Researching | GPXYGPFX Volume 44 Issue 10,2024

MAO Li-yu, BIN Bin, ZHANG Hong-ming, LÜ..., GONG Xue-yu, YIN Xiang-hui, SHEN Yong-cai, FU Jia, WANG Fu-di, HU Kui, SUN Bo, FAN Yu, ZENG Chao, JI Hua-jian and LIN Zi-chao|Show fewer author(s)

Currently, the traditional measuring methods ofgrain quality are mainly the traditional separation and manual inspection, which take a long time and have low efficiency. Near Infrared (NIR, 780~2 500 nm) spectral analysis technology has the advantages of a wide range of applicable samples, high accuracy of quantitative

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2768 (2024)

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A Diagnostic Method for Electron Density of Plasmas by Machine-Learning Combined With Stark Broadening

ZHANG Ting-lin, TANG Long, PENG Dong-yu, TANG Hao..., JIANG Pan-pan, LIU Bo-tong and CHEN Chuan-jie|Show fewer author(s)

Electron density is one of the key fundamental parameters of plasma discharges. H is the most used spectral line for spectroscopic diagnosis based on the Stark broadening method. The van der Waals broadening, which is related to the gas temperature, makes an important contribution to the broadening of the H line at atm Electron density is one of the key fundamental parameters of plasma discharges. H_β is the most used spectral line for spectroscopic diagnosis based on the Stark broadening method. The van der Waals broadening, which is related to the gas temperature, makes an important contribution to the broadening of the H_β line at atmospheric pressure. To extract the Stark broadening width, the gas temperature should be determined in advance from the rotational temperature of molecules, resulting in inevitable errors in measuring. During the nonlinear parameters fitting processes of a spectral line, the errors in gas temperature will transfer to electron density measurement. This work proposes combining a random forest regression model based on machine learning and a Stark broadening method based on optical emission spectroscopy. Compared with the error characteristic of the traditional least square method, this method is found to have a good performance in robustness and generalization capability so that it could diagnose the electron density of plasma more precisely and quickly. Because of the different states of plasma discharges, the training set of H_β standard theoretical line used for the machine learning is simulated by the model of spectral line broadening, in which the random errors are introduced into the gas temperature. A sample set, combined with the spectral line’s intensity distribution with each group’s temperature deviation and the corresponding electron density, is employed to train the random forest model. The hyperparameters (i.e., the minimum number of leaf nodes and the number of decision trees) that minimize the mean square error of the model are set to 2 and 100, respectively. It is found that the average relative error between the results predicted by the random forests regression model, which is well-trained, and the actual values are less than 3%. The model was evaluated by a test set of spectral data with a temperature error range of 0~±10%. With the increase in temperature error, the prediction results of the random forest model are better than those of the least squares method. When the error of gas temperature is ±10%, the mean squared error of predicted electron density is reduced by more than 30% compared with the least squares method. In the training set of spectral data, when the error of gas temperature introduced into the training set is in the range of 0~±10%, the minimum mean squared error of electron density is achieved, and the robustness of the model is better than that of the least squares method. However, the prediction results of the model become inaccurate when the temperature error introduced into the training set is beyond ±10%. In addition, the time spent analyzing the spectral line by the model, which is well-trained, is much less than that by the least square method..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2778 (2024)

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The Identification and Analysis of the Modern Binding Media Based on Multiple Analytical Methods

ZHANG Yao-yao, FU Ying-chun, and WEI Shu-ya

The study of natural organic binders in historical objects is one of the research highlights in cultural heritage. However,identifying and classifying modern binding media used for restoration is also very important in heritage conservation. It is necessary for the subsequent restoration of ancient ceramics to understa The study of natural organic binders in historical objects is one of the research highlights in cultural heritage. However,identifying and classifying modern binding media used for restoration is also very important in heritage conservation. It is necessary for the subsequent restoration of ancient ceramics to understand the composition of the binding media used in the restoration of ancient ceramics. To address this problem, a batch of modern binding media for restoring ancient ceramics was analyzed by Fourier Transform infrared spectroscopy (FTIR), Pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) and thermally assisted hydrolysis-methylation pyrolysis-gas chromatography-mass spectrometry (THM-Py-GC/MS) techniques. The results showed that the characteristic absorption peaks of the infrared spectrum of the binding media at 1 252 and 829 cm^-1 were in line with the range of characteristic absorption peaks of epoxy compounds and matched with the standard spectrum of epoxy resin in the infrared spectrum library, which indicated that the binding media was an epoxy adhesive. Moreover, additives such as pyridine compounds, benzoate compounds and primary alcohol compounds are also added to the binding media. Then, the chemical composition of the binding media was analyzed by applying Py-GC/MS and THM-Py-GC/MS techniques, and the binding media was detected as a bisphenol A-type epoxy resin. Moreover, to improve the performance of the epoxy resin adhesive, amine curing agent, diisooctyl phthalate plasticizers and diisooctyl phthalate inactive diluent were also detected to be added to the binding media. THM-Py-GC/MS technique affects the analysis and identification of amine curing agents in this binding media due to the presence of methylation reagents, whereas the Py-GC/MS technique is more suitable for the analysis and characterization of epoxy adhesives. In this study, the use of the Py-GC/MS technique can identify the types of modern binding media used for the restoration of ancient ceramic relics, providing a new approach for the analysis and identification of epoxy resin adhesives, as well as a new perspective for the identification of unknown restoration materials for cultural relics..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2785 (2024)

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The Experimental and Theoretical Study of Vibrational Spectroscopy for 2,5-Dichloropyrimidine

CHEN Heng-jie, FANG Wang, ZHANG Jia-wei, and CHEN Shuang-kou

Fourier transforms infrared (FT-IR) spectra in the range of 400~4 000 cm-1 was ollected for 2,5-dichloropyrimidine (2,5-DCP) in solid phase as well as in liquid phase using four sample preparation methods: KBr pressed (KBr), mineral oil (Nujol), attenuated total reflection (ATR) and melting (Liquid), while Fourier tran Fourier transforms infrared (FT-IR) spectra in the range of 400~4 000 cm^-1 was ollected for 2,5-dichloropyrimidine (2,5-DCP) in solid phase as well as in liquid phase using four sample preparation methods: KBr pressed (KBr), mineral oil (Nujol), attenuated total reflection (ATR) and melting (Liquid), while Fourier transforms Raman (FT-Raman) and laser Raman (Laser-Raman) spectra in the range of 80~3 200 cm^-1 was also recorded. To correctly interpret the experimentally obtained vibrational spectra, the geometry of 2,5-DCP was first optimized by applying 14 methods from density function theory (DFT) as well as second-order perturbation method (MP2), based on which its harmonic frequency, infrared intensity and Raman activity were obtained, followed by the conversion of Raman activity to Raman intensity. To consider the anharmonic effect, the perturbation calculation is performed near the equilibrium geometry to obtain the third and fourth-order force fields in normal coordinates, and the anharmonic vibration frequency and intensity of 2,5-DCP are obtained based on the vibration second-order perturbation (VMP2) theory. It is found that the anharmonic vibration frequencies calculated by B3LYP and B3PW91 have the smallest difference from the experimental values. Based on the preferred B3LYP method, the effect of the basis sets on the vibration frequency continued to be investigated, eight basis sets were adopted, and it was found that the difference between the 6-311++G(2pd, 2df) level and the experimental values was the smallest, with a root-mean-square error(RMSE) of 6.75 cm^-1 (4.63 cm^-1 under 22 vibration modes), the 6-311++G(d, p) greatly reduced the calculation time, while the accuracy of 6-311++G(d, p) is not much lost (6.79 cm^-1). In summary, the anharmonic vibrational spectra calculated based on the B3LYP method combined with the 6-311++G(2df, 2pd) basis set are the best choice for assigning the experimental vibrational spectra of the 2,5-DCP. Then, according to the optimal calculation results and the vibrational fundamental frequencies obtained by the scaling factor method, combined with the anharmonic vibrational intensity, the schematic diagram of the normal coordinates analysis, the potential energy distribution (PED) of the vibrations, and compared to the experimentally acquired infrared and Raman spectra, all fundamental frequencies and some overtones of the 2,5-DCP were assigned, and two vibrational couplings were found, One is caused between 3 054 cm^-1 and the combination tones of 1 554 and 1 540 cm^-1; the other is from the coupling between 1 132 cm^-1 and the sum frequency 793+351 cm^-1 and the difference frequency 1 370～230 cm^-1. Finally, the anharmonic vibrational spectra of 2,5-DCP under multiple isotopic substitutions were expected and the correctness of the attribution was checked again..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2795 (2024)

