A dynamic shelf-life prediction method considering actual uncertainty: Application to fresh fruits in long-term cold storage. (July 2023)

• Record Type: Journal Article
• Authors: Chen, Qian; Li, Jiali; Yang, Han; Qian, Jianping
• Is Part Of: Journal of food engineering. Volume 349(2023)
• Publication Date: 2023-07
• Subjects: Fresh fruit -- Shelf-life prediction -- Cold storage -- Uncertainty -- Kinetic reaction model -- Stochastic process; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2023.111471
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Shelf-life is an important tool for visually conveying remaining food quality. However, the traditional printed shelf-life is a fixed value based on a stable environment, whereas food supplies undergo long-term dynamic conditions. Moreover, the accumulating effects of real-world uncertainty will lead to stochastic changes in remaining shelf-life in a probabilistic manner. Therefore, we proposed a novel dynamic shelf-life prediction method integrating the kinetic reaction and stochastic process. First, to predict practical changes in food quality indices (FQIs), we established the stochastic kinetic model, which combines basic deterministic modeling with zero-order reaction and the Arrhenius equation, and stochastic factor modeling with the Wiener process. Second, we conducted real-time probability analysis of remaining shelf-life to quantify the potential degeneration of food quality based on the established model. By using datasets monitoring firmness and Vitamin C of Kiwifruit in long-term cold storage to verify the performance of our proposed integrated model, we showed that our method was more accurate in modeling stochastic changes in FQIs than the traditional reaction model, resulting in mean absolute error, mean absolute percentage error, and root mean squared error (RMSE) less than 0.2136, 0.0162, and 0.7402, respectively. Furthermore, the shelf-life probability analysis was efficient, with relative RMSE 5.26% and computing time 0.047 s. This valuable food quality information can be provided to managers or customers to reduce food loss and waste. Highlights: A dynamic shelf-life prediction method considering actual uncertainty is proposed. MAE, MAPE and RMSE of stochastic kinetic model are less than 0.2136, 0.0162, 0.7402. Relative RMSE of shelf-life prediction reaches 5.26%. The running time of the proposed algorithm is less than 0.047s.