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A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

In this paper, a novel strategy of “pick the best of the best” was proposed for the nondestructive identification of different‐origin and adulterated Poria cocos with near‐infrared spectroscopy. First, various preprocessing methods were divided into three classes: baseline correction, scattering and...

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Autores principales: Li, Jiayi, Yu, Mei, Li, Shangke, Jiang, Liwen, Zheng, Yu, Li, Pao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358339/
https://www.ncbi.nlm.nih.gov/pubmed/34401069
http://dx.doi.org/10.1002/fsn3.2383
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author Li, Jiayi
Yu, Mei
Li, Shangke
Jiang, Liwen
Zheng, Yu
Li, Pao
author_facet Li, Jiayi
Yu, Mei
Li, Shangke
Jiang, Liwen
Zheng, Yu
Li, Pao
author_sort Li, Jiayi
collection PubMed
description In this paper, a novel strategy of “pick the best of the best” was proposed for the nondestructive identification of different‐origin and adulterated Poria cocos with near‐infrared spectroscopy. First, various preprocessing methods were divided into three classes: baseline correction, scattering and trend correction, and scaling. The single preprocessing methods with the best predictions in each class were selected. Then, the selected preprocessing methods were combined in pairs according to three classes. The pair combination preprocessing methods with the best predictions and also better predictions than single methods were selected. Finally, the selected pair combination preprocessing method was combined with the methods in the unselected class. The three combination preprocessing methods with the best predictions and also better predictions than pair combination methods were selected as the final prediction. With this strategy, the optimized preprocessing combination can be obtained quickly, and the identification accuracy with principal component analysis method can be greatly improved. 0% identification accuracy of adulterated samples and 12.5% identification accuracy of different‐origin samples were obtained with the raw data. However, 100% accuracy of adulterated samples, 93.8% accuracy of calibration dataset, and 75% accuracy of validation dataset can be obtained with the novel strategy. The developed technology can be regarded as a simple, rapid, and accurate nondestructive identification method for different‐origin and adulterated samples, and has a broad application prospect in the future.
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spelling pubmed-83583392021-08-15 A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy Li, Jiayi Yu, Mei Li, Shangke Jiang, Liwen Zheng, Yu Li, Pao Food Sci Nutr Original Research In this paper, a novel strategy of “pick the best of the best” was proposed for the nondestructive identification of different‐origin and adulterated Poria cocos with near‐infrared spectroscopy. First, various preprocessing methods were divided into three classes: baseline correction, scattering and trend correction, and scaling. The single preprocessing methods with the best predictions in each class were selected. Then, the selected preprocessing methods were combined in pairs according to three classes. The pair combination preprocessing methods with the best predictions and also better predictions than single methods were selected. Finally, the selected pair combination preprocessing method was combined with the methods in the unselected class. The three combination preprocessing methods with the best predictions and also better predictions than pair combination methods were selected as the final prediction. With this strategy, the optimized preprocessing combination can be obtained quickly, and the identification accuracy with principal component analysis method can be greatly improved. 0% identification accuracy of adulterated samples and 12.5% identification accuracy of different‐origin samples were obtained with the raw data. However, 100% accuracy of adulterated samples, 93.8% accuracy of calibration dataset, and 75% accuracy of validation dataset can be obtained with the novel strategy. The developed technology can be regarded as a simple, rapid, and accurate nondestructive identification method for different‐origin and adulterated samples, and has a broad application prospect in the future. John Wiley and Sons Inc. 2021-06-19 /pmc/articles/PMC8358339/ /pubmed/34401069 http://dx.doi.org/10.1002/fsn3.2383 Text en © 2021 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Li, Jiayi
Yu, Mei
Li, Shangke
Jiang, Liwen
Zheng, Yu
Li, Pao
A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy
title A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy
title_full A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy
title_fullStr A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy
title_full_unstemmed A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy
title_short A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy
title_sort novel strategy of “pick the best of the best” for the nondestructive identification of poria cocos based on near‐infrared spectroscopy
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358339/
https://www.ncbi.nlm.nih.gov/pubmed/34401069
http://dx.doi.org/10.1002/fsn3.2383
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