Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning
Accurately predicting postharvest quality is crucial for optimizing storage and reducing losses in table grapes. This study explores the potential of fruit color parameters as non-invasive indicators of postharvest weight loss and textural changes. Using convolutional neural networks (CNNs), we deve...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Future Foods |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666833525001613 |
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| author | Xiaoyan Cheng Yao Zhou Zhengyang Huo Ruiying Li Shiqian Xu Hao Qi Jianyuan Zhu Fei Wang Yang Bi |
| author_facet | Xiaoyan Cheng Yao Zhou Zhengyang Huo Ruiying Li Shiqian Xu Hao Qi Jianyuan Zhu Fei Wang Yang Bi |
| author_sort | Xiaoyan Cheng |
| collection | DOAJ |
| description | Accurately predicting postharvest quality is crucial for optimizing storage and reducing losses in table grapes. This study explores the potential of fruit color parameters as non-invasive indicators of postharvest weight loss and textural changes. Using convolutional neural networks (CNNs), we developed predictive models based on colorimetric data, achieving high accuracy (R² > 0.80 for weight loss and R² > 0.97 for texture). Additionally, the effects of storage temperature on grape quality were examined, revealing that colder storage at 3°C significantly reduces weight loss and maintains texture better than storage at 10°C. Among tested cultivars, ‘Shine Muscat’ exhibited lower weight loss and superior textural stability compared to ‘Flame Seedless’. These findings highlight the potential of integrating color-based assessments and machine learning models into postharvest monitoring, offering a practical approach for improving quality control and storage management in the grape industry. |
| format | Article |
| id | doaj-art-9eb50a0fced7461b9fdf2be94ac172a5 |
| institution | OA Journals |
| issn | 2666-8335 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Future Foods |
| spelling | doaj-art-9eb50a0fced7461b9fdf2be94ac172a52025-08-20T02:37:35ZengElsevierFuture Foods2666-83352025-12-011210070310.1016/j.fufo.2025.100703Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learningXiaoyan Cheng0Yao Zhou1Zhengyang Huo2Ruiying Li3Shiqian Xu4Hao Qi5Jianyuan Zhu6Fei Wang7Yang Bi8College of Science, Gansu Agricultural University / State Key Laboratory of Aridland Crop Science, Co-Built by the Provincial and Ministerial Authorities, Lanzhou 730070, China; Corresponding authors.College of Science, Gansu Agricultural University / State Key Laboratory of Aridland Crop Science, Co-Built by the Provincial and Ministerial Authorities, Lanzhou 730070, ChinaCollege of Science, Gansu Agricultural University / State Key Laboratory of Aridland Crop Science, Co-Built by the Provincial and Ministerial Authorities, Lanzhou 730070, ChinaCollege of Science, Gansu Agricultural University / State Key Laboratory of Aridland Crop Science, Co-Built by the Provincial and Ministerial Authorities, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Science, Gansu Agricultural University / State Key Laboratory of Aridland Crop Science, Co-Built by the Provincial and Ministerial Authorities, Lanzhou 730070, ChinaCollege of Food Science and Engineering, Gansu Agricultural University, Lanzhou 730070, China; Corresponding authors.Accurately predicting postharvest quality is crucial for optimizing storage and reducing losses in table grapes. This study explores the potential of fruit color parameters as non-invasive indicators of postharvest weight loss and textural changes. Using convolutional neural networks (CNNs), we developed predictive models based on colorimetric data, achieving high accuracy (R² > 0.80 for weight loss and R² > 0.97 for texture). Additionally, the effects of storage temperature on grape quality were examined, revealing that colder storage at 3°C significantly reduces weight loss and maintains texture better than storage at 10°C. Among tested cultivars, ‘Shine Muscat’ exhibited lower weight loss and superior textural stability compared to ‘Flame Seedless’. These findings highlight the potential of integrating color-based assessments and machine learning models into postharvest monitoring, offering a practical approach for improving quality control and storage management in the grape industry.http://www.sciencedirect.com/science/article/pii/S2666833525001613Postharvest deteriorationNon-destructive evaluationPredictive analyticsStorage stabilityMachine learning applications |
| spellingShingle | Xiaoyan Cheng Yao Zhou Zhengyang Huo Ruiying Li Shiqian Xu Hao Qi Jianyuan Zhu Fei Wang Yang Bi Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning Future Foods Postharvest deterioration Non-destructive evaluation Predictive analytics Storage stability Machine learning applications |
| title | Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning |
| title_full | Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning |
| title_fullStr | Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning |
| title_full_unstemmed | Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning |
| title_short | Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning |
| title_sort | predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning |
| topic | Postharvest deterioration Non-destructive evaluation Predictive analytics Storage stability Machine learning applications |
| url | http://www.sciencedirect.com/science/article/pii/S2666833525001613 |
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