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2022 Vol. 42, No. 06
Published: 2022-06-01
 
1661 Research Progress of Spectroscopic Techniques in Foreign Object Detection of Aquatic Products
LI Xin-xing1, 2, MA Dian-kun1, XIE Tian-hua3, ZHANG Chun-yan1, HU Jin-you3*
DOI: 10.3964/j.issn.1000-0593(2022)06-1661-05
During the 14th Five-Year Plan period, the total fishery production in China is expected to continue to grow, and aquatic products further become an important dietary component for consumers. However, it is highly likely to lead to food safety incidents due to irregularities in the breeding, processing and cooking processes. Spectroscopy has become a hot spot for aquatic product testing technology because of its advantages of rapid, nondestructive, and high-test reproducibility, reflecting both the spectral properties of objects and the spatial information of samples, but mostly focusing on freshness testing. This paper reviews the literature related to the application and progress of spectroscopic techniques in foreign matter residues of aquatic products in the past 10 years. It introduces the common spectroscopic techniques in their application and progress from four aspects: fishbone detection, adulteration analysis, parasite detection and heavy metal detection, mainly including X-Ray technology, visible imaging, near-infrared imaging, hyperspectral imaging, etc. While introducing the current problems, we look forward to the development of spectroscopic techniques in aquatic products. The development prospect of foreign substance residue detection: traditional detection algorithms are further optimized, and multi-spectral technology is used for foreign substance residue detection of aquatic products; the great advantage of deep learning in feature extraction is applied, and the application field of spectral technology in foreign substance residue detection of aquatic products is studied more deeply; the organic integration of spectral technology and multiple detection technologies becomes an inevitable trend, and online real-time detection becomes possible.
2022 Vol. 42 (06): 1661-1665 [Abstract] ( 215 ) RICH HTML PDF (967 KB)  ( 184 )
1666 Fourier Transform Near-Infrared Spectral System Based on Laser-Driven Plasma Light Source
WANG Yue1, 3, 4, CHEN Nan1, 2, 3, 4, WANG Bo-yu1, 5, LIU Tao1, 3, 4*, XIA Yang1, 2, 3, 4*
DOI: 10.3964/j.issn.1000-0593(2022)06-1666-08
As a commonly used scientific research-grade near-infrared spectral detection instrument, the near-infrared Fourier transformation spectrometer is widely used in various scientific research fields. The current near-infrared spectrometer focuses on improving spectral resolution and pays less attention to the improvement of the spectral signal-to-noise ratio. The spectral signal-to-noise ratio directly affects the accuracy of spectral line index measurement, the higher the spectral signal-to-noise ratio. The higher the accuracy of spectral line index measurement, the more conducive to the fine spectral comparison of trace substances. Therefore, it is necessary to improve the spectral signal-to-noise ratio of the spectrometer. Compared with commonly used tungsten light sources, laser-driven plasma light sources (LDLS) not only have the advantages of high light intensity in near-infrared regions but also their unique high-frequency modulation output signal can be well suppressed by the background signal on the interference spectrum after modulation and deconstruction by the lock-phase amplifier. The combination of high brightness and radiation modulation has significantly improved the spectral signal-to-noise ratio of the near-infrared Fourier transformation spectral system with LDLS as the light source. For the above reasons, this paper proposes to use the new laser-driven plasma light source as the spectral signal output source of the near-infrared Fourier transformation spectral system, and with the modulation ability of tungsten light source set up by the near-infrared Fourier transformation spectral system to carry out a comparative experiment of signal-to-noise ratio. Firstly, the tungsten light source is used by chopper high-frequency modulation and then de-adjusted by the lock-phase amplifier, the integration time of the lock-phase amplifier is optimized, and the interference spectral signal-to-noise ratio is calculated by using the signal-to-noise ratio evaluation method given in the text. The signal-to-noise ratio is compared separately, the integral time is 0.5, 1, 5, 10 and 20 ms, respectively. Interference spectrum signal-to-noise ratio and symmetry, and the optimal integration time of the phase-lock amplifier in subsequent systems are determined to be 5ms. The interference spectrum signal-to-noise ratio of tungsten light sources in this state is calculated to be about 90∶1. The system built by using laser-driven plasma light source instead of a tungsten light source and chopper is used to build the system of the light source at the optimum integration time. The results of the comparative evaluation of interference spectrum signal-to-noise ratio of traditional light source systems show that the interference spectrum signal-to-noise ratio of laser-driven plasma light sources is 111 times higher than that of conventional tungsten light sources. The results show that the near-infrared Fourier transformation spectral system, constructed by the light source, has a peak error of 0.5 nm
2022 Vol. 42 (06): 1666-1673 [Abstract] ( 202 ) RICH HTML PDF (4686 KB)  ( 211 )
1674 Study on Performances of Transmitting Pressure and Measuring Pressure of [C4mim][BF4] by Using Spectroscopic Techniques
ZHU Xiang1, 2*, YUAN Chao-sheng1, CHENG Xue-rui1, LI Tao1, ZHOU Song1, ZHANG Xin1, DONG Xing-bang1, LIANG Yong-fu2, WANG Zheng2
DOI: 10.3964/j.issn.1000-0593(2022)06-1674-05
Diamond anvil cell (DAC) is a kind of high-pressure generator that is frequently used in the laboratory and plays an important role in the field of high-pressure research. When pressure-transmitting medium (PTM) in DAC can just provide non-hydrostatic pressure, it will be difficult to accurately measure the pressure of the sample by using the ruby fluorescence method. The same cases frequently emerge under ultra-high pressure conditions. If there is a material with dual functions of transmitting pressure and measuring pressure, the pressure values of the sample under non-hydrostatic conditions might be measured accurately according to the principle that the closer the positions are, the more similar the pressures are. Apparently, it is important to search for the material with dual functions of transmitting and measuring pressure. In this paper, a ruby particle and a small drop of ionic liquid [C4mim][BF4]were loaded into a DAC, a series of high-pressure environments were provided by compressing the [C4mim][BF4]. Simultaneously, the fluorescence spectrum of ruby and the Raman spectrum of [C4mim][BF4] was obtained under high pressures. By analyzing the positions of the R1 line of ruby, the pressure values of [C4mim][BF4] were obtained. By analyzing the widths of the R1 line of ruby, it was found that the hydrostaticand quasi-hydrostatic pressures provided by [C4mim][BF4] were in the ranges of 0~6.26 and 6.26~21.43 GPa, respectively. It could be speculated that [C4mim][BF4] can use as PTM in the pressure range of 0~21.43 GPa. In addition, [C4mim][BF4] is a liquid phase Ⅰ, liquid phase Ⅱ, amorphous phase I, and amorphous phase II in the ranges of 0~2.28, 2.28~6.26, 6.26~14.39, and 14.39~21.43 GPa, respectively. By analyzing the positions of ν(B-F) and ν(ring) as the characteristic Raman peaks from [C4mim][BF4], it was found that they followed linear changes with increasing pressure during the above four phases of [C4mim][BF4]. What’s more, the formulas of pressure and position of ν(B-F) and ν(ring) were given. The formulas are very important for [C4mim][BF4] to be a pressure gauge. To sum up, [C4mim][BF4] has the dual functions of transmitting pressure and measuring pressure and can use simultaneously as the PTM and the pressure gauge. The research results provided an important basis for accurate measurement of sample pressure in non-hydrostatic environments and provide a new solution to the inaccurate measurement of sample pressure under ultra-high pressure conditions.
2022 Vol. 42 (06): 1674-1678 [Abstract] ( 124 ) RICH HTML PDF (2122 KB)  ( 118 )
1679 Raman Spectroscopy Study of Reduced Nicotinamide Adenine Dinucleotide
HUANG Bin, DU Gong-zhi, HOU Hua-yi*, HUANG Wen-juan, CHEN Xiang-bai*
DOI: 10.3964/j.issn.1000-0593(2022)06-1679-05
Reduced nicotinamide adenine dinucleotide (NADH) plays a crucial role in many biochemical reactions in human metabolism. Noninvasive and in vivo monitoring of the NADH level in skin tissue is of great interest. In this paper, the Raman scattering experiment and density functional theory (DFT) calculation have been applied to investigate the vibrational properties of NADH in the spectral range of 200～3 300 cm-1. The DFT calculation was performed with hybrid exchange functional using B3LYP functions with a polarized 6-311+G(d,p) basis. To achieve accurate analytical vibrational frequency calculation, the ground-state geometry of NADH molecule was first optimized at B3LYP/6-311+G(d,p) level of theory without any symmetry restrain, and the bond lengths and bond angles of NADH molecule were calculated. Then, the calculated wavenumbers were normally scaled with a necessary wavenumber linear scaling procedure by accounting for anharmonicity in DFT calculation. The DFT calculated spectrum of NADH is in good agreement with the Raman experimental spectrum: a good linear correlation between calculated and experimental wavenumbers has been obtained in the spectral range of 200～3 300 cm-1, and the deviations are smaller than 5 cm-1. In addition, the characteristic vibrational modes of the three parts adenine, nicotinamide, and dinucleotide of NADH molecule have been assigned and discussed, which would be helpful for the noninvasive and in vivo analyses of NADH. The characteristic mode of adenine at 732 cm-1 can be chosen as the most representative model for analyzing NADH. The characteristic mode of nicotinamide at 1 690 cm-1 can be chosen as another representative mode for further analyzing NADH. The characteristic modes of dinucleotide at 1 086 and 1 339 cm-1 can be chosen as a combination for further more accurately analyzing NADH. Therefore, when applying the Raman method for noninvasive and in vivo monitoring of the NADH level in skin tissue, first, the most representative mode at 732 cm-1 can be used for quick analyses, then the mode at 1 690 cm-1 and/or the combination modes of 1 086 and 1 339 cm-1 can be used for further accurate analyses.
2022 Vol. 42 (06): 1679-1683 [Abstract] ( 241 ) RICH HTML PDF (1746 KB)  ( 80 )
1684 Comparison of Haematite and Goethite Contents in Aeolian Deposits in Different Climate Zones Based on Diffuse Reflectance Spectroscopy and Chromaticity Methods
ZHU Meng-yuan1, 2, LÜ Bin1, 2*, GUO Ying2
DOI: 10.3964/j.issn.1000-0593(2022)06-1684-07
Haematite (α-Fe2O3) and goethite (α-FeOOH) are two chromogenic minerals commonly found in aeolian sedimentsand soils. They are important indicators of environmental change. Haematite indicates dry and hot environmental conditions, while goethite indicates wet and cold environmental conditions. They compete with each other and coexist in sediments and soils. In practical research, the ratio of goethite to haematite is used to indicate changes in environmental conditions. Due to the low content and weak magnetism of haematite and goethite in natural samples, traditional methods, such as X-Ray diffraction, chemical analysis and rock magnetism, have difficulty accurately and conveniently detecting their contents. Based on their optical characteristics, an increasing number of researchers have tried to quantify haematite and goethite in natural samples using diffuse reflectance spectroscopy and chromaticity methods in recent years. There are some differences between these two approaches. The intensity of the characteristic peaks of haematite and goethite in the diffuse reflectance spectrum is affected by the substrate effect, lattice ion substitution and other factors. The redness (a*) of the chromaticity parameter is also affected by lattice-ion substitution, and the factors affecting the yellowness (b*) are more complicated. Therefore, the relevant parameters of the two methods cannot be simply equivalent to the content of haematite or goethite. It is particularly important to compare the two approaches. In this study, we use diffuse reflectance spectroscopy and chromaticity to conduct experiments on three aeolian sedimentary profiles: Bole, Xinjiang, in an arid region; Jinchuan, Sichuan, in a subhumid region; and Xuwen, Guangdong, in a humid region; to analyse the indicative significance and applicable range of different parameters for haematite and goethite and to discuss the environmental significances of the ratio of goethite to haematite. The results show that diffuse reflectance spectroscopy and chromaticity methods effectively identify the signals for goethite and haematite in aeolian sediments. However, due to the complex influencing factors, they can only be used as semiquantitative indicators in a large range of space. a* can better indicate haematite content; Gt/Hm and b*/a* can also better reflect the ratio of goethite to haematite; but Gt and b* cannot indicate well the content of goethite. Atmospheric hydrothermal conditions cannot be completely equal to soil hydrothermal conditions, atmospheric precipitation does not completely enter the soil, temperature affects the moisture content in the soil through evaporation, and the hydrothermal condition of the soil is affected by many factors. Temperature determines the rate of chemical reactions on a large geographic spatial scale, and the effect of temperature on the formation and preservation of haematite and goethite is greater than that of precipitation.
2022 Vol. 42 (06): 1684-1690 [Abstract] ( 147 ) RICH HTML PDF (5619 KB)  ( 74 )
1691 Rapid Detection of Total Organic Carbon in Oil Shale Based on Near Infrared Spectroscopy
LI Quan-lun1, CHEN Zheng-guang1*, SUN Xian-da2
DOI: 10.3964/j.issn.1000-0593(2022)06-1691-07
To quickly detect the total organic carbon (TOC) content of oil shale, the TOC content and near-infrared spectrum data of 230 rock samples were measured in a certain block of the Songliao basin. The Monte Carlo method eliminates 14 abnormal samples, and the remaining 216 samples are pretreated by the method of detrended and baseline correction. The feature wavelength is selected by successive projections algorithm (SPA), uninformative variable elimination(UVE) algorithm and competitive adaptive reweighted sampling(CARS) method. The SPXY method divides the sample set into calibration set(144 samples) and validation set(72 samples) according to the ratio of 2∶1. Then linear partial least squares (PLS) model, nonlinear support vector machine (SVM) model and random forest (RF) model are adopted to predict the TOC content of oil shale. The determination coefficient (R2) and root mean square error (RMSE) was used as the evaluation indexes of the model to explore the influence of different characteristic wavelength selection methods on TOC modeling of oil shale and to compare the accuracy of different modeling methods on TOC content prediction of oil shale. The results show that the feature wavelength extraction can optimize the model. SPA, UVE and CARS extract 16, 253 and 65 wavelength points respectively. After the feature wavelength extraction, the model determination coefficient is improved, and the root means square error is decreased. This shows that the feature wavelength extraction plays an important role in simplifying the model and improving model efficiency. In addition, The performance of the nonlinear RF and SVM model is better than that of the linear PLS model. The reason is that the carbon in oil shale exists in all kinds of hydrocarbons, and the absorption peaks of different hydrocarbon groups interact with each other, which makes the complex nonlinear relationship between the TOC content of oil shale and the near-infrared spectroscopy data. Therefore, the nonlinear SVM and RF model can show better performance. Compared with other models, the coefficient of determination (R2v) and root mean square error (RMSEV) of the CARS-SVM model invalidation set show better results, reaching 0.906 6 and 0.222 0 respectively. This model can be used to rapidly detect TOC content in oil shale. The results of this study show that the application of near-infrared spectroscopy in the rapid detection of TOC content in oil shale is feasible, and the CARS-SVM model can show good prediction performance, which provides a new method and idea for the rapid detection of TOC content in oil shale in China.