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Visible/Near Infrared Spectroscopic Modeling for Cadmium Contaminated Lettuce

ZHOU Lei-jinyu, ZHOU Li-na, CHEN Li-mei, KONG Li-juan..., QIAO Jian-lei and LI Ming-tang|Show fewer author(s)

To quickly and non-destructively monitor the degree of cadmium contamination in lettuce, visible-near infrared spectroscopy is used to classify cadmium contamination in lettuce. Lettuce leaves’ visible-near infrared reflectance spectra were collected to analyze the variation in reflectance spectra under different cadmi To quickly and non-destructively monitor the degree of cadmium contamination in lettuce, visible-near infrared spectroscopy is used to classify cadmium contamination in lettuce. Lettuce leaves’ visible-near infrared reflectance spectra were collected to analyze the variation in reflectance spectra under different cadmium pollution levels (0, 5, 10, 20 mg·kg^-1) in soil, with lettuce as the research subject. The spectral analysis reveals that within the wavelength range of 510 to 730 nm, the reflectance of lettuce leaves in the visible-near infrared spectrum decreases and then increases with the increase in cadmium content in the soil. Within the wavelength range of 730 to 799.53 nm, the reflectance of lettuce leaves under 5 and 20 mg·kg^-1 cadmium stress is higher than the CK, while under 10 mg·kg^-1 cadmium stress, the reflectance is lower than the control group. Additionally, an absorption valley was observed at 762.199 nm. In establishing a cadmium pollution monitoring model for lettuce, various preprocessing methods were applied to the raw spectra to improve the signal-to-noise ratio. These methods include smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), mean normalization (MN), SG+MSC, SG+SNV, SG+MN, SG+first derivative (FD), and SG+second derivative (SD). Based on the principal component analysis (PCA), dimensionality reduction was performed on the original spectra and spectra subjected to various preprocessing methods. Subsequently, the reduced data was divided into training and testing sets in a 4∶1 ratio. These sets were then used to establish classification monitoring models for cadmium pollution in lettuce by combining particle swarm optimization-random forest (PSO-RF), genetic algorithm-optimized support vector machine (GA-SVM), backpropagation neural network (BP-NN), extreme learning machine (ELM), and Naive Bayes, followed by analysis and comparison. The results demonstrate that among the different models, the PSO-RF (SG) model achieves the best recognition performance, followed by the GA-SVM (SG+FD) model and the ELM (MSC) model. The training accuracy of the PSO-RF (SG), GA-SVM (SG+FD), and ELM (MSC) models is 100%, while their testing accuracies are 100%, 83.33%, and 79.17% respectively. On the other hand, the BP-NN model and the Naive Bayes model perform relatively poorly. The training accuracy of the BP-NN (SNV) model is 42.72% with a testing accuracy of 50%. The Naive Bayes (SG+FD) model achieves a training accuracy of 71.84% and a testing accuracy of 83.33%. It indicates that applying visible-near infrared spectroscopy combined with particle swarm optimization random forest modeling can provide a novel approach for studying the monitoring of heavy metal contamination in lettuce..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2805 (2024)

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Discrimination of Apple Origin and Prediction of SSC Based on Multi-Model Decision Fusion

JIANG Xiao-gang, HE Cong, JIANG Nan, LI Li-sha..., ZHU Ming-wang and LIU Yan-de|Show fewer author(s)

Traceability of apple origin and prediction of apple SSC is of great practical significance, and the purpose of origin discrimination and SSC prediction is achieved by modeling. To overcome the limitations of a single model, the overall prediction performance is improved by combining the prediction results of multiple Traceability of apple origin and prediction of apple SSC is of great practical significance, and the purpose of origin discrimination and SSC prediction is achieved by modeling. To overcome the limitations of a single model, the overall prediction performance is improved by combining the prediction results of multiple models. Near-infrared spectroscopy (NIRS) detection technology combined with a multi-model decision fusion strategy is utilized for traceability identification of apple origin and prediction of apple SSC to verify the feasibility of the theoretical method.The spectra of apple samples were collected using a handheld near-infrared detector, and apple origin discrimination models were established using the sample spectra in combination with the random forest (RF) method, the partial least squares discriminant analysis (PLS-DA) method, and the support vector machine (SVM) method. The predictions from the three discrimination models are then used in a voting system decision fusion method to generate new discriminant results. Actual values of SSC were collected for all apple samples, and SSC prediction models were developed using the sample spectra and actual values of SSC combined with the random forest (RF) method, the partial least squares regression (PLSR) method, and the support vector regression (SVR) method. Using the outputs of the three regression models, the new SSC prediction is output through the weighting method decision fusion strategy. When the voting decision-making method was not used, the discrimination modeling using the RF method was the most effective among the three qualitative modeling methods, with a prediction accuracy of 88.71%. The worst prediction was made using the SVM method, with a prediction accuracy of 77.43%. After using the voting decision method, the accuracy of apple origin identification reached 93.42%, and its prediction precision and recall also reached a double high, both above 85%. All three quantitative modeling methods gave good results in predicting apple SSC without using the weighted decision fusion method. All three methods predicted coefficients of determination around 0.87 and root mean square errors of prediction (RMSEP) around 0.78. The prediction of the SSC level was improved after using the weighted decision fusion method. The prediction coefficient of determination was 0.91, and the RMSEP was 0.66. The feasibility of the proposed method was confirmed by using the multi-model decision fusion method in the identification of apple origin and the prediction of apple SSC to improve the accuracy of apple origin discrimination and the precision of the prediction of apple SSC. Meanwhile, the handheld NIR detector combined with the multi-model decision fusion method provides a new high-precision prediction approach for on-site non-destructive testing analysis..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2812 (2024)

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Near-Infrared Prediction Models for Quality Parameters of Culture Broth in Seed Tank During Citric Acid Fermentation

