Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents
The lily, valued for its edibility and medicinal properties, is rich in essential nutrients. However, storage conditions and sulfur fumigation during processing can degrade key nutrients like polysaccharides, phenols, and sulfur dioxide. To address this, we applied a deep learning model combined wit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/5/825 |
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| author | Pengfei Zhang Youyou Wang Binbin Yan Xiufu Wang Zihua Zhang Sheng Wang Jian Yang |
| author_facet | Pengfei Zhang Youyou Wang Binbin Yan Xiufu Wang Zihua Zhang Sheng Wang Jian Yang |
| author_sort | Pengfei Zhang |
| collection | DOAJ |
| description | The lily, valued for its edibility and medicinal properties, is rich in essential nutrients. However, storage conditions and sulfur fumigation during processing can degrade key nutrients like polysaccharides, phenols, and sulfur dioxide. To address this, we applied a deep learning model combined with hyperspectral imaging for the rapid prediction of nutrient quality. The CLSTM (convolutional neural network–long short-term memory) model, utilizing variable combination population analysis (VCPA) for wavelength selection, effectively differentiated sulfur fumigation patterns in lilies. In terms of nutrient content prediction, the CLSTM model combined with full-wavelength data demonstrated superior performance, achieving an R<sup>2</sup> value of 0.769 for polysaccharides and 0.699 for total phenols. Additionally, the CLSTM model combined with IRF-selected characteristic wavelengths exhibited remarkable performance in predicting sulfur dioxide content, with an R<sup>2</sup> value of 0.755. These findings highlight the potential of hyperspectral imaging and the CLSTM model in enhancing the quality assessment and ensuring the nutritional integrity of lily products. |
| format | Article |
| id | doaj-art-e24c5ed0084f4c7aa8380ee459915f2c |
| institution | DOAJ |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-e24c5ed0084f4c7aa8380ee459915f2c2025-08-20T02:59:14ZengMDPI AGFoods2304-81582025-02-0114582510.3390/foods14050825Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator ContentsPengfei Zhang0Youyou Wang1Binbin Yan2Xiufu Wang3Zihua Zhang4Sheng Wang5Jian Yang6Dexing Research and Training Center of Chinese Medical Sciences, Dexing 334220, ChinaState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, ChinaDexing Research and Training Center of Chinese Medical Sciences, Dexing 334220, ChinaDexing Research and Training Center of Chinese Medical Sciences, Dexing 334220, ChinaDexing Traditional Chinese Medicine Industry Development Service Center, Dexing 334220, ChinaDexing Research and Training Center of Chinese Medical Sciences, Dexing 334220, ChinaJiangxi Province Key Laboratory of Sustainable Utilization of Traditional Chinese Medicine Resources, Institute of Traditional Chinese Medicine Health Industry, China Academy of Chinese Medical Sciences, Nanchang 330115, ChinaThe lily, valued for its edibility and medicinal properties, is rich in essential nutrients. However, storage conditions and sulfur fumigation during processing can degrade key nutrients like polysaccharides, phenols, and sulfur dioxide. To address this, we applied a deep learning model combined with hyperspectral imaging for the rapid prediction of nutrient quality. The CLSTM (convolutional neural network–long short-term memory) model, utilizing variable combination population analysis (VCPA) for wavelength selection, effectively differentiated sulfur fumigation patterns in lilies. In terms of nutrient content prediction, the CLSTM model combined with full-wavelength data demonstrated superior performance, achieving an R<sup>2</sup> value of 0.769 for polysaccharides and 0.699 for total phenols. Additionally, the CLSTM model combined with IRF-selected characteristic wavelengths exhibited remarkable performance in predicting sulfur dioxide content, with an R<sup>2</sup> value of 0.755. These findings highlight the potential of hyperspectral imaging and the CLSTM model in enhancing the quality assessment and ensuring the nutritional integrity of lily products.https://www.mdpi.com/2304-8158/14/5/825lilyhyperspectral imagingnutrient contentdeep learning |
| spellingShingle | Pengfei Zhang Youyou Wang Binbin Yan Xiufu Wang Zihua Zhang Sheng Wang Jian Yang Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents Foods lily hyperspectral imaging nutrient content deep learning |
| title | Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents |
| title_full | Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents |
| title_fullStr | Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents |
| title_full_unstemmed | Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents |
| title_short | Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents |
| title_sort | integration of hyperspectral imaging and deep learning for discrimination of fumigated lilies and prediction of quality indicator contents |
| topic | lily hyperspectral imaging nutrient content deep learning |
| url | https://www.mdpi.com/2304-8158/14/5/825 |
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