Journals >Spectroscopy and Spectral Analysis
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2024
Volume: 44 Issue 3
42 Article(s)
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A Review of the Auxiliary Measurement of Nobble Gases in LIBS
XU Jie, GU Yi, SONG Bao lin, GE Liang quan, ZHANG Qing xian, and YANG Wen jia
Laser-induced breakdown spectroscopy (LIBS) is an emission spectrum analysis technology which has great potential in the analysis of high-risk environments and important cultural relics due to the advantages of real-time, multi-element, non-destructive and long-range. Currently, LIBS measurement technology is still affLaser-induced breakdown spectroscopy (LIBS) is an emission spectrum analysis technology which has great potential in the analysis of high-risk environments and important cultural relics due to the advantages of real-time, multi-element, non-destructive and long-range. Currently, LIBS measurement technology is still affected by experimental equipment and measurement environment, among which the influence of the atmosphere environment on LIBS measurement is particularly significant. Nobble gases are conducive to assisting LIBS measurement, enhancing the spectral signal, improving detection limit, etc., because of their excellent physical properties (specific heat capacity, conductivity, stable chemical properties, large relative molecular mass, etc.), which are increasingly used in laser-induced breakdown spectroscopy. Therefore, based on the detailed investigation of the application of inert gas in the LIBS measurement process, the influencing factors on the spectral signal are analyzed. For example, the very low chemical activity effectively reduces the absorption and annihilation of spectral signals by other gases in the air (oxygen, nitrogen, etc.). The large atomic volume greatly limits the outward expansion of the plasma, causing the temperature per unit volume to rise significantly; The abundance of electrons causes the free electrons in the generated plasma to rise greatly, which increases the electron density; The metastable state promotes the delayed excitation of inert gas to the sample, assists in the excitation of some lighter elements and greatly increases the plasma lifetime; The energy transfer can enhance the emission spectral of some special energy levels to obtain higher enhancement factors; The effect of radon gas on emission line enhancement through decay was also analyzed. However, the emission of inert gases can also be used to calculate the plasma temperature and electron density of samples, which has an important influence on the auxiliary measurement of LIBS. In addition, the application of metastable state in LIBS measurement and enhancement factor of inert gas on the spectral signal in different studies are also listed. Finally, the differences in signal enhancement, ablation quality and time evolution of different inert gases are analyzed and compared. It turns out that helium is special compared to other inert gases because of its physical properties and extra-nuclear structure. However, with the increase of cycle number, the signal enhancement factor of other gases gradually increases. At the same time, the ablation mass gradually decreases, and the time evolution of plasma is also getting longer, which provides theoretical support and reference for the selection of appropriate inert gases in LIBS measurement..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 601 (2024)
Research on Low Illumination Image Enhancement Method Based on Spectral Reflectance
MA Xiang cai, CAO Qian, BAI Chun yan, WANG Xiao hong, and ZHANG Da wei
Low illumination image enhancement technology is one of the research hotspots of computer vision. The theoretical algorithm of Retinex assumes that the image is the product of the reflection component and the illumination component. It restores the image by removing or correcting the illumination component and combininLow illumination image enhancement technology is one of the research hotspots of computer vision. The theoretical algorithm of Retinex assumes that the image is the product of the reflection component and the illumination component. It restores the image by removing or correcting the illumination component and combining the reflection component of the object, which is widely used in traditional algorithms and deep learning enhancement models. Spectral reflectance is the fingerprint of color, and multispectral images have more information than RGB images. Colorimetric theory and Retinex theory agree that the color of an image depends on reflection data, but spectral reflectance is obtained based on instrument measurement, and the image reflection component is obtained based on image hypothesis decomposition. The literature has not studied the enhancement of low-light images from the perspective of spectral reflectance. Inspired by Retinex theory and combined with the strong nonlinear fitting ability of deep learning, a low illumination image enhancement method based on spectral reflectance is proposed. The spectral reflectance of color is used to replace the image reflection component in the RetinexNet network, and the spectral power distribution of the CIE standard light source is used to replace the image illumination component in the network. Firstly, the spectral reflectance of normal light images in the image database is reconstructed to build a multispectral image dataset of low illumination and normal light images. Then, the deep learning network model is trained to convert low-illumination images into the multispectral images. Any low illuminance image is obtained from the multispectral image through the network model, and the multispectral image is obtained from the CIEXYZ tristimulus according to the colorimetric theory and then converted to the RGB color space for display through the standard color space.The method is trained and tested on the public LOL dataset, and the results show that this method is superior to the standard methods in image noise suppression and color restoration, which proves the superiority and effectiveness of this method for low illumination image enhancement..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 609 (2024)
Non-Invasive Blood Glucose Measurement Based on Near-Infrared Spectroscopy Combined With Label Sensitivity Algorithm and Support Vector Machine
MENG Qi, ZHAO Peng, HUAN Ke wei, LI Ye, JIANG Zhi xia, HANG Han wen, and ZHOU Lin hua
Near-infrared spectroscopy analysis technology has broad application prospects in biomedical engineering. Non-invasive and continuous measurement can monitor the human blood glucose level in real-time, which brings great convenience to diabetes patients, improves the quality of life of patients, and reduces the incidenNear-infrared spectroscopy analysis technology has broad application prospects in biomedical engineering. Non-invasive and continuous measurement can monitor the human blood glucose level in real-time, which brings great convenience to diabetes patients, improves the quality of life of patients, and reduces the incidence of complications of diabetes. The idea of non-invasive blood glucose monitoring was put forward earlier, but there are still difficulties, such as low prediction accuracy low correlation between prediction value and label value: up to now, it has not met the clinical requirements. In recent years, spectral detection technology has developed rapidly, and machine learning technology has obvious advantages in intelligent information processing. Combining the two can effectively improve the accuracy and universality of non-invasive blood glucose medical monitoring models. This paper proposes a label sensitivity algorithm (LS), and a prediction model of human blood glucose content is established by combining the support vector machine method. We used a near-infrared spectrometer to collect dynamic blood spectral data at the index finger of four volunteers (28 groups of data for each volunteer) and used the multivariate scattering correction (MSC) method to eliminate the influence of partial light scattering. Considering the difference in the absorption of blood glucose to light of different wavelengths, In this paper, a feature wavelength selection method based on blood glucose concentration label difference is proposed, and a label sensitivity support vector machine (LSSVR) prediction model is constructed Experiments were designed to compare the model with partial least squares regression (PLSR) and discriminant support vector machine (FSSVR, The predicted values are all in the A-region of Clark grid with allowable error. The excellent performance of the LSSVR model provides a new idea for the early realization of non-invasive blood glucose monitoring..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 617 (2024)
Application and Analysis of Multi-Component Simultaneous Measurement of Forest Combustibles Pyrolysis Gas Based on TDLAS
GUO Song jie, WANG Lu peng, CHEN Jin zheng, MA Yun2, LIANG An, LU Zhi min, and YAO Shun chun
Pyrolysis of forest combustibles is an important research topic in forest fires and is of great significance for early warning and control of forest fires. The pyrolysis of forest combustibles mainly produces carbonaceous gases such as CO,CO2,and CH4. The continuous release of these gases will likely trigger forest firPyrolysis of forest combustibles is an important research topic in forest fires and is of great significance for early warning and control of forest fires. The pyrolysis of forest combustibles mainly produces carbonaceous gases such as CO,CO2,and CH4. The continuous release of these gases will likely trigger forest fires and aggravate the greenhouse effect. The rapid and accurate detection of the concentrations of these three components is beneficial for early warning of fires and atmospheric environmental protection. In this paper, the concentrations of three CO,CO2,and CH4 components in the pyrolysis gases of six mountain forest tree species samples were accurately measured by frequency division multiplexing combined with tunable diode laser absorption spectroscopy (TDLAS) technique. The applicability of the frequency division multiplexing-TDLAS technique for simultaneous multi-component measurements of pyrolysis gases of forest combustibles is demonstrated. Firstly, the basic principle of the frequency division multiplexing-TDLAS technique is introduced, and the absorption spectra of the three components are determined without interfering with each other and with suitable spectral intensity. Secondly, the characteristics of the second harmonic (2f) signal and the second harmonic/DC (2f/DC) signal are investigated to invert the different concentrations accurately. The difference in the accuracy of inversion of different concentrations using the 2f signal and 2f/DC signal is compared using Simulink simulation, and the results show that the 2f/DC signal has a larger linear interval and is suitable for the measurement of different concentration components in the pyrolysis gas of forest combustibles. Finally, an experimental setup for simultaneous CO,CO2,and CH4 measurements was built using two distributed feedback (DFB) lasers with center wavelengths of 1 580.0 and 1 653.7 nm, respectively. The 2f/DC signals of the three components were measured using a Herriott absorber cell, and the absolute concentrations of the three components in the pyrolysis gas were obtained by establishing calibration models with standard gases. The results showed that the peak 2f/DC signals of the three components satisfied a good linear relationship with the concentrations withlinearity greater than 0.995, and the concentrations of CO in the pyrolysis gases of the six tree samples were significantly higher than those of CO2,and CH4 under the effect of the coke gasification reaction and Boundouard reaction. by analyzing the spectra of Chinese sweetgum leaf samples, it was shown that within the 2 s measurement time, the spectroscopic system for CO,CO2,and CH4 with a minimum detection limit lower than 0.008% and sensitivity better than 0.005%, which meets the demand of forest fire early warning. This study provides a methodological reference for the simultaneous multi-component measurement of pyrolysis gases of forest combustibles and forest fire early warning..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 625 (2024)
Measurement of Chlorine Distribution in Concrete Based on Laser-Induced Breakdown Spectroscopy
ZHANG Zhi, GUO Xin yu, HANG Yu hua, QIU Yan, WU Jian, SUN Hao, ZHOU Ying, LI Jing hui, MEI Jin na, and LIAO Kai xing
Chloride ions will corrode the concrete structure, cause the corrosion of the reinforcement inside the structure, lead to concrete cracking, and destroy the structures integrity. Conventional chlorine content measurement methods, such as chemical titration, have many problems, such as complicated operation and slow deChloride ions will corrode the concrete structure, cause the corrosion of the reinforcement inside the structure, lead to concrete cracking, and destroy the structures integrity. Conventional chlorine content measurement methods, such as chemical titration, have many problems, such as complicated operation and slow detection speed. Laser-induced breakdown spectroscopy (LIBS) has the advantages of no sample pretreatment required, two-dimensional scanning detection and in-situ fixed-point analysis. However, there are still some problems in the measurement of chlorine content in concrete, such as the difficulty in reducing the limit of chlorine quantification and the influence of non-cement components on the measurement results of the two-dimensional distribution of chlorine. In order to meet the application requirements of rapid detection of chlorine content in concrete structures of nuclear power plants, a method for detecting chlorine content distribution by dual-pulse LIBS is studied,and the erosion status of chloride ions in simulated concrete samples of nuclear power plants is evaluated in the paper. Firstly, the calibration models of chlorine are established by internal standard (IS), Principal Component Regression (PCR) and Support Vector Regression (SVR). The LOD and LOQ of chlorine calculated by the IS method are 0.006 02 wt% and 0.0180 6 wt% respectively. The Leave-One-Out Cross-Validation (LOOCV)method is used to evaluate the prediction performance of the three calibration models. Secondly, to exclude the influence of the non-cement matrix on chlorine detection, Logistic Regression combined with Principal Component Analysis (PCA) and SVM classification are established to identify aggregate and cement in concrete. The SVM model with the combination of Si, Ca and O has the best classification effect, and its recognition accuracy of the total components reaches 98.20%, including 97.84% for aggregate and 98.33% for cement. Finally, the quantitative analysis of chlorine in the concrete erosion surface corroded for 15 and 30 days iscarried out aPCR calibration model. The average predicted chlorine content of the two concrete samples reach the maximum value at about 2 mm, 0.890 wt% and 0.599 wt%, respectively, which is consistent with the result of the potentiometric titration method. In conclusion, based on the dual-pulse LIBS method, chlorines LOD the LOD of chlorine comes to 0.006 02 wt% under the total energy of 30 mJ. The precise identification of aggregate and cement in concrete is realized, and the two-dimensional distribution of chlorine content on the erosion and penetration surface of concrete is obtained, which provides an engineering solution for the field application of rapid detection of chlorine content in nuclear power plant concrete structures..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 632 (2024)
Application of Laser-Induced Breakdown Spectroscopy in Quantitative Analysis of Sediment Elements
FU Xiao fen, SONG You gui, ZHANG Ming yu, FENG Zhong qi, ZHANG Da cheng, and LIU Hui fang
Laser-induced breakdown spectroscopy can quickly measure the content or composition of various elements in samplesand is widely used in the testing and analysis of environmental samples.However,its application to analysis of multiple elements in geological samples is rarely reported.This study took the drill-core QuateLaser-induced breakdown spectroscopy can quickly measure the content or composition of various elements in samplesand is widely used in the testing and analysis of environmental samples.However,its application to analysis of multiple elements in geological samples is rarely reported.This study took the drill-core Quaternary Lake sediments of Qinghai Lake and national standard soil samples as the research objects.The original spectra were preprocessed by Savitzky-Golay convolution smoothing and standard normal variable transformation,and through univariate calibration analysis as well as partial least squares regression algorithm to quantitatively analyze nine elements of Na,Ca,Mg,Si,Al,Fe,Mn,Sr and Ba in Qinghai Lake sediment samples.The results of cross-validation were used as the criteria for optimizing the parameters of the PLSR model,and the quantitative accuracy and stability of the PLSR models were evaluated by the coefficient of prediction determination (R2),root mean square error of cross-validation (RMSECV),root mean square error of prediction (RMSEP) and residual predictive deviation,respectively.The results show that the PLSR algorithm significantly improves the quantitative effect of traditional univariate analysis;the coefficients of determination for prediction are 0.94,0.94,0.98,0.94,0.97,0.84,0.89,0.98 and 0.76,and the relative analysis errors are 2.74,2.35,3.27,2.97,3.56,1.68,1.54,4.18 and 0.75.Combined with the results of cross-validation root mean square error and prediction root mean square error,it can be seen that LIBS technology combined with the PLSR algorithm has high prediction accuracy for Na,Ca,Mg,Si,Al and Sr elements.However,the quantitative effects of Fe,Mn and Ba elements are not very satisfactory,indicating that the PLSR algorithm has certain limitations and applicability in the prediction accuracy.In order to further explore the reliability of the LIBS technique is applied to index test of geochemical elements,this paper compared the predicted content ratio of LIBS with the reference content ratio.The variation trend of the two curves isconsistent,which verifies the feasibility and effectiveness of LIBS technology applied to sediment element geochemistry.It provides a reliable analytical method for element quantification in sediment samples and also provide new technologies and ideas for the reconstruction of paleoclimate and paleoenvironment..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 641 (2024)
Physical and Chemical Indexes Were Determined Based on Multispectral Image Angle Fusion
LIU Hong yang1, KONG De guo, LUO Hua ping, GAO Feng, and WANG Cong ying
Based on a multispectral image angle fusion. Multispectral images were obtained from 10° to 90 °at 10° intervals. Multispectral image angle fusion and the region of interest (ROI) were extracted using ENVI5.1 software to obtain the multispectral data. The Pearson correlation analysis of the spectral reflBased on a multispectral image angle fusion. Multispectral images were obtained from 10° to 90 °at 10° intervals. Multispectral image angle fusion and the region of interest (ROI) were extracted using ENVI5.1 software to obtain the multispectral data. The Pearson correlation analysis of the spectral reflectance, band, and relative azimuth found that both the band and relative azimuth were extremely significantly correlated with the spectral reflectance, and the relative azimuth correlation coefficient of 0.1 is greater than the band correlation coefficient of 0.053. Therefore, it is necessary to add the relative azimuth factors in the modeling process. Using standard normal variable transformation (SNV), mean centralization transformation (MC), convolution smoothing treatment (S_G), normalization treatment (Nor), partial least squares regression (PLSR) to evaluate the full band set correlation coefficient (Rc), prediction set correlation coefficient (Rp), correction set root mean square error (RMSEC) and prediction set root mean square error (RMSEP) to explore the effect of the model. The results show that the prediction effect of the established PLSR and SVM models is significantly improved after adopting the angle fusion treatment. The optimal model is a partial least squares regression model (AF-PLSR) with Rc of 0.936, RMSEC of 0.298, Rp of 0.901, RMSEP of 0.285; the optimal prediction model is the support vector machine model (AF-SVM), Rc is 0.894, 0.527, 0.376;Rp is 0.830, 0.901, and RMSEP is 0.532, 0.379 respectively. Angle fusion combines the spectral data from different angles together to obtain more abundant information than a single angle and a more perfect spectral information. The established detection model has a higher accuracy. The results proved that it is feasible to predict the water content, hardness, and sugar content of Korlas fragrant pear based on the multispectral image angle fusion technology. The results provide a new idea for improving MMS and HMS NDE accuracy..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 649 (2024)
Determination of Soluble Solid Content in Peach Based on Hyperspectral Combination With BPSO
ZHANG Li-xiu, ZHANG Shu-juan*, SUN Hai-xia, XUE Jian-xin, JING Jian-ping, and CUI Tian-yu
Soluble solids (SSC) are an important index to evaluate the internal quality of Kubo peach. Traditional SSC content detection is destructive, time-consuming and laborious. Rapid and nondestructive detection of the SSC content of Kubo peach is of great importance for its quality classification. Binary particle swarm optSoluble solids (SSC) are an important index to evaluate the internal quality of Kubo peach. Traditional SSC content detection is destructive, time-consuming and laborious. Rapid and nondestructive detection of the SSC content of Kubo peach is of great importance for its quality classification. Binary particle swarm optimization (BPSO) is obtained by updating the speed formula based on standard particle swarm optimization (PSO). BPSO has the characteristics of high accuracy and fast convergence and is mostly used in optimization problems in separate spaces. Taking Kubo peach as the research object. Basedon hyperspectral technology combined with BPSO and based on BPSO combined characteristic wavelength selection algorithm to study the SSC content of Kubo peach. Firstly,hyperspectral information of 198 Kubo peaches was collected to obtain the spectral curve of Kubo peaches in the range of 900~1 700 nm. Meanwhile, theSSC value of Kubo peaches was. Used (Kennard-stone) algorithm to divide samples into a correction set (147) and a prediction set (51). The BPSO feature wavelength selection algorithm is used to extract the feature wavelength from Kubos original spectral data. It is compared with the Competitive Adaptive Reweighting algorithm (CARS), Successive projections algorithm (SPA), and Uninformative variable selection algorithm (UVE). A method of extracting characteristic wavelength based on BPSO is proposed for primary combination (BPS0+CARS, BPSO+SPA, BPSO+UVE) and secondary combination ((BPSO+ CARS)-SPA), (BPSO+SPA)-SPA), (BPSO+UVE)-SPA). Based on the10 characteristic wavelength extraction methods above. Established support vector machine (LS-SVM) model and the genetic algorithm (GA) optimized support vector machine (GA-SVM) model of Kubo peach SSC content. The results show that the prediction performance of the model based on the BPSO algorithm is higher than that of other single characteristic wavelength algorithm, and the coefficient of determination R2p of the prediction set of the two models is above 0.97. Among the combination algorithms based on BPSO, the LS-SVM based on the quadratic combination (BPSO+SPA)-SPA algorithm has the highest prediction performance for Kubo peach SSC content when the number of characteristic wavelengths is small. The coefficient of determination between the correction set and the prediction set are 0.982 and 0.955, respectively. The root mean square errors RMSEC and RMSEP were 0.108 and 0.139, respectively. The prediction performance of the proposed model is slightly lower than that of the BPSO algorithm, but only 22 characteristic wavelengths are used for modeling, which greatly simplifies the model. These results show that (BPSO+SPA)-SPA is an effective method for extracting characteristic wavelength, which provides a new method for nondestructive detection of fruit SSC content..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 656 (2024)
Simulative Study of Multiband Perfect Absorption and Sensing Properties of Plasmonic Silver Film Coupled Si3N4 Nanocavities in Visible NIR Region
WANG Jia zheng, LIU Jia, SUN Wei xin, ZHOU Jian zhang, WU De yin, and TIAN Zhong qun
Metamaterials with multiband optical perfect absorption effect are highly desired in applications like optical filtering and refractive index sensing. This paper proposes a multi narrowband perfect metamaterial absorber composed of a Si3N4 dielectric nanocavity array on a silver film. Simulation by finite element methoMetamaterials with multiband optical perfect absorption effect are highly desired in applications like optical filtering and refractive index sensing. This paper proposes a multi narrowband perfect metamaterial absorber composed of a Si3N4 dielectric nanocavity array on a silver film. Simulation by finite element method gives four absorption bands with peak absorptances up to 99.9% and narrow bandwidth down to 0.74 nm. These absorption bands come from surface lattice resonance mode and three surface plasmon polariton modes. Besides, these modes are highly susceptible to changes in geometrical and optical parameters, which means absorption peaks can be tuned in the visible near infrared range. The refractive index sensing capability of the structure was also investigated, giving 347 nm per refractive index unit sensitivity and 469 figure of merit. These features make the proposed structure suitable for applications such as refractive index sensors and optical filters..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 663 (2024)
A Combined CARS and 1D-CNN Method for the Analysis of Heavy Metals Exceedances in Soil by XRF Spectroscopy
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The more frequent human activities with the modernization of the society intensify the soil heavy metal pollution.When the content of heavy metal elements in the soil exceeds its risk screening value,there may be risks to human health.Therefore,screening out the soil with the risk of heavy metal pollution is an importaThe more frequent human activities with the modernization of the society intensify the soil heavy metal pollution.When the content of heavy metal elements in the soil exceeds its risk screening value,there may be risks to human health.Therefore,screening out the soil with the risk of heavy metal pollution is an important part of soil pollution control.The spectral data of 59 national standard soil samples were obtained by X-ray fluorescence (XRF) spectroscopy,and then pre-processed by wavelet soft threshold denoising and iterative discrete wavelet transform background deduction.Moreover,the competing adaptive reweighted sampling (CARS) algorithm was applied to screen the heavy metals in the soil.Finally,the screened results wereinput to the one-dimensional convolutional neural network (1D-CNN) model to predict whether soil samples were at risk of heavy metal contamination.The results showed that the number of feature channels sampled by the CARS algorithm was significantly reduced from 2048 to 37,53,37 and 45 for Ni,Cu,As and Pb respectively,which is 1.81%~2.59% of the original number of channels.Compared with the no screening (i. e.original data) and successive projections algorithm (SPA),the accuracy of the CARS-1D-CNN model can reach 96.67%,93.22%,91.67% and 88.33%, respectively in determining whether the soil samples are at risk of contamination with Ni,Cu,As and Pb.Based on CARS screening,1D-CNN has a significant advantage over traditional partial least squares regression (PLSR) methods regarding predictive accuracy.Therefore,the CARS combined with the 1D-CNN method proposed in this paper improves the model prediction accuracy while reducing its computing complexity,which is a good theoretical guidance for soil heavy metal elemental contamination risk screening..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 670 (2024)
The Non-Destructive Analysis of Fired Technology of Tang Sancai From Xing Kiln and Ding Kiln in Hebei
CHEN Dian, HOU Yu cun, HUANG Xin, LI Rong wu, PAN Qiu li, and CHENG Lin
This paper reports the results of the chemical composition and phase structures of the Tang Sancai from the Xing Kiln and Ding Kiln. The research results show that the bodies of Tang Sancai came from the Xing Kiln and Ding Kiln have different sources and formulations.The most remarkable thing is that Ca glazeand Pb glaThis paper reports the results of the chemical composition and phase structures of the Tang Sancai from the Xing Kiln and Ding Kiln. The research results show that the bodies of Tang Sancai came from the Xing Kiln and Ding Kiln have different sources and formulations.The most remarkable thing is that Ca glazeand Pb glaze are together in Tang Sancai sample XY4 from Xing kiln.On the other hand,the α-quartz and trace amounts of anorthite are commonly found in Tang Sancai in high-lead glazes both from the Xing kiln and the Ding kiln. Notably,there is mullite (3Al2O3·2SiO2) and a trace amount of α-Fe2O3 in Ding kiln yellow glaze. Besides,there is a trace amount of Pb8Cu(Si2O7)3 in Ding kiln green glaze;Furthermore,there is a small amount of potassium feldspar (KAlSi3O8) in the white glaze of Xing kiln.These trace amounts of crystalline phases are probably formed by complex physical and chemical changes during the firing and cooling processes or come from the glaze itself.It plays an important role in understanding the early Tang Sancai firing technology and authenticity identification and in further study of sources of Tang Sancai come from the tombs..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 675 (2024)
Double Fano Resonance Characteristics Based on Variable Period Subwavelength Dielectric Gratings Multilayer Films
XIAO Chunyan, YANG Chen, and ZHOU Xinde
Currently,many sensing structure models can only sense the refractive index of single variable samples to be measured.In order to achieve high-throughput detection of different samples to be measured and reduce the interference of environmental factors,a variable period subwavelength dielectric grating multilayer compoCurrently,many sensing structure models can only sense the refractive index of single variable samples to be measured.In order to achieve high-throughput detection of different samples to be measured and reduce the interference of environmental factors,a variable period subwavelength dielectric grating multilayer composite structure based on wavelength modulation is proposed.Take the double period as an example for analysis.The variable period grating layer is composed of two dielectric gratings, A and B, with different grating periods.The transmission characteristics are analyzed by the finite element method.The TE polarized incident light is incident on the surface of the dielectric grating in a manner perpendicular to the surface of the grating layer.When the phase matching conditions are met in the dielectric grating areas A and B,the variable period subwavelength dielectric grating will form GMR,Providing two double discrete resonance defect peaks with a single narrow band. Because the F-P-like cavity contains a periodic photonic crystal,the photonic band gap will be generated when the light wave propagates to the photonic crystal,providing a wide band continuous state.Under the condition that the phase matching condition is satisfied,the double discrete state resonance defect peak formed in the variable period subwavelength waveguide structure is coupled with the continuous state formed in the F-P-like cavity composed of the periodic photonic crystal multilayer dielectric film,and the double Fano resonance is realized.Then,by exploring the influence of waveguide layer thickness dw and photonic crystal cycle number N on the sensing characteristics,we choose dw=97 nm and N=3 to maximize the FOM value.Finally,the variable period dielectric grating layer is composed of two materials with different dielectric refractive indices,two sensing and detection units can be set in the grating groove part of the dielectric grating areas A and B,and a dual Fano resonance all-dielectric sensing model based on wavelength modulation is established.Different sensing and detection areas are set,and it is found that the dual Fano spectral curve can change with ns1 and ns2 in different sensing and detection areas;the dynamic detection of the refractive index of the sample to be measured is indirectly realized.Therefore,the multivariate detection of different refractive index intervals of the sample to be measured can be realized in the same sensing structure model.The results show that the FOM values of FR1 and FR2 in the sensor detection unit A are 631.53 and 463.7 RIU-1,respectively;in the sensor detection unit B,the FOM is 480.67 and 834.04 RIU-1 respectively.The sensor structure model designed has realized high reflectivity,high FOM value and wide detection range of the sensor structure through structural parameter optimization,which provides a theoretical reference for the dual Fano resonance and has certain research value for the multivariate detection of the samples refractive index to be measured..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 681 (2024)
Temperature Measurement of Atmospheric-Pressure CO2 Microwave Discharge With Optical Emission Spectroscopy
LI Rong yi, and ZHU Hai long
Atmospheric pressure microwave plasmas have presented significant application values in decomposing and converting carbon dioxide to treat the environment due to their unique advantages such as high density of active particles, high gas temperature, high energy conversion efficiency, and good controllability. In the woAtmospheric pressure microwave plasmas have presented significant application values in decomposing and converting carbon dioxide to treat the environment due to their unique advantages such as high density of active particles, high gas temperature, high energy conversion efficiency, and good controllability. In the work, the discharge characteristics and the temperature parameters of CO2 microwave plasma were studied and diagnosed to apply decomposition and conversion of carbon dioxide with atmospheric pressure microwave plasma in the future. The discharge characteristics were investigated by observing the discharge patterns of the plasma, and the rotational temperature of the C2 molecule in the exciting region of CO2 discharge was diagnosed by means of optical emission spectroscopy; thereby, its variations with respect to different positions, microwave power and gas flow rates were obtained. The results indicate that the discharge patterns of the atmospheric pressure microwave plasma exhibit abright central discharge region and afterglow region surrounding the central discharge region, and a clear boundary between these two regions can be observed. The length of the central discharge region increases linearly with the increase of microwave power and is weakly affected by an increase inthe gas flow rate. The diagnostic results of optical emission spectroscopy show that during the discharge process, in addition to the continuous chemical fluorescence spectrum, strong Swan bands of C2 molecules exist in the central discharge region. The plasma temperature in the central discharge region calculated based on the optical emission spectroscopy is approximately 6 000 K, and it almost no changes with varieties of power and gas flow rate and varies slightly (±100 K) at different locations of the central discharge region..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 688 (2024)
Energy Levels and Magnetic Dipole Transition Parameters of 1s22s22p3 Configuration for FeXX Ion
LI Dong-yuan, OUYANG Pin-jun, SUO Ming-yue, WANG Hao, WANG Yi-xuan, ZHOU Shu-shan, and HU Mu-hong
Atomic spectra data plays significant roles in the research of astrophysics measurements and plasma diagnosis,accurate and reliable atomic spectra data are very helpful in profound comprehensions of the nature of astrophysical sources and the characters of plasma.Focused on Fe XX ion,theoretical calculations on energy Atomic spectra data plays significant roles in the research of astrophysics measurements and plasma diagnosis,accurate and reliable atomic spectra data are very helpful in profound comprehensions of the nature of astrophysical sources and the characters of plasma.Focused on Fe XX ion,theoretical calculations on energy levels and magnetic dipole transition parameters of 1s22s22p3 ground configuration are performed using multi-configuration Dirac-Fock (MCDF) method in present work.Based on the relativistic computational code GRASP2k compiled within the framework of MCDF method,the relativistic effects and electron correlation effects in many-electron system are taken into account adequately,the fully relativistic atomic wave function is constructed with manageable size and most crucial correlation effects.Furthermore,considering and analyzing the competitions among the other physical interactions within the system,Breit interaction and quantum electrodynamics effect are treated in detail with perturbation approximation method.Then the accurate theoretical calculations on energy level structure and magnetic dipole transition rate,wavelength and weighted oscillator strengths of 1s22s22p3configuration for FeXX ion are accomplished.Compared with the existing experimental results,there lative differences of excited energies of atomic states and transition rates for magnetic dipole transition range from 0.175%~0.457% and 0.441% to 4.725%,respectively,good agreements are achieved.However,there is still insufficient in experimental data of wavelength and weighted oscillator strengths.The wavelengths computed in this paper agree well with other theoretical results available in literature,the maximum difference between them is only 6.138 88 .The results obtained are hoped to provide some valuable theoretical references for spectral experimental measurements.It can be inferred from present work that the fully relativistic MCDF method is suitable for various many-electron system and can be used in a widely range of applications for its accuracy and exactness in dealing with electron correlation effect and relativistic effects.The accuracy of results calculated meets the increasing data demands of storage ring experiment in which the highly charged ions can be prepared,and provides reliable theoretical reference datafor related studies,such as high-resolution spectroscopic measurement,astrophysics,plasma diagnosis,extra-nuclear inertial confinement and nuclear fusion,and so on..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 693 (2024)
Spectral Study on Anticoagulation Mechanism of Dabigatran
GONG Meng-jie, HAI Ying, Lu Kai-wen, GU Hong-bin, and ZU Li-li
Dabigatran etexilate (DE) is a new oral anticoagulant drug used to prevent non valvular atrial fibrillation stroke and vascular embolism. DE itself has no pharmacological activity. Its active component is Dabigatran (DAB), the product of DE by catalyzed hydrolysis in plasma and liver. However, the interaction mechanismDabigatran etexilate (DE) is a new oral anticoagulant drug used to prevent non valvular atrial fibrillation stroke and vascular embolism. DE itself has no pharmacological activity. Its active component is Dabigatran (DAB), the product of DE by catalyzed hydrolysis in plasma and liver. However, the interaction mechanism between the polar groups of DAB and thrombin is still unclear, especially in physiological conditions, although the drug has already been used for clinical treatment. No specific reversal agent has been found for dabigatran.This work used steady-state and time-resolved fluorescence spectroscopy methods to study the interaction between DAB and thrombin in pH 7.4 phosphate buffer. A combination of dynamic and static fluorescence quenching of thrombin when in contact with DAB wasobserved, suggesting that the electrostatic effect between thrombin and the benzamidine group of DAB was the key factor of a fast and effective formation of the DAB-thrombin complex. The molecular conformation of DAB when in interaction with thrombin was studied by molecular docking simulation and the effect of DAB polar groups on the binding energy was investigated. By comparing the fluorescence spectra and molecular docking simulation results of the interaction between thrombin and dabigatran ester (DE, esterification of both DAB polar groups), dabigatran ethyl ester (DAE, esterification of DAB carboxylic group), and dabigatran hexyl ester (DAH, esterification of DAB benzamidine group), the role of polar groups in the combination of dabigatran and thrombin was further verified. Steady-state and transient fluorescence spectra of DAB and DE interacting with bovine serum albumin (BSA) under physiological pH conditions were also obtained,confirming that the polar groups of DAB play an important role in the selective binding of DAB with thrombin. The results provide theoretical and experimental bases for improving the drug efficiency and finding reversal agents..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 699 (2024)
Study on the Detection Method of COD in Surface Water Based on UV-Vis Spectroscopy
ZHENG Pei-chao, ZHOU Chun-yan, WANG Jin-mei*, YIN Yi-tong, ZHANG Li, Lu Qiang, ZENG Jin-rui, and HE Yu-xin
Chemical Oxygen Demand (COD) is one of the important indicators of water quality detection, reflecting the organic content of water. Traditional chemical detection methods for COD have disadvantages, such as complicated operation, long waiting times and secondary pollution. UV-Vis spectrometer has been one of the most Chemical Oxygen Demand (COD) is one of the important indicators of water quality detection, reflecting the organic content of water. Traditional chemical detection methods for COD have disadvantages, such as complicated operation, long waiting times and secondary pollution. UV-Vis spectrometer has been one of the most widely acceptable methods for detecting COD because of its rapidity detection and no pollution. In order to satisfy the requirement of detecting COD of surface water rapidity, real-time and on-line, a model of kernel principal component analysis (KPCA) combined with particle swarm optimization extreme learning machine (PSO-ELM) was developed for COD prediction of surface water based on UV-Vis spectrometer. Savitzky-Golay filtering was employed to smooth the spectrum. The integral spectrum was substituted for the processed spectrum to decrease the impact of fluctuations. In the meantime, spectrum normalisation was used to eliminate the impact caused by different dimensions of spectrum data. The KPAC algorithm was used to compress the whole spectrum into 5 features, effectively solving the spectral information redundancy problem. PSO algorithm was used to optimize the weight and bias of ELM, which improved the models accuracy. 217 surface water samples, such as rivers, Yangtze River, lakes and reservoirs, were randomly divided into training sets and test sets according to 7∶3, and modeling tests were conducted. The R-squared (R2) of the training set was 0.930 2, the root mean square error (RMSE) of the training set was 0.363 0 mg·L-1, the R-squared (R2) of the test set was 0.931 9, and the root mean square error (RMSE) of test set was 0.400 7 mg·L-1. In order to verify the improvement of the KPCA based on the full spectrum compression method, data Compression algorithms such as principal component analysis (PCA), successive projection algorithm (SPA) and Lassoregression (LASSO) were compared. The RMSE of PCA-PSO-ELM model, SPA-PSO-ELM model and LASSO-PSO-ELM model was 0.715 1,0.473 7 and 0.412 6 mg·L-1, respectively. It was shown that the results of the KPCA-PSO-ELM model were better than the above three models, and RMSE decreased by 78.46%,18.22% and 2.97%, showing that KPCA is an efficient spectral dimension reduction algorithm, which can effectively eliminate spectral redundant information and improve the prediction accuracy of the model. The KPCA-PSO-ELM proposed can realize fast and real-time monitoring of COD in surface water, which can provide algorithm reference for the scene of online water quality monitoring of rapid pollution. As a basic chemical oxygen demand detection research, it provides method reference for online monitoring scenarios for chemical oxygen demand..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 707 (2024)
Study on the Interaction Between Theophylline and Pepsin by Multispectral and Molecular Docking Simulation
WANG Xiao-xia, MA Li-tong, SUN Ji-sheng, NIE Zhi-hua, SAI Hua-zheng, CHENG Jian-guo, and DUAN Jian-guo
In this study,the binding mechanism of theophyline (TPL) and pepsin (PEP) was studied for the first time by ultraviolet-visible absorption spectroscopy,Fourier infrared spectroscopy,fluorescence spectroscopy,three-dimensional fluorescence spectroscopy,synchronous fluorescence spectroscopy,circular dichography andmolecuIn this study,the binding mechanism of theophyline (TPL) and pepsin (PEP) was studied for the first time by ultraviolet-visible absorption spectroscopy,Fourier infrared spectroscopy,fluorescence spectroscopy,three-dimensional fluorescence spectroscopy,synchronous fluorescence spectroscopy,circular dichography andmolecular docking simulation method,and the mechanism of interaction between TPL and PEP was explored at themolecular level,which was helpful to conduct in-depth research on the pharmacotoxicity and efficacy of TPL.According to the Stern-Volmer equation,the dynamic fluorescence quenching rate constant Kq of TPL for PEP at three temperatures of 298,303,and 308 K is much greater than the maximum dynamic fluorescence quenching constant of 2.0×1010 L·(mol·s)-1,proving that TPL quenching PEP is static quenching.With the continuous increase of TPL concentration,the dynamic quenching constant Ksv of PEP shows a regular downward trend,and TPL can effectively quench the endogenous fluorescence of PEP and further infer that the quenching mechanism is static quenching.The three-dimensional fluorescence spectrum analysis showed that with the continuous increase of TPL concentration in the system,the peak fluorescence intensity representing tryptophan residues,tyrosine residues and peptide chain skeleton structure in PEP decreased significantly,and the peak position was redshifted,indicating that TPL a affected the secondary structure of PEP.Simultaneous fluorescence mapping analysis showed that when TPL binds to PEP,it is mainly concentrated on the tryptophan residues.Infrared spectroscopy showed is that TPL caused the functional groups in PEP to expand and vibrate,which changed the secondary structure of PEP.The ultraviolet absorption spectrum showed that the absorption peak and peak increased gradually with the increase of TPL concentration in the mixed system,indicating that TPL could change the secondary structure of PEP.Themolecular docking simulation method shows that the binding force between TPL and the amino acid residues GLU13,VAL30,TRP39,GLY76,GLY78 and PHE117 in PEP is van der Waals force,and hydrogen bonds are formed with amino acid residues THR77 and GLY217,and hydrophobic forces exist with amino acid residues TYR75,LEU112,ILE120 and PHE111,which proves that they are mainly bound to hydrogen bonds by van der Waals force.Further evidence that TPL changes the secondary structure of PEP.Circular dichromy chromatographic analysis showed that the proportion of β-folds in PEP decreased from 50.2% to 48.8%,and the proportion of α-helical structure increased from 8.1% to 8.4%.The proportion of β-corners increased from 18.3% to 18.7%;The proportion of random structures rose from 29.1% to 29.2%,indicating that TPL has changed the secondary structure of PEP.The results of this experiment are helpful to understand the binding mechanism of TPL and PEPand provide data basis for the use and research of TPL..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 714 (2024)
Rapid Detection of Total Organic Carbon Concentration in Water Using UV-Vis Absorption Spectra Combined With Chemometric Algorithms
, , , , , , , and
Total Organic Carbon (TOC) refers to the total amount of carbon contained in suspended or dissolved organic matter in water. It represents the concentration of organic matter in water by the mass of carbon contained in a unit volume of water. Total organic carbon can reflect more comprehensively the total amount of orgTotal Organic Carbon (TOC) refers to the total amount of carbon contained in suspended or dissolved organic matter in water. It represents the concentration of organic matter in water by the mass of carbon contained in a unit volume of water. Total organic carbon can reflect more comprehensively the total amount of organic pollutants in the water. Monitoring total organic carbon can promote China to achieve the goals of “carbon peaking” and “carbon neutrality”, and it also has great significant meaning to the study of Chinas ocean earth carbon cycle. The national standard method for measuring water quality TOC mainly adopts the high-temperature catalytic oxidation method or wet oxidation method. Although the above two methods are accurate in measurement and have a high interpretability, they have disadvantages, such as complicated test methods, long measurement time, secondary pollution, and huge workforce and material resources waste. These methods can only be completed in the laboratory, so it is impossible to realize the in-situ online measurement of TOC. Therefore, it is -greatly significant for us to study the method of rapid and in-situ monitoring of TOC in water. This paper has established a single wavelength concentration detection model for TOC standard solution based on UV absorption spectra. Duo to more complex substance content of real water samples, ACO-PLS and SPA algorithms were used to select characteristic wavelengths and the performance of different spectral pretreatment methods, including S-G smoothing, min-max normalization, Standard Normal Variation (SNV), elimination of constant offset, derivative correction, were compared. The fast detection model of real water samples based on spectral absorption was established the least squares support vector machine algorithm (LSSVM) optimized by particle swarm optimization (PSO). The experimental results show that the modeling effect of SNV algorithm pretreatment is generally better than that of other pretreatment methods when a different numbers of characteristic wavelengths are selected. Moreover, the optimal number of characteristic wavelengths is generally 50 with different preprocessing algorithms because too many or too few wavelengths will reduce the modeling accuracy. The optimal modeling parameters are the SNV preprocessing method with 50 characteristic wavelength combinations selected by the ACO-PLS algorithm. The optimal PSO-LSSVM model result shows Rc=0.984 3, RMSEC=0.457 4, Rp=0.974 5, RMSEP=0.481 1. The optimal TOC detection was successfully applied to newly collected water, demonstrating the robustness of the model. ACO-PLS can effectively select the characteristic wavelength combination. Thus, the rapid determination of TOC in water quality based on UV-Vis absorption spectroscopy can be realized with the PSO-LSSVM algorithm, which provides a fast and pollution-free measurement scheme for TOC in water and provides theoretical support for the development of TOC sensors..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 722 (2024)
Study on One-Dimensional Convolutional Neural Network Model Based on Near-Infrared Spectroscopy Data
TANG Jie, LUO Yan-bo, LI Xiang-yu, CHEN Yun-can, WANG Peng, LU Tian, JI Xiao-bo, PANG Yong-qiang, and ZHU Li-jun
Near-infrared spectroscopy technology has been widely applied for detection in various industries. However, traditional methods struggle to gather key information from the spectral data, leading to significant model prediction errors. This study explores the regression modeling of one-dimensional convolutional neural nNear-infrared spectroscopy technology has been widely applied for detection in various industries. However, traditional methods struggle to gather key information from the spectral data, leading to significant model prediction errors. This study explores the regression modeling of one-dimensional convolutional neural networks (1DCNN) on near-infrared data, focusing on the chemical composition of 452 plants from the Solanaceae family. Through parameter optimization, the study suggests that the optimal settings for the model include 64 channels in the intermediate convolutional layer, a maximum pooling layer of 1, 6 convolutional layers, and 5 channels in the final convolutional layer. These findings can serve as a reference for future model research. The root mean square error of the models test set ranges from 0.02 to 0.49, with an average relative error of 0.8%～1.7%, significantly lower than previous literature. Compared to traditional methods, 1DCNN can fully utilize all of the near-infrared spectral data while maintaining a simple model structure and strong predictive capabilities. This work provides new insights for data processing in near-infrared spectroscopy research and promotes the application and development of this technology..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 731 (2024)
Characteristic Wavelength Selection Method and Application of Near Infrared Spectrum Based on Lasso Huber
GUO Tuo, XU Feng-jie, MA Jin-fang, and XIAO Huan-xian
In near-infrared spectroscopy ( NIRS ) wavelength screening, selecting characteristic wavelengths is challenging problem when the number of variables is much larger than the sample size. Lasso and Elastic Net algorithms are used for variable selection for large-dimensional small-sample data, but both use the least squaIn near-infrared spectroscopy ( NIRS ) wavelength screening, selecting characteristic wavelengths is challenging problem when the number of variables is much larger than the sample size. Lasso and Elastic Net algorithms are used for variable selection for large-dimensional small-sample data, but both use the least square error to measure the loss function to select characteristic variables. Therefore, when the sample contains outliers, the model established using Lasso or Elastic Net algorithms is more sensitive to outliers, resulting in the model shifting to outliers and reduced robustness. Because of the above problems, this paper uses the Huber function as the loss function and proposes the Lasso-Huber wavelength selection method for near-infrared characteristic wavelength selection. Combined with the partial least squares ( PLS ) method, the quantitative correction model of the quality control index components of Antai pills is established and compared with the model performance of full wavelength modeling, Lasso and Elastic-Net method wavelength selection. In this experiment, 116 NIRS data from 21 batches of Antai Pills were collected, of which 101 data were used as calibration sets. The model was internally verified by the leave-one-out cross-validation method, and the other 15 data were used as validation sets for external verification. The Mahalanobis distance method ( MD ) based on principal component analysis ( PCA ) was used for detection for outliers in the calibration set. Taking ferulic acid, one of the quality control index components of Antai pills, as an example, Lasso, Elastic-Net and Lasso-Huber methods were used to screen 69, 155 and 87 characteristic wavelength points in the normal spectra of Antai pill samples. The prediction model established by the Lasso-Huber method combined with PLS was the best, and the R2p and SEP of the prediction set were 0.953 1 and 0.058 7. In addition, the Lasso-Huber method was found to be more advantageous in modeling with outliers by comparing the prediction performance of calibration models normal spectra and outliers in the calibration set. The results showed that the optimal number of wavelength points selected by the Lasso-Huber algorithm was 88, and the performance R2vof the model combined with PLS was 0.967 3, while the R2vof the Lasso method is 0.840 5, the R2vof the Elastic-Net method was 0.834 7, the of the full wavelength modeling is 0.852 0. It can be seen that in the samples with outliers, the Lasso-Huber method not only reduces the number of characteristic bands but also reduces the algorithms sensitivity to outliers, improving the accuracy and robustness of the model. From the perspective of the simplified model, the modeling time of Lasso and Elastic-Net is 61.826 0 and 79.959 9 s, while the modeling time of Lasso-Huber is only 1.360 8 s. Therefore, the algorithm is expected to be integrated into the near-infrared spectroscopy modeling software for practical production applications in the future..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 737 (2024)
The Improved Genetic Algorithm is Embedded Into the Classical Classification Algorithm to Realize the Synchronous Identification of Small Quantity and Multi Types of Lubricating Oil Additives
XIA Yan-qiu, XIE Pei-yuan, NAY MIN AUNG, ZHANG Tao1, and FENG Xin
Adding a small amount of additives to the lubricating oil can make the lubricating oil obtain some new characteristics or improve the properties of some existing characteristics in the lubricating oil.Aiming at the problem of identifying various kinds of tiny additives in lubricating oil of mechanical equipment.Based oAdding a small amount of additives to the lubricating oil can make the lubricating oil obtain some new characteristics or improve the properties of some existing characteristics in the lubricating oil.Aiming at the problem of identifying various kinds of tiny additives in lubricating oil of mechanical equipment.Based on Python,eight different samples were prepared with Base Oil PAO-10 and three Commercial lubricating oil additives, T321,T534,and T307, in different proportions.Thermo Scientific Nicolet IS5 Fourier collected the mid-infrared spectra of the samples transform infrared spectrometer in the range of 4 000～400 cm-1,and the infrared spectra of the samples were normalized by Min-Max.For nearly category mechanical equipment of tiny amount of additive in lubricating oil variety identification,four classical classification algorithms are studied,including the Support Vector Classifier (OVR SVMs),Random Forests Classifier (RF),embedded in the Genetic Algorithm (GA),and Local search Genetic Algorithm (LGA) optimization technologies,infrared spectrum characteristic band many category classification model building methods are established.Example test results show that the accuracy of the new model improves the original classical algorithms OVR SVMs (91.67%)and RF (79.17%) to OVR SVMs (100%)and RF (100%).With the new models embedded in LGA,the length of the characteristic band was shortened to 36.7% of the length of the original band.The new model applies to the case with only one additive and has a high recognition rate of 100% for the simultaneous identification of two or more additives.The results show that the model can effectively realize the rapid,accurate,and multi-type synchronous recognition of small amounts of lubricanting oil additives..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 744 (2024)
Double-Wavelength NIR Raman Spectroscopy and the Application on Corrosion Products
WANG Mao-cheng, LI Gan, CHENG Hao, JIANG Wei, CHEN Guang, and LI Hai-bo
In Raman spectroscopy tests, some substances have a fluorescence emission background.Since the fluorescence intensity is generally orders of magnitude higher than the Raman scattering intensity, it is almost impossible to obtain an effective Raman spectroscopy signal during fluorescence interference.In order to suppresIn Raman spectroscopy tests, some substances have a fluorescence emission background.Since the fluorescence intensity is generally orders of magnitude higher than the Raman scattering intensity, it is almost impossible to obtain an effective Raman spectroscopy signal during fluorescence interference.In order to suppress the fluorescence background, a near-infrared band laser can be used as the excitation light source, but the quantum efficiency of silicon-based detectors in the band above 1 000 nm is seriously reduced, resulting in the difficulty of near-infrared Raman spectroscopy instruments to detect the band above 3 000 cm-1, that is, there is a problem that the fluorescence suppression effect and detection range cannot be balanced.Since the Raman characteristic peaksat high wavenumber are important for identifying material types and analyzing material structures, such as the N—H telescopic vibration peak of the amino group around 3 300 cm-1 and OH- telescopic vibration peak at 3 650 cm-1. These groups are importantfor analyzing material corrosion products, hydrates, organic matter, etc.Therefore, a method is urgently needed to solve the problem that the fluorescence suppression effect and detection range cannot be balanced for Raman spectroscopy instruments.In this work, this project proposes a scheme using a spectrometer with fixed detection wavelength, 730 and 830 nm dual wavelength laser “relay” excitation (i. e.830 nm excitation ofthe Raman peaks with low wavenumber, 730 nm excitation the Raman peaks with high wavenumber), which can extend the detection range to 200～3 700 cm-1 without reducing the spectral resolution (up to 6 cm-1). At the same time, the whole machine optical path has no mechanical moving parts, and the high reliability is convenient to realize the portable design.The prototype testproved that the technical scheme has the advantages of high sensitivity, good fluorescence suppression effect, wide detection range and high spectral resolution.Through the dual-wavelength near-infrared Raman spectrometer, the clear Raman signal of uranium corrosion products and LiH deliquescence products under organic coating was obtained, which solved the technical problems of non-destructive testing of special materials.This technique is also suitable for other research and testing areas, especially substances with small Raman scattering cross-sections and high fluorescence backgrounds.Since the dual-wavelength NIR Raman spectrometer can cover the detection range of 200～3 700 cm-1, it provides a feasible technical approach for the Raman detection of most hydrate, hydroxyl and amino functional substances..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 756 (2024)
Scientific Studies on Beads Unearthed From the Rabat Cemetery,Uzbekistan
WU Chen, LIU Song, LIANG Yun, ZHAO Feng-yan, LI Qing-hui, and WANG Jian-xin
In 2017—2018,the China-Uzbekistan joint archaeological team conducted the excavations at the Rabat Cemetery at Boysun,Uzbekistan.94 tombs were excavated.Preliminary archaeological studies have shown that the cemetery is a Yuezhi culture cemetery from the end of the 2nd century BC to the early 2nd century AD,with rich cIn 2017—2018,the China-Uzbekistan joint archaeological team conducted the excavations at the Rabat Cemetery at Boysun,Uzbekistan.94 tombs were excavated.Preliminary archaeological studies have shown that the cemetery is a Yuezhi culture cemetery from the end of the 2nd century BC to the early 2nd century AD,with rich cultural factors,which provides important archaeological new data for the study of the ancient Yuezhi cultural features and the study of ancient culture from the 2nd century BC to the 2nd century AD in the North Bactria region.Over 1, 500 pieces of beads and pendants in various textures were unearthed in Rabat cemetery,providing abundant research materials.In this study,13 samples of typical synthetic silicate beads are analyzed by Optical Microscopy,Energy dispersive X-ray fluorescence spectroscopy,Scanning electron microscopy with energy dispersive spectromer,and laser Raman spectroscopy to figure out their chemical compositions and a part of the manufacturing technology.Moreover,possible provenances of these beads are discussed,combined with the morphological characteristics of specific samples found at this site.The results show that raw materials of the Rabat beads include sodium-rich faience,natron-based soda glass,plant-ash soda glass，hige-magnesia soda glass，mineral soda-alumina glass，plant ash soda-alumina glass and potash glass,and the productions cover Western Asia,the Mediterranean,Central Asian, India,Pakistan,and other regions.It argues that from the end of the 2nd century BC to the 2nd century AD,frequent economic exchanges and rich cultural interactions took place between the region of Rabat Cemetery and the areas of the Mediterranean,West Asia,and South Asia..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 762 (2024)
Extraction of Natural Gas Microleakage Stress Regions Based on Hyperspectral Images of Winter Wheat
LI Hui, LIU Xu-sheng, JIANG Jin-bao, CHEN Xu-hui, ZHANG Shuai, TANG Ke, ZHAO Xin-wei, DU Xing-qiang, and YU LONG Fei-xue
Natural gas has gradually occupied an important position in the energy structure. As natural gas pipelines and gas storage are buried underground all year round, oxygen-free corrosion, natural disasters, looseness of injection wells and pipelines, and other factors will lead to gas leakage. So, it is necessary to deterNatural gas has gradually occupied an important position in the energy structure. As natural gas pipelines and gas storage are buried underground all year round, oxygen-free corrosion, natural disasters, looseness of injection wells and pipelines, and other factors will lead to gas leakage. So, it is necessary to determine the location of leakage points and make early judgments and warnings before large-scale leakage from underground natural gas storage. This paper collected four periods of hyperspectral image data of winter wheat. It integrated the spatial-temporal-spectral features of hyperspectral data to explore the relationship between the radius and duration of winter wheat stress under natural gas stress, thus indirectly detecting the microleakage point of natural gas. On the one hand, the indexCWTmexh(CWTmexh=CW2770/(1-CW487)×CW550), constructed by continuous wavelet transform of the canopy spectra after continuum removal, was used to classify pixels into non-stress and stress with threshold segmentation. On the other hand, PCA features of hyperspectral image data are extracted, and natural gas stress regions are identified with the SVM classifier. Finally, the results of both threshold segmentation and SVM classification are analyzed by mathematical morphology, and the stress area is fitted with a circular curve using the least square to explore the relationship between the stress radius of natural gas leakage and the stress days. The results show that: (1) The CWTmexh index can be applied to imaging hyperspectral data, showing good recognition performance; (2) SVM classifier can recognize winter wheat stress areas based on spectral difference characteristics with good classification accuracy (i.e., the maximum classification accuracy of 99.25% and kappa coefficient is 0.97) and the recognition accuracy increases with the continuation of natural gas stress; (3) There is a strong linear correlation between the radius of the stressed area and the ventilation days of winter wheat. Results of thisstudy showed that it is feasible to indirectly identify natural gas micro-leakage points through hyperspectral remote sensing by monitoring surface vegetation at the canopy and low altitude scales and can predict time-dependent changes associated with underground natural gas micro leakage stress. The results can provide a theoretical basis for monitoring the leakage points of underground natural gas storage by spaceborne hyperspectral remote sensing and provide technical support for future engineering applications..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 770 (2024)
Spectroscopic Characteristics and Color Genesis of Yellowish-Green Montebrasite
ZHANG Juan, LI Ke-xin, QIN Dong-mei, BAO De-qing, and WANG Chao-wen
The amblygonite-montebrasite series are a kind of rare mineral gemstonewhich shows a complete solid solution containing end members amblygonite (LiAlPO4F) and montebrasite (LiAlPO4OH).However,little research has been paid attention toamblygonite-montebrasite in China,especially on the rapid and non-destructive estimatiThe amblygonite-montebrasite series are a kind of rare mineral gemstonewhich shows a complete solid solution containing end members amblygonite (LiAlPO4F) and montebrasite (LiAlPO4OH).However,little research has been paid attention toamblygonite-montebrasite in China,especially on the rapid and non-destructive estimation of F contents to determine the variety and the origin of color.In this study,conventional gemology,Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS),Infrared spectroscopy (IR),Raman spectroscopy (Raman),UV-VIS absorption spectroscopy (UV-VIS) were employed to test and analyze the gemstone-grade yellowish-green amblygonite-montebrasite series collected in the market.The results of conventional gemological analyses showed that the high refractive index and low relative density of the amblygonite-montebrasite series indicated that the studied samples belong to the endmember of montebrasite.The infrared spectra of the studied samples in the fingerprint region of 400~1 500 cm-1 showed an absorption band of 430 cm-1,belonging to the bending vibration of the Li—O bond.The absorptions of 494,546,604 and 664 cm-1 were caused by the joint actions of the bending vibration (ν4) of PO3-4 and the stretching vibration of the AlO6 bond.The absorptions at ~1 032,~1 090 and ~1 211 cm-1 were owing to the stretching vibration (ν3) of PO3-4. The absorptions at ~832 and 3 386 cm-1 were linked to the bending vibration of OH (δOH) and stretching vibration of OH (νOH),respectively.Raman spectra of the samples showed typical Raman peaks at 305,430,489,648,806,893,1 013,1 058,1 107,1 193,3 367 cm-1,respectively.The wide peak at ~3 367 cm-1 was the typical OH stretching vibration,and a group of peaks between 1 000 and 1 200 cm-1 were the asymmetric stretching vibration of PO3-4 (ν3).The Raman peaks below 600 cm-1 were more complex, relating to the asymmetric stretching vibration of PO3-4 (ν4) and the stretching vibration of the Al—O bond.Integrated estimations of F contents by using high-value refractive index,infrared and Raman spectral index indicated that the high refractive index nγ,the difference between the peak values of 3 367 cm-1 and of 1 058 cm-1 in Raman spectrum,and the FWMH index of the Raman peak near 3 367 cm-1 were good indicators of F contents in the series of amblygonite-montebrasite.It canquickly and indirectly semi-quantify the F contents of faceted amblygonite-montebrasite gemstones.The results of LA-ICP-MS showed that the main element compositions of the studied samples were P2O5,Al2O3 and Li2O,with a trace amount of Fe,consistent with the chemical composition of amblygonite-montebrasite.The Fe replaced the Al in the octahedral structure of amblygonite-montebrasite.The charge transfer between O2-—Fe3+,the intervalence charge-transfer transition between Fe3+—Fe2+,and spin forbidden transition of Fe3+ d—d orbit caused the absorptions of the blue-violet and the orange-red regions were consistent with the results of UV-VIS spectroscopy with observed absorptions in the blue-violet region (300～420 nm) and the yellow-orange region (near 590 nm),which may be the main reason for the color genesis of amblygonite-montebrasite showing yellowish-green tone..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 777 (2024)
Particulate Backscattering Characteristics and Remote Sensing Retrieval in the Zhanjiang Bay
HU Cheng-hao, WU Wen-yuan, MIAO Ying, XU Lin-xia, FU Xian-hao, LANG Xia-yi, HE Bo-wen, and QIAN Jun-feng
Hyperspectral remote sensing is a cutting-edge technology in remote sensing, which has the characteristics of multi-band and high spectral resolution, so it is increasingly widely used in rock identification and classification. In the current study of hyperspectral rock classification, many rocks are easily confused beHyperspectral remote sensing is a cutting-edge technology in remote sensing, which has the characteristics of multi-band and high spectral resolution, so it is increasingly widely used in rock identification and classification. In the current study of hyperspectral rock classification, many rocks are easily confused because of their similar mineral composition, and the classification accuracy is not always high. In the study of high spectral lithology in a wide range of field conditions, there is a lot of interference from the external environment, such as ground cover, pixel mixing and so on, so the spectral characteristics of rocks need to be further studied. The rocks with similar spectra are reclassified. In this study, from the point of view of the laboratory hyperspectral remote sensing system, the HySpex hyperspectral images of 81 common magmatic and metamorphic rock samples were taken as the research data images, and the images were preprocessed such as reflectance correction. Combined with the spectra of rock samples measured by ASD to verify the extraction of corresponding sample spectral curves in the images, the spectral information representing each rock sample was extracted, and the spectral similarity was classified. Finally, the preliminary classification system of 9 large and 28 small categories based on 81 rock samples is obtained. The initial classification system has similar composition properties and spectral characteristics of rock samples in large classes. The spectral characteristics of small classes are more similar than those of large classes. In order to verify the effect of preliminary classification experience on computer lithology classification, the follow-up study is based on the initial classification system of rock samples, and the minimum noise separation technique is used to extract the feature information of hyperspectral images. Finally, the computer classification algorithm model uses the maximum likelihood method and random forest classification, and the training samples set each rock as a single rock book and each subclass in the initial classification system as a sample. Complete the hyperspectral image classification of common magmatic and metamorphic rocks. The experimental results show that the accuracy of maximum likelihood method and random forest classification based on traditional model is 83.21% and 83.63%, while the accuracy of maximum likelihood classification and random forest classification based on initial classification can be improved to 85.46% and 89.39%. Random forest classifier is superior to the traditional maximum likelihood method, while the rock primary classification system has some advantages compared with simple original rock classification. It can be used as a reference for future rock classification work..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 784 (2024)
