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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9007301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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