Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics
Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe„ stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 met...
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MDPI AG
2019-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/2/419 |
| _version_ | 1798005750769909760 |
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| author | Dongdong Du Jun Wang Bo Wang Luyi Zhu Xuezhen Hong |
| author_facet | Dongdong Du Jun Wang Bo Wang Luyi Zhu Xuezhen Hong |
| author_sort | Dongdong Du |
| collection | DOAJ |
| description | Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe„ stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R<sup>2</sup> = 0.9928; testing: R<sup>2</sup> = 0.9928), SSC (training: R<sup>2</sup> = 0.9749; testing: R<sup>2</sup> = 0.9143) and firmness (training: R<sup>2</sup> = 0.9814; testing: R<sup>2</sup> = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles. |
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| last_indexed | 2024-04-11T12:44:03Z |
| publishDate | 2019-01-01 |
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| spelling | doaj.art-3a71267d67434e108ad6b7c4600a74762022-12-22T04:23:25ZengMDPI AGSensors1424-82202019-01-0119241910.3390/s19020419s19020419Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with ChemometricsDongdong Du0Jun Wang1Bo Wang2Luyi Zhu3Xuezhen Hong4College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of On Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, ChinaPostharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe„ stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R<sup>2</sup> = 0.9928; testing: R<sup>2</sup> = 0.9928), SSC (training: R<sup>2</sup> = 0.9749; testing: R<sup>2</sup> = 0.9143) and firmness (training: R<sup>2</sup> = 0.9814; testing: R<sup>2</sup> = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.https://www.mdpi.com/1424-8220/19/2/419electronic nosenondestructive detectionkiwifruitripenessSSCfirmness |
| spellingShingle | Dongdong Du Jun Wang Bo Wang Luyi Zhu Xuezhen Hong Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics Sensors electronic nose nondestructive detection kiwifruit ripeness SSC firmness |
| title | Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics |
| title_full | Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics |
| title_fullStr | Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics |
| title_full_unstemmed | Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics |
| title_short | Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics |
| title_sort | ripeness prediction of postharvest kiwifruit using a mos e nose combined with chemometrics |
| topic | electronic nose nondestructive detection kiwifruit ripeness SSC firmness |
| url | https://www.mdpi.com/1424-8220/19/2/419 |
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