Particulate Backscattering Characteristics and Remote Sensing Retrieval in the Zhanjiang Bay
YU Guo, ZHONG Ya-feng, FU Dong-yang, LIU Da-zhao2, and XU Hua-bing
Based on the in-situ investigation of Zhanjiang Bay in January 2018,the in-situ remote sensing reflectance (Rrs),particulate backscattering (bbp),chlorophyll a concentration (Chl a) and inorganic suspended matter concentration (ISM) were obtained.The backscattering characteristics of particulates in Zhanjiang Bay were Based on the in-situ investigation of Zhanjiang Bay in January 2018,the in-situ remote sensing reflectance (Rrs),particulate backscattering (bbp),chlorophyll a concentration (Chl a) and inorganic suspended matter concentration (ISM) were obtained.The backscattering characteristics of particulates in Zhanjiang Bay were analyzed,and the backscattering coefficients of particulates were retrieved by remote sensing.The research results showed that the coefficients of variation (CV) of bbp in the six bands (420,442,470,510,590 and 700 nm) were between 50%～60% in surface water,and the variation range was 0.026 1～0.211 2 m-1,which also mean the complexity of optical properties in water.In order to better quantify the spectral characteristics of bbp,the power function spectral model of bbp was constructed with 510 nm as the reference band,and the slope index of the spectral model was 1.55.In the meantime,the bbp(510) had a power relationship with ISM and an exponential relationship with particulate composition (Chl a/ISM),while the determination coefficient (R2) was 0.74 and 0.62,respectively.It indicated that the first-order driving factor of particulate backscattering in the bay was mainly the concentration of inorganic suspended matter,and the second-order driving factor of particulate composition also contributed to the variation of bbp(510).In addition,in order to accurately estimate the particulate backscattering coefficient in Zhanjiang Bay,a random forest model was constructed based on in-situ remote sensing reflectance,and compared with three semi-analytical algorithms such as QAA-v6,QAA-RGB and QAA-705.The R2 of random forest model was 0.86,the mean absolute percentage error (MAPE) was 12%,the root mean square error (RMSE) was 0.02 m-1,the R2 of QAA-v6,QAA-RGB and QAA-705 was 0.63,0.71 and 0.53,the MAPE was 186%,117% and 243%,and the RMSE was 0.16,0.09 and 0.18 m-1,respectively.Although the three semi-analytical algorithms also had high R2, there were significant differences between the estimatedand measured values,and the MAPE and RMSE were also large.The retrieval accuracy of three semi-analytical algorithms was significantly lower than that of the random forest method,which indicated that the random forest method had great potential application when using remote sensing to retrieve the bbp in Zhanjiang Bay..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 793 (2024)
FTIR Study on the Effect of DangGuiTongFengFang on Hyperuricemia Nephropathy in Mice
LIU Bi-wang, LU Rong-rong, CAO Yue, WANG Xiu-wen, ZHAO Huan, HAO Miao, MA Xiao-xia, MA Yan-mia, and WANG Yong-hui2
The Fourier transform infrared spectrometer (FTIR) is an effective method to identify and analyze the structure of substances. It can be used to characterize Quantitative analysis samples and is widely used in many fields, such as medicine and the chemical industry. FTIR, as a simple, practical, accurate and low-cost iThe Fourier transform infrared spectrometer (FTIR) is an effective method to identify and analyze the structure of substances. It can be used to characterize Quantitative analysis samples and is widely used in many fields, such as medicine and the chemical industry. FTIR, as a simple, practical, accurate and low-cost identification method, has been widely used in drug testing, but its application in pharmacological research is very limited. There are only reports on the study of various kinds of tumor tissues by infrared spectrum analysis. Hyperuricemia nephropathy is a condition in which a high uric acid load exceeds the ability of the kidneys to clear the elevated level of serum uric acid. The uric acid crystals are deposited in collecting ducts, the renal pelvis and the urethra. Therefore, it is feasible to use FTIR to evaluate the Hyperuricemia nephropathy model and evaluate the efficacy of drugs. To explore the feasibility of FTIR in evaluating the Hyperuricemia nephropathy model and the therapeutic effect of DangGuiTongFengFang, 60 km mice were randomly divided into 6 groups, the Rats in the normal control group, model group, positive control group and the low, middle and high groups of the Chinese angelica gouty prescription were treated with potassium oxonateto make the model of Hyperuricemia nephropathy, serum UA, urine UA, serum CRE, urine CRE, serum IL-1β, renal IL-1β and FTIR spectra were detected. The results showed that the indexes of the model group were significantly different from those of the normal group (p<0.05), and the serum levels of UA, CRE, IL-1β and renal IL-1β in the high-dose group were also significantly higher than those in the model group (p<0.05). The pathological changes of the kidney in the high-dose group were improved, with no inflammatory cell infiltration and no tubular type in the lumen, indicating that the high-dose group has a protective effect on the kidney injury. The FTIR spectra of the model group were lower than that of the normal group. The FTIR spectra of the positive control group and the high-dose group of DangGuiTongFengFang were close to that of the normal group. It is further indicated that DangGuiTongFengFang has a protective effect on renal injury. The study shows that FTIR can be used as a simple, quick, flexible and economical method to evaluate the animal model and the therapeutic effect of drugs. It also provides a significant technical means for the Hyperuricemia nephropathy model and the screening of effective drugs. This experiment extends the application range of the FTIR detection method..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 800 (2024)
Mix Convolutional Neural Networks for Hyperspectral Wheat Variety Discrimination
LI Guo-hou, LI Ze-xu, JIN Song-lin, ZHAO Wen-yi, PAN Xi-peng, LIANG Zheng, QIN Li, and ZHANG Wei-dong
Different varieties of wheat meet the markets diverse needs,but it also brings the risk of mixed wheat varieties.To improve the purity of wheat varieties and thus the economic value of the selection,breeding,and processing,the identification of wheat seeds plays a key role.Traditional methods of physical and chemical Different varieties of wheat meet the markets diverse needs,but it also brings the risk of mixed wheat varieties.To improve the purity of wheat varieties and thus the economic value of the selection,breeding,and processing,the identification of wheat seeds plays a key role.Traditional methods of physical and chemical analysis of wheat variety purity take a long time to identify and destroy seeds,which can no longer meet the urgent needs of modern agriculture. Hyperspectral imaging is a new,fast,efficient,and nondestructive identification technique that has achieved remarkable results in seed variety identification.Unfortunately,most existing methods use only spectral information without considering spatial information sufficiently to produce unsatisfactory classification results. A hyperspectral imaging device was used to acquire hyperspectral images of the front and back of wheat seeds of eight varieties in the paper.Meanwhile,we propose a hyperspectral wheat variety identification method based on a hybrid convolutional neural network with an attention mechanism,which mainly uses the advantageous complementary features of 3D convolution and 2D convolution to extract valuable features of wheat for driving the identification of wheat varieties.Precisely,we first extract the regions of interest of wheat varieties and use multiple scattering correction methods to weaken the spectral differences of the same variety due to differences in scattering levels.Meanwhile,we use principal component analysis to reduce the useless spectral bands of 3D data and thus retain and compress the valuable features for identifying wheat varieties.Subsequently,a 3D convolution module acquires spatial dimension and feature information between different spectra,a 2D convolution module is used to obtain spatial features and the images inherent feature information,and an attention mechanism is introduced into the 2D convolution model to refine the features.Finally,the identification of different wheat varieties in the same region is achieved at the fully connected layer.Extensive experiments on our collected dataset show that the proposed method performs better than the state-of-the-art methods,and its classification accuracy reaches 97.92%.Besides,the proposed method has better classification performance for a small sample.In short,the proposed method has good effectiveness and robustness for hyperspectral wheat seed identification and provides a new method for the online identification of wheat seeds..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 807 (2024)
Estimation of Leaf and Canopy Scale Tea Polyphenol Content Based on Characteristic Spectral Parameters
, , , , , , , , and
The content of tea polyphenols has strong physiological activity and antioxidant properties,which are important attributes of tea quality,and play an important role in human body fat metabolism and scavenging free radicals.Compared with the assay method of tea polyphenol content,although monitoring the content of tea pThe content of tea polyphenols has strong physiological activity and antioxidant properties,which are important attributes of tea quality,and play an important role in human body fat metabolism and scavenging free radicals.Compared with the assay method of tea polyphenol content,although monitoring the content of tea polyphenols based on remote sensing technology has the advantages of high efficiency,accuracy and real-time,there are few studies on how to use remote sensing data to monitor the content of tea polyphenols.This study took tea leaves from five tea gardens in Yingde City,Guangdong Province, as the research object,and measured the content of tea polyphenols and the corresponding hyperspectral data at two scales of leaf and canopy of spring tea,summer tea and autumn tea.Standard normal variate transformation (SNV) was used to preprocess leaf and canopy hyperspectral reflectance data;then,Successive projections algorithm (SPA) and competitive adaptive weighted sampling algorithm (CARS) to select the sensitive bands of tea polyphenols at two scales of leaf and canopy in different growing seasons;Finally,the tea polyphenol content models in different periods were constructed and verified by partial least squares (PLS),random forest (RF) and multiple linear regression (MLR).The results showed that:(1) The tea polyphenols content increased significantly with the passage of seasons,the content of tea polyphenols in spring tea (15.37%) was the lowest,the content of tea polyphenols in summer tea was the second (18.29%),and the content of tea polyphenols in autumn tea (20.77% in autumn tea) was the highest;(2) The characteristic bands of tea polyphenols are mainly concentrated in the short-wave near-infrared band (around 2 100~2 200 nm),near-infrared (around 1 300~1 400 nm),red wave-red edge band and green band;(3) The CARS-PLS,SPA-MLR and CARS-PLS have the highest precision among the tea polyphenol models constructed based on the spectral characteristics from spring，summer and autumn canopy,with R2 of 0.56，0.45 and 0.52 respectively，and RMSE of 1.15，1.68 and 1.77 respectively；The validation set R2 was 0.43，0.40 and 0.41 respectively，and the RMSE was 1.60，1.91 and 1.91 respectively；The SPA-PLS，CARS-PLS and SPA-MLR models based on the spectral characteristics of spring tea，summer tea and autumn tea canopy leaves have the highest precision，with R2 of 0.50，0.42 and 0.42 respectively，and RMSE of 1.25，1.70 and 1.66 respectively；The validation set R2 was 0.43，0.36 and 0.38 respectively，and the RMSE was 1.44,1.96 and 2.49 respectively. The results showed that it is feasible to measure the content of tea polyphenols at two scales of leaf and canopy in different seasons based on remote sensing data and has great potential for real-time monitoring of tea quality characteristics in large areas..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 814 (2024)
Study on Coal and Gangue Recognition by Vis-NIR Spectroscopy Under Different Working Conditions
LIU Tao, LI Bo, XIA Rui, LI Rui, and WANG Xue-wen
In the process of realizing the efficient utilization of coal, coal and gangue separation is a very important step, but the existing coal and gangue separation technology has the problems of resource waste and low efficiency. It can be seen that Vis-NIR(visible near-infrared) spectroscopy identification technology has In the process of realizing the efficient utilization of coal, coal and gangue separation is a very important step, but the existing coal and gangue separation technology has the problems of resource waste and low efficiency. It can be seen that Vis-NIR(visible near-infrared) spectroscopy identification technology has the advantages of being fast and reliable and has a certain research foundation in the field of coal and gangue recognition, but most of the studies have not been effectively analyzed in combination with actual conditions. Firstly, this paper set up a Vis-NIR spectrum acquisition device in the laboratory to simulate three conditions in the actual environment: different detection angles (0°, 10°, 20°, 30°), different detection distances (10, 15, 20 and 25 cm), and different illumination angles (15°, 25°, 35°, 45°). The spectral data of coal and gangue samples from Ximing Coal Mine in Shanxi are collected in the Vis-NIR spectrum band under the single-factorandmulti-factor conditions of orthogonal experimental design. Secondly, the collected spectral data were analyzed and successively underwent standard normal variable transformation and Savitzky-Golay convolution smoothing to reduce the impact of noise and error on the data. Finally, in the single factor experiment, the spectral data were trained based on five machine learning models, including decision tree (DT), k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and AdaBoost, combined with the preprocessing algorithm. The results of the single factor experiment show that the AdaBoost algorithm adopted in this paper has strong learning ability, and the recognition accuracy of coal and gangue under different working conditions is 100%, which is better than other recognition models. In orthogonal experiments, a support vector machine (SVM) is used as the recognition model for training. The results show that, in the rawand preprocessed data, the three conditions have different degrees of influence on the recognition accuracy of coal and gangue, and the order of influence from large to small is different illumination angles, different detection distances and different detection angles. In the preprocessed data, the optimal combination of working conditions is a detection angle of 0°, detection distance of 20 cm and illumination angle of 35°. At the same time, by comparing the experimental results, it can be found that the appropriate pretreatment and modeling methods can reduce the influence of different working conditions on recognition accuracy. A group of conditions were randomly selected to perform three repeated control trials with the optimal group. The results showed that the optimal group had better recognition performance than the random control group. The research results have reference significance for finding the optimal working conditions of coal and gangue identification and provide a theoretical basis for the practical application of Vis-NIR spectroscopy in coal and gangue identification..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 821 (2024)
