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Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

FORESTS 2023年第8期14卷 1566页

作者： Li, Huazhe Cui, Lijuan Dou, Zhiguo Wang, Junjie Zhai, Xiajie Li, Jing Zhao, Xinsheng Lei, Yinru Wang, Jinzhi Li, Wei Chinese Acad Forestry Inst Wetland Res Beijing 100091 Peoples R China Beijing Key Lab Wetland Serv & Restorat Beijing 100091 Peoples R China Chinese Acad Forestry Inst Ecol Conservat & Restorat Beijing 100091 Peoples R China Shenzhen Univ Coll Life Sci & Oceanog Shenzhen 518060 Peoples R China Shenzhen Univ MNR Key Lab Geoenvironm Monitoring Great Bay Area Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Urban Informat Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen 518060 Peoples R China

Mangroves have important roles in regulating climate change, and in reducing the impact of wind and waves. Analysis of the chlorophyll content of mangroves is important for monitoring their health, and their conservation and management. Thus, this study aimed to apply four regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Partial Least Squares (PLS) and Adaptive Boosting (AdaBoost), to study the inversion of Soil Plant Analysis Development (SPAD) values obtained from near-ground hyperspectral data of three dominant species, Bruguiera sexangula (Lour.) Poir. (B. sexangula), Ceriops tagal (Perr.) C. B. Rob. (C. tagal) and Rhizophora apiculata Blume (R. apiculata) in Qinglan Port Mangrove Nature Reserve. The accuracy of the model was evaluated using R-2, RMSE, and MAE. The mean SPAD values of R. apiculata (SPAD(avg) = 66.57), with a smaller dispersion (coefficient of variation of 6.59%), were higher than those of C. tagal (SPAD(avg) = 61.56) and B. sexangula (SPAD(avg) = 58.60). The first-order differential transformation of the spectral data improved the accuracy of the prediction model;R-2 was mostly distributed in the interval of 0.4 to 0.8. The accuracy of the XGBoost model was less affected by species differences with the best stability, with RMSE at approximately 3.5 and MAE at approximately 2.85. This study provides a technical reference for large-scale detection and management of mangroves.

关键词： hyperspectral data successive projections algorithm mangroves SPAD measurements

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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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A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm

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MICROCHEMICAL JOURNAL 2024年 205卷

作者： Zhao, Guangxia Xu, Zhuopin Tang, Liwen Li, Xiaohong Zhang, Pengfei Wang, Qi Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China

Accurate classification of sorghum varieties is crucial to the production and processing of liquor with sorghum as raw materials. Hyperspectral imaging (HSI) has the potential to achieve this goal quickly and nondestructively. This study proposes a novel algorithm, an improved Principal Component Analysis combined with SpectrumImage-Convolutional Neural Network (PCA-SICNN), which can combine spectral features and image features of HSI data, to enhance the accuracy of variety identification of sorghum seeds. To verify the effect of this algorithm, hyperspectral imaging data (939-1700 nm) of 13,200 sorghum seeds from 6 varieties were collected. The principal component analysis (PCA) was employed to select 20-dimension images from the origin hyperspectral imaging data. Spectrum-Image-Convolutional Neural Network (SICNN) extracts the spectral and image features of sorghum in the network and then fuses the features. By fully learning the HSI data features of sorghum, it achieves the classification of sorghum varieties. The results demonstrate that PCA-SICNN achieves an accuracy on the training set and on the test set reaches 98.67 % and 98.64 %, respectively. Compared with other control methods, the prediction accuracy of the PCA-SICNN model increased by at least 1.10 %. These results suggest the potential for the method to be widely applied in the production and processing of sorghum.

关键词： Sorghum variety classification Hyperspectral imaging Convolutional neural network successive projections algorithm

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Confocal Raman spectroscopy assisted by chemometric tools: A green approach for classification and quantification of octyl p-methoxycinnamate in oil-in-water microemulsions

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MICROCHEMICAL JOURNAL 2023年第PartA期184卷

作者： Silva do Nascimento, Danielle Volpe, Veronica Fernandez, Cintia J. Oresti, Gerardo M. Ashton, Lorna Grunhut, Marcos Univ Nacl Sur Dept Chem INQUISUR UNS CONICET Bahia Blanca Buenos Aires Argentina Univ Nacl Sur Dept Biol Bioquim & Farm INIBIBB UNS CONICET Bahia Blanca Buenos Aires Argentina Univ Lancaster Dept Chem Lancaster England

This work proposes a green analytical method based on confocal Raman spectrometry and chemometrics tools for the qualitative and quantitative analysis of oil in water microemulsions loaded with the UVB filter octyl p-methoxycinnamate (OMC). The method does not use reagents and only 10 mu L of sample are needed. The analyzed microemulsion samples were synthetized in the laboratory using decaethylene glycol mono-dodecyl ether (21.9 %) as non-ionic surfactant, ethyl alcohol (7.3 %) as co-surfactant, oleic acid (1.5 %) as oil phase and water (69.3 %). A physicochemical characterization of the samples was carried out obtaining expected values for droplet size (<20 nm), polydispersity index (<0.290) and conductivity (0.04-0.07 mS cm-1), among others. Linear discriminant analysis (LDA) after selection of variables using the successive projections algorithm (SPA) and soft independent modelling of class analogy (SIMCA) were employed to classify microemulsions with different concentrations of OMC (1.0 to 10.0 %). In the case of LDA, seven Raman spectral variables were previously selected by SPA and after this SPA-LDA model resulted in one error in the prediction set achieving an accuracy of 97.8 %. The SIMCA model (alpha = 0.05) presented an explained variance higher 97 % using four principal com-ponents and it allowed the correct classification of 100 % of the samples (N = 15). In the quantitative analysis, partial least squares (PLS) was used to determine OMC in a range according to international legislation. The model presented optimal statistical parameters (R2 = 0.9699;RMSEP = 0.54 %) and the prediction of samples were in close agreement with HPLC method. Moreover, the greenery of the method was estimated using the AGREE metric and an optimal value of 0.85 was obtained demonstrating the proposed analytical method results environmentally friendly.

