Packaging performance evaluation and freshness intelligent prediction modeling in grape transportation

文献类型： 外文期刊

第一作者： Yin, Maosong

作者： