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Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning
The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and ef...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146475/ https://www.ncbi.nlm.nih.gov/pubmed/37115737 http://dx.doi.org/10.1371/journal.pone.0284612 |
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author | Khorramifar, Ali Rasekh, Mansour Karami, Hamed Lozano, Jesús Gancarz, Marek Łazuka, Ewa Łagód, Grzegorz |
author_facet | Khorramifar, Ali Rasekh, Mansour Karami, Hamed Lozano, Jesús Gancarz, Marek Łazuka, Ewa Łagód, Grzegorz |
author_sort | Khorramifar, Ali |
collection | PubMed |
description | The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes. |
format | Online Article Text |
id | pubmed-10146475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101464752023-04-29 Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning Khorramifar, Ali Rasekh, Mansour Karami, Hamed Lozano, Jesús Gancarz, Marek Łazuka, Ewa Łagód, Grzegorz PLoS One Research Article The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes. Public Library of Science 2023-04-28 /pmc/articles/PMC10146475/ /pubmed/37115737 http://dx.doi.org/10.1371/journal.pone.0284612 Text en © 2023 Khorramifar et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Khorramifar, Ali Rasekh, Mansour Karami, Hamed Lozano, Jesús Gancarz, Marek Łazuka, Ewa Łagód, Grzegorz Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning |
title | Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning |
title_full | Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning |
title_fullStr | Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning |
title_full_unstemmed | Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning |
title_short | Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning |
title_sort | determining the shelf life and quality changes of potatoes (solanum tuberosum) during storage using electronic nose and machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146475/ https://www.ncbi.nlm.nih.gov/pubmed/37115737 http://dx.doi.org/10.1371/journal.pone.0284612 |
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