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Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy

[Image: see text] Near-infrared spectroscopy has been widely used to characterize the chemical composition of tobacco because it is fast, economical, and nondestructive. However, few predictive models perform ideally when applied to large spectral libraries of tobacco and its various chemical indica...

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Autores principales: Liang, Youyan, Zhao, Le, Guo, Junwei, Wang, Hongbo, Liu, Shaofeng, Wang, Luoping, Chen, Li, Chen, Mantang, Zhang, Nuohan, Liu, Huimin, Nie, Cong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631892/
https://www.ncbi.nlm.nih.gov/pubmed/36340111
http://dx.doi.org/10.1021/acsomega.2c04139
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author Liang, Youyan
Zhao, Le
Guo, Junwei
Wang, Hongbo
Liu, Shaofeng
Wang, Luoping
Chen, Li
Chen, Mantang
Zhang, Nuohan
Liu, Huimin
Nie, Cong
author_facet Liang, Youyan
Zhao, Le
Guo, Junwei
Wang, Hongbo
Liu, Shaofeng
Wang, Luoping
Chen, Li
Chen, Mantang
Zhang, Nuohan
Liu, Huimin
Nie, Cong
author_sort Liang, Youyan
collection PubMed
description [Image: see text] Near-infrared spectroscopy has been widely used to characterize the chemical composition of tobacco because it is fast, economical, and nondestructive. However, few predictive models perform ideally when applied to large spectral libraries of tobacco and its various chemical indicators. In this study, the just-in-time learning-integrated partial least-squares (JIT-PLS) modeling strategy was applied for the first time to quantitatively analyze 71 chemical components in Chinese tobacco. Approximately 18000 tobacco samples from China were analyzed to find appropriately similar measurements and propose suitable and flexible similar subsets from the calibration for each test sample. In total, 879 representative aged tobacco leaf samples and 816 cigarette samples were used as external instances to evaluate the practical predicting ability of the proposed method. The most suitable similar subsets for each test sample could be selected by limiting the Euclidean distance and number of similar subsets to 0–3.0 × 10(–9) and 10–300, respectively. The majority of the JIT-PLS models performed significantly better than traditional PLS models. Specifically, using JIT-PLS instead of traditional PLS models increased the R(2) values from 0.347–0.984 to 0.763–0.996, and from 0.179–0.981 to 0.506–0.989 for the prediction of 67 and 71 components in aged tobacco leaf and cigarette samples, respectively. Good prediction ability was demonstrated for routine chemical components, polyphenolic compounds, organic acids, and other compounds, with the mean ratios of prediction to deviation (RPD(mean)) being 7.74, 4.39, 4.05, and 5.48, respectively). The proposed methodology could simultaneously determine 67 major components in large and complicated tobacco spectral libraries with high precision and accuracy, which will assist tobacco and cigarette quality control in collecting as well as processing stages.
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spelling pubmed-96318922022-11-04 Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy Liang, Youyan Zhao, Le Guo, Junwei Wang, Hongbo Liu, Shaofeng Wang, Luoping Chen, Li Chen, Mantang Zhang, Nuohan Liu, Huimin Nie, Cong ACS Omega [Image: see text] Near-infrared spectroscopy has been widely used to characterize the chemical composition of tobacco because it is fast, economical, and nondestructive. However, few predictive models perform ideally when applied to large spectral libraries of tobacco and its various chemical indicators. In this study, the just-in-time learning-integrated partial least-squares (JIT-PLS) modeling strategy was applied for the first time to quantitatively analyze 71 chemical components in Chinese tobacco. Approximately 18000 tobacco samples from China were analyzed to find appropriately similar measurements and propose suitable and flexible similar subsets from the calibration for each test sample. In total, 879 representative aged tobacco leaf samples and 816 cigarette samples were used as external instances to evaluate the practical predicting ability of the proposed method. The most suitable similar subsets for each test sample could be selected by limiting the Euclidean distance and number of similar subsets to 0–3.0 × 10(–9) and 10–300, respectively. The majority of the JIT-PLS models performed significantly better than traditional PLS models. Specifically, using JIT-PLS instead of traditional PLS models increased the R(2) values from 0.347–0.984 to 0.763–0.996, and from 0.179–0.981 to 0.506–0.989 for the prediction of 67 and 71 components in aged tobacco leaf and cigarette samples, respectively. Good prediction ability was demonstrated for routine chemical components, polyphenolic compounds, organic acids, and other compounds, with the mean ratios of prediction to deviation (RPD(mean)) being 7.74, 4.39, 4.05, and 5.48, respectively). The proposed methodology could simultaneously determine 67 major components in large and complicated tobacco spectral libraries with high precision and accuracy, which will assist tobacco and cigarette quality control in collecting as well as processing stages. American Chemical Society 2022-10-20 /pmc/articles/PMC9631892/ /pubmed/36340111 http://dx.doi.org/10.1021/acsomega.2c04139 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Liang, Youyan
Zhao, Le
Guo, Junwei
Wang, Hongbo
Liu, Shaofeng
Wang, Luoping
Chen, Li
Chen, Mantang
Zhang, Nuohan
Liu, Huimin
Nie, Cong
Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy
title Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy
title_full Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy
title_fullStr Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy
title_full_unstemmed Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy
title_short Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy
title_sort just-in-time learning-integrated partial least-squares strategy for accurately predicting 71 chemical constituents in chinese tobacco by near-infrared spectroscopy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631892/
https://www.ncbi.nlm.nih.gov/pubmed/36340111
http://dx.doi.org/10.1021/acsomega.2c04139
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