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Measurement of potentially toxic elements in the soil through NIR, MIR, and XRF spectral data fusion

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 187卷 106257-106257页

作者： Li, Fang Xu, Li You, Tianyan Lu, Anxiang Beijing Res Ctr Agr Stand & Testing Beijing 100097 Peoples R China Jiangsu Univ Sch Agr Engn Inst Agr Engn Zhenjiang 212013 Jiangsu Peoples R China

This paper aims to investigate the feasibility combination of X-ray fluorescence (XRF), near-infrared (NIR), and mid-infrared (MIR) sensors for the detection of seven key monitoring elements in the soil. Two strategies for data fusion were adopted: (i) the XRF characteristic bands were fused with all spectral data of NIR and MIR separately, and (ii) the XRF characteristic bands were fused with the characteristic bands of NIR and MIR, respectively. Also, different feature extraction methods were compared. The best feature extraction methods for XRF-NIR and XRFMIR models were principal component analysis (PCA) and successive projections algorithm (SPA). The modeling results showed that strategy (ii) showed better performance, and the XRF-MIR model provided more accurate results than the XRF-NIR model. Both XRF-MIR and XRF-NIR methods improved the accuracy of As, Cr, Cu, Ni, and Zn. The XRF-MIR obtained the best predictions, and the determination coefficients (R2) were 0.93, 0.98, 0.98, 0.95, and 0.98. For Pb and Cd, the measurement could be obtained by XRF alone, and the corresponding R2 were both 0.98. The results confirmed that sensor fusion can effectively improve the accuracy of the spectrometer in detecting metal elements in the soil.

关键词： X-ray fluorescence Near-infrared Mid-infrared successive projections algorithm Partial least square regression

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Qualitative and quantitative analysis based on digital images to determine the adulteration of ketchup samples with Sudan I dye

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FOOD CHEMISTRY 2020年 328卷 127101-127101页

作者： Giselle Reile, Cynthia Susana Rodriguez, Maria de Sousa Fernandes, David Douglas de Araujo Gomes, Adriano Goncalves Dias Diniz, Paulo Henrique Vanesa Di Anibal, Carolina Univ Nacl Sur Dept Quim INQUISUR UNS CONICET Av Alem 1253 B8000CPB Bahia Blanca Buenos Aires Argentina Univ Fed Paraiba Dept Quim BR-58051970 Joao Pessoa Paraiba Brazil Univ Fed Rio Grande do Sul Inst Quim BR-90650001 Porto Alegre RS Brazil Univ Fed Oeste Bahia Nucleo Quim Campus Reitor Edgard SantosRua Bertioga 892 BR-47810059 Barreiras BA Brazil

Sudan I is a synthetic-azo dye commonly used to adulterate foods to increase sensory appearance. However, it is banned due to its carcinogenic, mutagenic and genotoxic properties, which represent a serious risk to human health. Thus, this paper proposes a feasibility study to identify and quantify Sudan I dye in ketchup samples using colour histograms (obtained from digital images) and multivariate analysis. The successive projections algorithm coupled with linear discriminant analysis (SPA-LDA) classified correctly all samples, while the partial least squares coupled with SPA for interval selection (iSPA-PLS) quantified adequately the adulterant, attaining values of RMSEP of 11.64 mg kg(-1), R-2 of 0.96, RPD of 5.28, REP of 13.63% and LOD of 39.45 mg kg(-1). Therefore, the proposed methodology provides a simple, fast, inexpensive, promising analytical tool for the screening of both the quality and safety of ketchup samples. As a consequence, it can help to protect the consumer's health.

关键词： Ketchup Adulteration Sudan I dye Colour histograms Multivariate analysis successive projections algorithm

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Assessment of calibration methods for nitrogen estimation in wet and dry soil samples with different wavelength ranges using near-infrared spectroscopy

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 186卷 106181-106181页

作者： Wang, Qinqin Zhang, Hao Li, Fadong Gu, Congke Qiao, Yunfeng Huang, Siyuan Insentek Co Ltd 2 Beixin RdEast Third Ring Rd Beijing 100027 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100094 Peoples R China

