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Sensors 2019, 19(2), 419; https://doi.org/10.3390/s19020419

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

1,2, 1,2,*, 1,2, 1,2 and 2,3
1
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2
Key Laboratory of On Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
3
College of Quality & Safety Engineering, China Jiliang University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Received: 3 December 2018 / Revised: 11 January 2019 / Accepted: 11 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Electronic Noses and Their Application)
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PDF [2982 KB, uploaded 21 January 2019]
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Abstract

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: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles. View Full-Text
Keywords: electronic nose; nondestructive detection; kiwifruit; ripeness; SSC; firmness electronic nose; nondestructive detection; kiwifruit; ripeness; SSC; firmness
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Du, D.; Wang, J.; Wang, B.; Zhu, L.; Hong, X. Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics. Sensors 2019, 19, 419.

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