Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content
Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation w...
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MDPI AG
2023-08-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/12/15/2957 |
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| author | Chunxia Dai Jun Sun Xingyi Huang Xiaorui Zhang Xiaoyu Tian Wei Wang Jingtao Sun Yu Luan |
| author_facet | Chunxia Dai Jun Sun Xingyi Huang Xiaorui Zhang Xiaoyu Tian Wei Wang Jingtao Sun Yu Luan |
| author_sort | Chunxia Dai |
| collection | DOAJ |
| description | Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R<sup>2</sup><sub>P</sub>) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage. |
| first_indexed | 2024-03-11T00:27:19Z |
| format | Article |
| id | doaj.art-5980ec90dbce485e894b1dad04041e98 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-11T00:27:19Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-5980ec90dbce485e894b1dad04041e982023-11-18T22:55:30ZengMDPI AGFoods2304-81582023-08-011215295710.3390/foods12152957Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene ContentChunxia Dai0Jun Sun1Xingyi Huang2Xiaorui Zhang3Xiaoyu Tian4Wei Wang5Jingtao Sun6Yu Luan7School of Electrical and Information Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, ChinaSchool of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, ChinaSchool of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, ChinaSchool of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaSchool of Food Science and Technology, Shihezi University, Shihezi 832000, ChinaZhenjiang Food and Drug Supervision and Inspection Center, Zhenjiang 212004, ChinaMaturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R<sup>2</sup><sub>P</sub>) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.https://www.mdpi.com/2304-8158/12/15/2957hyperspectral imaging technologytomato maturitylycopene contentclassificationregression modelvisualization |
| spellingShingle | Chunxia Dai Jun Sun Xingyi Huang Xiaorui Zhang Xiaoyu Tian Wei Wang Jingtao Sun Yu Luan Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content Foods hyperspectral imaging technology tomato maturity lycopene content classification regression model visualization |
| title | Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content |
| title_full | Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content |
| title_fullStr | Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content |
| title_full_unstemmed | Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content |
| title_short | Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content |
| title_sort | application of hyperspectral imaging as a nondestructive technology for identifying tomato maturity and quantitatively predicting lycopene content |
| topic | hyperspectral imaging technology tomato maturity lycopene content classification regression model visualization |
| url | https://www.mdpi.com/2304-8158/12/15/2957 |
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