Choosing an appropriate wavelength range, extracting optimal wavelength variables, and selecting suitable statistical analysis methods are of great importance for improving the prediction accuracy of soil nitrogen (N) with near-infrared (NIR) spectroscopy. In this study, the prediction performances of two different wavelength ranges, a short wavelength range (SWR) of 900-1,700 nm and a full wavelength range (FWR) of 900-2,500 nm, are evaluated for the measurement of soil N content. Spectral scanning is performed on wet and dry-sieve soil samples to assess the effect of moisture on the prediction performance of soil N. Two calibration methods, a commonly used linear partial least squares regression (PLSR) and a nonlinear back propagation neural network (BPNN), are used. To understand if it is possible to reduce the number of wavelength variables without decreasing prediction accuracy, we introduce a successive projection algorithm (SPA) to extract wavelength variables that are minimally redundant. The results show that models developed within FWR outperform those developed within SWR, regardless of wet or dry soil conditions, which can be attributed to the presence of more spectral information related to soil N in FWR. Compared with PLSR, BPNN is a better choice for predicting soil N, because BPNN models provide higher accuracy. The best prediction performance is achieved by BPNN method in FWR using a SPA with Rp2 = 0.93, RMSEP = 0.0297% and RPD = 4.00 of wet soil samples, and Rp2 = 0.99, RMSEP = 0.0132% and RPD = 8.76 of dry soil samples. Additionally, we demonstrate that using the SPA algorithm significantly reduces the number of wavelength variables while maintaining high prediction accuracy. The characteristic wavelengths selected by the SPA algorithm follow the principle of material spectral absorption. It is worth noting that dry soil conditions lead to superior performance over wet soil conditions for the measurement of soil N, which can be attributed to the r

关键词： Soil nitrogen Partial least squares regression Back propagation neural network successive projections algorithm

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Vis/NIR Chemical Imaging Technique for Predicting Sodium Humate Contents in Aquaculture Environment

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WATER AIR AND SOIL POLLUTION 2017年第5期228卷 177-177页

作者： Qu, Jia-Huan Sun, Da-Wen Pu, Hongbin South China Univ Technol Sch Food Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Guangzhou Higher Educ Mega Ctr Acad Contemporary Food Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Higher Educ Mega Ctr Engn & Technol Res Ctr Guangdong Prov Intelligent Guangzhou 510006 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr FRCFT Dublin 4 Ireland

This study aimed to develop a visible and near-infrared (Vis/NIR) chemical imaging (400-1000 nm) technique to provide rapid prediction of the contents of sodium humate dissolved in aquaculture environment. Gray reference image with 5% reflectance value was first used to correct the obtained raw images in order to promote the reflectance values as compared to that with 99% reflectance for further spectral analysis. successive projection algorithm (SPA) was introduced to extract four optimal wavelengths, which were then used for the establishment of back-propagation artificial neural network (BP-ANN) models. The results revealed that the BP-ANN model based on the selected four optimal wavelengths better performed (R-C(2) = 0.986, R-CV(2) = 0.985, R-P(2) = 0. 9 9 3, RMSEC = 0. 329 m g /L, RMSECV = 0.433, RMSEP = 0.734 mg/L) than that based on the whole 381 wavelengths (R-C(2) = 0.978, R-CV(2) = 0.996, R-P(2) = 0.977, RMSEC = 0.388 mg/L, RMSECV = 0.625, RMSEP = 0.734 mg/L). Finally, a series of chemical images were developed to clearly display the concentration distribution of the sodium humate dissolved in water, demonstrating that Vis/NIR chemical imaging technique was feasible to quantify the contents of sodium humate in the aquatic environment and could be further used for real-time monitoring the quality of aquaculture water.

关键词： Imaging spectroscopy Vis/NIR Humic Gray reference image successive projections algorithm Back-propagation artificial neural network

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Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry

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SENSORS 2024年第10期24卷 2994页

作者： Li, Changming Tan, Yong Liu, Chunyu Guo, Wenjing Changchun Univ Sci & Technol Sch Phys Changchun 130022 Peoples R China Changchun Guanghua Univ Engn Technol R&D Ctr Changchun 130033 Peoples R China

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

关键词： fluorescence spectroscopy rice origin identification successive projections algorithm support vector machine spectral analysis

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Non-destructive analytical strategies based on NIR spectroscopy for monitoring the quality of biodiesel/diesel blends in terms of the authentication of second-generation biodiesel feedstock using DD-SIMCA and quantification of the biodiesel content using iSPA-PLS

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VIBRATIONAL SPECTROSCOPY 2023年 129卷

作者： da Costa, Gean Bezerra Fernandes, David Douglas de Sousa Neto, Jose Germano Veras Diniz, Paulo Henrique Goncalves Dias Gondim, Amanda Duarte Univ Fed Rio Grande Do Norte Programa Posgrad Ciencia & Engn Petr Natal RN Brazil Univ Fed Paraiba Ctr Ciencias Exatas & Nat Joao Pessoa PB Brazil Univ Estadual Paraiba Programa Posgrad Quim Campina Grande PB Brazil Univ Fed Oeste Bahia Programa Posgrad Quim Pura & Aplicada BR-47810059 Barreiras BA Brazil

