman scattering spectra of sea water, the fluorescence spectra of experimental oil and various thick oil films are collected to invert the oil film thickness. Otsu algorithm is used to select the band of 420～476 nm to evaluate the oil film thickness. The evaluation results show that when the oil film thickness is ≤800 μm, the algorithm has high accuracy, with an average error of 10.5%; when the thickness is >800 μm, the average error is 28.8%, with a large evaluation error and rapidly increases with the increase of the oil film thickness. The analysis results of relative error and extinction coefficient are consistent with the experimental results. The results show that the method can effectively evaluate the thickness of the thick oil film on the sea surface, and judge the effectiveness of the evaluation results according to the measurement relative error and extinction coefficient.
		2021 Vol. 41 (01): 150-155 [Abstract] ( 195 ) RICH HTML PDF (2538 KB) ( 46 )

	156	The Influence of Suspended Particles on Backscattering Properties in the Coastal Waters of Bohai Sea
		JIANG Ling-ling^1*, DUAN Jia-hui¹, WANG Lin², CHEN Yan-long², GAO Si-wen¹, GUO Xiang-yu¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0156-08
		Studying the influence of the spatial and size distribution of suspended particulate matter on its backscattering properties is of great importance for understanding water body scattering properties and improving inversion accuracy, which is greatly helpful for marine environmental monitoring. Based on the field data of Bohai Sea in June and September 2017, the total suspended particulate matter concentration (SPM), particle size, backscattering coefficient as well as other parameters were obtained. The results showed that the backscattering coefficient (b_bp(λ)) decreased with the increase of wavelength in the visible wave bands. In summer and autumn, the changing trend of b_bp(550) was consistent with that of SPM at most stations; however, the correlation coefficient R² between them was only 0.24. In addition, the relationship between mean particle size (D_A), median particle size (D₅₀) and b_bp(λ) was established in the study respectively. D_A and b_bp(λ) had a linear relationship, and their relationship was better in summer than in autumn by the influence of particle composition, R² was 0.7 in summer and was only 0.3 in autumn. At the same time, it is concluded that the b_bp(λ) increased with the increase of D_A when the water body was dominated by small particles, but while the water body was dominated by large particles, b_bp(λ) decreased with the increase of D_A. D₅₀ and b_bp(λ) showed a different relationship and had a good power exponential relationship, the more small particles in the water, the higher the particle backscattering coefficients value, and the correlation coefficient was 0.66 and 0.5 in summer and autumn, respectively. The influence of particle size distribution on b_bp(λ) was difficult to be determined if without considering the seasonal difference.
		2021 Vol. 41 (01): 156-163 [Abstract] ( 203 ) RICH HTML PDF (5674 KB) ( 67 )

	164	High-Sensitive and Rapid Fluorescencet Detection of Hg²⁺ Based on Poly(adenine)-Templated Gold Nanoclusters
		OU Li-juan, AN Xue-zhong, LUO Jian-xin, WANG Ling-yun, BO Heng, SUN Ai-ming, CHEN Lan-lan
		DOI: 10.3964/j.issn.1000-0593(2021)01-0164-04
		Poly adenine (Poly A)-templated gold nanoclusters (Poly A-AuNCs) have many advantages, such as facile and fastly synthesis, excellent fluorescence intensity and good photostability. Based on Poly A-AuNCs, a sensitive, simple, rapid and novel fluorescent strategy was developed for the detection of Hg²⁺. AuNCs were prepared by heating-assisted methods with sodium citrate as a reductant. The AuNCs have been revealed by fluorescence spectra and transmission electron microscopy (TEM). TEM showed that the AuNCs were spherical in shape and well dispersed. The average size of AuNCs is approximately 7 nm. The fluorescence spectra showed that AuNCs emitted strong blue fluorescence with maxima fluorescence emission wavelengths at 471 nm upon excitation at 280 nm. Additionally, the AuNCs were highly stable and little fluorescence change was observed after storing at 4 ℃ for 1 month. In the presence of Hg²⁺, the fluorescence of AuNCs was quenched effectively due to the high affinity between Hg²⁺and gold. The pH and reaction time was investigated. The fluorescence of AuNCs were insensitive to pH. The quenching reaction between Hg²⁺ and AuNCs was very fast and was completed within the first 1 min. Thus, the fluorescence was recorded after the simple mixture of Hg²⁺ and AuNCs. Under optimum conditions, a series concentration of Hg²⁺ was detected. The linear equation is y=-335.57x+541.35. The relative fluorescence intensity displayed a good linear relationship with Hg²⁺ concentration in the range from 0.01 to 1 μmol·L^-1 with the detection limit of 3 nmol·L^-1. Nine metal ions were added in the system to assess the sensing selectivity. Furthermore, the assay was successfully applied for the detection of Hg²⁺ in environmental water samples with good recoveries from 95.33％~103.8％, and the relative standard deviation was lower than 4%. Thus, this novel strategy was rapid, simple and have high sensitivity and good selectivity for Hg²⁺.
		2021 Vol. 41 (01): 164-167 [Abstract] ( 190 ) RICH HTML PDF (2154 KB) ( 49 )

	168	Self-Assembled Nanocomposite Film of AgN In-Situ Grown on Polydopamine With Enhanced Fluorescence of CDs for Detection of Puerarin
		WENG Wen-ting, WANG Si-yu, ZHUANG Jun-yang
		DOI: 10.3964/j.issn.1000-0593(2021)01-0168-09
		The S, N co-dopedcarbon dots was obtained through the one-step hydrothermal treatment of cystine and citric acid. The results suggested that the prepared carbon dot solution has fluorescence emission at 455 nm with an excitation wavelength of 350 nm. This fluorescent Carbon Dots (FCDs) showed stableemission performancein pH 6~11 and bright blueemission with a quantum yield of 61.7% and an average lifetime of 10.75 ns. In this article, a sample preparation scheme with self-assembled nanocomposite film of silver nanoparticle (AgN) was designed to enhanced the optically stimulated luminescence emission of FCDs. The enhance PL signal of FCDs grown on sensing film leading to a higher sensitivity of drug content detection. A uniform nanocomposite film substrate was prepared via the reduction in situ of silver ions in polydopamine (PDA). The Enhanced Fluorescence film FTO/PDA-AgN/PDDA/[PSS/PDDA]₃/FCDs was achieved by self-assembled the polyelectrolyte molecular Layer-by-Layer (LbL) on the nanocomposite film that was utilized to control the distance of silver nanoparticle (AgN) and fluorescent Carbon Dots (FCDs). The synchronous reduction method was easy to operate, and theresults of Ultraviolet, Fluorescence, Raman spectra and scanning electron microscopy revealed that the composite silver nanostructure in polydopamine film result in the AgN has the advantage of not being oxidized. The silver nanoparticles could enhance the optically stimulated luminescence emission of FCDs as the separation distance was optimally designed between FCDs and AgN surface, the fluorescent intensity of FCDs in SAMs increased by nearly 3 times with the corresponding fluorescence lifetime reduced from 6.08 to 2.98 ns. The characteristics of the AgN-enhanced fluorescence which has distance-dependence, accelerated radiation attenuation and correlation with the reduction degree of AgN were additional evidence for a local surface plasmon resonance effect between AgN and FCDs. The experimental results showed that the addition of puerarin (Pue) quenched the fluorescence signal of FCDs, and the degree of quenching had a good linear relationship with the content of puerarin in the range of 3.33×10^-7~1.50×10^-5 mol·L^-1. The linear regression equation is I₀/I=2.843×10⁴c_Pue+1.068, the correlation coefficient R=0.998 56, and the detection limit QL=2.31×10^-7 mol·L^-1. This established AgN-enhanced fluorescence system based on the SAMs could increase the sensitivity of the detection for puerarin as the detection limit was reduced about an order of magnitude.
		2021 Vol. 41 (01): 168-176 [Abstract] ( 224 ) RICH HTML PDF (6604 KB) ( 60 )

	177	Two-Dimensional Correlation Raman Spectroscopic Analysis of CuCl₂/DMF Solution Under Temperature Disturbance
		WU Xiao-jing¹, LI Zhi¹, LI Zi-xuan¹, LI Xing-xing¹, CHENG Long-jiu²
		DOI: 10.3964/j.issn.1000-0593(2021)01-0177-06
		The spectroscopic study of the solution has always attracted the attention of chemists, but most of them are based on one-dimensional spectroscopy. There are many disadvantages, such as low resolution, greatly affected by the error, overlapping peaks are difficult to distinguish and so on. So that we cannot get the information we need clarification. These problems were well solved by the introduction of two-dimensional correlation spectroscopy. By correlation analysis and calculation of dynamic spectra under external disturbances, the overall change information of spectral intensity can be obtained, which can significantly improve the resolution of one-dimensional spectroscopy and the separation degree of overlapping peaks. It has unique advantages in judging the response order of different functional groups under specific external disturbance and studying the weak intermolecular and intramolecular interactions. In this article, two-dimensional correlation Raman spectroscopy and theoretical calculation have been combined to the analysis of the micro clusters in solution. The target solution (pure DMF and CuCl₂/DMF solution of 0.84 mol·L^-1) was studied by micro confocal laser Raman spectrometer. The results have shown that, in the range of C—N bond stretching vibration band, due to the addition of CuCl₂, thestrength of the characteristic peak decreases greatly, and the peak width becomes large. Furthermore, it could be found that there is a new peak at 1 115 cm^-1, With the rise of temperature, the strength of stretching vibration peak decreases gradually, and the peak shape becomes slow. It is concluded that different types of micro clusters have different sensitivity to temperature with the help of moving-window two-dimensional Raman (MW2D Raman) spectroscopy. in addition, with the increase of temperature, they transform into each other and change at different rates. In order to obtain the essence of the micro cluster movement in the solution, the target solution was analyzed by two-dimensional Raman(2D Raman)spectroscopy with temperature as the external disturbance. It is found that the addition of Cu²⁺ makes the solution system more complex. In addition to the cluster structure existing in the original solvent, there is also the cluster structure solvated with Cu²⁺, and there is a certain transformation between them. Furthermore, the optimized possible cluster structures and thermodynamic data were calculated by densityfunctional theory. The results confirmed the interaction between Cu²⁺ and DMF, and the stability of the cluster configurations [Cu(DMF)_n]²⁺(n=1~6) gradually deteriorated with the increase of n. The feasibility and correctness of two-dimensional correlation spectroscopy analysis are verified.
		2021 Vol. 41 (01): 177-182 [Abstract] ( 219 ) RICH HTML PDF (3248 KB) ( 79 )

	183	Color Recovery Method for Snapshot Narrow Band Spectral Imaging Technology
		YI Ding-rong¹, KONG Ling-hua^2*, ZHAO Yan-li¹, YANG Zi-han¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0183-05
		Due to its simultaneous spectral and spatial resolutions, snapshot narrow-band spectral imaging (SNBSI) will have wide applications in remote sensing of natural resources, accurate agriculture, and medical diagnosis. However, due to its usage of narrow bands to improve spectral resolution and to enhance contrast, gray images instead of colorful ones produced by SNBSI are not idea for experts to identify, evaluate and appreciate. Existing color recovery methods primarily applicable to either wide-band spectral imaging or continuous spectral imaging wherein multiple bands together cover the full visible spectral region; however, they are not directly applicable to SNBSI. In this paper, we study color recovery method for SNBSI. Further we propose the concept of color camera via narrow-band multi-spectral imaging method. First, two color recovery methods were proposed, one was based on CIE three primary coordinate system; the other was modified Bayer-filter-array based color interpolation method. Then applied the two proposed methods to resurrect color informationfor three representative targets including a plant, a forearm, and a piece of cervical tissues captured by an SNBSI camera, then selected one of the two methods which resulted in higher values of mean, standard deviation, entropy, and gradient of the recovered images. Finally, the modified gray world chromatic aberration correction algorithm was applied to make the recovered images closer to the true color. Experimental results suggested that CIE-three primary based method was more suitable than the Bayer-filter-array based interpolation method for color recovery from SNBI spectral images. The recovered color images of a plant, a human forearm, and tissues by the CIE three primary based recovery method followed by the modified gray world chromatic correction algorithm were close to the true color and were satisfactory for human observation. This study introduces acolor recovery method which exploits few narrow-band spectral images that jointly cover about 30% of the visible spectral range. The existence of such a color recovery method proves that SNBSI is capable of having both spectral resolution and retain color information. Therefore, this paper proposes a method to realize snapshot narrow-band multi-spectral color imaging, which opens up a color restoration method different from the conventional RGB color camera.
		2021 Vol. 41 (01): 183-187 [Abstract] ( 214 ) RICH HTML PDF (2195 KB) ( 54 )

	188	COD Concentration Prediction Model Based on Multi-Spectral Data Fusion and GANs Algorithm
		CHEN Ying¹，XU Yang-mei¹, DI Yuan-jian¹，CUI Xing-ning¹，ZHANG Jie¹，ZHOU Xin-de¹，XIAO Chun-yan²， LI Shao-hua³
		DOI: 10.3964/j.issn.1000-0593(2021)01-0188-06
		Excessive concentration of organic pollutants in water is harmful, which causes not only serious environmental pollution but also endangers human health. Chemical oxygen demand (COD) can be used to characterize the pollution degree of organic pollutants in water. A quantitative prediction model of COD concentration based on generative adversarial networks (GANs) algorithm is proposed, which combines ultraviolet (UV) and Near Infrared (NIR) spectra with data-level fusion (DLDF) and feature level data fusion (FLDF). In this paper, firstly, COD standard samples are prepared according to a certain concentration gradient, and the ultraviolet spectrum (190~310 nm) and near-infrared spectrum (830~2 100 nm) of the standard sample are collected respectively. The first derivative and Savitzky-Golay (S-G) smoothing pretreatment of the obtained ultraviolet and near-infrared spectrum data are carried out to eliminate the baseline drift of the spectrum and the interference noise. Then, the data fusion of data level and featural level are carried out directly basing on the pretreated ultraviolet and near-infrared spectra, and the COD concentration prediction model is constructed by GANs algorithm. The model is evaluated by using the square of the correlation coefficient of the evaluation parameters (R²), the mean square root error of the predicted value and the real concentration value (RMSEP) and the prediction deviation. The results show that neither FLDF model nor DLDF model is not ideal. The analysis shows that the model contribution of the ultraviolet spectrum is concealed in the near-infrared band due to the unbalanced data in the ultraviolet and near-infrared bands, which makes the spectral fusion meaningless. In order to avoid the problem of fusion failure, the normalizat-ion method is proposed to deal with the mixed spectrum in the text. The effects of standard normal variation (SNV), maximum and minimum normalization (MMN) and vector normalization (VN) on the modeling are discussed. Then the normalized ultraviolet and near-infrared spectral data are fused again under the given sub-interval number, the input X of GAN model is taken as the input X, and the real measured COD value is taken as the output Y. The prediction models of COD concentration are established after different normalization methods. The modeling results show that different normalization methods have a great influence on the hybrid spectral data fusion model, and the prediction accuracy of the data-level fusion model and the feature-level fusion model is significantly improved before it is normalized, among which the prediction model with the maximum and minimum normalization is the most obvious. Finally, in order to verify the accuracy of the multi-spectral data fusion GANs Prediction model, the GANs prediction model of the full wavelength ultraviolet band of a single spectral source and the GANs prediction model of the full wavelength near-infrared band of a single spectral source are established. The experimental results show that the correlation coefficient of the characteristic level spectral fusion model basing on the ultraviolet and near-infrared spectra is 0.994 7, the prediction mean square root error is 0.976, the prediction model error is reduced by 52.9% comparing with the data level fusion, and the predicted recovery rate is 98.4%~103.1%, which is much better than the other groups. The generalization ability of the model is strong and the prediction accuracy is high. Compared with the monitoring model of single spectral source, the data fusion of mixed spectra can reflect more the chemical information of water samples, and reveals the pollutant degree of a water body more comprehensively, reflects the difference of pollutants in a water body from different levels, provides some technical support for on-line monitoring of COD concentration in water.
		2021 Vol. 41 (01): 188-193 [Abstract] ( 214 ) RICH HTML PDF (2066 KB) ( 91 )

	194	Feature Wavelength Optimization Algorithm for Water Quality COD Detection Based on Embedded Particle Swarm Optimization-Genetic Algorithm
		QI Wei, FENG Peng^*, WEI Biao, ZHENG Dong, YU Ting-ting, LIU Peng-yong
		DOI: 10.3964/j.issn.1000-0593(2021)01-0194-07
		In the water quality measurement based on UV-Visible spectroscopy, the spectral signal is easily disturbed by system noise and the scattering of suspended solids, and there are information redundancy, multicollinearity and other characteristics, resulting in a large deviation in the selection of characteristic wavelength in the COD measurement of water quality. Therefore, this paper proposes an optimization algorithm of water COD detection characteristic wavelength based on embedded particle swarm genetic (EPSO_GA) algorithm to improve the accuracy of wavelength selection. In order to verify the feasibility of the optimization algorithm for detecting characteristic wavelength, spectral data of water samples from a university pond, domestic sewage and drainage ditch were collected, and EPSO_GA algorithm was used to select characteristic wavelength from the pre-processed spectral data. EPSO_GA algorithm adopts the real coding method to realize unified coding of particle swarm optimization (PSO) algorithm and genetic optimization (GA) algorithm. The operations of selection, crossover, and mutation of the GA algorithm are embedded when the particles are updated in the PSO algorithm, which improves the limitations of these two algorithms in spectral wavelength feature selection. The characteristic wavelength selected by EPSO_GA algorithm was combined with the partial least square method (PLS) to construct the water COD prediction model of EPSO_GA_PLS, and compared with the traditional PSO algorithm and the GA algorithm, the PSO_PLS, GA_PLS established by the characteristic wavelength and the PLS water quality COD prediction model constructed by the full spectrum are compared. The results showed that compared with PSO_PLS, GA_PLS and the PLS water quality COD prediction model constructed by full-spectrum, EPSO_GA improves the precocious and slow convergence speed of PSO and GA in spectral characteristic wavelength selection, and reduces the complexity of constructing PLS water quality COD prediction model in the whole spectrum, the prediction accuracy of the model is improved. In the EPSO_GA_PLS water quality COD prediction model established based on EPSO_GA algorithm, the root-mean-square error decreased to 0.212 3, and the prediction accuracy increased to 0.999 3, which can quickly and quantitatively detect water quality COD, provides a better prediction model for COD measurement by UV-Visible spectroscopy.
		2021 Vol. 41 (01): 194-200 [Abstract] ( 211 ) RICH HTML PDF (3039 KB) ( 92 )

	201	Identification of Wild Black and Cultivated Goji Berries by Hyperspectral Image
		ZHAO Fan, YAN Zhao-ru, XUE Jian-xin, XU Bing
		DOI: 10.3964/j.issn.1000-0593(2021)01-0201-05
		Hyperspectral image technology has a broad application in the detection and identification of agricultural products. Wild black Goji berries have remarkable economic benefits, and are often impersonated by growing black Goji berries. A nondestructive and fast identification method for wild black Goji berries using hyperspectral image technology is proposed. Obtained results were as follows:(1) a total of 256 samples of black Goji berries (Wild，Growing, 128 each) in the range of 900～1 700 nm were observed, and each average spectra were used as simple spectra. (2) spectral is preprocessed with standardized normal variate transform (SNV) based on the Kennard-Stone(K-S) method, the calibration set and prediction set samples ratio were observed in 2∶1 (pairs). However, the spectra were found reduced in dimension by the successive projections algorithm method (SPA), and the 30 characteristic wavelengths extracted by the full spectra (FS). Then the 30 characteristic wavelengths and the full spectra are used as model inputs, the support vector machine (SVM), extreme learning machine (ELM), and random forest (RF) recognition models were established. (3) In the identification of wild black Goji berries models, the results showed that the calibration identification rate of SVM, ELM, and RF model with reference to FS and SPA were higher than 98.8%, and the prediction set samples rate of SVM, ELM, and RF model were also higher than 97.7%. The identification model of FS was slightly better than the identification model of SPA. However, the characteristic wave constant extracted by SPA is 11.8% less compared to FS, which eventually reduces the calculated model. RF identification model was reported better compared to SVM, and ELM, RF identification rate is 100%. The study has shown that the use of hyperspectral image technology combined with classification models can quickly identify wild black Goji berries.
		2021 Vol. 41 (01): 201-205 [Abstract] ( 212 ) RICH HTML PDF (2679 KB) ( 167 )

	206	Variable Selection Method in the NIR Quantitative Analysis Model of Total Saponins in Red Ginseng Extract
		AN Si-yu^{1, 2}, ZHANG Lei¹, SHANG Xian-zhao¹, YUE Hong-shui¹, LIU Wen-yuan^2, JU Ai-chun^1
		DOI: 10.3964/j.issn.1000-0593(2021)01-0206-04
		Yiqi Fumai Lyophilized Injectionis a new type of freeze-dried powder injection made of red ginseng, ophiopogon japonicus and schisandrachinensis. The total saponin content of red ginseng extract is an important quality control index in the production process of Yiqi Fumai lyophilized injection. The results of traditional analysis methods lag far behind, which cannot feedback the quality information of the production process timely, it is necessary to establish a rapid method for the determination of total saponin. As a process monitoring tool, near-infrared spectroscopy (NIR) has been widely used in the quality control of traditional Chinese medicine. How to extract the effective information from the spectrum with weak absorption and serious overlapping of spectral regions is the key to improve the monitoring veracity. Model population analysis (MPA) provides a new idea for variable selection method. In this study, the near-infrared spectrum data of 55 batches red ginseng extracts were collected. Themulti scattering correction (MSC) method was used to preprocess the spectrum data, the variable screening methods derived from MPA, such as random frog (RF), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), VCPA combined with IRIV (iterative retaining information variables) and the variable selection method of OPUS were respectively used in the establishment of PLS quantitative analysis model. The results showed that the R_c of model established by OPUS, CARS-PLS and RF-PLS were only 0.601 3, 0.565 3 and 0.644 0, respectively. The R_c of model established by VCPA-PLS was 0.951 2, which was the highest, but this model did not present good robustness. The model established by VCPA-IRIV-PLS had the best prediction effects; its R_c was 0.928 , RSEP% was 7.99%.
		2021 Vol. 41 (01): 206-209 [Abstract] ( 187 ) RICH HTML PDF (1334 KB) ( 83 )

	210	Rapidly Detecting Chlorophyll Content in Oilseed Rape Based on Spectral Reconstruction and Its Device Development
		WENG Hai-yong¹, HUANG Jun-kun¹, WAN Liang², YE Da-peng^1*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0210-06
		In order to rapidly and nondestructively detect chlorophyll content in leaves, a handheld multi-spectral imaging system was developed in this study to collect multispectral images of oilseed rape leaves. The pseudo-inverse method was introduced to reconstruct the multispectral reflectance at 6 wavebands (460, 520, 660, 740, 840 and 940 nm) to the hyperspectral reflectance at 512 wavebands in the range of 379～1 023 nm with the aim to improve the spectral resolution. The partial least square regression (PLSR) was then used to build a model to predict chlorophyll content in leaves based on the reconstructed hyperspectral reflectance. The results showed that the reflectance in the visible range of the reconstructed hyperspectral presented a high relationship with the chlorophyll content. The performance of PLSR model using reconstructed spectrum as inputs was evaluated using the parameters of the determination coefficient of prediction set (R²_p), root mean square error of prediction (RMSEP) and residual prediction deviation (RPD) with the values of 0.78, 1.50 and 2.14, respectively, which was better than that using original spectrum at 4 wavebands (460, 520, 660 and 740 nm) with the values of R²_p, RMESP and RPD of 0.72，1.85 and 1.88, respectively. The results demonstrated that the combination multispectral imaging with spectral reconstruction technology could improve the predicting ability of the PLSR model and this technology can be used for monitoring physiology and nutrient status in oilseed rape leaves.
		2021 Vol. 41 (01): 210-215 [Abstract] ( 217 ) RICH HTML PDF (3747 KB) ( 82 )

	216	Recognition Method for Crop Canopies Based on Thermal Infrared Image Processing Technology
		MA Xiao-dan^1*, LIU Meng¹, GUAN Hai-ou¹, WEN Feng-rui¹, LIU Gang²
		DOI: 10.3964/j.issn.1000-0593(2021)01-0216-07
		In order to solve the problem that the gray level distribution of crop canopy thermal infrared image isuneven and has large noise, the traditional image segmentation method is difficult to realize the effective recognition of its target region. In this study, by the thermal infrared images of adzuki bean canopy’s in the seedling stage was taken as the research object, Combining fuzzy neural network and affine transformation, a crop canopy recognition model based on thermal infrared image processing technology was proposed. First, the adaptive characteristics of the five-layer linear normalized fuzzy neural network were used to select the Gaussian membership function to automatically calculate the inference rules for canopy visible light image recognition, effectively segmenting the canopy area in the visible light image. By analyzing three segmentation indexes and entropy, the canopy segmentation quality of visible light images was quantitatively evaluated. When the network iterates 38 times, the error precision was 0.000 952, and the visible light image of the crop canopy was obtained. The average effective partition rate of the algorithm was 96.13%, and the entropy value of the image source average information was 2.454 4~5.198, which was only 0.245 9 different from the entropy of the canopy image obtained by the standard algorithm. Then, using the effective area of the canopy to obtain the visible light image as a reference image, the affine transformation algorithm was used to adjust the image transformation factors such as optimal translation, rotation, and scaling. To register the raw thermal infrared image. And a canopy thermal infrared image recognition method based on affine transformation was proposed. For a crop thermal infrared image with an initial temperature range of 16.35~19.92, when the rotation amplitude was 1.0 and the zoom factor was 0.9, the maximum temperature difference of the target image obtained as the optimal registration parameter of the heterogeneous image was 3.17 ℃. Relative The average temperature of the original image decreased from 18.711 ℃ to 17.790 ℃, and the crop canopy recognition based on thermal infrared image processing technology was realized. Finally, mutual information was used as a monitoring index to evaluate the thermal infrared image recognition method of crop canopy. In the canopy thermal infrared image recognition method proposed in this study, the average mutual information between the acquired target image and the initial thermal infrared image was 4.368 7, while the average mutual information between the standard target image and the initial thermal infrared image was 3.981 8, and the difference between the two was only 0.486 9. At the same time, the average temperature difference between the two canopy thermal infrared images was 0.25 ℃, which effectively eliminates the background noise of the original thermal infrared images. The research results show that the effectiveness and practicability of this research method could provide a technical reference for the application of thermal infrared images to reflect the characteristic parameters of crop physiological and ecological information.
		2021 Vol. 41 (01): 216-222 [Abstract] ( 226 ) RICH HTML PDF (3158 KB) ( 88 )