2022 Vol. 42 (06): 1691-1697 [Abstract] ( 183 ) RICH HTML PDF (3314 KB)  ( 196 )
1698 Identification of Corn Varieties Based on Bayesian Optimization SVM
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3
DOI: 10.3964/j.issn.1000-0593(2022)06-1698-06
In order to detect corn varieties quickly, a classification model of corn varieties was established based on the combination of support vector machine (SVM) and near-infrared spectroscopy. 293 samples from five varieties, including Zhengdan 958, Xianyu 335, Jingke 968, Denghai 605 and Demeiya, were collected as research objects. After performing standard normal variable transformation (SNV) processing on the collected near-infrared spectra, the principal component analysis (PCA) method is used to reduce the dimensionality of the spectral data. According to the ratio of 6∶1, 251 samples were randomly selected as the training set and 42 samples as the test set to explore the influence of the Bayesian optimization (BO) algorithm on the performance of the SVM model. Three methods, including grid search(GS), genetic algorithm(GA) and BO algorithm, were used to optimize the two important parameters of the SVM model, namely, the penalty factor C and the radial basis kernel function parameter γ. The C and γ, corresponding to the highest recognition accuracy based on ten-fold cross-validation of each model, were used as modeling parameters, and the SVM classification model based on the three optimization algorithm methods were established. The SVM classification model based on BO is compared with the model based on GS and GA. The experimental results show that the performance of the SVM classification model optimized by BO is superior to that of the other two optimization algorithms, and the recognition accuracy on the test set can reach 100%. This shows that the parameters of the SVM model optimized by BO are the optimal global parameters, and the parameters optimized by the other two optimization algorithms may fall into the local optimal, resulting in poor performance of the model. BO-SVM models were established on the spectral data before and after PCA dimensionality reduction. The results show that BO is not good for high-dimensional data optimization, and it is more suitable for low dimensional data. For the problem of poor performance of the model caused by the imbalance of the number of different sample categories, the SVM models were re-established by removing the two small samples, Zheng Dan 958 and Xianyu 335, and using the remaining three categories, a total of 248 corn samples. The experimental results show that the performance of each model on the test set is improved after removing the two types of small samples, which indicates that for the problem of unbalanced sample number between classes, the more samples of a certain class, the more delicate the correction of model parameters, and the better the fitting effect of the model on this class. The results of this study can be used for rapid identification of corn varieties and can also provide references for the classification and origin identification of other agricultural products based on near-infrared spectroscopy.
2022 Vol. 42 (06): 1698-1703 [Abstract] ( 178 ) RICH HTML PDF (2244 KB)  ( 92 )
1704 Prediction Model of Soil Moisture Content in Northern Cold Region Based on Near-Infrared Spectroscopy
SHI Wen-qiang1, XU Xiu-ying1*, ZHANG Wei1, ZHANG Ping2, SUN Hai-tian1, 3, HU Jun1
DOI: 10.3964/j.issn.1000-0593(2022)06-1704-07
There is a large temperature difference between summer and winter in northern China. Soil temperature difference greatly influences the measurement of soil moisture by NIR (Near-Infrared). A prediction model for soil NIR spectrum and soil moisture content under a wide range of temperature stress (-20~40 ℃) was introduced. Soil samples were collected in the experimental field of Heilongjiang Bayi Agricultural University. After drying and sieving, soil samples were dampened to moisture content ranging from 15% to 50%. Prediction model for NIR and soil moisture content under different temperature stress was built. 69 groups of spectral data was used as training set to build model based on the full-band spectral data and five different spectral signal preprocessing methods. BP (Back-propagation) neural network, optimized support vector machine (SVM) algorithm and Gaussian process algorithm (GP) were used to establish the prediction model of soil near-infrared spectrum and moisture content in northern cold areas,and verify the effect of the model. The learning rate for BP neural network was 0.05, the maximum training time was 5 000, and the number of hidden layer units was 20. SVM used the radial basis function and Leave-One-Out Cross-Validation to determine the optimal penalty parameter (0.87), which improved the accuracy of the model prediction. Marton kernel internally was used for the GP model. GP model was evaluated by the coefficient of determination (R2), and root mean square error (RMSE). Results show that the S_G-BP neural network model has the best performance among the BP neural network models, with R2 of 0.960 9 and RMSE of 2.379 7. The SNV-SVM model has the best performance among the SVM models with R2 of 0.991 1 and RMSE of 1.081 5. The GP models, S_G-GP model has the best performance among GP models, with R2 of 0.928 and RMSE of 3.258 1. In conclusion, the SVM model based on SNV preprocessing has the best training performance. 35 groups of spectral data were used as a prediction set to verify the model performance. According to the model comparison and analysis, the prediction model based on the SVM algorithm is better than the other two algorithms, among which the S_G-based SVM model has the best performance. R2 and RMSE are 0.992 1 and 0.736 9, respectively. Combining the parameters of modeling set and prediction set, the SVM model based on S_G has the best performance in this study. This model can predict soil moisture content under a wide range of temperature stress in cold regions, providing a theoretical foundation for the design and optimization of portable NIR soil moisture rapid measurement instruments in the cold region.
2022 Vol. 42 (06): 1704-1710 [Abstract] ( 180 ) RICH HTML PDF (5500 KB)  ( 94 )
1711 Online Monitoring of Pesticides Based on Laser Induced Breakdown Spectroscopy
ZHANG Xing-long1, LIU Yu-zhu1, 2*, SUN Zhong-mou1, ZHANG Qi-hang1, CHEN Yu1, MAYALIYA·Abulimiti3*
DOI: 10.3964/j.issn.1000-0593(2022)06-1711-05
As an important tool of modern agriculture, pesticides are widely used in agricultural production with the advantage of their efficient ability to kill pests and diseases, but they also cause pollution to the environment while killing pests. In this paper, a spectral detection of pesticide spray was carried out to study the real-time monitoring of pesticide use by laser-induced breakdown spectroscopy(LIBS) technology. First, the atmospheric LIBS spectrum in a clean environment was detected. Many atoms emission spectra of nitrogen (N) and oxygen (O) were detected in the air spectrum, which has a good consistency with the air composition. At the same time, two hydrogen Balmer series atomic lines (Hα and Hβ) were also observed, which was mainly derived from water vapor in the air. It is worth noting that two Argon(Ar) atomic lines were also found in the air spectra, indicating that the LIBS technology has great potential in detecting trace elements. In this experiment, the pesticide Decis was selected as the research object, and LIBS tested its active ingredient deltamethrin (C22H19Br2NO3，CAS: 52918-63-5). The halogen element bromine(Br) was observed in the spectra of deltamethrin, and two Br atomic emission lines (827.294, 833.470 nm) were marked. During the detection of pesticide samples, many characteristic spectral lines that did not appear in the air spectrum were also found, including CN molecular band and C2 molecular band. What are more, elements natrium(Na) and calcium(Ca) that were not observed in the air spectra were also detected. Especially for Ca, the intensity and number of spectral lines in pesticides significantly increase. Finally, the temperature of the CN molecule was studied. The vibrational temperature of CN molecule in deltamethrin and pesticide were estimated to be 8 800 and 6 200 K respectively, and the rotational temperature to be 8 600 and 5 500 K respectively. The above results indicated that it is feasible to use LIBS technology to monitor pesticides online, and LIBS technology has a promising future in pesticide monitoring.
2022 Vol. 42 (06): 1711-1715 [Abstract] ( 117 ) RICH HTML PDF (3198 KB)  ( 88 )
1716 Characteristic Extraction Method and Discriminant Model of Ear Rot of Maize Seed Base on NIR Spectra
MENG Fan-jia1, LUO Shi1, WU Yue-feng1, SUN Hong1, LIU Fei2, LI Min-zan1*, HUANG Wei3, LI Mu3
DOI: 10.3964/j.issn.1000-0593(2022)06-1716-05
Ear rot of corn seeds is one of the main diseases that harm the yield of corn. A discriminant model of ear rot of corn seeds was studied by near-infrared spectroscopy. The study samples were provided by the Hainan Breeding Base of Jilin Academy of Agricultural Sciences. 246 corn seeds were selected as the research objects, 96 of which were infected with ear rot, and the other 150 were normal samples of the same kind of corn. A Matrix-Ⅰ Fourier NIR spectrometer was used to collect the NIR spectra of the samples in the range of 800~2 500 nm, and the NIR spectra were preprocessed by Multiplicative Scatter Correction (MSC). Four optimal regions were selected combined with the sensitive band of NIR spectrum of organic matter in maize and the absorption peak of the NIR spectrum of samples. Correlation analysis (CA), successive projections algorithm, SPA) and Competitive Adaptive Reweighted Sampling (Competitive Adaptive Reweighted Sampling, Cars), 4 (1 362, 1 760, 2 143 and 2 311 nm), 5 (1 227, 1 310, 1 382, 1 450 nm) were extracted by three characteristic wavelength extraction algorithms with different principles, respectively 1 728 nm) and 10 (1 232, 1 233, 1 257, 1 279, 1 313, 1 688, 1 703, 1 705, 2 302 and 2 323 nm).The characteristic wavelengths extracted were used as input variables of the corn seed ear rot identification model. The disease status of samples was represented by 0-1 (infected normal) as the output true value to establish the support vector machine (SVM) model. The model parameters were optimized by the grid search method and the 10-fold cross-validation method. The results show that the modeling accuracy of the training and test set in three discriminant models, CA-SVM, SPA-SVM and CARS-SVM, is above 90%. The research results in this paper provide a model basis for the maize seed disease diagnosis device. The method of selecting characteristic wavelengths for the optimal region can also provide a reference for establishing other seed disease discrimination models.
2022 Vol. 42 (06): 1716-1720 [Abstract] ( 216 ) RICH HTML PDF (3141 KB)  ( 76 )
1721 Quantitative Analysis of Diabetic Blood Raman Spectroscopy Based on XGBoost
WANG Ming-xuan, WANG Qiao-yun*, PIAN Fei-fei, SHAN Peng, LI Zhi-gang, MA Zhen-he
DOI: 10.3964/j.issn.1000-0593(2022)06-1721-07
The blood contains many biological information, such as hormones, enzymes, blood sugar and other components. High blood sugar will cause diabetes, which has many complications, such as cerebral infarction, cerebral hemorrhage, kidney damage, fundus damage, peripheral neuropathy and a series of diseases.At present, the routine blood component detection and analysis cycle are too long, the resulting feedback is slow, and it is not easy to achieve rapid and continuous detection. Optical detection technology can identify the chemical composition and relative content of the substance according to the spectrum of the substance to be tested. Because of its advantages, such as high sensitivity, strong applicability, and fast analysis speed, it gradually exerts its advantages in blood non-invasive detection. With the continuous advancement of laser technology, Raman spectroscopy technology, as a nonlinear scattering spectroscopy technology, has been widely used in blood detection technology. In order to improve the prediction accuracy of Raman spectroscopy in this paper, the XGBoost algorithm was firstly applied to the blood glucose concentration of Raman spectroscopy to improve the prediction accuracy. 106 sets of experimental blood samples and real concentrations were provided by the First Hospital of Qinhuangdao City, Hebei Province. Bruker’s Multi RAM spectrometer was used to measure blood Raman spectroscopy data. In the experiment, the power of the 1 064 nm excitation light source was 400 mW, the spectral resolution was 6 cm-1, the scanning rate was 10 kHz, and the scanning range was 400~4 000 cm-1. Each sample is collected 10 times, and the average value is calculated as the original spectrum to ensure the accuracy and repeatability of the experiment. In this paper, the method does not require preprocessing of the data. Firstly, the spectral data were randomly divided into a training and test sets with a ratio of 7∶3. The training set was used to train the model and determine the model parameters. The test set was used to verify the stability and prediction accuracy of the model. Then, the XGBoost model was established, and grid search and k-fold cross-validation were used to optimize the model parameters. We adopted model evaluation indicators and a Clark grid error analysis chart to analyze the prediction of blood glucose concentration of the XGBoost model. Finally, the XGBoost model was compared with Decision Tree (DT), Random Forest (RF) and Support Vector Machine Regression (SVR) models.The experimental results showed that the quantitative regression model established by XGBoost had the best effect. The model’s coefficient of determination was 0.999 99, the mean square error of the calibration set was 0.007 49, the mean square error of the prediction set was 0.007 17, and the relative analysis error was 331.973 18. The prediction points fell in area A of the Clark grid error analysis chart. The results prove that the application of the XGBoost algorithm to the quantitative analysis of blood components in Raman spectroscopy has high prediction accuracy, and the data is not pre-processed, which can effectively shorten the program’s running time. It has broad development prospects in Raman spectroscopy and near-infrared spectroscopy quantitative analysis.
2022 Vol. 42 (06): 1721-1727 [Abstract] ( 141 ) RICH HTML PDF (3958 KB)  ( 146 )
1728 Study on Detecting Method of Toxic Agent Containing Phosphorus (Simulation Agent) by Optical Emission Spectroscopy of Atmospheric Pressure Low-Temperature Plasma
YANG Jin-chuan1, 2, AN Jing-long1, 2, LI Cong3, ZHU Wen-chao3*, HUANG Bang-dou4*, ZHANG Cheng4, 5, SHAO Tao4, 5
DOI: 10.3964/j.issn.1000-0593(2022)06-1728-07
Gas chemical agent is fast-killing, highly diffusible, and difficult to decontaminate, threatening national security and social stability if used or leaked. Therefore, it is necessary to develop a gas detection method that can be used in real-time and on-site. Existing gas detection methods include infrared absorption spectroscopy, gas chromatography/mass spectroscopy, ion mobility spectrometry, and different gas sensors. Even so, these methods cannot achieve portability, sensitivity, and broad-spectrum simultaneously and meet the requirement of real-time and on-site detection. Based on the unique advantages of optical emission spectroscopy (OES), such as fast response, high sensitivity, broad-spectrum, and good repeatability, this work proposes a gas detection technology with OES from low-temperature plasma (LTP) at atmospheric pressure. Three excitation sources, i.e., nanosecond pulse, direct current (DC) self-pulse, and microwave (MW) generate LTP. Dimethyl methylphosphonate (DMMP) is used as the stimulant of sarin, of which OES is obtained. Ethanol is used as the organic interference in the environment. The principal component analysis (PCA) of OES from ethanol and DMMP is carried out. The relationship between pulse repetition rate and OES intensity from DMMP is explored. Results show that three sources can distinguish the characteristic OES from DMMP: the wavelengths of P atom are 213.82 and 215.09 nm, and those of PO radical are 253.67 and 255.6 nm. Regarding spectral discrimination, OES from DMMP in MW plasma is the clearest, while the continuous background is strong when using nanosecond pulse and DC self-pulse. In terms of device applicability, MW plasma, sustained with argon, can avoid electrode contamination and be an effective method to establish an OES database for chemical agents. Nanosecond pulse and DC self-pulse discharges can be directly operated in ambient air. The gas temperature (Tg) of MW plasma is the highest (about 1 300 K), whileTg of nanosecond pulse and DC self-pulse is similar (980 K vs 880 K). A linear relationship between OES intensity from DMMP and pulse repetition rate is observed in the range of 1~40 kHz, with correlation coefficients greater than 0.98. The OES detection method proposed in this work has the advantage of fast response and easy operation, and the potential of extensibility and miniaturization. This work verifies the feasibility of OES from LTP for chemical agent detection and provides a technical reference for equipment development in the future.
