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Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters

In this study, moisture contents and product quality of Pu‐erh tea were predicted with deep learning‐based methods. Images were captured continuously in the sun‐drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation wer...

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Autores principales: Chen, Cheng, Zhang, Wuyi, Shan, Zhiguo, Zhang, Chunhua, Dong, Tianwu, Feng, Zhouqiang, Wang, Chengkang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007301/
https://www.ncbi.nlm.nih.gov/pubmed/35432968
http://dx.doi.org/10.1002/fsn3.2699
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author Chen, Cheng
Zhang, Wuyi
Shan, Zhiguo
Zhang, Chunhua
Dong, Tianwu
Feng, Zhouqiang
Wang, Chengkang
author_facet Chen, Cheng
Zhang, Wuyi
Shan, Zhiguo
Zhang, Chunhua
Dong, Tianwu
Feng, Zhouqiang
Wang, Chengkang
author_sort Chen, Cheng
collection PubMed
description In this study, moisture contents and product quality of Pu‐erh tea were predicted with deep learning‐based methods. Images were captured continuously in the sun‐drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation were collected with a portable meteorological station. Sensory scores of aroma, flavor, liquor color, residue, and total scores were given by a trained panel. Convolutional neural network (CNN) and gated recurrent unit (GRU) models were constructed based on image information and EP, which were selected in advance using the neighborhood component analysis (NCA) algorithm. The evolved models based on deep‐learning methods achieved satisfactory results, with RMSE of 0.4332, 0.2669, 0.7508 (also with R (2) of .9997, .9882, .9986, with RPD of 53.5894, 13.1646, 26.3513) for moisture contents prediction in each batch of tea, tea at different sampling periods, the overall samples, respectively; and with RMSE of 0.291, 0.2815, 0.162, 0.1574, 0.3931 (also with R (2) of .9688, .9772, .9752, .9741, .8906, with RPD of 5.6073, 6.5912, 6.352, 6.1428, 4.0045) for final quality prediction of aroma, flavor, liquor color, residue, total score, respectively. By analyzing and comparing the RMSE values, the most significant environmental parameters (EP) were selected. The proposed combinations of different EP can also provide a valuable reference in the development of a new sun‐drying system.
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spelling pubmed-90073012022-04-15 Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters Chen, Cheng Zhang, Wuyi Shan, Zhiguo Zhang, Chunhua Dong, Tianwu Feng, Zhouqiang Wang, Chengkang Food Sci Nutr Original Articles In this study, moisture contents and product quality of Pu‐erh tea were predicted with deep learning‐based methods. Images were captured continuously in the sun‐drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation were collected with a portable meteorological station. Sensory scores of aroma, flavor, liquor color, residue, and total scores were given by a trained panel. Convolutional neural network (CNN) and gated recurrent unit (GRU) models were constructed based on image information and EP, which were selected in advance using the neighborhood component analysis (NCA) algorithm. The evolved models based on deep‐learning methods achieved satisfactory results, with RMSE of 0.4332, 0.2669, 0.7508 (also with R (2) of .9997, .9882, .9986, with RPD of 53.5894, 13.1646, 26.3513) for moisture contents prediction in each batch of tea, tea at different sampling periods, the overall samples, respectively; and with RMSE of 0.291, 0.2815, 0.162, 0.1574, 0.3931 (also with R (2) of .9688, .9772, .9752, .9741, .8906, with RPD of 5.6073, 6.5912, 6.352, 6.1428, 4.0045) for final quality prediction of aroma, flavor, liquor color, residue, total score, respectively. By analyzing and comparing the RMSE values, the most significant environmental parameters (EP) were selected. The proposed combinations of different EP can also provide a valuable reference in the development of a new sun‐drying system. John Wiley and Sons Inc. 2022-02-22 /pmc/articles/PMC9007301/ /pubmed/35432968 http://dx.doi.org/10.1002/fsn3.2699 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 Articles
Chen, Cheng
Zhang, Wuyi
Shan, Zhiguo
Zhang, Chunhua
Dong, Tianwu
Feng, Zhouqiang
Wang, Chengkang
Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters
title Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters
title_full Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters
title_fullStr Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters
title_full_unstemmed Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters
title_short Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters
title_sort moisture contents and product quality prediction of pu‐erh tea in sun‐drying process with image information and environmental parameters
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007301/
https://www.ncbi.nlm.nih.gov/pubmed/35432968
http://dx.doi.org/10.1002/fsn3.2699
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