Considering relevant environmental, economic, and social factors, the discussion on the use of second-generation biofuels has gained prominence in recent years, especially supported by the Sustainable Development Goals (SDGs) and the Paris COP21 climate change agreement. One of the policies implemented by different countries has been the mandatory blend of biodiesel in commercial petrodiesel. Thus, this work proposes the use of NIR spectroscopy to monitor the quality of biodiesel/diesel blends. The analytical strategies involve the authentication of second-generation biodiesel feedstock using Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and the quantification of biodiesel content regardless of the biodiesel feedstock using the successive projections algorithm for interval selection in Partial Least Squares (iSPA-PLS). To avoid dilution of the samples with organic solvents, two spectral regions (881-1651 and 1911-2203 nm) were investigated. As a result, the spectral range of 1911-2203 nm achieved the best results for both the qualitative and quantitative analysis. Accordingly, the authentication efficiencies for DD-SIMCA were 97.6% and 99.2% for jatropha and beef tallow biodiesel/diesel blends using multiplicative scattering correction (MSC) and Savitzky-Golay smoothing (with a second-order polynomial and a 15-point window) coupled with the first derivative (SGD) as pre-processing algorithms, respectively. Furthermore, a relative error of prediction of only 2.85% was obtained for the biodiesel content quantification using SGD/iSPA-PLS. Hence, the proposed analytical methodology provides valuable support to governments and regulatory bodies in overseeing the quality of this product, ensuring that consumers are safeguarded against economic repercussions resulting from improper or fraudulent blending. Moreover, it aligns with the principles of Green Analytical Chemistry and contributes to the SDG 7 (affordable and clean energy) and SDG 13 (climate a

关键词： Second-generation biodiesel Near-infrared spectroscopy One-class classification successive projections algorithm Partial Least Squares

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Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index

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JOURNAL OF BIOPHOTONICS 2024年第1期17卷 e202300323-e202300323页

作者： Yang, Cheng-bo Cai, Zhi-long Li, Qing-zhi Tang, Feng Wu, Jing-jun Yang, Jia Zhang, Yu-rong Li, Bo Yang, Ping Ye, Xin Yang, Li-ming China Acad Engn Phys Res Ctr Laser Fus Mianyang Peoples R China Sichuan Sci City Hosp Mianyang Peoples R China China Acad Engn Phys Res Ctr Laser Fus Mianyang 621900 Peoples R China Sichuan Sci City Hosp Mianyang 621900 Peoples R China

To achieve high-accuracy urine specific gravity discrimination and guide the design of four-waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m-SPA-SI can effectively distinguish USG and provide design guidance for 4-wavelength multispectral sensors.

关键词： random forest spectral analysis spectral index successive projections algorithm urine specific gravity

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Rapid determination of moisture content in compound fertilizer using visible and near infrared spectroscopy combined with chemometrics

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INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 103045-000页

作者： Wang, L. S. Wang, R. J. Lu, C. P. Wang, J. Huang, W. Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Anhui Peoples R China

Moisture is a very important index for quality control of compound fertilizer. In this study, a rapid method to determine moisture content in compound fertilizer using visible and near infrared (Vis-NIR) spectroscopy combined with chemometrics is proposed. Continuous wavelet transform (CWT) with Daubechies family was used to preprocess the spectra of compound fertilizer. CWT was optimized by comparison the performance of partial least squares regression (PLSR) models. For comparison, raw spectra and three common pre-processing transforms, namely multiplicative scatter correction (MSC), first derivative (FD) and second derivative (SD) with Savitzky-Golay (SG) smoothing, were also carried out. successive projections algorithm (SPA) was employed to select effective wavelengths with moisture under optimized continuous wavelet transforms. CWT-SPA-PLSR models were built with the effective wavelengths selected by SPA. Furthermore, CWT-SPA-LS-SVM model was developed with the best effective wavelengths. The results indicated that the optimized continuous wavelet transforms (db3 at scale 7, db4 at scale7 and db4 at scale 10) were superior;the best effective wavelengths were selected by SPA under db4 at scale 10;the performance of the CWT-SPA-LS-SVM model (R-2 = 0.8054, RMSEP = 0.1795, RPD = 2.2672) is better than that of CWT-SPA-PLSR model (R-2 = 0.7863, RMSEP = 0.1958, RPD = 2.0781). This study demonstrated visible and near infrared spectroscopy using continuous wavelet transform combined with successive projections algorithm and linear regression (PLSR) or nonlinear regression (LS-SVM) to determine moisture content in compound fertilizer was feasible.