2022 Vol. 42 (06): 1728-1734 [Abstract] ( 134 ) RICH HTML PDF (3696 KB)  ( 78 )
1735 Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection
YU Zhi-rong, HONG Ming-jian*
DOI: 10.3964/j.issn.1000-0593(2022)06-1735-06
As a typical structure in deep learning, a fully connected network appears in almost all neural network models. In the quantitative analysis of near-infrared spectroscopy, the number of spectral samples is small, but the dimension of each sample is high. It leads to two problems: if the spectrum is directly input into the network, the number of parameters of the network will be very large, which requires more samples to train the model. Otherwise, the model is prone to over fitting; reducing the dimension of the spectrum before inputting it into the network solves the problem that the number of parameters of the network is too large, but it will lose some information and cannot give full play to the learning ability of the network. According to the characteristics of near-infrared spectrum, a group fully connected near-infrared spectrum quantitative analysis network(GFCN) is proposed. Based on the traditional two-layer fully connected network, the network uses several small fully connected layers to replace the first fully connected layer, which overcomes the disadvantage of too many network parameters caused by a direct input spectrum. The GFCN model was tested with Tecator and IDRC2018 datasets and compared with a fully connected network (FCN) and partial least squares (PLS). The results show that the prediction effect of GFCN is better than that of FCN and PLS on the two datasets. In the case of only a small number of samples participating in the modeling, GFCN can still maintain a high prediction effect. The experimental results show that the GFCN can be used for the quantitative analysis of near-infrared spectrum and adapt to the scene with few samples. It indicates that the proposed model has important research value and good application prospects.
2022 Vol. 42 (06): 1735-1740 [Abstract] ( 149 ) RICH HTML PDF (3196 KB)  ( 52 )
1741 Study on Quantitative Detection of Tomato Seedling Robustness in Spring Seedling Transplanting Period Based on VIS-NIR Spectroscopy
JI Jiang-tao1, 2, LI Peng-ge1, JIN Xin1, 2*, MA Hao1, 2, LI Ming-yong1
DOI: 10.3964/j.issn.1000-0593(2022)06-1741-08
To screen the key indicators that affect the robustness of tomato plug seedlings during the spring nursery and transplanting period and realize its rapid non-destructive testing, this paper measured 5 seedling indicators, then used vector normalization and the independence weight coefficient method to determine each indicator. According to the weighting results, two indicators containing more comprehensive information and greater influence are selected: chlorophyll and dry quality. The simplified seedling evaluation value composed of the two indicators can approximate the comprehensive evaluation value. The correlation coefficient is 0.92, which greatly reduces the number of indicators required for quality testing, and can well represent the robustness of tomato seedlings during the spring seedling transplanting period. At the same time, the visible-near infrared spectrum data of each plug seedling is extracted and pre-processed by denoising and multi-scattering correction (MSC). This way, it can eliminate the spectral interference information caused by light scattering and make it more usable than the original spectral information. Subsequently, the spectrum-physical and chemical value symbiosis distance (SPXY) algorithm is used to divide the sample set. The distance between the samples is calculated using two variables of the band value and the evaluation value to maximize the characterization of the sample distribution to improve the difference and representativeness of the samples. Secondly, the competitive adaptive weighting algorithm (CARS) and the uninformative variable elimination-successive projections algorithm (UVE-SPA) are used to optimize the spectral feature wave number, reduce the spectral data dimension and obtain simplified spectral information that can better reflect the spectral characteristics and reduce redundancy. Finally, partial least squares-support vector machine (LS-SVM) and convolutional neural network (CNN) based on U-Net model transformation are applied. After extracting the characteristic wavelength, the preprocessed spectral data and the spectral data are respectively used as the input of the model and established a non-linear mapping model of spectral data and comprehensive evaluation value. We can carry out comparison and selection. The results show that the spectral information of the bands filtered by the UVE-SPA preprocessing method is more abundant and effective. The regression effects of the models built for the two preprocessed optimal bands are overall better than the models built for the full bands; the modeling effect of the CNN model is overall. It is better than the LS-SVM model, and the UVE-SPA-CNN model has the best effect on the regression analysis of spectral data and seedling evaluation values. The correlation coefficients of the modeling set and prediction set are 0.988 and 0.946, respectively, and the values of the root mean square error are 0.085 and 0.025, respectively, which provide a theoretical basis for directly using spectral data to obtain the evaluation value of tomato seedlings that incorporate multiple factors, thereby judging the robustness of the seedlings.
2022 Vol. 42 (06): 1741-1748 [Abstract] ( 132 ) RICH HTML PDF (3534 KB)  ( 49 )
1749 Scattering Characteristics of Marine Mixed Suspended Particles to Blue and Green Lasers
WANG Ming-jun1, 2, 3, WANG Zhu-yu1, HUANG Chao-jun2
DOI: 10.3964/j.issn.1000-0593(2022)06-1749-06
The transmission of laser underwater is largely affected by suspended particles in seawater. At present, most theoretical studies on the light scattering of suspended particles in the ocean are carried out on single-component suspended particles. However, suspended particles in the real ocean exist in a group of particles mixed with multiple components. Therefore, it is of great significance to study the scattering characteristics of blue-green lasers by mixed suspended particles in the real ocean. In this paper, five common suspended particles which have a greater impact on the transmission of blue-green lasers are planktonic algae, suspended sediments, debris, suspended bubbles, and minerals as the research objects. Fully considering the different mixing situations of these five suspended particles in real sea conditions, a model of the scattering characteristics of blue-green lasers by mixed spherical suspended particles in seawater is constructed. The statistical average light scattering parameters and average scattering phase function of 532 nm blue-green lasers of spherical suspended particles mixed with five seawater substances are calculated numerically. They are analyzing the influence of the mixing ratio of different mixed suspended particles on the average scattering, absorption and extinction coefficients and single albedo with the effective radius of the particles and the change of the particle number concentration. At the same time, the influence of different mixing ratios under different particle sizes on the average scattering phase function of mixed suspended particles with angle changes is analyzed. Numerical results show that the average scattering coefficient increases when the proportion of suspended sediment in the entire mixed model increases, while the average absorption coefficient increases with the proportion of suspended algae particles in the entire mixed model. It can be seen that among the five common suspended particles that have a major impact on the light in the ocean, suspended sediment has the greatest impact on light scattering, and suspended algae particles have the greatest impact on light absorption. As the concentration of suspended particles increases, the single albedo of the mixed particles remains unchanged. One can see that the average light scattering parameters of the mixed suspended particles are consistent with the increase in particle concentration. The average scattering phase function of mixed suspended particles in the ocean increases with the increase of the effective radius of the particles. The average scattering phase function of suspended particles under the mixing ratio with the largest scattering effect is the largest, and the forward scattering of suspended particles is strong. This work has important theoretical significance for the blue-green laser transmission in seawater, channel modeling, the research of underwater wireless optical communication and the laser target detection in seawater.
2022 Vol. 42 (06): 1749-1754 [Abstract] ( 99 ) RICH HTML PDF (5033 KB)  ( 36 )
1755 Study on Coal-Rock Identification Method Based on Terahertz Time-Domain Spectroscopy
MIAO Shu-guang1, SHAO Dan1*, LIU Zhong-yu2, 3, FAN Qiang1, LI Su-wen1, DING En-jie2, 3
DOI: 10.3964/j.issn.1000-0593(2022)06-1755-06
Coal-rock identification is one of the key problems restricting unmanned coal mining. Because of the extremely complicated working environment, the traditional manual coal mining is difficult to find the interface of coal and rock accurately, which is easy to cause the phenomenon of undercutting or overcutting. As a non-destructive detection method, Terahertz spectroscopy can reflect the physical and chemical information of the object under test and be an effective method to study the identification of coal and rock. In this paper, the terahertz time-domain spectroscopy and multivariate statistical method-cluster analysis (CA) and principal component analysis (PCA) are used to identify different types of coal and rock. The THz spectra of six coal and rock samples are obtained by transmission terahertz spectrometer. FFT and other mathematical calculations can obtain various samples’ refractive index, absorption coefficient and dielectric constant. The results show differences in the refractive index and absorption coefficient of different types of coal and rock. By analyzing the relationship between the refractive index and absorption coefficient of various coal samples and the content of each component of the samples, it can be found that carbon content is one of the factors affecting the refractive index of the samples, and ash content is one of the factors affecting the absorption coefficient of the samples.The Euclidean distance of two kinds of samples in cluster analysis and the score of PC1 in principal component analysis can reflect the similarity and dissimilarity between coal and rock samples, and the results of CA and PCA are consistent. The refractive index and absorption coefficient of various samples in the 0.5~2.5 THz frequency range are combined with CA and PCA to form a model between terahertz data and coal and rock. According to the analysis,the six types of coal samples in the two models can be divided into two types based on the similarity between different samples. In the CA-PCA model with the absorption coefficient of various samples adopted, four kinds of coal are clustered together. Moreover, quartz sandstone (GSR-4) has a unique characteristic: quartz sandstone has the smallest PC1 score value, and the Euclidian distance between quartz sandstone and the second type is the largest, up to 219.03. It can be seen that the combination of terahertz technology and multivariate statistical method can realize the accurate identification of coal and rock, and the recognition accuracy can reach 100%.
2022 Vol. 42 (06): 1755-1760 [Abstract] ( 140 ) RICH HTML PDF (2477 KB)  ( 58 )
1761 Study on Properties of Azaanthracene Derivatives With Triplet-Triplet Annihilation Upconversion and One-Photon Hot Band Absorption Upconversion
XU Lei, ZHU Lin, ZHANG Chun, YE Chang-qing*, CHEN Shuo-ran, LI Lin, LIANG Zuo-qin, WANG Xiao-mei*
DOI: 10.3964/j.issn.1000-0593(2022)06-1761-08
Upconversion is a phenomenon that converts low-energy photons into high-energy photons. It has a wide range of potential applications in three-dimensional fluorescence microscopy, solar cells, photocatalysis and other fields, and thus has become an attractting topic in the field of organic fluorescent materials. At present, the research about organic low light upconversion materials based on the triplet-triplet annihilation (TTA) mechanism has been investigated in much more depth, and there have been many reesearch reports discussing the TTA mechanism and application; while the research discussing on the other organic upconversion mechanism, the one-photon hot band absorption upconversion (OPA-UC), is still relatively rare. Azaanthracene derivatives are ideal model molecular structures for studying TTA-UC and OPA-UC organic upconversion luminescence due to their good structural rigidity, planarity, and high fluorescence quantum yield. This work compares three azaanthracene derivatives: Phenosafranine (PSF), Safranine T (Safranine T, SFT), Methylene Violet (MTV) and their respective TTA-UC and OPA-UC difference in luminescence performance, analyze and explore the structure-activity relationship of molecular structure to OPA-UC luminescence performance and TTA-UC sensitization efficiency. Experiments have found that phensafranine and safranine T had higher fluorescence quantum yields and a larger radiation attenuation constant. The main attenuation process was radiation attenuation, while methylene violet had a higher intramolecular charge transferability (Intramolecular charge transfer, ICT), so the non-radiation attenuation part was more. It was found that the triplet energy level of methylene violet was too low to carry out the triplet-triplet energy transfer process, and safranine T had a higher triplet lifetime due to its higher triplet lifetime. The upconversion luminous efficiency (9.69%) was 3 times (3.16%) of the phensafranine system. Further studying the OPA-UC performance difference between phensafranine and methylene violet and found that the OPA-UC luminous efficiency of methylene violet (0.12%) under the same concentration condition (10-3 mol·L-1) was much higher compared with that of phensafranine (0.059%) and as the concentration increased, the OPA-UC luminescence enhancement effect of methylene violet was greater. Further results showed that in the TTA-UC luminescence process, the sensitization efficiency of the photosensitizer was mainly affected by the molecular triplet lifetime and the inter-system inter-system transitioning ability. The longer the lifetime, the stronger the inter-system inter-system transitioning ability and the higher the sensitization efficiency; In the OPA-UC luminescence process, the luminescence efficiency of the luminescent agent molecules were mainly affected by ICT. The greater the ICT degree, the higher the molecular luminescence efficiency. In this work, azaanthracene molecules have a low cost and are easy to obtain, which has certain practical significance for designing high-performance TTA-UC and OPA-UC luminescent molecules in the future.
2022 Vol. 42 (06): 1761-1768 [Abstract] ( 119 ) RICH HTML PDF (5005 KB)  ( 42 )
1769 DFT Calculation of Absorption Spectra for Planar Porphyrin Derivatives
ZHOU Cai-hua, DING Xiao
DOI: 10.3964/j.issn.1000-0593(2022)06-1769-05
The photosensitizer was applied photodynamic therapy (PDT), a molecule that can absorb light with a certain wavelength. And the photosensitizer can transfer excited energy to ground state oxygen. As a result, the ground state oxygen gets the energy and becomes the singlet oxygen. Currently, those applied photosensitizers are almost planar molecules containing porphyrin ring, and those planar molecules have a big delocalized π bonds. Simultaneously, there is a slight inter-system crossing and a long triplet lifetime after the planar molecules are excited by light. So those planar molecules have a high yield of singlet oxygen. However, the absorption bands of the applied photosensitizers always lie in the UV regions, which easily damage the body issue. Due to the photo-damage character is not beneficial for the therapy, so the study on photosensitizers with Vis-IR absorption bands were widely concerned. Based on the above reason, we investigate three photosensitizers (earing-porphyrin (a), trisulfo-phthalocyanine (b) and trisulfo-phthalocyanine Ni(Ⅱ) (c)) using the DFT and TD-DFT. The optimized results show that all atoms of (a) are in a plane, the radius of (a) is almost 7 Å, and the cavity radius is 5 Å. All atoms of (b) are also in a plane, the radius of trisulfo-phthalocyanine is 8 Å, and the radius of the cavity is 4 Å. But (c) is a distorted plane due to the coordinated mode of Ni(Ⅱ). Therefore, the earing-porphyrin (a) in a big cave can capture more ground-state oxygen. The orbital energies and populations show that the HOMO energy of (a) is the biggest among them. That is, the electrons of the earing-porphyrin (a) were excited to higher energy levels easily. The energy gaps (Ehomo-lumo) of three molecules are 0.072, 0.076 and 0.075 a.u. The earing-porphyrin (a) has the lowest energy gap. The orbital populations show that the atomic p orbitals constitute the big delocalized bond, and the d orbitals of Ni also join in the delocalized bond in the molecule (c). At last, the absorption spectra of three molecules were simulated by TD-DFT/B3LYP/6-311G(d, p). For three planar molecules, there are the Soret band and Q band in their spectra. The Q band lies at 450~900 nm for molecules (a) and (c) and about 400~800 nm for molecule (b). In conclusion, this paper has calculated and discussed the structural optimization, the orbital energies, and the absorption bands of three planar molecules. The investigated results will improve the discovery and development of photosensitizers with near-infrared absorption bands, and it also will pro-vide the theoretical basis for the study of the photosensitizer.
2022 Vol. 42 (06): 1769-1773 [Abstract] ( 122 ) RICH HTML PDF (2183 KB)  ( 51 )
1774 Research on Identification of Danshen Origin Based on Micro-Focused Raman Spectroscopy Technology
LI Qing1, 2, XU Li1, 2, PENG Shan-gui1, 2, LUO Xiao1, 2, ZHANG Rong-qin1, 2, YAN Zhu-yun3, WEN Yong-sheng1, 2*
DOI: 10.3964/j.issn.1000-0593(2022)06-1774-07
The origin is an important factor affecting the quality of Chinese herbal medicine. The difference of origin leads to the uneven quality of Chinese herbal medicine. In order to maintain the market order, it is necessary to establish the method of identification of the origin of Chinese herbal medicine to identify and analyze the quality of Chinese herbal medicine more accurately. This article takes danshen, a major clinical medicinal material, from many origins as the research object, and 150 samples of danshen were collected from different origins. The surface of each root of danshen sample was scanned 1~n times randomly by micro focusing Raman spectroscopy under non-destructive conditions, and the average spectrum of each sample was calculated. By analyzing the original spectral data, it is found that the surface spectral signal of danshen contains both the Raman spectra of tanshinones and the fluorescence spectra of impurities. Mainly reflected in that danshen from different origins has their aggregation regions, and the signal intensity of the surface spectral signal of danshen is significantly weaker or stronger than that of tanshinones in the specific wavelength range. After preprocessing the spectral data of 1~n scans, the classification model of danshen origin was established by partial least squares discriminant analysis (partial least squares-discriminant analysis, PLS-DA) and random forest classification algorithm [no screening (random forest, RF) or screening of important variables (RF-VS)]. Results the training set and test set accuracy of the optimal model obtained by random 1 scanning were 88% and 87% respectively, and the samples with low quality and high quality could be distinguished with an accurate of 97%; the training set and test set accuracy of the optimal model obtained by random scanning 2 and 3 times were both 89% and 87% respectively. Combined with the operation efficiency of the model The spectrum obtained by random 1 scanning was selected, and the identification model of the origin of danshen was obtained by first derivative (1ST-D) pretreatment and RF-VS calculation. In conclusion, the micro focused Raman spectroscopy technology can establish a rapid and accurate prediction model of the origin of danshen under non-invasive conditions and provide a reference for the further application of this technology in identifying the origin and authenticity of expensive and scarce Chinese herbal medicine.