	223	Study on the Adaptability of Polarization Parameter Model of Winter Jujube in South Xinjiang to Outdoor Light Conditions
		SUO Yu-ting^1，2, LUO Hua-ping^1，2*, LI Wei^1，2，WANG Chang-xu^1，2, XU Jia-yi^1，2
		DOI: 10.3964/j.issn.1000-0593(2021)01-0223-06
		In agricultural remote sensing detection, clear weather is the necessary condition to collect spectrum. The main purpose of this study is: (1) to explore the influence of different weather (sunny, cloudy, cloudy) on the polarization parameters (S₀, ε₀, f₀₀), linear degree of polarization (Dolp) and physical and chemical value modes of different wavelengths of the BRDF of Dongzao in South Xinjiang through the hyperspectral polarization detection technology; (2) to compare the experimental results under different weather conditions for the remote control of outdoor jujube quality sensing detection provides a certain reference for environmental adaptability and prospective application. In this study, the statistical relationship between the water content of Dongzao jujube in South Xinjiang and its spectral polarization characteristics were analyzed. The first derivative spectral form of S₀, ε₀, f₀₀, Dolp and the statistical equation of water content of Dongzao jujube in South Xinjiang were established under different weather conditions. Three indexes were selected: correlation coefficient r, the standard deviation of calibration set (RMSEC) and standard deviation of prediction set (RMSEP) The performance and prediction ability of the model are evaluated. The results of correlation analysis showed that there was a good correlation between water content and hyperspectral polarization data when the quality of winter jujube was detected in sunny, cloudy and cloudy weather conditions, but the correlation between polarization parameter, linear polarization and water content was better than that in sunny and cloudy weather conditions. The former model had the largest correlation coefficient, and its r-score was the highest They are 0.913 and 0.914, and the value of correlation coefficient r of dark box spectrum and moisture content model is closest to 0.926. The feasibility analysis results of the model show that the maximum standard deviation of the calibration set samples of polarization parameter, linear polarization degree and water content model under three weather conditions are 0.009 71 and 0.008 73, respectively. The smaller the RMSEC value is, the better the model is established. The results show that the maximum standard deviation of the three weather models is 0.012 3 and 0.011 7, respectively. The smaller the RMSEP value is, the better the prediction ability of the model is. The results of different weather experiments show that the polarization can weaken the strong light and strengthen the weak light. By comparing the experimental results of sunny, cloudy and cloudy days, the method has better environmental adaptability and has a wide application prospect in outdoor jujube quality remote sensing detection.
		2021 Vol. 41 (01): 223-228 [Abstract] ( 180 ) RICH HTML PDF (4697 KB) ( 52 )

	229	Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy
		ZHANG Xu¹, ZHANG Tian-gang², MU Wei-song¹, FU Ze-tian^2,3, ZHANG Xiao-shuan^2,3*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0229-07
		The maturity of wine grape is an important quality index to determine the harvest time of grape. Aiming at the problem that the maturity of wine grapeis difficult to be detected in the field, the internal relationship between SSC and spectral data of wine grape was studied by Vis/NIR spectroscopy and chemometrics. The Vis/NIR spectral data of five varieties of grape and their leaves in different mature periods were obtained by USB2000+spectrometer. The spectral data were extracted by OMNIC 8.0 software, and the chemical values and spectral absorption values were modeled by TQ analyst 8.0 software. The wave band 450~1 000 nm which had high signal-to-noise ratio was selected, and PCA was adopted to eliminate the abnormal spectral data. The first derivative (FD), Savitzky-Golay smoothing (S-G), multiple scattering correction (MSC) and standard normal variate (SNV) were combined into four methods to preprocess the spectral data. Based on the spectral data of five varieties of grape berry and the spectral data of five varieties of grape leaf, the prediction models of SSC were established by PLS. The model effects with different pretreatment methods were compared, and the optimal pretreatment method was selected for modeling. Finally, the prediction models of SSC were verified by external samples. The results show that the performance of most prediction models is the best when S-G smoothing+FD+MSC preprocessing method is applied. The correlation coefficient of calibration sets and validation sets of grape berries were above 0.93 and 0.86, respectively, and the maximum root means square error is 0.30 and 0.48, respectively. The correlation coefficient of calibration sets and validation sets of grape leaves were above 0.73 and 0.65, respectively, the maximum root mean square error is 0.95 and 0.75, respectively. The highest average relative error between the predicted value and the real value of grape berry samples was 0.43%. The SSC prediction model built by the spectra of grape berry has a good predictive ability, which is superior to the SSC prediction model built by the spectra of the grape leaf. The prediction model of SSC can provide a theoretical reference for the study of grape maturity evaluation. Therefore, Vis/NIR spectroscopy is suitable for rapid and non-destructive detection of solid soluble content in the wine grape field.
		2021 Vol. 41 (01): 229-235 [Abstract] ( 177 ) RICH HTML PDF (4263 KB) ( 91 )

	236	Analyze on the Response Characteristics of Leaf Vegetables to Particle Matters Based on Hyperspectral
		KONG Li-juan, YU Hai-ye, CHEN Mei-chen, PIAO Zhao-jia, LIU Shuang, DANG Jing-min, ZHANG Lei, SUI Yuan-yuan^*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0236-07
		Particle matter (PM) pollution not only affects human life but also influences the photosynthesis, growth, yield as well as quality of the plant. In this paper, Pakchoi, oilseed rape(Brassica napus) and Italian lettuce which were at harvest periods were investigated in the simulated particulate pollution environment. The hyperspectral and photosynthetic data of leaf were obtained and their response mechanism to particle matters was studied by hyperspectral technique and plant phenotypic. The spectral and photosynthetic characteristics of leaf vegetables to particle matters were analyzed. The results showed that under the influence of particle matter only, the changing trend of hyperspectral response curves did not alter with the kinds of leaf vegetables, while reflectance value was very different. The leaf showed higher reflectivity within the visible region. The red edge position was “blue shift”. Oilseed rape was the most sensitive to PM, and Pakchoi had the strongest ability to absorb PM. The correlations between the net photosynthetic rate and the original spectra, the first derivative spectra, the ten hyperspectral characteristic parameters and the four vegetation indices were compared. The sensitive bands were extracted by the correlation analysis method. The characteristic wavelengths were extracted by original spectrum, first derivative (FD), multiple scatter correction (MSC) and correlation analysis method, and the net photosynthetic rate quantitative inversion model was established by ln logarithm operation, polynomial function and several combination methods. Characteristic parameters were got such as Dr, SDr, SDr/SDb, SDr/Sdy for Italian lettuce, SDr, Dy, NIRRP, （SDr-SDy）/（SDr+SDy） for oilseed rape and λr, SDy, （SDr-SDy）/（SDr+SDy） for Pakchoi. Moverover, the pretreatment methods were FD, second derivative (SD), Savitzky-Golay smooth (SG) and MSC. Also, four modeling methods were classical least squares（CLS）, principal component regression (PCR), stepwise multiple linear regression（SMLR）and partial least squares (PLS). The inversion model of the net photosynthetic rate of three kinds of leaves during the collecting period was established with correlated coefficients as an evaluation index of model. The combination of FD+SG+PLS was finally determined the best method for the net photosynthetic rate of Pakchoi and Italian lettuce inversion model, and the combination of FD+SG+MSC+SMLR was finally determined the best method for the net photosynthetic rate of Brassica napus inversion model. The results could provide references for the model modification of leaf vegetables under particle matter pollution environment. This study may provide the theoretical basis for the diagnosis and analysis of physiological changes of leaf vegetables under particle matter pollution using hyperspectral technology, and bring novel ideas for disease identification and early warning of facility agricultural vegetables.
		2021 Vol. 41 (01): 236-242 [Abstract] ( 172 ) RICH HTML PDF (3244 KB) ( 69 )

	243	Detection on Firmness and Soluble Solid Content of Peach During Different Storage Days
		LIU Yan-de, ZHANG Yu, JIANG Xiao-gang, SUN Xu-dong, XU Hai, LIU Hao-chen
		DOI: 10.3964/j.issn.1000-0593(2021)01-0243-07
		Soluble solid content (SSC) and firmness are two important indexes of peach, which determine its internal quality. However, the water in the peach fruit is lost, the surface begins to soften and rot, and the internal quality changes during transportation or sale. This paper aims to investigate the feasibility of visible/near-infrared spectroscopy（VIS-NIR）in predicting SSC and firmness of peach during different storage days and to predict the optimal storage period of peaches further. The spectrum of peach in 4 storage stages was collected by diffuse transmittance and reflectance, and the sugar and hardness were measured. The mean spectrum of peach in four stages was analyzed. The spectral intensity increased with the storage days, and the peak shift was caused by the changes in the color and pigment of the peel in the region of 650~680 nm. Meanwhile, the changes in SSC and firmness were analyzed. The SSC gradually increased during storage, while the firmness rapidly decreased during storage. Finally, the SSC increased by 3.31% and the firmness decreased by 58.8%. Pretreatment methods such as multivariate scattering correction(MSC), S-G smoothing, normalization and baseline are used to reduce the impact of noise and errors in the spectrum, and uninformative variable elimination (UVE) and successive projections algorithm (SPA) is used to select characteristic wavelengths, then the partial least squares regression(PLS) is used to establish prediction models for SSC and firmness. Analyzing the PLS regression coefficient of SSC and firmness with the mean spectrum, it is found that SSC has many high regression coefficient bands, and the high regression coefficient of firmness is near the peaks and troughs. Therefore, the SSC model established by the characteristic wavelength obtained by SPA and UVE is not good, while the firmness model is good. The results show that the best prediction correlation coefficient (R_p) and root mean square error of prediction (RMSEP) of SSC under the diffuse transmittance and reflectance detection methods are 0.886, 0.727, 0.820, 1.003, respectively. The pretreatment methods are MSC and S-G smoothing with 3 smoothing window width, respectively. In addition, the SPA-PLS model of firmness established by diffuse transmittance uses 15 spectral variables to obtain R_p and RMSEP of 0.798 and 0.976. The UVE-PLS model established by the diffuse reflectance uses 113 spectral variables to obtain R_p and RMSEP of 0.841 and 0.829. It can be seen that the diffuse transmittance method predicts SSC better, and the diffuse reflectance predicts the firmness better during peach storage. The SSC and firmness prediction models established by VIS-NIR can reliably predict the changes of SSC and firmness during the storage of peaches and have certain reference value to guide picking and selling time and reduce decay.
		2021 Vol. 41 (01): 243-249 [Abstract] ( 237 ) RICH HTML PDF (4795 KB) ( 157 )

	250	Estimation of Grassland Aboveground Biomass From UAV-Mounted Hyperspectral Image by Optimized Spectral Reconstruction
		KANG Xiao-yan^1,2, ZHANG Ai-wu^1,2*, PANG Hai-yang^1,2
		DOI: 10.3964/j.issn.1000-0593(2021)01-0250-07
		The accurate and timely estimation of above-ground biomass (AGB) is crucial for grassland monitoring and rational grazing. The unmanned aerial vehicle (UAV)-based hyperspectral remote sensing can obtain images with high spatial, spectral and radiometric resolutions in a short time and has been widely used in many fields such as precision agricultural and high-throughput plant phenotype. To explore the applicability of UAV-based hyperspectral image (UAV-HSI) in grassland AGB prediction, we collected UAV-HSI, grassland AGB, and relevant auxiliary data in a grassland sample area of Qinghai Province. However, it is not only inconvenient to widely collect, store and transmit, but also inefficient in data processing for UAV-HSI because of large data volumes, which may restrict practices of UAV-HIS widely. For resolving the above problems, a spectral reconstruction and optimization method considering both data simplification and spectral fidelity was proposed, attempting to ensure the performance of grassland AGB prediction and effective reduction of data volumes. First, using the residual quantization, we obtained several binary cubes (H_i) and corresponding coefficient matrices (β_i) with low volumes. H_i and β_i can replace the original data for storage and transmission. Second, preliminarily reconstructed spectra (PRS) can be produced by H_i and β_i. Third, through the Savitzky-Golay (SG) filter, optimized PRS (OPRS) can be achieved by enhancing the spectral fidelity. To demonstrate the effectiveness of OPRS, we carried out the spectral fidelity experiment. Taking a grassland canopy spectrum as an example, three fidelity indices, i. e., the spectral vector distance (SVD), spectral correlation coefficient (SCC), and spectral angle mapping (SAM), were analyzed. Results showed that, on the three fidelity indices, OPRS was superior to PRS. And then, the correlations between AGB and OPRS bands were discussed. Compared with raw spectra and PRS, OPRS achieved the relatively high and most stable potential in forage AGB prediction. Furthermore, the partial least squares regression (PLSR) was used to calibrate models of grassland AGB prediction. Results demonstrated that, among raw spectra, order -1 to -4 PRS, and order -1 to -4 OPRS, The prediction performances of order -4 and -3 OPRS reached the optimal and sub-optimal levels with RPD (the ratio of performance to deviation)=2.31 and 2.23, respectively. Their RPD values were 0.26 and 0.18 higher than that of the original spectra, respectively. Therefore, with a reduction of one order of magnitude, OPRS achieved a better performance than the original spectrum in the grassland AGB prediction. In other words, OPRS had advantages with both data simplification and accurate grassland AGB prediction. This study provides a new solution to estimate grassland AGB for UAV-HIS effectively.
		2021 Vol. 41 (01): 250-256 [Abstract] ( 208 ) RICH HTML PDF (5028 KB) ( 187 )

	257	Estimations of Winter Wheat Yields in Shandong Province Based on Remote Sensed Vegetation Indices Data and CASA Model
		ZHANG Sha^{1, 2}, BAI Yun^2*, LIU Qi², TONG De-ming², XU Zhen-tian², ZHAO Na², WANG Zhao-xue², WANG Xiao-peng², LI Yong-sha^{1, 2}, ZHANG Jia-hua^{3, 4}
		DOI: 10.3964/j.issn.1000-0593(2021)01-0257-08
		Accurate estimation of regional winter wheat yields is of great significance for understanding the agricultural production status and ensuring national food security. Light use efficiency (LUE) model is one of the most used models for crop yield estimation, however an important parameter, maximum light use efficiency (ξ_max), still remains large uncertainties, and whether the crop ξ_max changes along with time is also to be explored. In this paper, Savitzky-Golay (S-G) method is used to filter the time-series moderate resolution imaging spectroradiometer (MODIS) vegetation indices data, and a quadratic difference method and a spectral mutation method are used to extract the winter wheat planted areas during 2000—2015 in Shandong Province. Then a fixed ξ_max and a changed ξ_max are used to drive the CASA (the Carnegie-Ames-Stanford approach) model for years from 2000 to 2016 respectively. Using harvest index (HI) and winter wheat planted areas, the winter wheat yield during 2000—2016 in Shandong Province are obtained, to explore the effect of ξ_max on estimating winter wheat yield. The results show that the filtered time-series vegetation indices data capture the spectral features of winter wheat during the growth stages, and the extracted method used in this paper shows a good universal property. The extracted winter wheat planted areas agree well with the planted areas from statistical yearbooks at the city level, and the determination coefficient (R²) between those reaches 0.71, which indicates the extracted winter wheat planted areas are reliable in this paper. The R² between statistical yields and yields estimated with a changed ξ_max is 0.32, which is higher than that between statistical yields and yields estimated with a fixed ξ_max. This indicates that the ξ_max of winter wheat is changed along with time, and the varieties replacement of winter wheat may be responsible for this. Both the statistical and estimated yields of winter wheat during 2000—2016 show increasing trends with increasing rates of 93.12 and 149.79 kg·hm^-2·a^-1, respectively. The winter wheat yields in the western Shandong province are overall higher than those in the eastern study area.
		2021 Vol. 41 (01): 257-264 [Abstract] ( 208 ) RICH HTML PDF (7936 KB) ( 82 )

	265	Research on Maize Growth Monitoring Based on Visible Spectrum of UAV Remote Sensing
		WANG Xiang-yu¹, YANG Han², LI Xin-xing², ZHENG Yong-jun³, YAN Hai-jun⁴, LI Na^5*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0265-06
		Maize is one of the most important food crops in China, which has the largest planting scale and the fastest growing trend. The growth of maize will directly affect its yield and quality. Therefore, through effective monitoring of the growth of maize can provide macro information for field management and yield estimation, and provide an important basis for decision-making by the relevant national departments. In this study, Unmanned Aerial Vehicle (UAV) equipped with an image sensor was used as a low-altitude remote sensing platform to obtain visible spectral remote sensing images of maize. First we made geometric and radiometric correction of maize canopy visible spectrum image by ENVI software, and then we made gray and enhancement processing of the color image. AP-HI algorithm was used to obtain the maize coverage information, for it has strong light adaptability to the complex background of farmland. The image was segmented by AP-HI algorithm and converted it into a binary image to remove the background of the land, water pipes, roads and residues in the image, so as to retain the binary image of maize. The road existed in the farmland of the image, which needed to be excluded when calculating the actual crop coverage. The road area appeared in the four boundaries and center of the image. The number of black pixels in the road area was counted and the road width was calculated according to the number of pixels, and then the road part was removed from the binary image. In the processed image, the white pixels are the non-crop area, and the black pixels are the maize planting area. In order to calculate the size of maize crops, the proportion of black pixels to total pixels in the binary image needed to be counted. The unit area was selected as 80×80 pixels, and the image was marked by blocks from top to bottom and left to right and got the number of blocks was 720. The unit area was scanned, and the proportion of the black pixels per unit area to the total number of pixels (6 400 pixels) was calculated. Until the 720 blocks were completely counted, the proportion of the number of black pixels to total pixels in the image could be calculated, which is the maize coverage. The relational model between coverage and Leaf Area Index (LAI) through canopy porosity was established to complete maize LAI inversion, so as to provide theoretical basis for monitoring maize growth. The results show that low altitude UAV visible spectrum remote sensing can be used as an effective method to extract crop coverage, which has a good prospect.
		2021 Vol. 41 (01): 265-270 [Abstract] ( 280 ) RICH HTML PDF (4937 KB) ( 155 )

	271	Study on Inversion Model of Soil Heavy Metal Content Based on NMF-PLS Water Content
		WU Xi-jun¹, ZHANG Jie¹, 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)01-0271-07
		The excessively high content of heavy metals in the soil is hugely harmful, not only causing serious environmental pollution, but entering the human body through the food chain poses a serious threat to human health, so it is very important for heavy metal detection. X-ray fluorescence spectroscopy has been widely used because of its short detection time, non-destructive testing, and low testing costs. However, the detection of spectral data is severely disturbed by soil moisture factors, which leads to lower accuracy in estimating the heavy metal content in the soil directly. Taking the soil samples of Mancheng District, Baoding City, Hebei Province as the research object, the collected soil samples were cleaned, screened, dried, and then added with a certain amount of heavy metal solution to prepare samples with different water content and heavy metals for detection. The Mahalanobis distance and NJW clustering were calculated for the abnormal data in the experiment, and the influence of soil moisture content on the heavy metal spectrum was analyzed, the results show that the spectral repeatability of different water content is poor, and the spectral intensity decreases nonlinearly with the increase of soil water content. The Savitzky-Golay convolution smoothing denoising method and linear background method are used to preprocess the spectrum to solve the problems of noise and baseline drift caused by the environment and the instrument itself. A non-negative matrix factorization algorithm was used to deal with the peak signal-to-noise ratio evaluation model to determine the number of end elements. The results show that the peak signal-to-noise ratio tends to increase when the number of end elements increases to 10. The stable fluctuation is very small. After the non-negative matrix decomposition treatment, the spectrum repeatability and similarity are good among the same heavy metal content and different water content. The correlation coefficient between the spectra is calculated to prove the similarity between the spectra further. A partial least squares prediction model was established after removing the water content for spectral interference. In order to verify the accuracy of the prediction model, a partial least squares prediction model with no water content removed was established, and the partial water content was removed by orthogonalization with external parameters The least squares prediction model is evaluated using the evaluation parameter determination coefficient (R²), cross-validated root mean square error (RMSECV), average absolute error (MAE), and relative analysis error (RPD). Validation results show that compared to models built without removing water content, non-negative moments are used partial least squares model established by matrix decomposition and removal of water content R² and RPD increased by 0.019 7 and 1.029 2, RMSECV and MAE decreased by 2.386 3 and 1.439 6; Compared to the partial least squares model established by the external parameter orthogonalization method, the RPD and RPD increased by 0.009 9 and 0.108 1, and the RMSECV and MAE decreased by 0.244 7 and 0.356 6, it is shown that the model established after denoising by non-negative matrix decomposition can effectively improve the accuracy and robustness of prediction. Non-negative matrix factorization can effectively eliminate the effect of soil water content on the spectrum, and the partial least squares model established on this basis has realized the inversion of soil heavy gold content and provided certain technical support for quantitative detection of heavy metals.
		2021 Vol. 41 (01): 271-277 [Abstract] ( 203 ) RICH HTML PDF (3508 KB) ( 59 )

	278	Study on Repairing Mechanism of Wind Quenching Slag Powder in Heavy Metal Contaminated Soil by XRD and SEM
		ZHANG Hao¹，YU Xian-kun²，XU Xiu-ping^2*，YANG Gang³
		DOI: 10.3964/j.issn.1000-0593(2021)01-0278-07
		Steel slag tailings are the main solid waste in metallurgical industry, with the production of 15%~20% of crude steel. The utilization ratio is quite low and only reaches 10% of steel slag tailings production due to limited technology. Meanwhile, steel slag tailings are disposed of in direct stacking and landfill in general since the management system is not perfect, which pollutes land source, underground water source and air quality. In the face of the above problems, the development of an inexpensive curing agent is used for repairing of heavy metal contaminated soil has become not only one of the main methods to the sustainable development of metallurgical solid waste, but also one of the main methods to achieve the greatly reduce the production cost of heavy metal contaminated soil remediation. Wind quenching slag powder was used as curing agent to repair the heavy metal contaminated soil including Cd, Cu, Pb, Ni and Zn in this paper. This study investigates the wind quenching slag grinding time, wind quenching slag powder content and curing time on the repairing effect of heavy metal contaminated soil. The particle size distribution of wind quenching slag powder was characterized by LPSA, the pore structure of wind quenching slag powder was characterized by BET, the microstructure of mixture of wind quenching slag powder-heavy metal contaminated soil was characterized by SEM, mineral composition of wind quenching slag powder was characterized by XRD in order to the mechanism of wind quenching slag powder repairing heavy metal contaminated soil was analyzed. The results indicate that the properties of wind quenching slag are safe and there is no pollution to the ecological environment, which can be used as a curing technology for remediation of heavy metal contaminated soil. The solidification effect of wind quenching slag powder (the wind quenching slag grinding time, wind quenching slag powder content and curing time are 100 min, 20% and 14 d, respectively) on Cd, Cu, Pb, Ni and Zn of heavy metal contaminated soil was more than 91%. With the extension of wind quenching slag grinding time, the particle size of wind quenching slag powder is decreased and its particle size distribution tends to be uniform. The damage of porous structure and the increase of the specific surface area of wind quenching slag are beneficial to improve the effect of wind quenching slag powder on heavy metal contaminated soil restoration. With the increase of wind quenching slag powder content, the amount of hydrated gel (C—S—H) formed by wind quenching slag powder is increased, which is beneficial to improve the effect of wind quenching slag powder covering heavy metal contaminated soil, so as to achieve the purpose of curing Cd, Cu, Pb, Ni and Zn in heavy metal contaminated soil. Wind quenching slag powder can selectively be curing Cu, Cd, Ni, Zn and Pb in heavy metal contaminated soil. Under different curing times, heavy metals exist in the form of Cd₂SiO₄, Cu(OH)₂·2H₂O, PbCO₃, 3Ni(OH)₂·2H₂O, Ni₂SiO₄, Zn(OH)₂ and Zn₂SiO₄, respectively.
		2021 Vol. 41 (01): 278-284 [Abstract] ( 196 ) RICH HTML PDF (3456 KB) ( 60 )

	285	X-Ray Fluorescence Spectroscopy Combined With Discriminant Analysis to Identify Imported Iron Ore Origin and Brand: Application Development
		LIU Shu¹, ZHANG Bo^1，2, MIN Hong¹, AN Ya-rui^2, ZHU Zhi-xiu¹, LI Chen^1
		DOI: 10.3964/j.issn.1000-0593(2021)01-0285-07
		Iron ore is an important raw material for the iron and steel industry. China is an iron ore import-demand country and the world’s largest iron ore consumer. The main goal of the customs’ inspection of imported iron ore is to prevent the risk of safety, health, environmental protection, fraud and other aspects of imported iron ore. The compliance verification of the origin and brand of imported iron ore can quickly screen the phenomena of adulteration, adulteration, and inferior charging, which support the risk management of imported iron ore and ensure trade facilitation. This article expands the application based on previous research. The research objects are 422 imported iron ore samples from 5 countries. In this paper, the accuracy of the non-standard sample analysis method of wavelength dispersive X-ray fluorescence spectrum is investigated. For the elements not detected in the measurement process, the detection limit was chosen to replace the missing values. For the outliers in the measurement process, F-test based on residual variance is used to eliminate the outliers. Each of the Pilbara Blend Lumps, Newman Blend Lumps, and Newman Blend Fines has one F statistic calculated from one set of data is greater than the F-test critical value (a=0.01), so these three sets of data are eliminated. The contents of Fe, O, Si, Ca, Al, Mn, Ti, Mg, P, Na, Cr, K, Sr, S, Zn, V, Cu, Ba, Ni, Mo, and Pb are selected by the stepwise discriminant method as the characteristic variable of the original identification model, and a four-dimensional Fisher discriminant model is established to identify the origin of the iron ore. The contents of Fe, O, Si, Ca, Al, Mn, Ti, Mg, P, Na, Cr, K, Sr, S, Zr, Zn, V, Cu, Ba, Cl, Ni, Mo, and Pb are selected by the stepwise discrimination method as the feature variables of the brand recognition model, and a 20-dimensional Fisher discriminant model is established to realize the recognition of 21 brand iron ores. The contribution of characteristic elements to the classification and recognition model is investigated, and the element characteristics of misidentified brand iron ore are analyzed. On this basis, the paper summarizes the whole data processing flow of the discrimination analysis model of the origin and brand of imported iron ore.
		2021 Vol. 41 (01): 285-291 [Abstract] ( 212 ) RICH HTML PDF (1974 KB) ( 94 )

	292	Study on the Spectral Identification Characteristics of “Heiqing” and “Heibi”
		DAI Lu-lu¹, JIANG Yan¹, YANG Ming-xing^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2021)01-0292-07
		“Heiqing” refers to dolomite-related nephrite with the color of the nearly black whose main component is tremolite. “Heibi” refers to serpentinite-related nephrite with the color of the nearly black whose main component is actinolite. EPMA, LA-ICP-MS and FTIR spectrum were applied to obtain the mineral species of “Heiqing” and “Heibi”. Raman spectrum, Micro-UV-Visible spectrophotometer and FTIR spectrum were used to investigate the spectral identification characteristics of “Heiqing” and “Heibi”. “Heiqing” have the standard tremolite peak position, and main peak positions of “Heibi” have several wavenumber deviations, which move in the direction of small wavenumber. In visible-near infrared band “Heiqing” appear 445 nm absorption peak and 680, 940nm wide absorption band, which were attributed to the role of Fe²⁺ and Fe³⁺; “Heibi” appears 445 nm absorption peak, 660, 690 nm double absorption peak and 970 nm , which were attributed to the role of Fe²⁺ and Fe³⁺, Cr³⁺. The near-infrared region of the samples can be analyzed by Micro-UV-Visible spectrophotometer. The strong absorption peaks of “Heiqing” appear at 1 397, 2 310, 2 387 and 2 466 nm, and the weak absorption peaks appear 1 915 and 2 120 nm; The absorption peaks of “Heibi” appear at 1 400, 2313 and 2 394 nm. The results of FTIR spectrum indicates that the absorption peaks of “Heiqing” were at 5 225, 4 738, 4 692, 4 349, 4 317, 4 190, 4 064 cm^-1and the absorption peaks of “Heibi” were at 4 708, 4 307, 4 178 and 4 031 cm^-1. Although there aresome small differences between the results of Microscopic UV-Visible spectrum and FTTR spectrum analysis, the results are basically consistent, the results of FTIR spectrum analysis shall prevail. By comparing “Heiqing”, “Heibi” and actinolite jade of Dahua, the near-infrared spectrum identification characteristics of “Heiqing”(tremolite) and “Heibi”(actinolite) are “Heiqing”(Tremolite) have two absorption peaks at 4 800～4 600 cm^-1, and split double absorption peaks at 4 350～4 300 cm^-1. “Heibi”(actinolite) have a weak absorption peak at 4 800～4 600 cm^-1, and a single absorption peak at 4 350～4 300 cm^-1. Moreover, the whole near-infrared absorption peaks of “Heibi” (actinolite) moves in the direction of smaller wavenumber than “Heiqing” (Tremolite).
		2021 Vol. 41 (01): 292-298 [Abstract] ( 259 ) RICH HTML PDF (2885 KB) ( 110 )

	299	Classification of Changbai Mountains Pork Based on Laser-Induced Breakdown Spectroscopy
		LIN Xiao-mei¹, SUN Hao-ran², XU Yu-ting³, LIN Jing-jun², WANG Yue⁴, WANG Zhen-xing⁴, GAO Xun^3*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0299-06
		The internal structure of pork is complex, the matrix effect is strong, the components of each part are similar, so it is not easy to distinguish. Combined with laser-induced breakdown spectroscopy, the classification accuracy is improved by spectral analysis. Five different parts of Changbai Mountains Pork (tenderloin, plum blossom, hind leg, front leg and streaky pork) were used as the samples to be tested. The feasibility of identifying pork fat, muscle and their different parts by laser-induced breakdown spectroscopy was explored by means of cold storage, slice and other pretreatment methods. Firstly, by collecting LIBS spectral line information of fat pork samples and muscle samples, it is found that Mg, K, Fe, Cu, CA, Na and other elements are abundant in pork, and C—N bond is found in the spectrum of fat samples. Compared with LIBS spectral line information of muscle samples, the background and noise signal of the spectral line information of fat samples are greatly disturbed due to the influence of their internal moisture and organic matter composition. There are some differences, which indicates that LIBS can be used to distinguish adipose tissue from muscle tissue. Through the detection of the LIBS characteristic spectral intensity of the target elements Ca, Na, Mg, K and Al, the ratios of mg/Ca, Al/Ca, Na/Ca and K/Ca were calculated. It was found that the distribution of element ratio of Na/Ca and K/Ca was significantly different from that of Al/Ca and Mg/ca. On this basis, according to the ratio of Na/Ca and K/Ca, the decision threshold of element distribution of pork was calculated [(1-α)=90%]. It is found that Na/Ca and K/CA can reflect the distribution of elements more clearly than Al/Ca and Mg/ca. The threshold value of the ratio distribution can be used to distinguish different parts of pork. Taking the front leg meat and the back leg meat as an example, the Na/Ca and K/Ca ratios of the front leg meat were 1.29～1.58 and 0.31～0.42 respectively, and the Na/Ca and K/Ca ratios of the back leg meat were 0.98～1.18 and 0.15～0.23 respectively. There was no obvious overlap in the distribution of element ratio. Finally, in order to improve the reliability of LIBS technology in the classification of different tissues of pork, the spectral element intensity ratio data and principal component analysis method are combined, which can basically achieve the classification of various parts of pork, indicating that the element characteristic spectral line intensity ratio has certain prediction accuracy in the classification of various parts of pork. The whole work has proved that it is feasible to use laser-induced breakdown spectroscopy in qualitative analysis of pork, such as classification and identification, which is expected to be suitable for other biological tissue detection and analysis.
		2021 Vol. 41 (01): 299-304 [Abstract] ( 190 ) RICH HTML PDF (4113 KB) ( 65 )

	305	Research on Radiation Spectroscopy Thermometry of Plume of Solid Rocket Motor
		GUO Xiao-xu¹, PAN Ke-wei², HOU Long-feng¹, YANG Bin^1*, PING Li¹, XU Qiu-li², LIU Jin-liang¹, WANG Ying¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0305-07
		The plume of solid rocket motor has the characteristics of high temperature, high speed and intense radiation. The temperature of the plume is an essential parameter of condition and performance.The accurate temperature measurement of the plume of a solid rocket motor is important to provide a valuable reference for understanding the internal combustion condition and the overall performance of the motor. With the development of laser and spectroscopy, the laser spectroscopy technology is gradually applied to the measurement of combustion of solid propellant and plume temperature. Radiation spectroscopy thermometry can realize the non-intrusive and on-line measurement of temperature by measuring the radiation spectrum of flame. It has the advantages of wide temperature measuring range, fast response and high reliability. It can be applied to measure the temperature of the plume of the solid rocket motor. In this paper, the thermometry based on radiation spectroscopy was proposed to measure the temperature of the plume of the solid rocket motor. The measurement system of the radiation spectrum of the plume of the solid rocket motor was built using a 350～1 000 nm fiber spectrometer. Moreover, the spectral response coefficient was calibrated with a standard radiation blackbody furnace. The curve of response coefficient with wavelength was obtained to revise the measured radiation spectrums of the plume. Then the measurement system was applied to ground tests of standard Φ118 solid rocket motors, the radiation spectrums of the plume of the solid rocket motor, which with a typical 12% aluminum mass content propellant, were measured. The characteristics of radiation spectrums at different working times were analyzed. Furthermore, the graybody assumption was discussed based on the two-color gray judgment principle. The radiation of plume in a 675～745 nm spectral range can be considered as graybody.The maximum relative deviation of emissivity with wavelength was 4.01%, and the corresponding mean-variance was 1.53%. Therefore, the parameters of temperature and emissivity at the different working times were obtained by spectral fitting based on Planck radiation law. The maximum deviation between the temperature measurement and the theoretical thermodynamic calculation is 5.40%. Besides,the relationship between the measurement results and conditions were discussed, the radiation spectrums of the plume of the solid rocket motors with 12%, 15%, and 19% aluminum mass content propellants were measured, and the characteristics of radiation spectrums with different aluminum mass content were discussed. In addition, the influences of aluminum mass content on radiation spectrums, temperature, and emissivity of the plume were analyzed with the measurement results. This research on radiation spectroscopy thermometry of the plume of the solid rocket motor can provide the tool for performance evaluation and formulation optimization of the solid rocket motor. The influences of aluminum mass content of propellant on radiation spectrums, temperature, and emissivity of the plume can provide the experimental data support for reducing the characteristic signal of the plume of the solid rocket motor.
		2021 Vol. 41 (01): 305-311 [Abstract] ( 279 ) RICH HTML PDF (1841 KB) ( 324 )

	312	Research on Constant Temperature Two-Color Light Sources for Nighttime Visibility Estimation
		TANG Qi-xing¹, ZHOU Yi¹, DAI Pang-da¹, GAO Yan-wei², FAN Bo-qiang^1,3, LI Meng-qi^1,3, HE Ying¹, YOU Kun¹, ZHANG Yu-jun^1*
		DOI: 10.3964/j.issn.1000-0593(2021)01-0312-07
		Visibility is the maximum horizontal distance that can be seen. Its observations and forecasts have been widely used in various fields of weather forecasting, environmental pollution analysis, and transportation. The existing visibility estimation methods are mainly divided into scattering type and transmission type. Among them, digital camera method for measuring visibility is the closest to the definition. With the development of digital camera technology, the research and application of digital camera measurement methods have been accelerated. However, in the process of nighttime visibility measurement by using a digital camera, the measurement is inevitably affected by the background light, the gray level of the light source, etc., resulting in unstable visibility measurement, low precision of observation results and small observation range. It is known that the accuracy of visibility measurement can be guaranteed by using the stability of dual light sources. Most studies have used a white light source to solve the measurement visibility instability problem. From the perspective of quasi-monochromatic light sources, the penetration ability of light sources in different frequency bands is different. In the visible range, the characteristics of the penetrating ability are analyzed. Based on the existing dual light sources, a method for nighttime visibility estimation based on constant temperature two-color light sources has been proposed, which realized high-precision and wide-range visibility estimation under different weather conditions. By designing the constant temperature duallight sources, the influence of ambient temperature change on the light intensity is reduced. Constant voltage and the constant current module are used to ensure the consistency of the dual sources light intensity. The integrating sphere is used to ensure the uniformity of the light intensity. According to different penetrating powers of different frequency bands, the two-color light sources are used to achieve high-precision and wide-range visibility. A visibility observation system based on constant temperature two-color light sources has been established. A series of experiments have been carried out. The experimental results show that the consistency of the two light sources reaches 0.99. When the visibility is not good, the light intensity of the blue light reaching the camera is weak, and the measurement result of the red light is close to the true value. When it is sunny, nighttime visibility is good. At this time, the difference in blue transmittance is large, which is beneficial to improve the signal-to-noise ratio. The standard deviation of the blue light source is 36.90, and the measurement result of blue light is close to the true value. When the visibility range is up to 15 000 m, one month of experimental observation is performed. By comparing with real values, the proposed method has great accuracy within the visibility range.
		2021 Vol. 41 (01): 312-318 [Abstract] ( 212 ) RICH HTML PDF (2730 KB) ( 48 )

	319	Efficient Saponification and Separation of Unsaponifiables for Authentication of Sesame Oil Using FT-IR Spectroscopy and Chemometrics
		HE Wen-xuan^*, LIN Qi
		DOI: 10.3964/j.issn.1000-0593(2021)01-0319-08
		Sesame oil is one of the essential cooking oil, and it has been consumed in daily life. The intake of adulterated sesame oil leads to severe health problems. Thus the method to identify adulteration is significant research. Saponification is one of the simple and inexpensive processes have been used to identify the adulteration in edible oil. The saponification takes a long time, higher temperature and the isolation of unsaponifiable from saponifiable is tedious. In the present research, the enriched saponification process has been developed using ultrasonication technique instead of a common conventional method. The process has been significantly reduced to ten minutes. The special solid phase extraction (SPE) cartridge has been designed and prepared to separate the unsaponifiable. The combined FT-IR with chemometrics based on the isolated unsaponifiable was first used to authenticate sesame oil. The partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) was employed to establish the models to identify the adulteration detection and authentication of sesame oil. The results indicated that the OPLS-DA is better than PLS-DA, which was chosen for the authentication of sesame oil. The prediction of samples was accurate by the constructed model. The results suggested that the combined FT-IR spectroscopy with chemometrics based on isolated unsaponifiable could be used for authentication of sesame oil.
		2021 Vol. 41 (01): 319-326 [Abstract] ( 191 ) RICH HTML PDF (2592 KB) ( 58 )

	327	Study on Catalytic Combustion of Dioxins From Iron Ore Sintering Flue Gas Over Ce-V-Ti Catalysts by XRD and FTIR
		SHI Qi¹, DING Long¹, LONG Hong-ming^1,2*, CHUN Tie-jun¹
		DOI: 10.3964/j.issn.1000-0593(2021)01-0327-06
		Dioxins are a group of chlorinated volatile organic pollutants (VOCs) with environmental persistence, biological accumulation and long-term residual properties. It can cause teratogenic, carcinogenic and mutagenic hazards. During the iron ore sintering process, dioxins can be catalytically synthesized from chlorine-containing precursors by Ullman reaction in the alkaline environment or by some catalytic components on the surface of fly ash. Besides, dioxins can be synthesized by de novo through elementary reaction. Iron ore sintering process is one of the most emission sources of dioxins. Physical adsorption technology can only remove pollutants from gas phase to the solid phase and increase the aftertreatment problem of fly ash. Besides, there is a risk of dioxins regeneration under 250～350 ℃. Catalytic combustion can be completely degradation dioxins into CO₂，H₂O and HCl/Cl₂ over catalysts. It is an efficient, energy conservation and low-cost method to avoid secondary pollution. However, the working temperature of traditional catalysts is too high to the end temperature of the sintering flue gas. It is important to improve the catalytic activity at low temperature to achieve high efficiency catalytic combustion of VOCs from iron ore sintering flue gas. As Ce has the 4f orbital coordination effect and Lewis acid site, which plays a crucial role in the activation of C—H and C—Cl bonds in organic pollutants, the anti-chlorine toxicity and combustion activity of the catalystcan be improved by doping transition metal or adjusting the proportion of active components of catalysts. Hence, the effect of different Ce/V weight ratio of Ce-V-Ti catalysts prepared by sol-gel method were studied in this paper. Chlorobenzene was used as the model molecule of dioxins. The phase, specific area, molecular structure and functional groups of Ce-V-Ti catalysts were characterized by XRD, BET,XPS and FTIR. The results show that the catalytic activity of chlorobenzene over Ce-V-Ti catalysts with 15 Wt% Ce and 2.5 Wt% V can achieve CB conversion of 60% at 150 ℃ and 95% at 300 ℃ under the reaction conditions of GHSV=30 000 h^-1, 20% O₂ and 100 ppm CB. The chemical interaction between the barrier and the active component affected the catalytic activity of catalysts. According to the spectroscopic analysis, the XRD pattern of Ce-V-Ti catalysts was mainly anatase TiO₂. The specific surface area was 95.53 m²·g^-1, the volume of the pore was 0.29 cm³·g^-1，and D_BJH was 6.5 nm. Most of the functional groups on the Ce-V-Ti catalysts were C—H groups and O—H, which was expedited the adsorption and desorption of CB. The introduction of V as co-catalytic compositioninto Ce-Ti catalyst promoted the solid solution reaction of Ce element and increased the oxygen vacancy on the surface of the catalyst, which was conducive to improving the catalytic activity of the catalyst. Meanwhile, the oxidation reaction of V in low-price promotes the reduction reaction of Ce.
		2021 Vol. 41 (01): 327-332 [Abstract] ( 180 ) RICH HTML PDF (2520 KB) ( 76 )

　CopyRight © OFFICE OF SPECTROSCOPY AND SPECTRAL ANALYSIS
　Address: NO.76 Collage South Road of Beijing 　Postalcode:100081
　Tel. (010)62181070　E-mail:chngpxygpfx@vip.sina.com
　Support by Beijing Magtech Co.ltd　E-mail:support@magtech.com.cn