Detection of Trace Methane Gas Concentration Based on 1D-WCWKCNN
KAN Ling-ling, ZHU Fu-hai, and LIANG Hong-wei
In detecting methane concentration by tunable laser absorption spectroscopy (TDLAS), the second harmonic signal amplitude of methane transmitted light intensity is directly proportional to the concentration of trace methane gas. How to accurately and quickly screen the amplitude of the second harmonic signal of the tarIn detecting methane concentration by tunable laser absorption spectroscopy (TDLAS), the second harmonic signal amplitude of methane transmitted light intensity is directly proportional to the concentration of trace methane gas. How to accurately and quickly screen the amplitude of the second harmonic signal of the target methane transmitted light intensity is crucial. The photodetector obtains the 1 000 methane gas transmitted light intensity signal samples, and it is demodulated to obtain the second harmonic signal. When obtaining a variety of trace methane gas transmitted light intensity and demodulating the second harmonic signal by transmitted light intensity, noise and artificial operation affect the amplitude of the second harmonic signal，resulting in an increase in the time for manual screening of the second harmonic signal. Using traditional TDLAS technology to screen trace methane second harmonic signals had the problem of high time cost. A trace methane concentration detection method based on wide convolution and wide kernel 1D convolutional neural networks (1D-WCWKCNN) was proposed. Firstly, the 1D-WCWKCNN model is trained with the help of the methane gas dataset, and the model parameters are continuously adjusted according to the training results. Secondly, the method used a wide convolution layer and wide convolution kernel 1D convolution layer to extract the features of the trace methane second harmonic signal so that the network obtained the characteristic relationship between a longer sequence and the sequence boundary information in the methane concentration signal and the gas concentration after one convolution. The second harmonic signal of methane transmitted light intensity is extracted through the 6-layer convolutional layer to extract the main characteristics of the relationship between the signal and methane gas concentration.The 6-layer maximal pooling layer retains the main characteristics. The Flatten layer processes the signal data processed by the previous layer in one dimension. Finally, the trained 1D-WCWKCNN model outputs trace methane gas concentration through the Dense layer. The trace methane gas concentration detection model based on 1D-WCWKCNN replaces manually screening second harmonic signals for detecting trace methane gas concentration in a fitted straight line in TDLAS technology. The effectiveness of this method is verified in actual experiments. The results show that it can effectively detect the concentration of trace methane in 50~1 000 mg·L-1, and its accuracy reaches 99.85%. Compared with other methods, it has strong signal feature extraction ability and high detection accuracy of methane gas.This method facilitates the screening of gas concentration signals to be measured in the field of gas detection..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 829 (2024)
Development of Dynamic High-Speed True Temperature Measurement System for Welding Head Based on Infrared Radiation Thermometry
XIAO Peng, TAI Hong-bing, XIANG Mao-lin, WANG Wei-chen, and ZHANG Fan
Temperature is an important basic parameter used to characterize the properties of objects,and is widely used in various fields such as metal processing,production life,aerospace,etc.The accuracy of temperature measurement has a crucial impact on each industry.With the continuous miniaturization of the size of electronTemperature is an important basic parameter used to characterize the properties of objects,and is widely used in various fields such as metal processing,production life,aerospace,etc.The accuracy of temperature measurement has a crucial impact on each industry.With the continuous miniaturization of the size of electronic devices and the popularity and development of various wearable smart devices,miniaturization has become an important technical indicator of the level of technology of electronic components,so electronic components have also been developing in the direction of small size and high integration,and its welding temperature fluctuations caused by the product yield is also decreasing.Therefore,achieving real-time access to the solder head temperature during welding electronic components has become an urgent research topic for many related companies.Based on the working characteristics of transistorized welding power supply,this paper starts by analyzing the structural characteristics of the solder head,using the infrared spectral radiation characteristics of the solder head,and designs a laser-targeted optical system to measure the temperature of the solder head using the Lambertian method,to obtain the real-time true temperature of the solder head when working.The whole temperature measurement system consists of a hardware system part and a software system part.Among them,the hardware system includes the design of the optical system,I/V conversion and amplification circuit and the high-speed data acquisition system of the upper computer.The software partof the system mainly includes the design of the interface of the upper computer system.The software of the upper computer adopts LabVIEW for program design,which mainly includes the configuration and driving of AD acquisition card,data filtering,zero point measurement,temperature calibration,temperature calculation,real-time temperature curve and data storage.After the system was established and the software was debugged,the temperature measurement system was calibrated using a standard cavity blackbody furnace.First,the emissivity of the weld head during operation was tested using the integral blackbody method,and the results showed that the emissivity was in general agreement with literature data in both oxidized and non-oxidized states.Then,using this emissivity value,the cavity blackbody emissivity was calculated for the small holes in the solder head.Finally,system stability and repeatability experiments were performed using a constant-current transistorized welding power source.The uncertainty analysis of the method yielded an overall uncertainty of the method within 3%.By calibrating and configuring the temperature measurement system,the system can also be applied to other scenarios where high-speed accurate temperature measurement is required..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 836 (2024)
Correction Method of Multispectral Satellite Images Based on Spaceborne Synchronous Atmospheric Parameters
XU Ling-ling, XIONG Wei, YI Wei-ning, QIU Zhen-wei, LIU Xiao, and CUI Wen-yu
The atmospheric state varies significantly in terms of the temporal and spatial scales. The atmospheric correction of remote sensing satellite images is limited because it is difficult to dynamically obtain atmospheric parameters matching with the images to be corrected. As the civil optical remote sensing satellite wiThe atmospheric state varies significantly in terms of the temporal and spatial scales. The atmospheric correction of remote sensing satellite images is limited because it is difficult to dynamically obtain atmospheric parameters matching with the images to be corrected. As the civil optical remote sensing satellite with the highest spatial resolution in China, the Gao Fen Duo Mo satellite is equipped with the first civilian Synchronization Monitoring Atmospheric Corrector (SMAC). The SMAC onboard the GFDM satellite platform is capable of multispectral and polarization detection and can offer time-synchronized, and field-of-view overlapped atmospheric measurements to obtain atmospheric parameters synchronized with the main sensor. This study proposed a synchronous atmospheric correction method for high-spatial resolution image based on the atmospheric parameters retrieved from SMAC. Firstly, based on the principle of time synchronization, the original data of SMAC was processed to form the SMAC-Level1 product, combining with the auxiliary data of the main camera. Then, according to the SMAC-Level1 data, the SMAC pixels covered with cloud were discriminated, and the aerosol and water vapor parameters of the pixels without cloud coverage were retrieved to form the SMAC-L2 product. Finally, based on the 6SV radiative transfer model, the atmospheric radiometric correction and proximity effect correction were carried out on the remote sensing image from the GFDM satellite (Level1), and the surface reflectance product of the main camera (Level2) was obtained. In the experimental part, Syn-AC was applied to the remote sensing image from the GFDM satellite, and the image quality before and after the atmospheric correction was evaluated. Furthermore, the surface reflectance after the correction was compared with the ground-measured value to discuss the accuracy of the synchronous atmospheric correction method. In addition, the classical correction method FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method.The results show that the reflectance obtained from the corrected image of Syn-AC is in good agreement with the ground-measured value, and the mean absolute error is 0.012 2 (the mean absolute error of FLAASH is 0.027 4). The atmospheric correction method based on synchronous atmospheric parameters retrieved from SMAC has great potential in improving satellite image quality and remote sensing quantitative applications..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 843 (2024)
Determination of Trace Gaseous Contaminants in FCV Hydrogen Fuel by Modular Fourier Transform Infrared Spectroscopy
YUAN Hui, LIU Dan, and XU Guang-tong
A modular Fourier Transform Infrared Spectroscopy （FTIR）characterization platform was designed and set up.The analytical method for determining hydrogen trace impurities in proton exchange membrane fuel cell vehicles (FCV) has been developed.After reducing the purity requirement of blank gas,specific target contaminantA modular Fourier Transform Infrared Spectroscopy （FTIR）characterization platform was designed and set up.The analytical method for determining hydrogen trace impurities in proton exchange membrane fuel cell vehicles (FCV) has been developed.After reducing the purity requirement of blank gas,specific target contaminants, including HCOOH，CO，CO2，NH3，H2O，CH4，C2H4，C2H6，C3H8，HCHO still could be rapidly determined simultaneously without any pre-process with high accuracy and reproducibility.The detection limits of the nine impurities still reach the ASTM D7653-18 reference value,and their quantitative limits are also lower than the corresponding limits of ISO 14687：2019.The method meets applicability verification rules in ISO 21087：2019. According to the requirements of hydrogen trace impurities in different scenarios,the modular FTIR system can be organically combined with good practicability,and the hydrogen concentration can be reduced to 100μmol·mol-1 after treatment, which meets thesafety and environmental protection requirements.It is consistent with the measured values of other reference methods in the laboratory.It will be gradually developed into an on-line method for different industrial scenarios.This is significant for the establishment of fuel-grade hydrogen quality systems,the application of purification technology and the development of new materials for fuel cell catalysts..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 853 (2024)
Identification of Ginkgo Fruit Species by Hyperspectral Image Combined With PSO-SVM
ZHANG Fu, ZHANG Fang-yuan, CUI Xia-hua, WANG Xin-yue, CAO Wei-hua, ZHANG Ya-kun, and FU San-ling
Ginkgo fruit with antioxidant,anti-tumour and cardiovascular disease prevention functions is rich in vitamins,ginkgo lactones and ginkgo flavonoids,and can be used for both medicine and food.Due to the different varieties of Ginkgo fruit,the content of the main ingredientsis different and there are differences in qualiGinkgo fruit with antioxidant,anti-tumour and cardiovascular disease prevention functions is rich in vitamins,ginkgo lactones and ginkgo flavonoids,and can be used for both medicine and food.Due to the different varieties of Ginkgo fruit,the content of the main ingredientsis different and there are differences in quality.In addition,the content of certain components in ginkgo fruit has a greater impact on their storage and processing.In order to achieve efficient and non-destructive identification of ginkgo fruit varieties,the Support Vector Machine (SVM) classification model based on hyperspectral imaging technology was proposed,and Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) was used to optimizethe parameters of the model to improve the accuracy of species identification.In this study,630 ginkgo fruits of three species were regarded as the research objectsand divided into training and test sets according to 2∶1,with 420 and 210 samples respectively.The hyperspectral acquisition system acquired Ginkgo fruit images in the range of 900～1 700 nm. Then region of interest (ROI) of 25×25 pixel in the center of mass position was selected after black and white correction,and the average spectrum in the region was extracted as the original spectral data.Because of the large noise at both ends of the original spectra,the signal noise ratio was lower and the effective information was less.The spectral band in the range of 945.98~1 698.75 nm was intercepted as the effective band,which was pre-processed by Standard Normal Variate transformation (SNV).Successive Projection Algorithms (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to extract the characteristic wavelengths.The wavelength reflectivity was used as the input matrix X,and the sample varieties 1,2,3 were used as the output matrix Y. Six identification models were established for the SNV-SPA/CARS-(GA/PSO)-SVM.The experimental results showed that the SNV-CARS-PSO-SVM model had the best identification performance, and the classification accuracy was 96.67%,indicating that the characteristic wavelength variables extracted by CARS could represent all wavelength information,and the PSO-SVM model had a better species identification effect, which could realize the identification of ginkgo fruit.This study provides a new idea for the efficient and non-destructive identification of ginkgo fruit species..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 859 (2024)
A Hyperspectral Deep Learning Model for Predicting Anthocyanin Content in Purple Leaf Lettuce
ZHANG Mei-ling, CHEN Yong-jie, WANG Min-juan, LI Min-zan, and ZHENG Li-hua
The leaves of purple leaf lettuce are rich in anthocyanins,carotene,vitamins,minerals and other elements.Among them,anthocyanin,as the main pigment in the leaf tissue,provides a variety of repair and protection functions for the plants,and its content can reflect the physiological state of purple leaf lettuce,so that tThe leaves of purple leaf lettuce are rich in anthocyanins,carotene,vitamins,minerals and other elements.Among them,anthocyanin,as the main pigment in the leaf tissue,provides a variety of repair and protection functions for the plants,and its content can reflect the physiological state of purple leaf lettuce,so that the high-accuracy prediction of anthocyanin content has practical significance.In order to efficiently and accurately estimate the anthocyanin content of purple-leaf lettuce,this paper collected hyperspectral data from purple-leaf lettuce and carried out high-precision modeling research.Five preprocessing operations,first derivative (FD),second derivative (SD),standard normal variate transformation (SNV),S-G filter and multiple scattering correction (MSC),were performed on the original average reflectance spectral data.Based on different pretreatment spectra,the partial least squares regression (PLSR) model of anthocyanin content in purple leaf lettuce was established, five preprocessing performances were compared,and MSC was illustrated as the ideal spectral pretreatment method.The competitive Adaptive Reweighted Sampling (CARS) algorithm was used to select characteristic wavebands for the original spectra and the spectra preprocessed by MSC.Based on the full band (original spectra,MSC preprocessed spectra) and characteristic wavebands based on the original spectraand the MSC preprocessed spectra separately),the PLSR model was constructed respectively,the coefficient of determination (R2) and root mean square error (RMSE) of the best-performing MSC-CARS-PLSR on the validation set were 0.872 and 0.070mg·L-1,respectively,and the residual prediction deviation (RPD) was 2.862.In order to improve the prediction accuracy further,this paper proposes a regression analysis framework marked as Ensemble that integrates deep convolutional features and extreme learning machines (ELM).Based on the improved Inception module,a one-dimensional convolutional neural network (1DCNN) matching the input spectral signal is designed as a feature extractor. ELM is used as an advanced regressor to replace a fully connected layer to calculate the extracted features.Through comparative analysis,the performance of Ensemble is better than that of a single 1DCNN model,ELM model and the best PLSR model based on preprocessed spectra,and its R2 and RMSE on the validation set were 0.905 and 0.060mg·L-1,respectively,and the RPD was 3.319,showing high prediction accuracy and excellent stability.The impact of preprocessing operations on the prediction accuracy of Ensemble is further analyzed. The experimental results show that Ensemble is much less dependent on preprocessing operations than PLSR,indicating that the model inherits the deep feature representation of 1DCNN and the high generalization of ELM at the same time,and can realize end-to-end high-precision prediction of anthocyanin content based on the original spectrum,which provides theoretical support for timely and accurate grasp of the growth situation of purple leaf lettuce..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 865 (2024)
Residual Quantization of Radiation Depth in Hyperspectral Image and Its Influence on Terrain Classification
WANG Juan, ZHANG Ai-wu, ZHANG Xi-zhen, and CHEN Yun-sheng
Most of the current research focuses on the improvement and application of spatial and spectral resolution of Hyperspectral Image(HSI). It pays little attention to the comprehensive application of radiation resolution. The radiation resolution reflects the range of the dynamic change of the radiation energy received byMost of the current research focuses on the improvement and application of spatial and spectral resolution of Hyperspectral Image(HSI). It pays little attention to the comprehensive application of radiation resolution. The radiation resolution reflects the range of the dynamic change of the radiation energy received by the sensor. It detects the small change of the radiation energy of the ground object, which also contains rich ground object information. This study proposes a HSI Radiation Bit Depth Residual Quantization Method to construct Low Bit Depth Hyperspectral Image (LHSI) and Residual Hyperspectral Image (RHSI)with different radiation bit depth levels. Through experiments, LHSI and RHSI of different radiation bit depth levels of HSI and their combinations are used to classify ground objects, and their effects on the classification accuracy of ground objects are analyzed. Experiments show that, based on ensuring a certain classification accuracy, 9-bit LHSI retains the main information of HSI; 4-bit RHSI highlights more details of ground objects than the HSI. The combination of 13-bit LHSI and 3-bit RHSI can not only retain the main information of HSI but also highlight the details of the ground object..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 872 (2024)
Comparison of Different Detection Modes of Visible/Near-Infrared Spectroscopy for Detecting Moldy Apple Core
, , , , , , , , , , , , , , , , , and
Moldy apple core is a kind of internal fruit disease that threatens consumers health. Rapid, nondestructive detection of moldy apple core is helpful to improve the quality of the apple and ensure the safety of consumers before entering the consumer market. In recent years, Vis/NIR spectroscopy has been widely used in Moldy apple core is a kind of internal fruit disease that threatens consumers health. Rapid, nondestructive detection of moldy apple core is helpful to improve the quality of the apple and ensure the safety of consumers before entering the consumer market. In recent years, Vis/NIR spectroscopy has been widely used in the nondestructive detection of fruit quality by its advantages of rapid, nondestructive, simple operation, low cost and batch online detection. The selection of spectral detection mode according to the actual detection requirement is an important prerequisite for developing fruit spectral nondestructive detection. Three kinds of spectral data from 243 samples were obtained based on diffuse reflection, diffuse transmission and transmission spectrum acquisition systems built by the laboratory. Five spectral pretreatment methods, including S-G smoothing (S-G), multiplicative scatter correction (MSC), standard normal variation (SNV), first derivative (FD), and normalize (NOR), were used for spectral data preprocessing. Four manifold learning algorithms, including locally linear embedding (LLE), multidimensional scaling (MDS), distributed neighbor embedding (SNE) and t-distributed neighbor embedding (t-SNE), were systematically used for spectral data dimensionality reduction. These were compared with the traditional principal component analysis (PCA) dimensionality reduction method. Finally, the least squares-support vector machine (LS-SVM) classification model was established based on the dimensionality-reduced data. The results show that the transmission detection mode is better than the diffuse transmission detection mode, and the diffuse transmission detection mode is better than the diffuse reflection detection mode in three different detection modes. The distributed neighborhood embedding algorithm is better than other dimension reduction algorithms. The model constructed by transmission detection mode combined with the distributed neighborhood embedding dimension reduction algorithm performs best. The accuracy of the calibration set and test set is 99.52% and 97.14%, respectively. The research provide a reference for establishing a spectral nondestructive detection platform and developing detection equipment for moldy apple core..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 883 (2024)
Tentative Study on Theory and Application of Remote Sensing Statistical Inference
ZHU Wei-ning
We propose a new qualitative-quantitative remote sensing analytical method,remote sensing statistical inference, which is different from the remote sensing classification (qualitative analysis) and remote sensing inversion (quantitative analysis).Theoretically based on statistical optics and probability distribution trWe propose a new qualitative-quantitative remote sensing analytical method,remote sensing statistical inference, which is different from the remote sensing classification (qualitative analysis) and remote sensing inversion (quantitative analysis).Theoretically based on statistical optics and probability distribution transformation,remote sensing statistical inference mainly studies how the probability distributions of the ground parameters (e. g.,soil moisture and temperature,water salinity and chlorophyll concentration,etc.) within the interested area change and affect the probability distributions (called spectral probability distribution,SPD) of the optical parameters (e. g.,surface reflectance,remote sensing reflectance of water bodies,etc.) observed by the remote sensors,and how to infer the statistical distributions of the ground parameters in this region based on the SPDs observed by the sensor,to provide the corresponding qualitative and quantitative information for characterizing the ground parameters.Compared with the traditional remote sensing classification and inversion,the advantages of remote sensing statistical inference are:(1) it can quickly obtain the global statistical characteristics of the ground parameters,such as mean,variance,max/min,etc.,without inversion of each image pixels,which is especially important for many current remote sensing applications based on big data and high-resolution images,because some application department managers are most interested in the overall statistical distributions of the management objects (such as lakes and reservoirs);(2) the observed SPDs can be directly used in the classification study of ground objects,providing a new classification method different from the traditional remote sensing classification based on the signature features of each pixel,which provides the overall classification of the study object (such as the classification of a lake) rather than the classification of each pixel (e. g.,the classification of water quality);(3) remote sensing statistical inference can provide auxiliary information for remote sensing inversion modeling,and based on the inferred information,the functions and/or parameters of inversion modelscan be adjusted so that the statistical characteristics of the inversed results match the inferred results.This paper briefly introduces some basic concepts and principles of remote sensing statistical inference,its advantages over remote sensing classification and inversion,the applicable objects of inference and remote sensing data processing methods for inference,analyzes the characteristics of the spectral probability distribution of major lakes in China,and proposes a bootstrap-based method for inference using the field measurement data of West Lake in Hangzhou.A simple inference method based onthe bootstrap-based method is proposed to infer the key statistical distribution parameters,for example,the mean concentration of the suspended particles in West Lake..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 891 (2024)
Spectral Detection of Desloratadine
GE Xue-feng, SHI Bin, TANG Meng-yuan, JI Kang, ZHANG Yin-ping, and GU Min-fen
Desloratadine (DL) is a classic antihistamine for treating allergic rhinitis and chronic urticaria. When the structure-related N—H bond is studied using spectroscopy, different pretreatment and testing methods significantly affectthe characteristic absorption peaks with the corresponding height ratio. With Fourier tranDesloratadine (DL) is a classic antihistamine for treating allergic rhinitis and chronic urticaria. When the structure-related N—H bond is studied using spectroscopy, different pretreatment and testing methods significantly affectthe characteristic absorption peaks with the corresponding height ratio. With Fourier transform infrared transmission method (TR-FTIR), the spectra of DL show obvious differentiation at 3 325 and 3 304 cm-1 two characteristic absorption peaks with peak height ratio of 1∶1, no peak, at 3 327 and 3 304 cm-1 two peaks with peak height ratio of 1∶2.3 respectively corresponding to three different pretreatments of conventional potassium chloride tablet, nujol mull, potassium chloride tablet with nujol mull. Furthermore, the characteristic peaks assigned N-H stretching vibration can hardly be detected using attenuated total reflection FTIR (ATR- FTIR) accessory with neither the crystal type of ATR nor the detector of the instrument changed, indicating the sensitivity of detector also plays an important role. Subsequently, using the diffuse reflection spectrum FTIR method (DRS-FTIR), it is easy to find two characteristic absorption peaks at 3 327 and 3 304 cm-1 with a peak height ratio of 1∶5 without any pretreatment. For the Fourier transform Raman (FT-Raman), two absorption peaks at 3 327 and 3 304 cm-1with a higher peak height ratio of 1∶6 also can be achieved at a high signal-to-noise ratio without pretreatment. For the Laser Raman with the excitation wavelengths of 532, 633 and 785 nm, no useful signal can be observed above 1 700 cm-1 because of the strong fluorescence. To analyze the effect of pretreatment on the IR N—H band of DL further, differential scanning calorimetry (DSC) is applied to detect the melting point change in the current study. The melting points of 156.4, 156.6 and 156.6 ℃ of the raw DL, the grinding DL and the mixture of DL and KCl indicate that the physical processes of grinding and mixture cannot destroy the crystalline from of DL. The melting points of 153.0, 141.5 and 145.0 ℃ for the ground samples of mixed DL and KCl, mixed with paraffin, and mixed with paraffin and KCl are lower than the raw DL with a broader distance of DSC peaks and indicate the structure change of DL during pretreatment. Therefore, DRS-FTIR and FT-Raman are the suggested methods forspectrum detection to avoid the uncertainty introduced by pretreatment of DL..
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 7511 (2024)
[in Chinese]
Spectroscopy and Spectral Analysis
- Publication Date: Aug. 06, 2024
- Vol. 44, Issue 3, 1 (2024)
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