2022 Vol. 42 (06): 1774-1780 [Abstract] ( 157 ) RICH HTML PDF (2507 KB)  ( 54 )
1781 NIR Band Assignment of Tanshinone ⅡA and Cryptotanshinone by 2D-COS Technology and Model Application Tanshinone Extract
PENG Yan-fang1, WANG Jun1, WU Zhi-sheng2*, LIU Xiao-na3, QIAO Yan-jiang2*
DOI: 10.3964/j.issn.1000-0593(2022)06-1781-05
The near-infrared (NIR) band assignment of Tanshinone ⅡA and Cryptotanshinone were performed by 2D-COS technique in deuterated chloroform. According to the two-dimensional synchronous slice spectra of Tanshinone ⅡA and Cryptotanshinone, Tanshinone ⅡA and Cryptotanshinone have characteristic absorption at 1 600~1 800, 1 900~2 230, and 2 300~2 400 nm. Tanshinone ⅡA has characteristic bands at 1 640 and 2 140 nm which connected with the first double-frequency and combination frequency of furan ring double bond. 1 696 nm was the second double-frequency of methyl stretching vibration in Tanshinone ⅡA and Cryptotanshinone molecules, the absorption at 1 726 and 1 740 nm were the second double-frequency of Tanshinone ⅡA and Cryptotanshinone which connected with cyclohexene methylene stretching vibration, 2 146 and 2 220 nm were the combined frequency of Tanshinone ⅡA and Cryptotanshinone which linked with benzene ring C—C and C—H stretching vibration, a series of peaks at 2 300~2 400 nm were the combination frequencies of stretching vibration and bending vibration of methyl in Tanshinone ⅡA and Cryptotanshinone molecules. Taking Tanshinone Extract as a carrier, the characteristic band by 2D-COS and the band by synergy interval Partial Least Squares (SiPLS) were used to establish Partial Least Square (PLS) quantitative models. The coefficients of determination R2 were all greater than 0.9, the Root Mean of Square Error of Calibration (RMSEC) and Root Mean of Square Error of Cross-Validation (RMSECV), and the Root Mean of Square Error of Prediction (RMSEP) were very small. The results showed that the PLS model established by 2D-COS and SiPLS were both good. The quantitative model based on the 2D-COS technique was explanatory. 2D-COS can be used to analyze the characteristic absorption connected with a structural differences. The simultaneous quantitative determination of structural analogues can be realized in the same band.
2022 Vol. 42 (06): 1781-1785 [Abstract] ( 145 ) RICH HTML PDF (2753 KB)  ( 37 )
1786 Density Functional Theory Studies on Structure and Spectra of Salidroside Molecule
XIE Yu-yu1, 2, 3, HOU Xue-ling1, CHEN Zhi-hui2, AISA Haji Akber1, 3*
DOI: 10.3964/j.issn.1000-0593(2022)06-1786-06
At present, with the continuous development of science and technology, more and more new techniques emerge in quality control quality evaluation of Traditional Chinese Medicines (TCMs). In the standardization process of TCMs, modern pharmaceutical research has made many remarkable achievements. Detection methods and technologies have made great progress from single-index detection to multi-index detection. Rhodiola Rosea is a kind of minority nationality medicine that is an indispensable part of the development of traditional Chinese medicine. Salidroside is one of the main components of Rhodiola Rosea. There are many reports on its extraction, separation and purification. However, far few reports have been reported up to now on its molecular parameters, such as bond length, bond angle, dihedral angle, frontier orbital distribution and surface electrostatic charge Distributions, which are critical factors determining its chemical properties and reaction mechanism. The parameters of bond length, bond angle and dihedral angle of salidroside were obtained by DFT / B3LYP method and 6-31 (d) basis set from Gaussian09W software. As the result of optimization, the surface electrostatic charge (ESP), the lowest occupied orbit (LUMO), the highest occupied orbit (HOMO). Infrared (IR) and nuclear magnetic resonance (NMR) data were calculated, and their peak positions were assigned and compared with the reported data. The results reveal that there is no imaginary frequency in the infrared absorption frequency, which indicates that the optimization result is reasonable and reliable; the highest occupied orbit energy E=-5.82 eV, the lowest orbit energy E=-0.000 42 eV, and the difference is 5.81 eV. By drawing the electron cloud distribution map of the orbit, we can see that the HOMO orbit is the π bonding orbital of electron mainly distributing on the benzene ring with a node; the LUMO orbit is the π antibonding orbital of electron mainly distributing on the benzene ring with two nodes. The drawing of surface electrostatic charge can directly determine which part of the molecule is prone to nucleophilic substitution reaction and which is prone to electrophilic reaction. The electron migration direction can be obtained directly by drawing the electron difference between the first excited state and the ground state. The theoretical study of salidroside molecular calculation in this dissertation can provide important references and new ideas for further exploring the chemical reaction mechanism, structural modification and identification of active sites.
2022 Vol. 42 (06): 1786-1791 [Abstract] ( 139 ) RICH HTML PDF (1951 KB)  ( 53 )
1792 Research on Non-Destructive Testing of Navel Orange Shelf Life Imaging Based on Hyperspectral Image and Spectrum Fusion
LIU Yan-de, WANG Shun
DOI: 10.3964/j.issn.1000-0593(2022)06-1792-06
Fruit shelf life is one of the important factors affecting fruit quality. Rapid non-destructive testing of fruit shelf life is an increasingly concerned issue for consumers and food processing enterprises. In order to explore the feasibility of prediction and discrimination methods for different shelf life of fruits, navel oranges with different shelf life were used as experimental samples, and hyperspectral imaging technology combined with chemometric methods were used to predict and discriminate navel oranges with different shelf life. The hyperspectral images of navel orange samples on day 0, day 7 and day 14 of the shelf life of navel orange were collected and corrected. From the spectral point of view, the average spectrum of navel orange samples was extracted, each spectrum had 176 wavelength points ; from the perspective of image, the R, G, B, H, S and I eigenvalues of navel orange samples in RGB and HSI color space were extracted, and the mean values of six components were obtained. Then, five image texture information of energy, entropy, contrast, inverse moment and correlation of gray level co-occurrence matrix were extracted, and a total of 11 image eigenvalues were extracted, and the image features were normalized. Combining spectral and image information, namely 176 original spectral and 11 image information, a total of 187 eigenvalues. Partial least squares support vector machine ( LS-SVM ) and partial least squares discriminant analysis ( PLS-DA ) models were established by using spectral information, image information, spectrum and image fusion information. When the original 176 spectral variables are used as input variables and the kernel function is LIN-Kernel, the LS-SVM model has the best prediction effect, and the misjudgment rate of prediction set is 5.33%. When 11 image feature variables are used as input variables and the kernel function is LIN-Kernel, the LS-SVM model has the best prediction effect, and the misjudgment rate of prediction set is 20%. When the fusion features of the original 176 spectral variables and 11 image feature variables are used as input variables and the kernel function is LIN-Kernel, the LS-SVM model has the best prediction effect, and the misjudgment rate of the prediction set is 1.33%. The experimental results show that the LS-SVM model based on spectral and image fusion information has the best effect, which improves the accuracy of navel orange recognition in different shelf life, and can realize accurate and effective classification and recognition of navel oranges in different shelf life. The misjudgment rate is 1.33%. The rapid identification of navel oranges in different shelf life by hyperspectral imaging technology has a certain degree of theoretical guidance for consumers to purchase fresh fruit and fruit deep processing enterprises, and lays a foundation for the development of related instruments in the future.
2022 Vol. 42 (06): 1792-1797 [Abstract] ( 164 ) RICH HTML PDF (1728 KB)  ( 317 )
1798 Study on Qualitative and Quantitative Detection of Pefloxacin and Fleroxacin Veterinary Drugs Based on THz-TDS Technology
CAO Yao-yao1, 2, 4, LI Xia1, BAI Jun-peng2, 4, XU Wei2, 4, NI Ying3*, DONG Chuang2, 4, ZHONG Hong-li5, LI Bin2, 4*
DOI: 10.3964/j.issn.1000-0593(2022)06-1798-06
As two commonly used quinolone veterinary drugs, pefloxacin and fleroxacin residues have attracted great attention, and it is a demand to develop rapid and efficient detection methods. This paper uses Terahertz Time Domain Spectroscopy (THz-TDS) to study pefloxacin and fleroxacin in fish meal feeds matrix. Firstly, 106 compressed samples of pure substances of pefloxacin, fleroxacin, polyethylene and fish meal feed, and binary mixtures of pefloxacin-fish meal feed, and fleroxacin-fish meal feeds with 17 different concentrations were prepared. Secondly, terahertz spectrum measurement and analysis were carried out on all tableting samples. Then, the continuous projection algorithm (SPA) combined with a support vector machine (SVM) and backpropagation neural network (BPNN) were used to establish a qualitative discrimination model to classify and discriminate the mixture of pefloxacin-fish meal feeds and fleroxacin-fish meal feeds. At last, partial least square regression (PLSR), BPNN and multiple linear regression (MLR) quantitative prediction models were established using the absorption coefficient at the characteristic frequency. The results showed obvious absorption peaks of pure pefloxacin at 0.775 and 0.988 THz, and obvious absorption peaks of pure fleroxacin at 0.919 and 1.088 THz. Polyethylene had no absorption of THz wave, and fish meal feeds had no absorption peak. The absorption peaks of two mixtures appeared near the absorption peaks of pure antibiotics. In qualitative discrimination, SVM was the best model, and the accuracy, precision, recall and F1 scores of the prediction set were 97.06%, 97.22%, 97.06% and 97.14%, respectively. In the quantitative regression, SPA-BPNN was the best model for predicting pefloxacin-fish meal feeds, with correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set being 0.984 9 and 0.009 5 respectively, and SPA-MLR was the best model for predicting fleroxacin-fish meal feeds, with Rp and RMSEP being 0.982 7 and 0.040 6 respectively. This study shows that THz-TDS technology is feasible for qualitative and quantitative detection of pefloxacin and fleroxacin in fish meal matrix, which provides theoretical and technical reference for practical detection of pefloxacin and fleroxacin in the livestock and poultry industry.
2022 Vol. 42 (06): 1798-1803 [Abstract] ( 119 ) RICH HTML PDF (3858 KB)  ( 207 )
1804 Quantitative Analysis of Mn and Ni Elements in Steel Based on LIBS and GA-PLS
YANG Lin-yu1, 2, 3, DING Yu1, 2, 3*, ZHAN Ye4, ZHU Shao-nong1, 2, 3, CHEN Yu-juan1, 2, 3, DENG Fan1, 2, 3, ZHAO Xing-qiang1, 2, 3
DOI: 10.3964/j.issn.1000-0593(2022)06-1804-05
The content of manganese and nickel in the steel refining process will affect the hardness and brittleness of the final product, but the added content needs to be strictly controlled. At the same time, the traditional steel composition detection equipment had a high cost, low efficiency and slow speed. Therefore, a high-precision, fast and real-time analysis method is needed. This article used genetic partial least squares (GA-PLS) combined with LIBS technology to quantitatively detect the two elements of Mn and Ni in the spectrum of steel samples and compared the results with the quantitative analysisof traditional PLS to verify the predictive performance of the GA-PLS model. This experiment used 12 steel samples purchased in the steel market, the spectral information of 9 samples was used as the calibration set training model, and the spectral information of 3 samples was used as the test set to verify the quantitative performance. GA-PLS continuously raised the threshold of the selected frequency of the variable, established the PLS model with the variables under different thresholds, and compared the threshold when the lowest RMSECV was selected (the optimal thresholds for the selected frequency of the spectral input variables of Mn and Ni were 8 and 7 respectively). The results of GA-PLS showed that the R2P and RMSEP of the GA-PLS manganese prediction results were 0.999 0 and 1.347 3, and the relative analysis error (RPD) was 2.5; the R2P and RMSEP of the nickel prediction results were 0.999 5 and 0.525 4, respectively, and the RPD was 8.6. The final predicted result was better than PLS. The results show that the GA-PLS algorithm has the potential for sustainable mining in metallurgical metal element analysis, and will also promote the deeper application of LIBS technology in the field of steel smelting.
2022 Vol. 42 (06): 1804-1808 [Abstract] ( 155 ) RICH HTML PDF (2415 KB)  ( 123 )
1809 Abundance and Spectral Characteristics of Molecular Weight Separated Dissolved Organic Matter Released From Biochar at Different Pyrolysis Temperatures
WEI Si-ye1, 2, FAN Xing-cheng3, MAO Han1, 2, CAO Tao4, 5, CHENG Ao3, FAN Xing-jun3*, XIE Yue3
DOI: 10.3964/j.issn.1000-0593(2022)06-1809-07
Biochar (BC) returned to soil will release amounts of dissolved organic matters (DOM), which can change soil-DOM’s content and chemical properties and then have important impacts on their environmental behaviors. The molecular composition and structures of BC DOM would determine their complex environmental behaviors. However, the studies on chemical composition, especial on molecular weight (MW) separated fractions, are still limited. In this study, two types of biochar were firstly produced by pyrolyzing rice straw, and pig manure at different temperatures (300, 400 and 500 ℃), and then DOM therein was fractionated into three MW fractions, including <1, 1~5 and >5 kDa, using ultrafiltration method. Subsequently, the content and optical properties of MW fractions were investigated based on dissolved organic carbon (DOC), ultraviolet-visible (UV-vis) spectra, and excitation-emission matrix spectra combined with the regional integral protocol (EEM-FRI). The results showed that the proportional DOC distributions of <1, 1~5 and >5 kDa into bulk BC DOM were 42%~60%, 16%~23% and 23%~29%, respectively. The corresponding distributions on α254 were 4%~27%, 8%~49% and 26%~81%. These results suggested that the major OC species in bulk BC DOM were portioned into <1 kDa fractions, whereas the major chromophores were partitioned into >5 kDa fractions. The >5 and 1~5 kDa fractions in BC DOM produced at 400, and 500 ℃ were generally contained higher MW and aromaticity than those at 300 ℃. The >5 kDa fractions within rice straw-derived BC DOM contained more aromatic structures than pig manure-derived ones, while the <1 kDa fractions within latter ones contained more aromatic ones. It was worth noting that MW fractions in rice straw and pig manure-derived BC DOM almost exhibited similar EEM spectra characteristics, indicating that BC DOM were complex organic compounds with a chemical continuum. Additionally, EEM spectra showed that fulvic-like and short-wavelength tryptophan-like fluorophores were predominant in MW fractions within rice straw- and pig manure- derived BC DOM, respectively. With increasing MW fractions, fluorescence index (FI) and autochthonous index (BIX) of DOM decreased, while humification index (HIX) increased, implying that high MW fractions enriched organic matters with high aromatic and humification degree. The study enhanced our understanding of the molecular composition and structures of BC DOM, which could also provide beneficial references for accurately evaluating environmental behaviors of BC DOM.
2022 Vol. 42 (06): 1809-1815 [Abstract] ( 138 ) RICH HTML PDF (3709 KB)  ( 42 )
1816 Study on the Effects of Planting Years of Vegetable Greenhouse on the Cucumber Qualties Using Mid-IR Spectroscopoy
ZHANG Yan-ru1, 2, SHAO Peng-shuai1*
DOI: 10.3964/j.issn.1000-0593(2022)06-1816-06
Greenhouse vegetable cultivation plays a crucial role in global vegetable supply. The planting year of vegetable greenhouses greatly affects vegetable yield and quality, whereas the study on vegetable quality using infrared spectroscopy is still unclear. Therefore, the mid-IR spectroscopy was used to detect how planting year of vegetable greenhouses (i.e., 1 year, 10 years, and 18 years) influenced cucumber quality by analyzing the specific peaks of cucumber fruits and leaves. In our study, the polysaccharides and protein components in cucumber fruits initially increased (the highest in 10 years) and subsequently decreased during the progression of the planting year. Raising planting years (i.e., 10 years and 18 years) increased lignin components of cucumber fruits (mainly in cucumber peel), which reduced the cucumber’s taste. In addition, the ratio of organic components in cucumber can reflect cucumber quality under different greenhouse planting years. Polysaccharide/protein components and polysaccharide/lignin components in 18 years were significantly lower than those in 1 year and 10 years, implying that cucumbers of 1 year and 10 years had well-balanced carbohydrates and nutrients. Our finding suggests that short-term planting years (e.g., within 10 years) can improve cucumber quality, but long-term planting years inhibit cucumber quality. Therefore, comprehensively considering cucumber quality, we suggested that the planting year of the cucumber greenhouse should not be too long. Additionally, the organic components of cucumber leaves showed a similar trend to organic cucumber components across greenhouse planting years. Linear regression analysis demonstrated that cucumber fruits’ protein and lignin components were positively associated with the protein and lignin components of cucumber leaves, which indicated that cucumber leaves might represent the nutrient and taste cucumber fruit. Overall, this study revealed the changes in organic cucumber components by mid-IR spectroscopy paralleled changed cucumber quality, providing scientific evidence for vegetable greenhouses management and improving vegetable quality.
2022 Vol. 42 (06): 1816-1821 [Abstract] ( 245 ) RICH HTML PDF (3034 KB)  ( 39 )
1827 Study on UV-Vis Absorption Spectra of Jadeite From Different Origins
MA Ping1, 2, Andy Hsitien Shen1*, ZHONG Yuan1, LUO Heng1
DOI: 10.3964/j.issn.1000-0593(2022)06-1827-05
Jadeite is a kind of precious jade. The value of jadeite from different producing areas varies greatly, and jadeite from other origins passes off as Myanmar Jadeite to obtain a price increase. There is an urgent need for a reliable method to determine the geographical origin of jadeite. The research on the origin of jadeite has important gemmological significance. At present, jadeite from different origins is mainly discussed in the aspects of the generation age, mineral assemblage, jadeite component content, etc. There is no rapid and effective method to identify the origin. This paper takes jadeite from Myanmar, Russia and Guatemala as the research objects. It is found that there are two obvious absorption regions in the UV-Vis absorption spectrum of jadeite from different areas. The absorption peak at 437 nm in the ultraviolet region is the absorption of Fe3+, and the absorption peak at 430 nm is caused by the spin forbidden transition of Mn2+, but the absorption coefficient ranges of the UV-Vis absorption spectra of jadeite from the three origins are different. The absorption coefficient peak range at 430 nm is less than 0.62, and the absorption coefficient peak at 437 nm is less than 0.66, which is Myanmar Jadeite, and the absorption coefficient peak range at 430 nm is greater than 1.1, When the peak absorption coefficient at 437 nm is greater than 1.1, it is Guatemala jadeite. When the peak absorption coefficient at 430 nm is 0.62~1.14, when the peak absorption coefficient at 437 nm is 0.66~1.1, the jadeite areas of Russia, Guatemala and Myanmar coincide, which is the common area of jadeite from three origins. The MnO and FeO elements’ content was determined by laser denudation inductively coupled plasma mass spectrometry (LA-ICP-MS). It was found that the peaks of UV-Vis absorption coefficients at 430 and 437 nm in different jadeite origins were positively correlated with the content of MnO and FeO elements. This study UV-Vis absorption spectroscopy technology to the rapid identification of jadeite origins, which has certain practicability and feasibility.
2022 Vol. 42 (06): 1827-1831 [Abstract] ( 145 ) RICH HTML PDF (3334 KB)  ( 120 )
1832 Study of Germinated Rice Seeds by FTIR Spectroscopy Combined With Curve Fitting
LI Shu-jie1, LIU Jie1, DENG Zi-ang1, OU Quan-hong1, SHI You-ming2, LIU Gang1*
DOI: 10.3964/j.issn.1000-0593(2022)06-1832-09
Seed germination is one of the main components of the seed life course. Agricultural production needs to understand the physiological and biochemical changes in seed germination and accurately determine the vigor of seeds. Therefore, it is of great significance to study seed germination. In order to explore the mobilization of storage materials during seed germination, Fourier transforms infrared spectroscopy (FTIR) combined with curve fitting was used to study rice seeds with different germination days. The rice seeds with different germination times were studied by original infrared spectra, second derivative spectra, two-dimensional correlation infrared spectra and curve fitting. The results showed that the original infrared spectra were overall similar. The spectra reflected that the main storage substances of rice seeds were starch, protein and fat. The absorption peak intensity ratios of A1 659/A1 019, A1 740/A1 019, A1 157/A1 019 and A1 157/A1 081 decreased with germination time. The results of two-dimensional correlation infrared spectroscopy in the range of 814～1 000 and 1 028～1 340 cm-1 showed that the number of auto-peaks, and the position and intensity of the strongest auto-peaks changed with the increase of seed germination time, indicating that carbohydrate and protein changed during seed germination. The second derivative spectra showed seven peaks in the range of 1 200～950 cm-1. The 988 cm-1 peaks shifted to the higher wavenumber with the increase in germination time, while the peaks at 1 053 and 1 158 cm-1 were shifted to a lower wavenumber, which indicated that the structure and content of polysaccharides in rice seeds changed during germination. Nine peaks appeared in the range of 1 700～1 600 cm-1, among which the peaks at 1 641 and 1 692 cm-1 moved to lower wavenumber with the increase in germination time, indicating that the protein structure and content of rice seeds may have changed during germination. In the range of 1 800～1 700 cm-1, only two peaks at 1 712 and 1 744 cm-1. There are observed in the second derivative spectra, which 1 744 cm-1 is caused by the C═O stretching vibration of the lipid substance. In order to further explore the specific changes of storage substances during the germination of rice seeds, curve fitting analysis was carried out in the regions of 1 200～950 and 1 800～1 600 cm-1 of original infrared spectra based on the location and number of sub-peaks determined by the second derivative spectra. The curve fitting results showed that with the increase of germination time, the relative polysaccharide and protein content showed a downward trend, while the relative content of fat first decreased and then increased. The results show that FTIR combined with curve fitting can be an effective method for seed germination study.
2022 Vol. 42 (06): 1832-1840 [Abstract] ( 208 ) RICH HTML PDF (6926 KB)  ( 64 )
1841 Discrimination of Millet Varieties and Producing Areas Based on Infrared Spectroscopy
TIAN Xue1, CHE Qian1, YAN Wei-min1, OU Quan-hong1, SHI You-ming2, LIU Gang1*
DOI: 10.3964/j.issn.1000-0593(2022)06-1841-07
There are significant differences in taste and nutritional value among different varieties and producing areas of millet. Therefore, it is of reference significance for consumers to distinguish different kinds of millet. In this paper, Fourier transforms infrared (FT-IR) spectroscopy, two-dimensional correlation infrared (2D-IR) spectroscopy combined with curve fitting, principal component analysis (PCA) was used to distinguish varieties and origins of millet. The results showed that the original spectra of millet were similar, which were mainly composed of carbohydrates, proteins and lipids. The obvious differences in intensity were observed near 3 012, 2 962, 2 928, 2 856, 1 748 and 1 548 cm-1 in SD-IR. The numbers, positions and intensities of auto-peaks and cross-peaks were different in the range of 1200~860 and 1700~1180 cm-1. The curve fitting results showed that the ratio of the sub-peak areas of millet in the range of 1 700~1 600 cm-1 was different, which indicated that the protein content of millet was different among different varieties, to realize the identification of millet varieties. The range of 1 800~800 cm-1 in the derivative spectra was used for PCA analysis. The results showed that the cumulative contribution rate of the first three principal components was 97%, and millet from different producing areas was correctly classified. The study demonstrates that IR combined with statistical analysis methods could be effectively used to identify and analyze varieties and producing areas of millet.
2022 Vol. 42 (06): 1841-1847 [Abstract] ( 174 ) RICH HTML PDF (5613 KB)  ( 310 )
1848 Study on the Fractional Baseline Correction Method of ATR-FTIR Spectral Signal in the Fermentation Process of Sodium Glutamate
HE Nian, SHAN Peng*, HE Zhong-hai, WANG Qiao-yun, LI Zhi-gang, WU Zhui
DOI: 10.3964/j.issn.1000-0593(2022)06-1848-07
In this paper, Attenuated Total Reflection Fourier Transformed Infrared Spectroscopy (ATR-FTIR) combined with the multivariate calibration model was used to realize the indirect measurement of the concentration of two main substrates (glucose and sodium glutamate) during the fermentation process of γ-polyglutamic acid (γ-PGA), which could provide feedback information for the fermentation process. The frequent baseline drift phenomenon in the spectrum measurement will seriously affect the performance of the subsequent multivariate calibration model, and it is necessary to use the baseline calibration algorithm to preprocess the spectrum. Most of the popular baseline correction algorithms are based on the Whittaker Smoother (WS) smoothing algorithm. And use integer-order differentials with limited expressive power to constrain the fitted baseline. Because of the poor adaptability of integer-order differential in the existing baseline correction algorithms, we use more flexible fractional-order differentials to constrain the baseline and then propose a baseline correction algorithm based on fractional-order, which realizes the extension of the integral order baseline correction. 5 batches of γ-PGA fermentation experiments were carried out, and the ATR-FTIR spectra of different batches and all batches were subjected to fractional baseline correction respectively; subsequently, the prediction accuracy of each model was improved to some extent. The experimental results show that only in batch 2 the baseline correction effect based on the integer-order is the best; the orders to obtain the best baseline correction effect for other batches were all fractional-order. Italso reflects that the constraint of the fractional-order derivative (including the integer-order derivative) on the baseline is reasonable. At the same time, it is found that the overall baseline correction effect of all batches is far worse than that of a single batch. The reason may be that the baseline of the spectra for each fermentation batch is different. Different orders need to be selected for different batches to achieve the best effect of baseline correction. In addition, the background spectrum was acquired with distilled water as the reference before measuring each γ-PGA fermentation sample. Anegative water peak thus inevitably appears in the wavenumber range of 3 100~3 600 cm-1 and forms harmful interference signals; the fractional baseline-corrected spectra show that the fractional-order baseline correction algorithm regards the negative water peak as the baseline and eliminates it to a certain extent. In summary, the fractional-order baseline correction algorithm expands the application range of the traditional integer-order baseline correction algorithm and provides a new solution to eliminate negative water peaks in the ATR spectra with water as the background spectrum.
2022 Vol. 42 (06): 1848-1854 [Abstract] ( 123 ) RICH HTML PDF (2557 KB)  ( 149 )
1855 Quantitative Analysis of NO-3，SO2-4，ClO-4 With Water as Internal Standard by Raman Spectroscopy
WANG Gan-lin1, LIU Qian1, LI Ding-ming1, YANG Su-liang1*, TIAN Guo-xin1, 2*
DOI: 10.3964/j.issn.1000-0593(2022)06-1855-07
Internal standard is often required when using Raman spectroscopy for quantitative analysis due to the poor reproducibility of the Raman spectrum. In aqueous solutions, the stretching vibration Raman peak of water at 2 700~3 900 cm-1 has a strong intensity and may be used as an internal standard, but the interaction of water and solute will cause the shape of the water stretching vibration Raman peak to change. In addition, the concentration of water will also change with the change the solute concentration. When the solute concentration is high, the water concentration needs to be corrected. Taking these two factors into consideration, quantitative analyses of NO-3, SO2-4, ClO-4 in aqueous solutions with Raman spectroscopy are investigated, focusing on evaluating water as an internal standard. The Raman spectra of different concentrations of NaNO3，Na2SO4，NaClO4 solutions show that with the increase of salt concentration, the Raman peak of water in the range of 2 700~3 900 cm-1 presents a trend that the left shoulder drops and the right shoulder rise. However, there exists a good linear relationship between Asalt/AH2O and csalt/cH2O in NaNO3，Na2SO4，NaClO4 solutions, where A represents the area of the Raman peak and c represents the concentration, and the R2 of the three fitting curves are 0.999 1, 0.999 1, 0.999 4, respectively. This indicates that the Raman scattering coefficients of acid ions and water do not change or change in the same proportion. So, although the shape of the water Raman peak having changed, the feasibility of water as an internal standard is not affected. After introducing the correction of the concentration of water, it is theoretically deduced that csalt and conform to the relationship: csalt=ARS/(1+BRS), where RS=Asalt/AH2O. In a wide concentration range from 0.1 mol·L-1 to near saturation, the standard working curves for NaNO3, Na2SO4, and NaClO4 are obtained to be cNaNO3=18.8RS/(1+0.6RS) (R2=0.999 1), cNa2SO4=20.2RS/(1+1.0RS) (R2=0.998 8), and cNaClO4=15.0RS/(1+0.7RS) (R2=0.999 8), respectively. The limit of detection (LOD) of NaNO3, Na2SO4 and NaClO4 are found to be 0.008 0, 0.005 2 and 0.007 3 mol·L-1, respectively. On the basis that the shape change of the water Raman peak does not affect its feasibility as an internal standard, when there are two salts in a solution, a water concentration correction for the second salt can be made to improve the quantitative analysis based on the standard curves for the single salt solutions. However, the correction result is limited when the second salt concentration is too large, and the first salt concentration is relatively small because the accuracy of the Raman peak area of the first salt will be affected due to the too large Raman intensity of the second salt.