关键词： UV filters Octyl p-methoxycinnamate Microemulsions Confocal Raman Spectroscopy Linear Discriminant Analysis successive projections algorithm Soft Independent Modelling by Class Analogy Partial Least Squares Green Analytical Chemistry

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Quantitative Monitoring of Leaf Area Index in Rice Based on Hyperspectral Feature Bands and Ridge Regression algorithm

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REMOTE SENSING 2022年第12期14卷 2777-2777页

作者： Ji, Shu Gu, Chen Xi, Xiaobo Zhang, Zhenghua Hong, Qingqing Huo, Zhongyang Zhao, Haitao Zhang, Ruihong Li, Bin Tan, Changwei Yangzhou Univ Jiangsu Key Lab Crop Genet & Physiol Jiangsu Co Innovat Ctr Modern Prod Technol Grain Joint Int Res Lab Agr & Agriprod SafetyMinist Ed Yangzhou 225009 Jiangsu Peoples R China

Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R-2) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R-2 of 0.878 and RMSE of 0.773 for the validation set 1, and R-2 of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.

关键词： leaf area index hyperspectral successive projections algorithm ridge regression rice

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Screening analysis of beer ageing using near infrared spectroscopy and the successive projections algorithm for variable selection

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TALANTA 2012年 89卷 286-291页

作者： Ghasemi-Varnamkhasti, Mandi Mohtasebi, Seyed Saied Luz Rodriguez-Mendez, Maria Gomes, Adriano A. Ugulino Araujo, Mario Cesar Galvao, Roberto K. H. Shahrekord Univ Dept Agr Machinery Engn Fac Agr Shahrekord Iran Univ Valladolid ETS Ingenieros Ind Dept Inorgan Chem Valladolid Spain Univ Tehran Fac Agr Engn & Technol Agr Machinery Engn Dept Karaj Iran Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa Paraiba Brazil Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil

This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the successive projections algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40 degrees C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500-5405 cm(-1) was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the nonalcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550 cm(-1) for non-alcoholic samples and 7228 cm(-1) for alcoholic samples. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Beer Ageing Near infrared spectroscopy Wavelength selection Classification successive projections algorithm Linear Discriminant Analysis

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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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A graphical user interface for variable selection employing the successive projections algorithm

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2012年 118卷 260-266页

作者： Paiva, Henrique Mohallem Carreiro Soares, Sofacles Figueredo Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa PB Brazil Mectron Engn Ind & Comercio SA BR-12227000 Sao Jose Dos Campos Brazil Inst Tecnol Aeronaut Div Engn Elet BR-12228900 Sao Jose Dos Campos Brazil

The present work describes a MATLAB graphical user interface for the successive projections algorithm (SPA), which is a variable selection technique aimed at reducing collinearity problems in Multiple Linear Regression (MLR) modelling. The interface also offers the possibility of pre-processing the data using Savitzky-Golay smoothing/differentiation and/or wavelet denoising. Sample selection routines for dividing the samples into calibration and validation sets are also implemented. At the end, prediction statistics (PRESS, RMSEP, SDV, WAS and correlation coefficient) are calculated to evaluate the performance of the resulting MLR model. All these operations can be carried out by the user without the need for MATLAB programming skills. For illustration, an example involving near-infrared spectrometric determination of moisture in corn samples is presented. The software is freely available at ***/similar to kawakami/spa. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Multivariate calibration Variable selection successive projections algorithm Graphical user interface MATLAB

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Detection of fat content in peanut kernels based on chemometrics and hyperspectral imaging technology

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INFRARED PHYSICS & TECHNOLOGY 2020年 105卷 103226-000页

作者： Sun, Jianfei Wang, Guangxian Zhang, Hui Xia, Lianming Zhao, Wenping Guo, Yemin Sun, Xia Shandong Univ Technol Sch Agr Engn & Food Sci 12 Zhangshou Rd Zibo 255049 Peoples R China Shandong Prov Engn Res Ctr Vegetable Safety & Qua 12 Zhangshou Rd Zibo 255049 Peoples R China Zibo City Key Lab Agr Prod Safety Traceabil Zibo Peoples R China

Hyperspectral imaging technology combined with chemometrics were applied to detect fat content in peanut kernels. Four varieties of peanuts were scanned to acquire hyperspectral images by using a "push-broom" system. Then, the spectral data was extracted from hyperspectral images. Principal component analysis (PCA) was used to detect outliers. After outliers removed, five different pre-processing methods were used to preprocess spectral data. successive projections algorithm (SPA) and regression coefficient (RC) were adopted to select effective wavelengths. Finally, based on the full wavelengths and the effective wavelengths, the models of partial least squares regression (PLSR), support vector machine regression (SVR) and multiple linear regression (MLR) were established respectively. Comparing these models, Baseline-SPA-MLR was the most excellent with determination coefficient (R-p(2)) of 0.9736, root mean square errors (RMSEp) of 0.4635% and residual prediction deviation (RPD) of 6.1273 in the prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology provided an efficient and non-destructive method for detecting the fat content in peanut kernels.

关键词： Hyperspectral imaging Fat content Peanut kernel successive projections algorithm Multiple linear regression

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