MU Liang-yin, ZHAO Zhong-gai, JIN Sai, SUN Fu-xin, and LIU Fei

The quality of the bacterial strain cultivation in the seed tank during the citric acid fermentation process directly affects the fermentation level. Hence, it is crucial to accurately and rapidly detect the quality parameters of the culture solution in the seed tank. However, these parameters are currently largely mea The quality of the bacterial strain cultivation in the seed tank during the citric acid fermentation process directly affects the fermentation level. Hence, it is crucial to accurately and rapidly detect the quality parameters of the culture solution in the seed tank. However, these parameters are currently largely measured manually, which does not meet real-time monitoring and precise control requirements. This paper builds a chemometric model for measuring the total acidity (TA) and reducing sugars (RS) in the seed tank’s culture solution, based on near-infrared spectroscopy. Initially, the original spectra were analyzed, and to eliminate random noise and reduce batch variability effects on the sample spectra, the SG-DT method of smoothing (SG) and detrending (DT) were sequentially used for spectral preprocessing. Then, the Interval Partial Least Squares (iPLS) method was used for feature wavelength selection, the effect of different division intervals on the selection result was discussed, and the optimal division interval number for the target quality parameter of TA was determined to be 21, with 495 feature wavelengths. For RS, it was 20, with 361 feature wavelengths. Subsequently, the correlation between spectral variables and quality parameter variables was analyzed. A BP network was introduced to establish the calibration model for TA, and both PLSR and BP networks were used to establish the calibration model for RS, and model prediction effects were compared to determine the optimal model. Finally, the optimal prediction model for TA based on the BP network had an

R p 2

of 0.808 5 and an RMSEP of 0.123 4. The model prediction effect of RS based on the BP network was superior to the PLSR model, with an

R p 2

of 0.964 7 and RMSEP of 0.173 9. This paper has realized online prediction of multiple quality parameters during the bacterial strain cultivation process in the complex citric acid fermentation system, providing a basis for real-time intelligent control of the fermentation process..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2819 (2024)

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Research and Characterization of Laboratory Dual Dispersions and DuralFocuses X-Ray Fluorescence Spectrometer Instrument Development

SHEN Ya-ting, HAN Ling-yun, CHEN Jun-ru, GUO Rong..., ZHU Shuai, LI Ying-chun, MA Sheng-feng, ZHU Yun, ZHANG Bao-ke and LUO Li-qiang|Show fewer author(s)

X-ray emission spectroscopy is an in situ non-destructive method to obtain chemical species of elements, and the development of laboratory-type XES devices worldwide is still in the exploratory stage. Energy-Dispersive X-Ray Fluorescence Spectrometer (EDXRF) and Wavelength-Dispersive X-Ray Fluorescence Spectrometer (WD X-ray emission spectroscopy is an in situ non-destructive method to obtain chemical species of elements, and the development of laboratory-type XES devices worldwide is still in the exploratory stage. Energy-Dispersive X-Ray Fluorescence Spectrometer (EDXRF) and Wavelength-Dispersive X-Ray Fluorescence Spectrometer (WDXRF) are widely used in geological, environmental and archaeological fields. However, although EDXRF has a simple structure and can realize rapid, non-destructive detection of multiple elements, the resolution is not ideal, the spectral line interference is serious, and the detection limit is poor. Although WDXRF can distinguish most elements with spectral overlapping characteristics in conventional applications, the structure and cost are complicated. To explore the development of laboratory-type XES devices, this study synthesizes the performance advantages of the Dual Dispersions and Dural Focuses X-Ray Spectrometer (DDF-XRS) design concept and successfully develops the principle prototype. The experimental data and analysis results show that this DDF-XRS spectrometer combines the advantages of both micro EDXRF and WDXRF with a simple structure, good signal-to-noise ratio, high resolution, and low detection limit characteristics. Through the wavelength-energy double dispersion technology, X-rays are firstly diffracted by the crystal to undergo wavelength dispersion, thus obtaining monochromatic light, and at the same time, the energy dispersion of the silicon drift detector can be used to observe the degree of monochromaticity, reduce the risk of misjudgment of spectral line interference, and improve the accuracy and reliability of the analysis results, which overcomes the limitations of the complex structure for WDXRF and the insufficient energy resolution of the EDXRF, and highlights the necessity and superiority of the double dispersion. This technology overcomes the limitations of WDXRF multi-element peak determination and EDXRF energy resolution, emphasizing the necessity and superiority of double dispersion. At present, the resolution of the DDF-XRS spectrometer is 45 eV, which can reduce the spectral line overlap of transition metal K_β to K_α peaks; at the same time, it significantly reduces the background of the continuum spectrum, and the optimal signal-to-noise ratio is >1 000; and the detection limit of Cr in the determination of geologic samples can be up to 0.26 mg·kg^-1. With the application of DDF-XRS, the transition metal K_α1 and K_α2 spectral lines can be resolved to a certain extent, and it is expected that the resolution can be further improved by combining with linear or two-dimensional array detectors to realize the determination of X-ray emission fingerprint spectra to obtain the chemical forms of the analyzed elements. Since the current crystal properties cannot fully resolve the overlapping spectral lines of transition metals, the search for curved crystals with high fractionation capacity and diffraction intensity will be the next research focus..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2827 (2024)

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Study on the Influence of Argon Environment on the Determination of C, P and S Elements in Steel by Laser-Induced Breakdown Spectrometry

ZHANG Jia-wei, WU Dong-sheng, ZHOU Yang, LI Yang, and SUN Lan-xiang

Laser-induced breakdown spectroscopy has showngreat-potential for application in the online analysis of molten steel composition. However, detecting the important C, P, and S component elements in molten steel has been challenging because their effective emission lines cannot be transmitted over long distances in the a

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2834 (2024)

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Remote Sensing Inversion of Soil Carbon Emissions in Desertification Mining Areas

LIU Ying, LIU Yu, YUE Hui, BI Yin-li..., PENG Su-ping and JIA Yu-hao|Show fewer author(s)

With the policy of “carbon peaking and carbon neutrality” put forward in China, carbon emissions in the mining area have become the focus of attention. The study was based on soil samples taken from the mine areas, combined with 6 mathematical transformation methods (R, R, Log(1/R), 1st, MSC, SNV) and spectral feature With the policy of “carbon peaking and carbon neutrality” put forward in China, carbon emissions in the mining area have become the focus of attention. The study was based on soil samples taken from the mine areas, combined with 6 mathematical transformation methods (R,

R

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2840 (2024)

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Quantitative Inversion Model of Soil Heavy Metals Zn and Ni Based on Fractional Order Derivative

JIANG Yu-heng, YAN Bo, ZHUANG Qing-yuan, WANG Ai-ping..., CAO Shuang, TIAN An-hong and FU Cheng-biao|Show fewer author(s)