关键词： Visible and near infrared spectroscopy Continuous wavelet transform successive projections algorithm Moisture content Compound fertilizer

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Non-destructive determination of four tea polyphenols in fresh tea using visible and near-infrared spectroscopy

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INFRARED PHYSICS & TECHNOLOGY 2022年第0期123卷

作者： Luo, Wei Tian, Peng Fan, Guozhu Dong, Wentao Zhang, Hailiang Liu, Xuemei East China Jiaotong Univ Coll Elect & Automation Engn Nanchang 330013 Peoples R China East China Jiaotong Univ Coll Civil Engn & Architecture Nanchang 330013 Peoples R China

Tea is one of the drinks with the largest consumption in the world. It is widely welcomed by people due to its unique taste and its great nutritive value. And tea polyphenol is a crucial indicator to evaluate the quality of tea, so the detection of tea polyphenols have a great significance. In this paper, a total of 159 tea samples of Juhuachun (J), Zhenong25 (Z) and Yingshuang (Y) were selected. The visible and near-infrared spectrum data of the samples and the physical and chemical values of four tea polyphenols were collected, and three pretreatment methods were used to preprocess the spectra. The optimal pretreatment method was selected by comparative analysis of modeling methods. Subsequently, three feature selection methods of successive projections algorithm, competitive adaptive reweighted sampling and random frog were used to extract the characteristic wavelengths. Based on the characteristic wavelength, the prediction models for partial least squares, multiple linear regression, and least squares-support vector machines were established respectively. After comparative analysis, the results show that the LS-SVM model based on SPA is the most appropriate for detecting tea polyphenols. And its RP2 was 0.996, 0.991, 0.997 and 0.988, respectively. This research shows that Vis-NIR spectroscopy can be used to rapidly detect the content of tea polyphenols in tea. The results of the research provide technical guidelines for non-destructive detection of the quality of tea.

关键词： Visible and near-infrared spectroscopy Characteristic wavelengths successive projections algorithm Competitive adaptive reweighted sampling Random frog Tea polyphenols

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Quantitative Inversion of Lunar Surface Chemistry Based on Hyperspectral Feature Bands and Extremely Randomized Trees algorithm

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REMOTE SENSING 2022年第20期14卷 5248-5248页

作者： Wu, Shuangshuang Chen, Jianping Li, Li Zhang, Cheng Huang, Rujin Zhang, Quanping China Univ Geosci Sch Earth Sci & Resources Beijing 100083 Peoples R China Beijing Key Lab Dev & Res Land Resources Informat Beijing 100083 Peoples R China Xian Univ Sci & Technol Coll Geomat Xian 710054 Peoples R China

In situ resource utilization (ISRU) is required for the operation of both medium and long-term exploration missions to provide metallic materials for the construction of lunar base infrastructure and H2O and O-2 for life support. The study of the distribution of the lunar surface elements (Fe, Ti, Al, and Si) is the basis for the in situ utilization of mineral resources. With the arrival of the era of big data, the application of big data concepts and technical methods to lunar surface chemistry inversion has become an inevitable trend. This paper is guided by big data theory, and the Apollo 17 region and the area near the Copernicus crater are selected for analysis. The dimensionality of the first-order differential spectral features of lunar soil samples is reduced based on Pearson correlation analysis and the successive projections algorithm (SPA), and the extremely randomized trees (Extra-Trees) algorithm is applied to Chang'E-1 Interference Imaging Spectrometer (IIM) data to establish a prediction model for the lunar surface chemistry and generate FeO, TiO2, Al2O3, and SiO2 distribution maps. The results show that the optimum number of variables for FeO, TiO2, Al2O3, and SiO2 is 17, 5, 8, and 30, respectively. The accuracy of the Extra-Trees model using the best variables was improved over that of the original band model, with determination coefficients (R-2) of 0.962, 0.944, 0.964, and 0.860 for FeO, TiO2, Al2O3, and SiO2, and root mean square errors (RMSEs) of 1.028, 0.672, 0.942, and 0.897, respectively. The modeling feature variables and model preference methods in this study can improve the inversion accuracy of chemical abundance to some extent, demonstrating the potential of IIM data in predicting chemical abundance and providing a good data basis for lunar geological evolution studies and ISRU.

关键词： moon machine learning successive projections algorithm extremely randomized trees lunar chemistry

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