Shelf life prediction model of postharvest table grape using optimized radial basis function (RBF) neural network

• Published in: British food journal (1966) 2019-10, Vol.121 (11), p.2919-2936
• Main Authors: Li, Yue, Chu, Xiaoquan, Fu, Zetian, Feng, Jianying, Mu, Weisong
• Format: Article
• Language: English
• Subjects: Accuracy; Analytic hierarchy process; Back propagation networks; Clustering; Fruits; Grapes; Growth models; Laboratories; Life prediction; Model accuracy; Neural networks; Prediction models; Radial basis function; Relative humidity; Safety management; Sensory perception; Shelf life; Storage conditions; Storage temperature; Temperature; Weight loss
• ISSN: 0007-070X; 1758-4108
• DOI: 10.1108/BFJ-03-2019-0183

Purpose The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods. Design/methodology/approach First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data. Findings The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R2 is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R2 are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties. Originality/value This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.