Integer-order derivative methods (such as 1st or 2nd order) are traditional preprocessing methods for soil heavy-metal inversion models, which ignore the fractional-order spectral reflectance information associated with the target variable. Fractional order derivative (FOD) can flexibly select the differential order to Integer-order derivative methods (such as 1st or 2nd order) are traditional preprocessing methods for soil heavy-metal inversion models, which ignore the fractional-order spectral reflectance information associated with the target variable. Fractional order derivative (FOD) can flexibly select the differential order to enhance the spectral signal effectively. This study focused on the farmland soil in Mojiang Hani Autonomous County, Pu’er City, Yunnan Province, China. Sixty-one soil hyperspectral reflectance information and soil heavy metal content data (zinc and nickel) were measured. The spectral reflectance information underwent 0 to 2 fractional-order derivative preprocessing with intervals of 0.05. The preprocessed spectral reflectance information at each order was input into the Successive Projections Algorithm (SPA) to select characteristic bands. Subsequently, three soil heavy metal prediction models were separately established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Bagging methods. The results show that after the fractional order derivative processing from 0 to 2 orders (41 orders in total with an interval of 0.05), the overall spectral intensity gradually weakens and gradually approaches zero with the increase of fractional orders. The spectral absorption band gradually narrows, and the differences between different spectral curves gradually decrease. As the derivative order increases, more abundant peaks and valleys are produced. The best-order models based on fractional derivatives are better than the original spectral model and the integer order model, and most of the better orders of the model are concentrated in low-order fractional orders. For heavy metal zinc, the best prediction model accuracy was achieved by the RF model of 0.75 order (R²=0.675, RMSE=6.149, RPD=1.755), followed by the Bagging model of 0.75 order (R²=0.633, RMSE=6.534, RPD=1.652), and the lowest was achieved by the PLSR model of 0.25 order (R²=0.551, RMSE=7.230, RPD=1.493). For the heavy metal nickel, the best prediction model accuracy was the RF model of order 0.80 (R²=0.854, RMSE=127.823, RPD=2.618), the Bagging model of order 0.80 was the next best (R²=0.841, RMSE=133.304, RPD=2.510), the PLSR model of order 0.40 lowest (R²=0.762, RMSE=163.162, RPD=2.051). Visible, the nonlinear models (RF and Bagging) constructed based on FOD preprocessing and SPA dimensionality reduction in this study have certain applicability in estimating heavy metal content in farmland soil. They can be a reference for predicting heavy metal content in similar regions..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2850 (2024)

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Near-Infrared Random Forest Classification and Recognition Based on Multi-Feature Fusion

XIE Xi-ru, LUO Hai-jun, LI Guo-nan, FAN Xin-yan..., WANG Kang-yu, LI Zhong-hong and WANG Jie|Show fewer author(s)

To solve the problem of low data classificationaccuracy and poor model stability of functional near-infrared brain functional imaging signals in brain-computer interfaces, this paper proposes to study the changes of cerebral blood oxygen concentration in the prefrontal brain region during the stimulation period and car To solve the problem of low data classificationaccuracy and poor model stability of functional near-infrared brain functional imaging signals in brain-computer interfaces, this paper proposes to study the changes of cerebral blood oxygen concentration in the prefrontal brain region during the stimulation period and carry out a study of the state binary classification identification of task and rest on the experimental data of the prefrontal brain region, and take the extracted single feature and multi-feature fusion as the inputs of the model, respectively, and validate the results of the classification through the model The conjecture that multi-feature fusion can improve the classification accuracy to some extent. Firstly, this paper designs an experimental paradigm for the prefrontal brain region: word fluency cognition experiment. The device used to collect the data is NirSmart, a Danyang Huichuang device from China, with a sampling rate of 11 Hz and a spatial resolution of 3 cm. The device uses avalanche diode and ultramicro photo detection technology, and the sensitivity is up to 0.1 pW. After that, the collected data are preprocessed using the Homer2 toolbox, the features are extracted using MATLAB, and the Random Forest model is constructed using two metrics of feature importance and error curve to evaluate the classification results. Importance and error curves were used to evaluate the performance of the model. Finally, the average of 20 runs of the random forest model was used as the final classification result. The experimental results show that multiple features can improve the final classification result compared with a single feature. The best second classification result for the case of three-feature fusion is 93.84%, 2.32%, 4.25%, and 5.33%, higher than the single-feature mean, slope, and peak-to-peak value, respectively. From the results of the experimental data, it can be seen that multi-feature fusion can improve the accuracy of near-infrared brain functional imaging classification to a certain extent. The performance of the random forest model is stable, which is expected to solve the previous problems of low classification accuracy and poor model stability and promote the development and application of brain-computer interface systems..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2858 (2024)

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Differences in the Effects of Night Blue Light and White Light Exposure on Liver Circadian Rhythm and Inflammatory Response

LIANG Xin-yue, ZENG Jing, AI Chang-peng, LUO Jie-cheng..., FAN Xiao-jing, WU Sheng-nan and GU Ying|Show fewer author(s)

The regulation of circadian rhythm by light is a non-imaging visual function.Non-imaging visual effect cells, mainly intrinsically photosensitive retinal ganglion cells, significantly impact the body’s biological rhythm, metabolism, cognition, etc., primarilymediated by blue light. This study aimed to explore the effec

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2865 (2024)

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Estimation of Leaf Physical and Chemical Parameters Based on Hyperspectral Remote Sensing and Deep Learning Technologies

YUE Ji-bo, LENG Meng-die, TIAN Qing-jiu, GUO Wei..., LIU Yang, FENG Hai-kuan and QIAO Hong-bo|Show fewer author(s)

Plant leaf physical and chemical parameters, such as leaf chlorophyll content, carotenoid content, water content, protein content, and Carbone-based constituents content, are crucial for accurately monitoring plant growth status. In recent years, with the rapid development of deep learning technology in vegetation remo Plant leaf physical and chemical parameters, such as leaf chlorophyll content, carotenoid content, water content, protein content, and Carbone-based constituents content, are crucial for accurately monitoring plant growth status. In recent years, with the rapid development of deep learning technology in vegetation remote sensing, the combined use of deep learning and hyperspectral remote sensing for plant leaf parameters estimation has been widely applied; however, currently, few leaf parameters estimation works based on the combination of deep learning and hyperspectral remote sensing technology have been conducted. This study explores the possibility of estimating leaf chlorophyll, carotenoid, water, protein, and Carbone-based constituent content by combining hyperspectral remote sensing and deep learning techniques. The main work of this paper is to propose a leaf physical and chemical parameter estimation method based on hyperspectral remote sensing and deep learning. Firstly, this study determines the sensitive spectral regions of multiple vegetation leaf physical and chemical parameters based on the PROSPECT-PRO radiative transfer model. Then, we designed a LeafTraitNet deep learning model; the LeafTraitNet model is trained and tested based on the lobex93 dataset, and a high-precision leaf parameter estimation result is obtained. The conclusions of this study are as follows: (1) It is vital to select leaf spectral absorption features based on the PROSPECT-PRO radiative transfer model. The leaf chlorophyll (434 and 676 nm) and carotenoids (445 nm) spectral absorption regions are located in the visible bands. However, the absorption regions with the most significant correlation coefficients (absolute values) are not their maximum spectral absorption bands, which the mutual influence of leaf chlorophyll and carotenoid absorptions may cause. (2) The leaf water spectral absorption regions are mainly located in the bands 950～2 500 nm, which overlaps with the spectral absorption regions of leaf protein and carbon-based component content, thus weakening the hyperspectral remote sensing estimation accuracy of the latter. The correlation coefficients between leaf protein (and carbon-based component content) and the spectral reflectance in the 950～2 500 nm range are notably lower than the leaf water. The correlation coefficients analysis results of leaf parameters and hyperspectral for the PROSPECT-PRO radiative transfer model and lobex93 dataset show similar correlation coefficients. (3) The three traditional methods and the LeafTraitNet model can be ranked as LeafTraitNet (total nRMSE=0.84)<RF (total nRMSE=1.59)<MLP (total nRMSE=1.73)<MLR (total nRMSE=1.74), which means the leaf parameters estimation performance is notably higher than RF, MLP, and MLR. However, further experiments are needed to validate the LeafTraitNet model at the canopy scale..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2873 (2024)

