Combination of unsupervised and supervised models to predict the maturity of peaches during shelf‐life. Issue 7 (27th May 2021)

• Record Type: Journal Article
• Authors: Zhong, Yuming; Bao, Yao; Ye, Jiaming; Liu, Jianliang; Liu, Huifan
• Is Part Of: Journal of food processing and preservation. Volume 45:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-27
• Subjects: Food -- Preservation -- Periodicals; Food industry and trade -- Periodicals; 664.005
• DOI: 10.1111/jfpp.15624
• Languages: English
• ISSNs: 0145-8892

Abstract

Abstract: Maturity is one of the important factors affecting peach ( Prunus persica L. Bastsch ) quality and shelf‐time. In this study, a combination of unsupervised and supervised models was employed to predict the maturity period of peach using 178 species from seven areas in China. Three types of nonparametric tests were used to analyze the data. The results emphasized that maturity may not be influenced by a single factor. Furthermore, PLS‐PM analysis emphasized that the color module was the most important factor during maturity. As a result, k‐means and random forest (RF) models were successfully established to predict the maturity of peaches during shelf‐life; the accuracy was 97.15%, and the kappa was 0.94. In double‐blind tests, the RF model achieved 100% classification over the maturity stages of peaches, and 81.25% confirmed the optimum maturity level, verifying our model. This study provides useful information on the maturity‐associated rapid analysis of peaches during their shelf‐life. Novelty impact statement: In total, 178 species of peaches, obtained from seven areas in China, were analyzed. Multivariate analyses were conducted to determine peach maturity. PLS‐PM analysis emphasized the color module as affecting maturity and random forest model reaches 100% classification over the peach maturity stage.