2022 Vol. 42 (06): 1855-1861 [Abstract] ( 164 ) RICH HTML PDF (2939 KB)  ( 75 )
1862 Determination of Rare Earth Elements in High-Salt Water by ICP-MS After Pre-Concentration Using a Chelating Resin
ZHU Zhao-zhou1*, YANG Xin-xin1, LI Jun1, HE Hui-jun2, ZHANG Zi-jing1, YAN Wen-rui1
DOI: 10.3964/j.issn.1000-0593(2022)06-1862-05
Based on inductively coupled plasma mass spectrometry (ICP-MS), a novel method for the accurate determination of ultra-trace rare earth elements (REEs) in high-salt surface water was established. The interfering with organic matter in the surface water was eliminated by UV/H2O2. The concentration of REEs in surface water is at the ng·L-1level, making the quantitative determination of dissolved REEs very difficult. The matrix effect of ICP-MS is serious when the total dissolved solid concentration is higher than 1 g·L-1. Moreover, nebulizer, sampler cone, and skimmer Cone may be blocked in the process of measuring. Therefore, it is necessary to remove salt from the water when the concentration of REEs in high-salt water is determined. REEs in water is needed to preconcentrate before measurement. However, the concentrations of organic matter are usually high in the surface water. The complexation of organic matter can lead to a fraction of REEs during -pre-concentration. The preconcentration of dissolved REEs is also a challenge. In this work, H2O2was added to the sample before the preconcentration. The sample was subsequently irradiated with a digester, which destroyed the organic ligands of REEs. The dissolved organic carbon (DOC) concentration in water could be reduced to approximately 0.5 mg·L-1. The REEs in water were pre-concentrated through a Nobias PA1 chelating resin column. Proceed as follows: initially, the preconcentrated system was respectively rinsed with HNO3, pure water, and NH4AC solution in sequence at a flow rate of 2.2 mL·min-1 to remove the possible residual REEs. Then, the column was respectively rinsed with NH4AC solution, sampler, and NH4AC solution in sequence at a flow rate of 2.0 mL·min-1 to preconcentrate REEs and remove the loaded salts. Finally, the REEs were eluted with HNO3 at a flow rate of 0.7 mL·min-1 and analyzed by ICP-MS. A 115In internal standard was used to correct instrument fluctuation and matrix effect. Results showed that the procedural blanks, detection limits, and relative standard deviations (RSD) of the REEs were 0.34～12.9 and 0.34～22.0 ng·L-1, and <5% (n=5), respectively, at a pH of 4.6±0.1. All REEs could be quantitative, and their recoveries were 97%～101%. The results from applying this method to coastal water, estuary water, and saline lake water showed that the recoveries of Tm internal standard were 98%～101%, and RSD of the samples (n=3) were <5%. It indicates that the method is suitable for analysing REEs in high-salt surface water.
2022 Vol. 42 (06): 1862-1866 [Abstract] ( 126 ) RICH HTML PDF (1651 KB)  ( 316 )
1867 Infrared Spectral Characterization of Ultraviolet Ozone Treatment on Substrate Surface for Flexible Electronics
WANG Xue-pei1, 2, ZHANG Lu-wei1, 2, BAI Xue-bing3, MO Xian-bin1, ZHANG Xiao-shuan1, 2*
DOI: 10.3964/j.issn.1000-0593(2022)06-1867-07
In recent years, with the progress of nanotechnology, polymer materials and advanced manufacturing technology, emerging flexible electronic devices represented by flexible sensors are playing an increasingly important role in the fields of wearable, healthcare, Internet of Things terminal and so on. As the carrier of flexible electronic devices, the flexible substrates are of great significance to the mechanical reliability and electrical sensing performance of sensors. However, the high hydrophobicity caused by the dominant non-polar bonds on the flexible substrate surface restricts the deposition of functional materials on the surface, which results in the unstable interfacial bond between the substrate and the electrode＼sensitive layer. Therefore, the surface modification of flexible substrates by ultraviolet-ozone (UVO) treatment has received extensive attention. In this study, we explored the rapid and accurate evaluation of the UVO treatment effect of the flexible substrate by near infrared (NIR) spectroscopy, aiming to characterize the modification effect from the level of group and molecule, which is an effective supplement to the contact angle measurement method. In particular, four kinds of flexible substrates, polydimethylsiloxane (PDMS), polyethylene terephthalate (PEN), polyethylene terephthalate (PET) and polyimide (PI) were modified by 1/2/5/10 minutes with UVO treatment, and the modification effects were characterized by NIR spectroscopy. Finally, the characterization analysis results were verified by the contact angle measurement. The NIR spectrum analysis showed that the UV energy was not enough to break the methyl (—CH3) functional group and (O—Si—O) chemical bond in the flexible PDMS substrate, so the hydrophilic groups such as hydroxyl group and carboxyl group could not be introduced. For flexible PEN and PET substrates, the treatment effect of UVO was better than that of flexible PDMS substrates, and the treatment effect of flexible PET substrates was better than that of flexible PEN substrates. The reason may be that the naphthalene ring double-ring structure in the PEN substrate has a strong ultraviolet light absorption ability, which blocks most ultraviolet energy below 380 nm. For flexible PI substrates, UVO treatment can effectively introduce active groups such as hydroxyl (C—OH) and carboxylic acid (OC═O), and the strength and number of these functional groups increase with the increase of modification time, so that the surface energy of PI substrates increases in a short time, the contact Angle decreases, and the wettability improves. The contact angle measurement results showed that the UVO treatment had no obvious effect on the flexible PDMS substrate (the contact angle decreased by 8.4%). The modification effect of flexible PET substrate (39.6% contact angle decline) was better than that of flexible PEN substrate (9.4% contact angle decline). UVO treatment was the most effective for the flexible PI substrate, since the contact angle decreased by 62.7%.
2022 Vol. 42 (06): 1867-1873 [Abstract] ( 171 ) RICH HTML PDF (3859 KB)  ( 112 )
1874 Analysis of Pigments of Colored Paintings From Early Qing-Dynasty Fengxian Dian in the Forbidden City
YOU Gui-mei1, ZHANG Wen-jie1, CAO Zhen-wei2, HAN Xiang-na1*, GUO Hong1
DOI: 10.3964/j.issn.1000-0593(2022)06-1874-07
Fengxian Dian is an ancestral worship hallinside the Forbidden City where the emperor’s family of the Ming and Qing Dynasties offered sacrifices to their ancestors, ranking second only to the Imperial Ancestral Temple (Tai Miao). Fengxian Dian was built in the Ming Dynasty and destroyed at the end of the Ming Dynasty. It was reconstructed during the reign of Shunzhi in the Qing Dynasty, and the existing buildings were mainly built in the Kangxi period, which is recorded in the archives. Fengxian Dian preserves a few extant colored paintings of the early Qingdynasty, which is the precious material to study the decorative paintings art in this period.The pigment samples from the Fengxian Dian were analyzed and identified for the first time using microscopic observation, laser Raman spectroscopy and SEM-EDS.The results show that there is a stratification phenomenon in some red and blue color layers. What’s more, the color of the surface layer is brighter, while the color of the middle layer and the lower layer is light, and the pigment formula used in each layer is different. The red pigments are vermilion(HgS), red lead (Pb3O4) and red ochre (Fe2O3), green pigment is atacamite [CuCl2·3Cu(OH)2], blue pigment is azurite [2CuCO3·Cu(OH)2], white pigments are lead white [2PbCO3·Pb(OH)2]. Light color pigment is mineral pigments mixed with white pigment dominated by lead white. Among them, the light red color pigment is composed of red lead and lead white, and the light green color pigment is made of atacamite and lead white. However, no lead white is found in light blue, and kaolin [Al2Si2O5(OH)4] is speculated because of many Al and Si elements present. The light blue color pigment is probably composed of azurite and kaolin. The absence of synthetic pigments such as ultramarine and emerald green, which were commonly used in the late Qing dynasty, confirms no major reparation of the polychrome paintings in Fengxian Dian after the Kangxi period. It is rare to use kaolin as the white pigment to confect light pigments. The discovery of kaolin in colored paintings of Fengxian Dian has enriched the materials of pigment production of the early Qing Dynasty, which have certain academic significance.
2022 Vol. 42 (06): 1874-1880 [Abstract] ( 164 ) RICH HTML PDF (8831 KB)  ( 277 )
1881 Classification of 2D Stellar Spectra Based on FFCNN
LU Ya-kun1, QIU Bo1*, LUO A-li2, GUO Xiao-yu1, WANG Lin-qian1, CAO Guan-long1, BAI Zhong-rui2, CHEN Jian-jun2
DOI: 10.3964/j.issn.1000-0593(2022)06-1881-05
Automatic classification of many stellar spectra is a basic task in celestial spectral processing. So far, the classification of star spectra is based on one-dimensional (1D) spectra. This paper proposes a new method based on two-dimensional(2D) stellar spectral classification. In the data processing process of LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope), 1D spectra are extracted and combined with 2D spectra, which are the images produced by a spectrometer, including blue end and red end. Based on LAMOST 2D spectra, a convolutional neural network (FFCNN) classification model is proposed for stellar spectral classification. The model is a supervised algorithm which extracts the features of the blue end and red end respectively through two CNN models. And the model fuses the two features to get new features and uses CNN to classify the new features. The data used in this work are all from LAMOST. A batch of sources are randomly selected in LAMOST DR 7, and their 2D spectra are obtained. There are 14 840 F, G, and K stars in 2D spectra for training the FFCNN model, including 7 420 blue end and 7 420 red end spectra. The number of three kinds of stellar spectra is not balanced. Different weights are set for each kind of stellar spectra in the training process to prevent the classification imbalance. At the same time, to accelerate the model’s convergence, the Z-score normalization method is used for 2D spectra. In addition, five-fold cross-validation is used to improve the model’s sample utilization and reliability. 3 710 2D spectra are used as the test set, and the accuracy, precision, recall and F1-score are used to evaluate the performance of the FFCNN model. Experimental results show that the precision of F, G, and K type stars reach 87.6%, 79.2%, and 88.5%, respectively, and they exceed the results of 1D spectral classification. The experimental results prove that the 2D stellar spectral classification based on FFCNN is an effective method, and it also provides new ideas and methods for the processing of stellar spectra.
2022 Vol. 42 (06): 1881-1885 [Abstract] ( 132 ) RICH HTML PDF (2460 KB)  ( 85 )
1886 Spectroscopic Characteristics and Coloring Mechanism of Smithsonite Jade
LUO Jie1, 2, YUE Su-wei1, 2*, GUO Hong-ying1, LIU Jia-jun3
DOI: 10.3964/j.issn.1000-0593(2022)06-1886-05
There are few studies on the mineralogical characteristics, spectral characteristics and the cause of color of the Smithsonite jade which appears to take on various hues, such as yellow, blue, pink, green, etc. In this paper, yellow-green smithsonite samples were selected and determined by X-ray powder diffractometer (XRD), electron probe (EPMA), laser denudation inductively coupled plasma mass spectrometer (LA-ICP-MS), Fourier infrared transform spectrometer (FTIR), Raman spectrometer (Raman), UV-Vis spectrophotometer (UV-Vis), electron paramagnetic resonance spectrometer (EPR). XRD test results show that the main component of the sample is smithsonite. EPMA test result shows that the main composition of smithsonite is ZnO, with an average content of 61.3%, and the secondary components are CaO, FeO, MnO, CdO and PbO. LA-ICP-MS test result shows that the content of transition elements Fe and Mn in the samples was relatively high, with an average content of 7 363×10-6 and 3 558×10-6, respectively. FTIR spectroscopy analysis showed that there are characteristic peaks of smithsonite, namely 740, 883, 1 490 cm-1, which are caused by in-plane bending vibration, out-of-plane bending vibration and antisymmetric stretching vibration CO2-3. The calcite Raman characteristic shifts of 300, 728, 1 091 cm-1 were shown in every sample caused by the symmetric stretching vibration of ZnO, in-plane bending vibration and symmetric stretching vibration of CO2-3. UV-Vis tests show that the absorption bands around 377, 395 and 417 nm are caused by the 6A1→4E(D), 6A1→4T2(D) transition of Fe3+, and d—d transition Mn2+ were responsible for the yellow-green color of the samples. The EPR spectrum also shows the characteristic six-fold hyper-fine resonance lines of Mn2+ at g=2.0 and Fe3+ at g=1.98. Combined with the results above, it is believed that the yellow-green color of the diamond is caused by the electron transition of Fe3+ and Mn2+ d—d orbitals.
2022 Vol. 42 (06): 1886-1890 [Abstract] ( 106 ) RICH HTML PDF (2270 KB)  ( 85 )
1891 Based on Color Calculation and In-Situ Element Analyze to Study the Color Origin of Purple Chalcedony
LUO Heng, Andy Hsitien Shen*
DOI: 10.3964/j.issn.1000-0593(2022)06-1891-08
Purple Grape Chalcedony from Indonesia has a special spherical appearance and violet color. Its double sides polishing waferis purple under the reflected light and brownish-yellow under the transmission light, while the color is concentrated in the center of the spherules. To investigate the origin of its color, a polarizing microscope, Scanning Electron Microscope, Microscopic UV-Vis Spectroscopy, heat treatment and LA-ICP-MS in situ composition analysis were performed. Chalcedony has a structure of fibrous core and micro quartz shell. The micro quartz shell has a particle size of 500 m, while the cryptocrystalline part is mainly composed of irregular SiO2 particles with particle sizes less than 1 m. UV-Vis spectra show that the purple is mainly from the absorption of about 540 nm, while the yellow is due to the strong absorption of near-ultraviolet area and blue light generated by the “left-leaning” the spectrum. In the UV-Vis spectrum, the surface reflection error was corrected by the Selmeier equation, and the instrument error was subtracted from the intensity of the unabsorbed band. The intensity information of the 540nm absorption peak was obtained by deducting the baseline with the least square smoothing. Calculate the L*a*b* and E* values of chalcedony in purple tone under reflected light and yellow tone under transmitted light. Thus the color can be quantified. In the heat treatment experiment, the purple hue of chalcedony began to fade at about 350 ℃, and the absorption peaks of UV-Vis spectra at 390 and 540 nm disappeared, while the color difference between reflected and transmitted light decreased; both of them are yellow. As the temperature rises to 400 ℃, the brown tone deepens, and the peak of about 478 nm appears. The intensity of spectral increases during the heat treatment, the “left-leaning” intensification, and the peak “redshift”. This phenomenon is like the change of spectral of Fe /SiO2 nanoparticles (Fe /SiO2 NPs) during its growth.It may be related to the change of Fe-related fine structure or inclusions in chalcedony during heat treatment. Color parameter was combinate with in-situ composition analysis, the data were normalized by standard score (Z - score), compared the relationship of the value of E* of purplecolor, the intensity of 540 nm peak and element content, found that 540 nm peak intensity can well reflect the concentration of purple. However, the linear correlation between color and the transition metal elements content is not significant. The E* value of yellow tone has an approximately negative correlation with Fe content. Fe does not exist in the form of impurity minerals, and the color of chalcedony is not determined by the element content independently but is affected by Fe’s existence in chalcedony, fine internal structure or inclusion.
2022 Vol. 42 (06): 1891-1898 [Abstract] ( 128 ) RICH HTML PDF (6040 KB)  ( 120 )
1899 Variation of Water Leaving Radiance Originated From Bioluminescence in the Yellow Sea and Its Relationship With Inherent Optical Properties and Depth
ZHANG Yu-xiao1, WANG Xi3, CHEN Shu-guo1, 2, 3*, LIU Zhao-wei3, HU Lian-bo1, 2
DOI: 10.3964/j.issn.1000-0593(2022)06-1899-08
Based on the radiative transfer simulation model, using the measured bioluminescence intensity data from different seasons in the Yellow Sea, combined with the simultaneous measured inherent optical properties data, this study analyzes the numerical and spectral variation of the water leaving radiance originating from bioluminescence (Lw-bio) and discusses its relationship with the inherent optical properties and the depth of bioluminescence. The main research results of this paper are as follows: (1) Lw-bio in the Yellow Sea has significant seasonal and spatial variation characteristics. Besides the bioluminescence abundance and ability of the water column, the Lw-bio is also related to the inherent optical properties of the water column and the depth of the bioluminescence source. (2) In terms of spectral variation, the maximum wavelength shift of Lw-bio increases with the deepening of the depth of bioluminescence source and related to the inherent optical properties. In the water column with a large value of inherent optical properties, when the depth of the bioluminescence source is below the surface water column, the maximum wavelength of Lw-bio can change from blue band (474 nm) to green band (up to 578 nm); In the water column with a low value of inherent optical properties, the variation of radiance spectrum is weak. Even if the depth of the bioluminescence source increases, the maximum wavelength of Lw-bio is still in the blue band (up to 500 nm). (3) Although the broad inherent optical properties of the Yellow Sea have a great impact on the geometric depth retrieval of the bioluminescence source, the geometric depth of the bioluminescence source can be retrieved from the spectral information of Lw-bio.