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Fluorescence Spectral Characteristics of Forest Soil Dissolved OrganicMatter and Its Correlation With Heavy Metal

SHI Chuan-qi, LI Yan, HU Yu, MENG Ling-bo..., JIN Liang and YU Shao-peng|Show fewer author(s)

Dissolved organic matter (DOM) is the most active component in soil organic matter, affecting heavy metal’s migration and transformation. Studying their correlation is of great value for environmental monitoring and pollution assessment. In this study, the topsoil (0~30 cm) was collected from the typical coniferous and Dissolved organic matter (DOM) is the most active component in soil organic matter, affecting heavy metal’s migration and transformation. Studying their correlation is of great value for environmental monitoring and pollution assessment. In this study, the topsoil (0~30 cm) was collected from the typical coniferous and broad-leaved mixed forest in Heilongjiang Province Maolan’gou National Nature Reserve, located in Xiaoxing’anling, China, and three-dimensional fluorescence spectroscopy- parallel factor analysis method was used to reveal the characteristics of forest soil DOM fluorescence spectra, and further to analyze the correlation between DOM fluorescence component and heavy metal content. The results showed that the forest soil DOM fluorescence index ranged from 1.468 to 1.635, with an average value of 1.531, indicating the DOM source had both autogenic and exogenous characteristics. The biological index ranged from 0.563 to 0.646, with an average value of 0.603, indicating a low contribution rate of recent autogenic source; The humification index ranged from 4.607 to 8.993, with an average value of 6.491, indicating a low degree of humification. Threetypes of fivekinds of fluorescent components were identified from the forest soil DOM fluorescence spectrum, including humus-likesubstances ［ultraviolet fulvic acid-like component (C1) and visible fulvic acid-like component (C2)］, humic acid-like substance (humic acid component, C3), and proteinlike substance ［tryptophanlike component(C4) and tyrosinelike component (C5)］. Humus-like substances accounted for the largest proportion of total components (60.12%), significantly higher than humic acid-like and protein-like substances, while humic acid-like substances accounted for the smallest proportion (11.25%) of total components. C1, C2, and C3 had a significant positive correlation, while C5 was significantly negatively correlated with the other four fluorescent components. There was a significant correlation between the fivekinds of fluorescent components and the fluorescence index, respectively, with only C5 showing a positive correlation with the fluorescence index.The forest soil heavy metal contentswere significant differences in the spatial distribution, with significant positive correlations between As and Cr, Cu and Zn, Ni and Zn, Hg and Pb, and significant negative correlations between Hg and Cu, Ni and Zn, Cr, and Pb, respectively.The correlationsamong the three types of fluorescent substances, Cr and Pb, were not significant, but all fluorescent substances had a significant correlation with Zn. Moreover, there was a significant correlation between humus-like substances and Cu and Hg; humic acid-like substances and Cu, Hg, and Ni; protein-like substances and Ni, respectively.The results of this study provide basic data for monitoring the forest soil environment of Maolan’gou National Nature Reserve and provide references for the assessment of heavy metal pollution in the soil of typical coniferous and broad-leaved mixed forests in Xiaoxing’anling..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2884 (2024)

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Wheat Yield Prediction Based on Continuous Wavelet Transform and Machine Learning

FAN Jie-jie, QIU Chun-xia, FAN Yi-guang, CHEN Ri-qiang..., LIU Yang, BIAN Ming-bo, MA Yan-peng, YANG Fu-qin and FENG Hai-kuan|Show fewer author(s)

Timely and accurate crop yield estimation is crucial for making informed decisions regarding crop management and assessing food security. This study aims to develop a method that combines continuous wavelet transform (CWT) with machine learning to predict wheat yield accurately. This research is based on the spectral d Timely and accurate crop yield estimation is crucial for making informed decisions regarding crop management and assessing food security. This study aims to develop a method that combines continuous wavelet transform (CWT) with machine learning to predict wheat yield accurately. This research is based on the spectral data of canopy height and yield data obtained from two-year field trials conducted during wheat growth’s flowering and filling stages in 2020—2021. Initially, CWT is employed to extract three wavelet features (WFs), namely Bortua-WFs based on the Bortua method, 1% R²-WFs representing WFs along with the top 1% determination coefficient for wheat yield, and SS-WFs encompassing all WFs under a single decomposition scale. Subsequently, three machine learning algorithms Random Forest (RF), K-nearest neighbor (KNN), and extreme gradient Lift (XGBoost) are utilized to construct the yield prediction model. Finally, optimal spectral features are selected using the same methodology for modeling and comparison purposes. The results demonstrate that: (1) all three WFs models combined with machine learning methods perform well, with higher accuracy and stability observed in the model built based on Boruta-WFs. (2) Compared to the spectral characteristic model, improved accuracy was achieved by utilizing Bortua-WFs at each growth stage; specifically, an increase in R² accuracy by 17.5%, 4%, and 39.6% during flowering stage, as well as an increase by 8.4%, 5.6%, and 16.9% during filling stage respectively were observed across different models.(3) The estimation model at the grouting stage outperformed that at the flowering stage; particularly noteworthy was the performance of XGBoost when combined with Bortua-WFs, which yielded an R² value of 0.83 accompanied by an RMSE value of 0.78 t·ha^-1. This study compared the performance of different characteristics and methods. It determined the best model accuracy under different schemes, which can provide technical references for the accurate wheat yield prediction by spectral technology..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2890 (2024)

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Optical System Design of High-Precision Line-Scanning Spectral Confocal Displacement Sensor

YANG Wei-guang, ZHANG Zhou-feng, QI Mei-jie, YAN Jia-yue, and CHENG Mo-han

Spectral confocal displacement sensor is a new geometric precision measurement sensor with high accuracy, high efficiency, and non-contact technical advantages. It is now widely used in measuring micro or macro geometric quantities. Conventional geometric measurement sensors use contact mechanical probes, which cause d

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2900 (2024)

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Identification and Detection of Multi-Component Trace Gases Based on Near-Infrared TDLAS Technology Based on SVM

