Shelf Life Prediction of ‘Royal Gala’ Apples Based on Quality Attributes and Storage Temperature

• Published in: Weon'ye gwahag gi'sulji Vol. 39; no. 3; pp. 343 - 355
• Main Authors: Cao, Mengke, Wang, Dong, Qiu, Lingyu, Ren, Xiaolin, Ma, Huiling
• Format: Journal Article
• Language: English
• Published: 한국원예학회HST 01.01.2021; 한국원예학회
• Subjects: 농학; correlation analysis; artificial neural network; model; storage; sparse principal component analysis
• ISSN: 1226-8763; 2465-8588
• DOI: 10.7235/HORT.20210031

Phenotypic changes caused by postharvest deterioration of the quality attributes of apples causesubstantial economic losses. Thus, strategies for accurate prediction of the shelf life of apples isurgently needed. In each of the three consecutive years from 2016 to 2018, freshly harvested ‘RoyalGala’ apples were stored at 0, 5, 15, and 25°C, respectively. Subsequently, 11 quality attributeswere measured at periodic intervals until the end of storage. To screen fewer and more usefulindexes, three input datasets were considered: temperature, color value (L*, a*, b*, △E, and C*),weight loss, firmness, titratable acidity, soluble solids content, starch, and reducing ascorbic acid(D1). The key quality attributes were screened by sparse principal component analysis (SPCA)(D2) and correlation analysis (CA) (D3), using shelf life as the output layer of the artificial neuralnetwork based on the back propagation (BP ANN) model. The results showed that the correlationcoefficients (r) of the predicted and measured shelf life for D1, D2, and D3 were 0.996, 0.997, and0.993, respectively, while the mean relative errors were 0.071, 0.074, and 0.074, respectively. Meanwhile, the relative percent root mean square (RMS) values were 0.088, 0.092, and 0.112,respectively. The application of SPCA reduced the quality attributes for the input dataset from 12 to6. Therefore, SPCA-BP ANN was shown to be a useful model for accurate prediction of thepostharvest shelf life of ‘Royal Gala’ apples. KCI Citation Count: 7