2022 Vol. 42 (06): 1899-1906 [Abstract] ( 97 ) RICH HTML PDF (5909 KB)  ( 40 )
1907 Optimization of Online Determination Model for Sugar in a Whole Apple Using Full Transmittance Spectrum
TIAN Xi1, 2, 3, CHEN Li-ping2, 3, WANG Qing-yan2, 3, LI Jiang-bo2, 3, YANG Yi2, 3, FAN Shu-xiang2, 3, HUANG Wen-qian2, 3*
DOI: 10.3964/j.issn.1000-0593(2022)06-1907-08
In Vis/NIR nondestructive detection, the accuracy of the prediction model is affected by many factors such as spectral quality, biological variability, detection system and modeling method. In this study, the multi-point full-transmittance spectra (650~1 000 nm) of “Fuji” apple were acquired at a speed of 0.5 m·s-1 with an integration time of 0.5 ms using an on-line spectrum measurement system. The spectral intensity changed with the detection orientations significantly, but the spectral curves of different orientations were similar, with an obvious peak at 920 nm and an obvious valley at 850 nm. To establish a reliable, accurate, and stable sugar calibration model of intact apple, three spectra preprocessing methods, including moving average smoothing, standard normal variate, and multiplicative scatter correction (MSC), were used to reduce the influence of noise from the environment and instrumental fluctuations. In order to analyze the effect of detection orientations on prediction accuracy, a local model based on single orientation and a universal model based on global orientations were built respectively. The result showed that the prediction accuracy was limited by the detection orientation in the local model, while the universal model had better applicability for multiple detection orientation than that of the local model. In order to further improve the prediction ability, a modeling method named efficient spectrum optimization was proposed to select the spectra with a high signal-to-noise ratio and remove the inefficient transmittance spectrum by investigating the interference of transmittance spectral intensity on the accuracy of the prediction model. The result showed that it is beneficial to optimize the prediction model after removing the spectrum collected from the central zone of the apple. The universal intensity optimization model considered the spectral quality of different orientations comprehensively. The prediction model was best with Rp,RMSEP and RPD of 0.79，0.84% and 1.58 respectively, when the spectral intensity threshold was 5 000. Our result illustrated that the multi-potion spectrum measurement system is promising for on-line detection of apple quality. The modeling method of efficient spectrum optimization could be selective the transmittance spectrum with a high signal-to-noise ratio and optimize the prediction model.
2022 Vol. 42 (06): 1907-1914 [Abstract] ( 104 ) RICH HTML PDF (4477 KB)  ( 65 )
1915 Discrimination of Four Black Heartwoods Using FTIR Spectroscopy and Clustering Analysis
MA Fang1, HUANG An-min2, ZHANG Qiu-hui1*
DOI: 10.3964/j.issn.1000-0593(2022)06-1915-07
A fast discrimination method of four black heartwoods was developed by Fourier Transform Infrared Spectroscopy (FTIR) combining with clustering analysis. In FTIR spectra, the principal chemical components of heartwood were characterized for cellulose (the bands at around ~1 370, ~1 158, ~1 034 and ~895 cm-1), lignin (the bands around ~2 935, ~1 510, ~1 462 and ~1 426 cm-1) and calcium oxalate (peaks at ~1 615, ~1 318 and ~781 cm-1). The correlation coefficient and relative intensity among samples with standard material result that D. ebenum and D. melanoxylon are lignin-rich, while C. imberbe contains more calcium oxalate. G. conjugate has peaked at 1 738 cm-1 means it contains resin. Based on the correlation coefficient, the method of Compare clustering analysis was used to classify four blackwoods. The classification rate was above 95% during blind sample testing. Meanwhile, four blackwood were successfully classified by the method of SIMCA clustering analysis. The recognition rate and rejection rate reached up to 100%. The accuracy of clustering reached up to 100% during blind sample testing. It explained that the four tree species could be classified and identified completely by SIMCA clustering analysis. Besides, cellulose showed high thermal sensitivity in D. ebenum and D. melanoxylon through the 2DCOS-IR synchronous spectra. Calcium oxalate showed high thermal sensitivity in C. imberbe and lignin showed high thermal sensitivity in D. melanoxylon. Combined with cluster analysis calculation and 2DCOS-IR, FTIR can analyze the relative content of the main composition of wood and quickly and effectively classify the pattern recognition of wood species based on the improving clustering model.
2022 Vol. 42 (06): 1915-1921 [Abstract] ( 143 ) RICH HTML PDF (5085 KB)  ( 47 )
1922 Simulation and Noise Analysis of Pushbroom Multi-Gain Spectral Imaging
JIN Peng-fei1, 2, 3, TANG Yu-yu2, 3*, WEI Jun2, 3
DOI: 10.3964/j.issn.1000-0593(2022)06-1922-06
Comprehensive remote sensing monitoring requires high sensitivity and dynamic range of the sensor, so a spectral imaging method based on a pixel by pixel gain switching is proposed. Unlike from the frame gain or row column gain switching imaging methods, this method can optimize the gain at pixel level by using the nondestructive readout characteristics of the 4T-APS CMOS detector. This method can take into account the imaging needs of water, vegetation, cloud and other factors and greatly improve the development efficiency of the payload. The basic principle is that the detector firstly carries out global exposure and then according to the saturation judgment of the multi-stage integral capacitance, selects the unsaturated highest gain signal to be transmitted down in the form of gain code plus signal. The real radiation value of the pixel is derived from the calibration coefficient of the gain code. Due to the multi spectral segment and piecewise response, it is important to establish the multi-gain spectral imaging model and analyze the noise to ensure the system’s quantitative application. Based on the analysis of noise types, the Poisson Gaussian noise model with multi-gain is established. Based on the model, the probability of low gain readout is calculated. The results show that although the noise will affect the change of the readout gain, the influence range is very small. When the radiance is within 5 mW·cm-2·μm-1·sr-1, the signal is less 0.05 mW·cm-2·μm-1·sr-1 than the value of the gain gear, so the probability of normal readout is greater than 99.6%. With the enhancement of the signal, the photon noise increases, the gain decreases, and the influence range expand. According to the multi-gain SNR model, the changes of SNR in spectral mode and combined channel mode are analyzed. Finally, the push broom spectral imaging simulation of four gains is carried out using the wideband imaging spectrometer (WIS) data as the entrance pupil radiance, and the inherent characteristics of the multi-gain spectral image are analyzed. Based on the noise model, add 1~3σ random noise to the spectral image with center wavelength of 0.443 μm, the influence of the noise on the gain of ground objects is analyzed. The results show that on the premise of meeting the SNR index, the single gain dynamic range of the system is 74 dB, and the total dynamic range is 114 dB. This method improves the SNR of weak signals such as water and ensures the unsaturation of bright targets such as buildings and clouds. Imaging simulation and noise analysis are conducive to the subsequent development of the sensors and provide a reference for the design of similar spectral instruments.
2022 Vol. 42 (06): 1922-1927 [Abstract] ( 115 ) RICH HTML PDF (3416 KB)  ( 37 )
1928 Study on Metallic Samples Determination Based on Prompt Gamma Neutron Activation Analysis Technique
CHENG Can1, HEI Da-qian2*, JIA Wen-bao1, SHAN Qing1, LING Yong-sheng1, ZHAO Dong1
DOI: 10.3964/j.issn.1000-0593(2022)06-1928-06
In the productive process of alloy materials, the change of element contents can significantly affect the quality and reliability of the products. The on-line monitoring techniques can determine the element contents of metallic materials in real-time, which can then guide their manufactureing processes and improve their quality and reliability. The normal techniques have limited penetration depth in the sample and are not efficient for volume analysis, which will affect the analytical accuracy. Prompt gamma-ray neutron activation analysis (PGNAA) is a non-destructive, high sensitivity and multi-elemental determination technique and can be used for bulk sample analysis. This work studied the feasibility of determination for metallic materials by using PGNAA. The metallic samples were analyzed by measuring prompt gamma rays produced by inelastic scattering of fast neutrons. A PGNAA system consisting of a D-T neutron generator, neutron reflector, collimator, high-purity germanium (HPGe) detector, and shielding was built and used for analysis of metallic materials. Firstly, Fe, Ti, Cr, Ni and Cu samples with various masses were conducted with the designed system. The prompt gamma rays of the elements were fitted using GAMMAFIT software to obtain the net areas of prompt gamma rays, and the response between sample mass and net peak area was analyzed. The non-linear response caused by variation of detection efficiency was corrected to obtain a calibration curve. A good linear relationship could be observed after correction. The mass detection limit of these elements was calculated, and the values were Fe (44 g), Ti (25 g), Cr (33 g), Ni (108 g) and Cu (72 g), respectively. Secondly, the determination of Fe and Cr of stain steel samples was studied by using the system. The calibration curves of Fe and Cr were obtained with the standard samples. An unknown sample was then measured and analyzed. The results were compared with the X-ray fluorescence (XRF) measurement data. The experimental data showed that the relative deviations of Fe and Cr obtained with the two methods were 4.08% and -2.97%, respectively. The results demonstrated that the PGNAA technique could be applied for determining many metals and alloys, which provides a basis for other metallic samples analyses.
2022 Vol. 42 (06): 1928-1933 [Abstract] ( 123 ) RICH HTML PDF (4562 KB)  ( 50 )
1934 Study on the High Temperature Annealing Process of Thermal Regeneration Fiber Bragg Grating
CHEN Huan-quan1, DONG Zhong-ji2, CHEN Zhen-wei1, ZHOU Jin1, SU Jun-hao1, WANG Hao1, ZHENG Jia-jin1, 3*, YU Ke-han1, 3, WEI Wei1, 3
DOI: 10.3964/j.issn.1000-0593(2022)06-1934-05
Fiber Bragg Grating (FBG) is a key device widely used in optical fiber communication and sensing. It has many advantages such as high sensitivity, small size and anti-electromagnetic interference. However, it will gradually decline and even be completely erased in a high-temperature environment for a long time, which greatly limits the application of FBG in some special fields such as industrial production, petroleum and electric power, aerospace, etc. Through high-temperature annealing treatment, it is expected that FBG can regenerate thermal regenerated FBG (RFBG), which can work stably in high-temperature environments after high-temperature erasure. Therefore, it is of great significance to study the influence of high-temperature annealing process on RFBG performance. In this paper, based on a 248 nm excimer laser, an initial FBG with a reflection spectrum center wavelength of 1 548.5 nm, a reflectivity of 97.8%, and a 3 dB bandwidth of 0.36 nm is produced by the phase mask method. It is found that FBG achieves thermal regeneration at 950 ℃, and an RFBG with a reflection spectrum center wavelength of 1 546.7 nm, reflectivity of 50.6%, and 3dB bandwidth of 0.19 nm is obtained; further research found that the annealing program after high-temperature thermal regeneration at 950 ℃ has an effect on RFBG The performance has a great impact. The RFBG is annealed by four methods: rapid cooling, slow cooling, natural cooling, and natural cooling in an argon atmosphere, and compared with the initial grating. It is found that the RFBG treated with rapid cooling has the best mechanical performance. It retains about 50% of the mechanical strength of the initial grating, which is better than the slow cooling and natural cooling treatments, which only retain 22.2% and 29.9% of the mechanical strength of the initial grating, respectively. It is found that annealing in argon is beneficial to the mechanical strength of RFBG. The improvement is also natural cooling, and the RFBG annealed in an argon atmosphere retains 43% of the mechanical strength of the initial grating. Further tests on thermal cycling and thermal stability of the RFBG treated with rapid cooling. The results show that the results of the three heating cycles of RFBG at 150~1 050 ℃ completely overlap, the temperature sensitivity is 16.30 pm·℃-1, the temperature sensitivity correlation coefficient R2 is 0.995 38, and the thermal stability test is carried out at 800 ℃ for 7 h, the total wavelength drift amount is only 0.08 nm, indicating that the RFBG prepared in this article has good temperature measurement performance and stability. The research work in this paper provides a certain theoretical and experimental basis for the practical and engineering application of RFBG high-temperature sensors.
2022 Vol. 42 (06): 1934-1938 [Abstract] ( 143 ) RICH HTML PDF (2229 KB)  ( 50 )
1939 Study on Classification and Recognition of Mountain Meadow Vegetation Based on Seasonal Characteristics of Hyperspectral Data
ZHENG Yi1, 2, 3, WANG Yao1, 2, LIU Yan1, 2*
DOI: 10.3964/j.issn.1000-0593(2022)06-1939-09
The mountain meadow on the north slope of the Tianshan Mountains has the highest grassland productivity, and the grassland degradation is serious. The classification and recognition of grassland vegetation play an important role in monitoring the background status of the grassland ecosystem. It is also the key to carrying out ecological reconstruction, which can quickly, accurately, and effectively evaluate grassland the dynamics and degree of grassland degradation. In this paper, we explored the classification method in grassland vegetation of the typical mountain meadow vegetation in the middle section of the north slope of Tianshan Mountain in Xinjiang. Firstly, a hyperspectral imaging spectrometer obtained original reflectance spectra of typical vegetation in four key growth periods (SOC710VP). Then, Savitzky-Golay filtering and the minimum noise fraction transformation (MNF) were used to smooth and reduce the dimensions of the spectrum data. Thirdly, classification models were established by the support vector machine (SVM), the backpropagation artificial neural network (BP-ANN) and the spectral angle mapper (SAM). Finally, a comparative analysis of the classification results from three models was made. The results showed that the dimension reduction and noise removal of grassland vegetation hyperspectral data could be effectively carried out by using the S-G filter and MNF transform preprocessing method. This processing reduced the redundancy of data and shortened the classification time while obtaining a smoother classification image. The parameters such as “green peak”, “red valley”, and “red edge” of mountain meadow vegetation varied greatly in different seasons. The spectral curve characteristics in the vigorous vegetation growth period (from April to May) were easier to distinguish than those in the withering date. Thus, the classification accuracy was higher in this period. The overall classification accuracy of the SVM model exceeded 90%, and the Kappa coefficient exceeded 0.9 in the green-up date (April) and tillering stage (May). Based on the SVM model, the classification accuracy of the polynomial kernel function was higher in the vigorous growth period (from April to May), and the radial basis function (RBF) showed better performance in the mature period (from June to September). BP-ANN had higher classification accuracy in the tillering stage, the overall classification accuracy was 91.07%, and the kappa coefficient was 0.89. However, the classification effect was general in other periods. Moreover, the classification time was still longer than that of SVM although after the reduction of MNF transformation dimensionality. SAM had the fastest classification speed, but the classification accuracy was low in each growth stage. The highest value was 77.80% of the overall classification accuracy in tillering stage, and the kappa coefficient was 0.73. Therefore, the SVM classification model using the polynomial kernel function was suitable for classifying and recognising mountain meadow vegetation, which had complete classification category results, higher accuracy and relatively few misclassification. It was a better classification method than BP-ANN and SAM.