FANG Xiao-meng, WANG Hua-lai, XU Hui, HUANG Meng-qiang, and LIU Xiang

Based on tunable semiconductor laser absorption spectroscopy (TDLAS) and frequency division multiplexing (FDM) method, a near-infrared multi-component trace gas identification and detection system based on support vector machine (SVM) classification was studied. When laser spectroscopy technology characterizes gas abso Based on tunable semiconductor laser absorption spectroscopy (TDLAS) and frequency division multiplexing (FDM) method, a near-infrared multi-component trace gas identification and detection system based on support vector machine (SVM) classification was studied. When laser spectroscopy technology characterizes gas absorption spectral lines, the absorption capacity of gas in the near-infrared band is lower than that in the far-infrared band. The absorption signal of gas detected by single-band laser spectrum is weak, and each gas component interferes with each other greatly. To improve detection accuracy, accurately identify gas components and perform multi-component detection at the same time, based on tunable semiconductor laser absorption spectroscopy technology, the frequency division multiplexing near-infrared TDLAS technology method is used, and the SVM classification algorithm is used to perform the real-time detection process of mixed gases. It effectively avoids cross-interference of various gases and realizes trace detection of eight gas markers: nitric oxide NO, hydrogen sulfide H₂S, ammonia NH₃, nitrogen dioxide NO₂, acetylene C₂H₂, carbon dioxide CO₂, methane CH₄, and hydrogen chloride HCl. When eight lasers work simultaneously, the system controls the band-pass filter to perform time-sharing filtering. It sequentially transmits the second harmonic data after differential phase locking to the host computer for real-time display. The recognition rate is over 96.3%, and the average content prediction accuracy is higher than 99.6%. It has achieved high-precision detection results with the lowest detection limit of CH₄ being 0.01 μL·L^-1, NO₂ being 0.05 μL·L^-1, and C₂H₂ being 0.03 μL·L^-1, and the detection limits of other gases are below 5 μL·L^-1. Conduct anti-interference analysis and detection lower limit analysis on the multi-channel detection of the system to verify that the system can achieve high-precision concentration detection of mixed gases when the system is operating stably. This system uses a distributed feedback laser drive and lock-in amplifier combined with the SVM algorithm model of data processing to realize multi-component trace gas identification and detection of near-infrared TDLAS technology, which can meet the trace level detection of trace gases and provide ultra-low performance for the future. The detection of concentration mixed gases is of very important significance..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2909 (2024)

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Classification of Hybrid Convolution Hyperspectral Images Based on Attention Mechanism

LIU Yu-juan, LIU Yan-da, YAN Zhen, ZHANG Zhi-yong..., CAO Yi-ming and SONG Ying|Show fewer author(s)

Hyperspectral Imagery (HSI), based on its high-resolution spatial and spectral information, has important applications in military, aerospace, civil, and other remote sensing fields, which has great research significance. Deep learning has the advantages of strong learning ability, wide coverage, and strong portability Hyperspectral Imagery (HSI), based on its high-resolution spatial and spectral information, has important applications in military, aerospace, civil, and other remote sensing fields, which has great research significance. Deep learning has the advantages of strong learning ability, wide coverage, and strong portability, which has become a hot spot in the research of high-precision hyperspectral image classification. Convolutional Neural Networks (CNN) are widely used in the research of hyperspectral image classification because of their powerful feature extraction ability and have achieved effective research results. Still, such methods are usually based on 2D-CNN or 3D-CNN alone. For the single feature of hyperspectral image, the complete feature information of hyperspectral data cannot be fully utilized. Secondly, the local feature optimization of the corresponding extraction network is good, but the overall generalization ability is insufficient. There are limitations in the deep mining of spatial and spectral information of HSI. Because of this, this paper proposes a Hybrid Spectral Convolutional Neural Network Attention Mechanism (HybridSN_AM) based on attention mechanism. The principal component analysis method is used to reduce the dimension of hyperspectral images, and the convolutional neural network is used as the main body of the classification model to screen out more distinguishable features through the attention mechanism so that the model can extract more accurate and more core joint space-spectral information, and realize high-precision classification of hyperspectral images. The proposed method was applied to three datasets: Indian Pines(IP), the University of Pavia (UP), and Salinas (SA). The experimental results show that the overall classification accuracy, average classification accuracy, and kappa coefficient of target images based on this model are higher than 98.14%, 97.17%，and 97.87%. Compared with the conventional HybridSN model, the classification accuracy of the HybridSN_AM model on the three data sets increased by 0.89%, 0.07%，and 0.73%, respectively. It effectively solves the problem of hyperspectral image joint space-spectral feature extraction and fusion, improves the accuracy of HSI classification, and has strong generalization ability. It fully verifies the effectiveness and feasibility of the attention mechanism combined with a hybrid convolutional neural network in hyperspectral image classification, which has important theoretical value for developing and applying hyperspectral image classification technology..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2916 (2024)

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Spectral Characteristics Identification Method for the Incubation Period of Chili Early Blight Disease

SHEN Meng-jiao, BAO Hao, and ZHANG Yan

Early blight of chili peppers is a common biological disaster that affects the safe production of chili peppers. It is characterized by suddenness and susceptibility and can easily cause significant economic losses. During the growth process of chili peppers, scientific monitoring and early warning of disease infestati Early blight of chili peppers is a common biological disaster that affects the safe production of chili peppers. It is characterized by suddenness and susceptibility and can easily cause significant economic losses. During the growth process of chili peppers, scientific monitoring and early warning of disease infestation during the incubation period is an important prerequisite for ensuring the healthy growth of crops. This paper establishes the spectral characteristics discriminative model of crop disease incubation period using the hyperspectral image with the working band of 400~1 000 nm and the spectral similarity measurement method. Continuous and dynamic monitoring of hyperspectral images of pepper leaves and healthy leaves inoculated with early blight pathogens at different infection stages using a hyperspectral imager.Extract the average spectrum of the region of interest from a series of hyperspectral images collected in the experiment and preprocess it using convolutional smoothing, multivariate scattering correction, and maximum minimum normalization method (SG-MSC-MMN). Then, two measures, spectral angle cosine correlation coefficient, and Chebyshev distance, are proposed as spectral characteristics evaluation parameters for the incubation period of early blight. Finally, principal component analysis (PCA) was used to verify the results of the spectral characteristics discriminative model of the incubation period to realize the visual distribution of the incubation period of samples.The experimental results show that it is feasible to use the spectral angle cosine correlation coefficient and Chebyshev distance as the spectral characteristic evaluation parameters of the incubation period of early blight of pepper and establish the corresponding discriminative model, respectively, and the earliest identifiable time of incubation period of early blight of pepper obtained from these two discriminative models is 24 hours after inoculation.According to the PCA drawing, the spatial distribution of health vaccination samples during inoculation for 24 hours was verified by the two spectral characteristics-based discriminative models for the incubation period proposed in this paper.The discriminative model of the incubation period of early blight of pepper established in this paper can be extended to monitor and identify the incubation period of other crop diseases and provide theoretical reference and method reference for scientific control of the incubation period of crop diseases..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2923 (2024)

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Study on Sugar Content Detection of Kiwifruit Using Near-Infrared Spectroscopy Combined With Stacking Ensemble Learning

GUO Zhi-qiang, ZHANG Bo-tao, and ZENG Yun-liu

In this study, we employ near-infrared spectroscopy with Stacking ensemble learning to perform non-destructive sugar content analysis in kiwifruit. Our research focuses on the “Yunhai No.1” kiwifruit variety from Hubei. Using an infrared analyzer, we gathered spectral data from 280 samples, spanning 1 557 wavelengths i In this study, we employ near-infrared spectroscopy with Stacking ensemble learning to perform non-destructive sugar content analysis in kiwifruit. Our research focuses on the “Yunhai No.1” kiwifruit variety from Hubei. Using an infrared analyzer, we gathered spectral data from 280 samples, spanning 1 557 wavelengths in the 4 000～10 000 cm^-1 range, and measured sugar content with a refractometer. Outliers were identified and excluded using a singular sample identification algorithm that combines Monte Carlo random sampling with a T-test. The SPXY algorithm was then employed to split the data into training and testing sets in a 4∶1 ratio. Data preprocessing involved multiple scattering corrections (MSC), Savitzky-Golay smoothing (SG), de-trending (DT), vector normalization (VN), and standard normal variable (SNV) transformations. Feature wavelengths were initially selected using uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and interval variable iterative space shrinkage approach (iVISSA), followed by a secondary selection with the successive projections algorithm (SPA) to remove collinear variables. To address the limitations of single models in generalization, we designed an integrated learning model using the Stacking algorithm. This model incorporated Bayesian ridge regression (BRR), partial least squares regression (PLSR), support vector regression (SVR), and artificial neural networks (ANN) as base learners, with linear regression (LR) serving as the meta-learner. We assessed the performance of various ensemble model combinations and analyzed the influence of base learners on ensemble performance using the Pearson correlation coefficient. Experimental results indicated that vector normalization was the most effective among the five preprocessing methods. The VN-CARS-PLSR model demonstrated superior performance, with

R p 2

of 0.805 and RMSEP of 0.498, identifying 177 feature wavelengths and reducing data volume by 88.6% compared to the original spectrum. Comparisons of different base learner combinations in the Stacking algorithm revealed that the PLS+SVR+ANN integrated model achieved the highest predictive accuracy, with

R p 2

of 0.853 and RMSEP of 0.433. The study concludes that the stacking ensemble model offers more comprehensive modeling capabilities and superior generalization than single models, providing valuable technical support for non-destructive sugar quality detection in kiwifruit..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2932 (2024)

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Red Soil Organic Matter Content Prediction Model Based on Dilated Convolutional Neural Network

DENG Yun, WU Wei, SHI Yuan-yuan, and CHEN Shou-xue

Soil Organic Matter (SOM) content is one of theimportant indicators used to measure soil fertility, and it is of great significance in accurately predicting SOM content from hyperspectral remote sensing images. Traditional machine learning methods require complex feature engineering. Still, they are not highly accurate

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2941 (2024)

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Effect of Pericyclic Substitutes on the Spectral Properties of Phthalocyanine in Different Substrates

QIN Jing, WANG Ke-dong, HU Xue-fang, CHEN Xiao-yuan, and LIU Chao

Metal phthalocyanines with different peripheralsubstituents were doped into silica (SiO2) gel glass matrix by the Sol-gel process to prepare metal phthalocyanine doped composite gel glass. The effects of the types and positions of different cyclic substituents in lead phthalocyanine series, aluminum phthalocyanine seri Metal phthalocyanines with different peripheralsubstituents were doped into silica (SiO₂) gel glass matrix by the Sol-gel process to prepare metal phthalocyanine doped composite gel glass. The effects of the types and positions of different cyclic substituents in lead phthalocyanine series, aluminum phthalocyanine series, and zinc phthalocyanine series on the UV visible absorption spectra of phthalocyanine in DMF solution and silica gel glass matrix were systematically studied to detect the existence status of phthalocyanine in different matrices and the influencing factors of the types and positions of different cyclic substituents. Firstly, the influence of the types of pericyclic substituents was studied: in DMF solution, the introduction of different pericyclic substituents -CP, -OAr, t-Bu can lead to the formation of a larger conjugated system of large π electrons, resulting in a red shift in the maximum electron absorption wavelength; In the composite gel glass, the doped metal phthalocyanine exists in the form of polymer, which makes the characteristic absorption peaks in the ultraviolet-visible spectrum become dispersed. Secondly, the influence of the position of cyclic substituents was studied: for the aluminum phthalocyanine series, the cyclic substituents OAr were studied, respectively α and β Bit time, α- AlTPOPcCl and Ultraviolet-visible absorption spectrum of β-AlTPOPcCl in DMF solution and silica composite gel glass. The results indicate that in DMF solution, due to the electron conjugation effect, α Compared to the substituent of position β, The characteristic peak has a redshift. In composite gel glass, The UV visible absorption spectra of α- AlTPOPcCl are almost identical to those in DMF solution, indicating that α- AlTPOPcCl exists as a monomer in these two matrices. The absorption spectra of β-AlTPOPcCl dispersed in the composite gel system, indicating that the phthalocyanine mainly existed in aggregate in the composite system. The above research results lay the experimental foundation for further research on the optical properties of phthalocyanine-doped composite gel glass and also laid the theoretical foundation for the research on nonlinear optical limiting materials and devices..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2953 (2024)

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FTIR and XPS Studies on the Effect of Ca²⁺ on the Fotation of Monazite by Octyl Hydroxamic Acid

LIU Rong-xiang, YANG Zhan-feng, LI Jie, CAO Zhao..., LI Qiang and LI Ji-chuan|Show fewer author(s)

The monazite flotation system often has many associated minerals such as calcite, fluorite, and dolomite. These associated minerals will dissociate a large amount of Ca2+ in the monazite ore, and Ca2+ often affects the pulp flotation environment. In this paper, the surface properties of monazite were studied by FTIR an The monazite flotation system often has many associated minerals such as calcite, fluorite, and dolomite. These associated minerals will dissociate a large amount of Ca²⁺ in the monazite ore, and Ca²⁺ often affects the pulp flotation environment. In this paper, the surface properties of monazite were studied by FTIR and XPS when Ca²⁺ affected the flotation of monazite by octyl hydroxamic acid. The research method is mainly through the solution chemical calculation of Ca²⁺, the flotation test of monazite, and the surface infrared spectroscopy ( FTIR ) and X-ray photoelectron spectroscopy ( XPS ) of monazite under flotation conditions. The solution chemical calculation of Ca²⁺ shows that an aqueous solution with increased pH value, Ca²⁺ exists in an ionic state, hydroxyl complex, and hydroxide compound, respectively. At the same time, the dominant components are Ca²⁺ and Ca(OH)⁺ at pH between 7 and 8. The results of monazite flotation showed that OHA could not fully float monazite without adding Ca²⁺, and the recovery rate was 75.37%. When the pH is (8±0.5), and the dosage of Ca Ca²⁺ is 3×10^-4 mol·L^-1, the flotation performance of OHA on monazite can be significantly improved, and the recovery rate reaches 96.48%. According to the solution chemistry calculation, Ca(OH)⁺ is the dominant component of activated monazite. When the Ca²⁺ dose is greater than 3×10^-4 mol·L^-1, the flotation recovery rate decreases greatly, indicating that the further increase of Ca²⁺ dose inhibits the flotation of monazite. Only a certain dose of Ca²⁺ effectively promotes the flotation of monazite, which may be attributed to the consumption of OHA concentration by Ca²⁺ dose, which in turn affects the flotation of monazite. Infrared spectroscopy analysis showed that under the action of Ca²⁺, two key new peaks appeared in the spectrum, one was the N—O—H bending vibration peak at 1 454 cm^-1, the other was the O—N stretching vibration peak at 880 cm^-1, and the organic peaks at 2 974 and 2 928 cm^-1. The —CH₃ and —CH₂— peaks were significantly enhanced, and these groups appeared to indicate that chemical adsorption occurred and the adsorption intensity was greater than that of pure monazite adsorption of OHA. XPS analysis showed that the relative content of N element on the surface of monazite was 0.61 % when only OHA was used to adsorb monazite. In comparison, the relative content of N element on the surface of monazite was 2.36% when OHA was used to adsorb monazite after Ca²⁺ treatment. It can be concluded that Ca²⁺ will promote the adsorption of OHA on the surface of monazite. It can be seen from the peak fitting that the addition of Ca²⁺ reacts with Ca(OH)⁺ to form O—Ca—OH groups on the cleavage surface of monazite to form O—Ca—OH groups, which can be used as a new adsorption site for adsorbing OHA. At the same time, the cerium atom of monazite and the two oxygen atoms on OHA form a five-membered chelate, which also acts as an adsorption site. It is concluded that there can be two adsorption sites on the surface of monazite. The active sites of Ca and Ce atoms on the surface of monazite can adsorb OHA, which is beneficial to the adsorption of OHA on the surface of monazite and forms a more uniform and dense OHA hydrophobic adsorption layer. This is why the performance of Ca²⁺ in OHA flotation of monazite is improved. This study helps to enrich the activation theory of metal ions in pulp and also confirms that effective mineral flotation separation depends not only on the strength of collector-mineral interaction but also on the chemical properties of the flotation solution to a large extent. Utilizing or controlling the surface reaction should be the main goal of developing a more efficient and economical flotation process..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2959 (2024)