2022 Vol. 42 (06): 1939-1947 [Abstract] ( 136 ) RICH HTML PDF (6876 KB)  ( 61 )
1948 Intelligent Recognition of Corn Residue Cover Area by Time-Series Sentinel-2A Images
TAO Wan-cheng1, 2, ZHANG Ying1, 2, XIE Zi-xuan1, 2, WANG Xin-sheng1, 2, DONG Yi1, 2, ZHANG Ming-zheng 1, 2, SU Wei1, 2*, LI Jia-yu1, 2, XUAN Fu1, 2
DOI: 10.3964/j.issn.1000-0593(2022)06-1948-08
Crop residue covering is an important way to reduce soil erosion and increase soil organic carbon, which is very important for black soil protection. Therefore, the accurate and rapid identification of corn residue cover area plays an important role in local government monitoring and promoting conservation tillage. The study area is located in Siping City, Jilin Province. Moreover, the time-series Sentinel-2A images collected from GEE (Google Earth Engine) cloud platform are used to capture spectral index based on the characteristics of the corn growing season and after harvest. Index features include Normalized Difference Vegetation Index (NDVI) and Normalized Difference Residue Index (NDRI). The time series feature values are sorted by size, and the quantile method is used to select quartile (QT) features at 0%, 25%, 50%, 75%, and 100% to construct datasets. On this basis, the random forest method after parameter optimization is applied to train and verify the sample datasets divided according to 7∶3, and then the datasets are classified, combined with the connected domain calibration method to remove the small connected domains generated in the classification process, and further optimize the global result. Through the quantitative and qualitative evaluation of Kappa and Overall Accuracy (OA), the experimental results show that: (1) The quantitative evaluation results of the classification model (M1/M2/M3/M4/M5) based on the dataset composed of the different feature are superior 90%. Among them, the classification model M5 of the dataset designed in this paper has the best performance, of which Kappa and OA are 97.41% and 97.91%, respectively. Compared with the classification model M2 without the QT feature, the Kappa and OA are increased by 4.52% and 3.64%, respectively. At the same time, the M5 recognition result can effectively retain edge detail information; (2) For QT feature of different time scales, using the QT feature classification model M5_6/M5 of time series remote sensing images from May to November can greatly restrain another crop residue. Compared with the Kappa and OA of the M5_1 model classification result using only the QT features of the time series images in November, the Kappa and OA increased by 3.9% and 3.12%, respectively; (3) Based on the M5 model, the Kappa and OA of the classification model M6 combined with the connected domain calibration method are 96.76% and 97.36%, respectively, second only to the recognition results of the M5 model. The model M6 avoids fine-grained patches while ensuring high accuracy and optimizing the classification visualization effect. Therefore, the M6 model proposed in this paper is suitable for identifying areas covered by corn residue in the study area. This method can be quickly implemented in the GEE cloud platform environment and is suitable for popularization and application in a corn residue covered areas in Northeast China.
2022 Vol. 42 (06): 1948-1955 [Abstract] ( 144 ) RICH HTML PDF (6057 KB)  ( 60 )
1956 Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*
DOI: 10.3964/j.issn.1000-0593(2022)06-1956-09
The use of hyperspectral remote sensing technology to monitor the protein content related to grain quality before rice matures is important. It can promptly adjust cultivation management methods and guide reasonable fertilization and help to grasp rice grain quality information in advance and clarify market positioning. This study took typical high-quality Indica rice in Guangdong Province as the research goal. Two-year nitrogen gradient experiments were carried on in 2019 and 2020. The canopy level hyperspectral data and rice nitrogen parameters, including leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), plant nitrogen content (PNC), and plant nitrogen accumulation (PNA), were collected at the rice panicle initiation stage and heading stage. Then, four individual machine learning algorithms, Partial Least Square Regression (PLSR), K-Nearest Neighbor (KNN), Bayesian Ridge Regression (BRR), Support Vector Regression (SVR), and three ensemble learning algorithms, Random forest (RF), Adaboost, Bagging were used for monitoring and modeling the nitrogen status of rice at different growth stages. After that, the rice grain protein content estimation models based on rice canopy spectral information, and spectral information combined with rice nitrogen parameterswere constructed by different machine learning algorithms. The rice nitrogen and grain protein content estimation models’ accuracy were evaluated and compared. The study results showed that for rice nitrogen nutrition monitoring, using the rice canopy spectral information from 454~950 nm, the R2 of LNC, LNA, PNC and PNA estimation models based on RF and Adaboost algorithms achieved above 0.90 at the rice, heading stage, with low RMSE and MAE. Panicle initiation stage When using full-band spectral information to estimaterice grain protein content, RF had the highest accuracy and stability, with R2 of 0.935 and 0.941, RMSE of 0.235 and 0.226, and MAE of 0.189 and 0.152 at rice panicle initiation and heading stage, respectively. Adaboost model has the highest accuracy and stability for seed protein monitoring with full-band spectral information combined with growth parameters at both fertility stages, at the panicle initiation stage, the Adaboost algorithm with full-band spectral and PNA data can reach the bestfor rice grain proteinestimation, the R2, RMSE and MAE was 0.960, 0.175, and 0.150. While at heading stage, the R2,RMSE and MAE was 0.963, 0.170, 0.137,when using Adaboost algorithm with the full-band spectral and PNC data as input parameters. The results showed that the ensemble algorithms RF, Adaboost and Bagging have good ability to deal with multiple covariance compared with several individual learner algorithms PLSR, KNN, BRR and SVR.And they are suitable for the analysis and processing of hyperspectral data, which have obvious advantages in crop nitrogen nutrition monitoring and rice quality early monitoring through remote sensing.
2022 Vol. 42 (06): 1956-1964 [Abstract] ( 186 ) RICH HTML PDF (5183 KB)  ( 62 )
1965 Identification of Two-Dimensional Material Nanosheets Based on Deep Neural Network and Hyperspectral Microscopy Images
PENG Ren-miao1, 2, XU Peng-peng2, ZHAO Yi-mo2, BAO Li-jun1, LI Cheng2*
DOI: 10.3964/j.issn.1000-0593(2022)06-1965-09
In recent years, two-dimensional materials have received widespread attention due to their unique properties. Among the various methods for preparing two-dimensional layered crystals, the thin-layer two-dimensional material crystals obtained by mechanical exfoliation are of high quality, suitable for basic research and performance demonstration. However, mechanically exfoliated crystals on substrates exhibit a certain degree of randomness, including a few layers and relatively thick flakes. The effective, rapid and intelligent characterization method of these two-dimensional nanostructures is beneficial to further research on the properties of two-dimensional materials. This paper proposes a method based on deep learning, which can segment and quickly identify two-dimensional material nanosheets based on optical microscope images through a convolutional neural network semantic segmentation algorithm built with an encoding-decoding structure. As a typical algorithm for deep learning in the field of image processing, Convolutional neural networks can be applied to the feature extraction in optical microscope images. Firstly, MoS2 nanosheet samples were prepared by mechanical exfoliation, and high spectroscopic images were acquired by optical microscopy. The nanosheet samples were labeled, and the marked images were further processed, including color calibration and sliding shear operation, to obtain datasets for network training and testing. A semantic segmentation algorithm based on encoding-decoding network structure was designed to rapidly identify nanosheets. Aiming at some flakes in images showing the characteristic of low contrast and fragmentation, residual convolution and pyramid pooling models were added to strengthen the extraction of features during encoding. The shallow feature information extracted from the encoding stage was reused during decoding to improve the network segmentation results. In the experiment, the weighted cross-entropy loss function was used to solve the problem of unbalanced classes, and the dataset was enlarged with data augmentation. Testing on the trained network show that the pixel accuracy was 97.38%, the mean pixel accuracy was 90.38%, and the mean intersection over union was 75.86%. Then, the exfoliated monolayer and bilayer graphenes were identified by transfer learning, and the mean intersection over union reached 81.63%, showing that this technique is universal for the identification of two-dimensional nanosheets. The identification of MoS2 and graphene nanosheets realizes the application of deep learning in optical microscopy images of two-dimensional materials. This method is expected to apply to more two-dimensional materials and break through the problem of automatic dynamic processing of optical images. Moreover, it provides a reference for hyperspectral images processing of other nanomaterials.
2022 Vol. 42 (06): 1965-1973 [Abstract] ( 145 ) RICH HTML PDF (6165 KB)  ( 65 )
1974 The Effect of Instrument Resolution on Passive Ranging of Oxygen A Band
LI Jin-hua1, 2, ZHANG Min-juan1, 2, WANG Zhi-bin1, 2, LI Shi-zhong1, 2*
DOI: 10.3964/j.issn.1000-0593(2022)06-1974-05
The calculation of transmittance is the core of infrared target passive ranging technology, the accuracy of the measured distance is directly affected by accuracy, while the instrument’s resolution directly affects the precision of the spectral signal. In order to study the influence of spectral resolution for transmittance, the calculation method of transmittance and the influence factors of the instrument function in the passive ranging system are analyzed. The experimental system is set up to verify the influence of instrument resolution on passive ranging. Firstly, the calculation method of oxygen A band transmittance is introduced, and the theoretical value is calculated. With a halogen lamp as the light source, the spectral curve under different resolutions was measured by the spectrometer at a certain distance, and the measured atmospheric transmittance was calculated. Then, based on the analysis and comparison of the transmittance model and measured transmittance spectrum, the calculation model was fitted and corrected. For a single spectral line, the measured spectral lines with a different resolution of the instrument differ greatly, while for the spectral line with an average point, the spectral signal with the same wave point is averaged to obtain the average effect so that the measured spectral line almost does not change. Specific resolution model selection should be based on different applications, different precision requirements to choose the appropriate resolution. The experimental results show that resolution greatly influences on the transmittance of a single spectral line. With the decrease of resolution, the less the value of captured spectral information points is, the smaller the correlation coefficient of measured target distance will be. The resolution has little effect on the calculation of the average band transmittance, and the measured target distances under different resolutions almost coincide. The higher the instrument’s resolution is, the longer the measurement time will be. When the average transmittance is used to calculate the distance of the measured target, the instrument resolution can be appropriately reduced within the accuracy requirements to greatly improve the measurement speed, achieve real-time measurement, and reduce the cost of system construction. The conclusion can provide a basis for the engineering application of transmittance measurement.
2022 Vol. 42 (06): 1974-1978 [Abstract] ( 100 ) RICH HTML PDF (2047 KB)  ( 41 )
1979 XRD Structural Analysis of Raw Material Used as Coal-Based Needle Coke in the Coking Process
FAN Qing-jie, SONG Yan, LAI Shi-quan*, YUE Li, ZHU Ya-ming, ZHAO Xue-fei
DOI: 10.3964/j.issn.1000-0593(2022)06-1979-06
Soft coal tar pitch (SCTP) with low QI content is the preferred raw material for preparing coal-based needle coke. The study on its structure changes in the cooking process is helpful to prepare high-quality needle coke. In this paper, the CarbX software developed by the Smarsly team was used to fit the full spectrum X-ray diffraction (XRD) data of the samples toquantify the microcrystalline structure parameters of SCTP at different carbonization temperatures (400, 500, 600, 800, 1 000, 1 200 and 1 400 ℃), and then investigate the thermally induced structural changes of SCTP at the nanoscale. The results show that the average graphene layer size of microcrystalline stack La gradually increases from 10.3 Å for the pristine pitch to 47.9 Å at 1 400 ℃ with the rising of the carbonization temperature, but La increases slowly before 500 ℃. A significant increase of La is found only when the temperature exceeds 800 ℃, indicating that high temperatures above 800 ℃ are needed to recombine the atoms in the cross-linked graphene layers and lead to the growth of the microcrystals. However, the C—C bond length (lcc) of the graphene carbon network is slightly affected by temperature and varies in the range of 1.41~1.42 Å. Because of mesophase transformation during the liquid-phase carbonization of SCTP into semi-coke, the average stack size Lc gradually increases before 500 ℃ and reaches the maximum at 500 ℃ (Lc=31.1 Å). Subsequently, due to further pyrolysis and polycondensation of semi-coke, Lc gradually decreases and reaches the lowest point (Lc=15.4 Å) at 1 000 ℃, and increases again after 1 000 ℃. Similar to Lc, the average number of graphene layers per stack N increases from 2.66 layers for the raw pitch to 9.05 layers at 500 ℃, then decreases to 4.55 layers at 1 000 ℃, and then begins to increase after 1 000 ℃. The samples are still in the pitch state before 500 ℃ the average graphene interlayer spacing a3 is large, about 3.50 Å at this stage. When the pitch becomes semi-coke at ca. 500 ℃, a3 rapidly decreases to 3.44 Å, continues to decrease, reaches the minimum at 1 000 ℃ (a3=3.39 Å), and begins to increase again after 1 000 ℃, indicating that the coke has undergone a shrinkage and re-expansion process. By using CarbX software to fit the XRD data of the sample, the main size (La, Lc, N, a3) of carbon microcrystals of the sample can be obtained, as well as the dispersion (ka，kc，σ3，ε3) of these parameters and the orientation (q), homogeneity (η) of per stack and disordered carbon content (cun). It is helpful to deeply understand the sample’s microstructure and to produce high-quality needle coke.
2022 Vol. 42 (06): 1979-1984 [Abstract] ( 153 ) RICH HTML PDF (2533 KB)  ( 53 )
1985 Nondestructive Determinations of Texture and Quality of Preserved Egg Gel by Hyperspectral Method
CHEN Yuan-zhe1, WANG Qiao-hua1, 2*, TIAN Wen-qiang1, XU Bu-yun1, HU Jian-chao1
DOI: 10.3964/j.issn.1000-0593(2022)06-1985-08
As an important quality parameter, texture can significantly affect the gelatin quality of preserved eggs. There is no effectively rapid detection method at present. In this study, hyperspectral imaging technology was used to predict preserved eggs’ textural characteristics and classify different qualities. Hyperspectral data of high-quality eggs, qualified eggs and unqualified eggs were collected. The original spectra were transformed by single and combined transformation to analyze the correction between one-dimensional spectral and textural hardness and springiness. It was found that the spectral reflectance after CR-FD transformation was most correlated with the hardness and springiness of the gel texture, and the maximum values were 0.882, 0.86 5 at 683, 715 nm, respectively; The hardness and springiness were taken as disturbance factors to explore the optimal research area of the hardness and springiness, the results showed that: When hardness was used as disturbance factor, autocorrelation peaks existed at 476, 539, 647, 672, 728 and 851 nm, Spectral signals at 483, 572, 657, 739 and 826 nm were more sensitive to springiness value. Therefore, two sensitive bands, 476~851 and 483~826 nm, were finally selected as the study regions for gel hardness and springiness, respectively. Comparing five different variable selection methods(SPA, CARS, GA, PSO and UVE), it was found that PSO-PLSR model had the highest detection accuracy: theR2p and RMSEP for predicted hardness were 0.826 and 0.874 with an RPD of 2. TheR2p and RMSEP for predicted springiness were 0.886 and 0.402 with an RPD of 1.9. Three different classifiers were used to predict preserved eggs, and the classification accuracy of high-quality eggs, qualified eggs and unqualified eggs reached 97%, 92% and 100%, respectively. The accuracy and generalization ability of the PLS-DA model were better than the BP and RF model based on the confusion matrix and ROC curves of prediction results. In conclusion, the hyperspectral technique can be used to predict preserved eggs’ textural characteristics and classify different quality of preserved eggs.
2022 Vol. 42 (06): 1985-1992 [Abstract] ( 128 ) RICH HTML PDF (5623 KB)  ( 55 )

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