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Potentiality of Synchronous Fluorescence Technology for Identificationof Pork Adulteration in Beef

LI Yue, LIN Yi-li, ZHOU Yun-yun, YANG Xin-ting..., WANG Zeng-li and LIU Huan|Show fewer author(s)

Beef is an important edible meat in China. In recent years, with the increasing demand for beef, the phenomenon of pork being impersonated or added to beef for sale has become increasingly serious, and there is an urgent need for simple and rapid detection methods to monitor adulteration behavior. This study analyzed t Beef is an important edible meat in China. In recent years, with the increasing demand for beef, the phenomenon of pork being impersonated or added to beef for sale has become increasingly serious, and there is an urgent need for simple and rapid detection methods to monitor adulteration behavior. This study analyzed the three-dimensional fluorescence spectra of beef and pork to determine the wavelength difference of synchronous fluorescence. A synchronous fluorescence spectrum with a fixed wavelength difference of 160 nm was used to qualitatively distinguish and quantitatively analyze the doping of beef with pork. The discriminant accuracy of the test set, verification set, and prediction set samples are taken as the evaluation index of the qualitative Discriminative model: Correlation coefficient (r), corrected Root-mean-square deviation (RMSEC) and predicted Root-mean-square deviation (RMSEP) were used as the evaluation indicators of the quantitative analysis model. The experimental results show a significant difference in the three-dimensional fluorescence spectra between beef and pork. Beef has fluorescence peaks at Ex/Em values of 270/320, 330/400, 350/500, 430/515 and 410/570 nm, while pork has three fluorescence peaks at Ex/Em values of 270/320, 330/400 and 430/515 nm. By setting the synchronous fluorescence wavelength difference to 160 nm, three fluorescence peaks of beef can be collected, with two of them located at the peak. The correction set accuracy of the SVM qualitative Discriminative model for beef, pork, and adulterated meat was 97.56%, and the prediction accuracy was 92.31%. The PLS prediction model for pork addition in beef without treatment, MSC treatment, and SNV treatment was compared. The PLS model without treatment was the best, with r_c, r_p, RMSEC, and REMSP reaching 0.978 6, 0.959 0, 0.059 7, and 0.092 7, respectively. Therefore, the qualitative discrimination and quantitative analysis detection model for beef adulterated pork based on synchronous fluorescence technology combined with SVM and PLS has a high recognition rate and detection accuracy, which can accurately and quickly detect whether pork is adulterated in beef..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2968 (2024)

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Application of Generalized Regression Neural Network With Ultraviolet-Visible Spectrometry Methods for Detection of Extra Virgin Olive Oil

YUAN Yuan, and ZHANG Jin

With the continuous prosperity of China’s economy, people have put forward higher requirements for material living standards. Food that prevents diseases and improves physical function has become a “hot spot” in the consumer market. Oil can provide energy for the human body. Edible oil is one of the major ways for huma With the continuous prosperity of China’s economy, people have put forward higher requirements for material living standards. Food that prevents diseases and improves physical function has become a “hot spot” in the consumer market. Oil can provide energy for the human body. Edible oil is one of the major ways for human beings to get oil, and high-quality vegetable oil contains substances that are more beneficial to human health, such as monounsaturated fatty acids, polyphenols, squalene, vitamin E, and other nutrients. Because of the physical cold pressing process, extra virgin olive oil keeps almost all the nutrients in its olive fruit, and the oleic acid content is as high as 70%. Therefore, although an “imported product”, extra virgin olive oil has been a “favorite” in the vegetable oil market since it entered the Chinese market, and its price is also significantly higher than ordinary vegetable oil on the market. Driven by interests, the phenomenon of making and selling fake super virgin olive oil has been repeatedly banned, and the means of making and selling fake olive oil have been constantly updated and iterated, resulting in the repeated prohibition of fake and inferior products in the domestic olive oil market. Adulterated oil products will not only harm the lives and property of consumers but also affect the production and sales of legitimate operators, disrupt the sales market, destroy the market order, and affect the public’s recognition of super virgin olive oil. The adulteration of vegetable oil is one of the urgent problems facing food safety at present. To realize the rapid, accurate, and low-cost identification and detection of the adulteration of vegetable oil and extra virgin olive oil, a method for qualitative and quantitative analysis of vegetable oil based on a generalized regression neural network and UV-Vis spectrum is proposed. Because the generalized regression neural network performs well in learning speed and nonlinear mapping ability, and the diffusion factor is the only optimization parameter of the network, it does not need backpropagation and repeated iteration. Compared with other detection technologies, UV-Vis technology has overwhelming advantages in terms of the detection cycle, stability, and low maintenance cost. This method has achieved 100% discrimination in the qualitative identification of vegetable oil and achieved the results that the determination coefficient (R²) is better than 0.988 75 and the root mean square error (RMSE) is better than 0.038 33 in the quantitative detection of extra virgin olive oil adulteration. The results showed that the model showed excellent predictive ability in identifying vegetable oils and in the quantitative detection of adulteration in extra virgin olive oil. Therefore, the method based on a generalized regression neural network algorithm and UV-Vis spectrum has important potential for application in vegetable oil’s qualitative and quantitative detection..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2973 (2024)

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A New Method for Determination of Eburicoic Acid in Fomes Officinalis Ames by NIR Combined With PLS

XIE Yu-yu, CHEN Zhi-hui, HOU Xue-ling, and LIU Yong-qiang

The traditional method for determining the content of Eburicoic acid is HPLC, which is inefficient and cumbersome to operate. To achieve rapid and non-destructive monitoring of Eburicoic acid, this paper attempted to establish a partial least squares (PLS) regression model based on near-infrared spectroscopy (NIR) to p

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2981 (2024)

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Spectral Reflectance Reconstruction Based on Multi-Target Screening Stacking Regression

LI Ri-hao, MA Yuan, and ZHANG Wei-feng

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2988 (2024)

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Estimation of Chlorophyll Content in Spartina Alterniflora Leaves Basedon Continous Wavelet Transformation and Random Forest Algorithm

GUAN Cheng, LIU Ming-yue, MAN Wei-dong, ZHANG Yong-bin..., ZHANG Qing-wen, FANG Hua, LI Xiang and GAO Hui-feng|Show fewer author(s)

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 2993 (2024)

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[in Chinese]

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 10, 1 (2024)

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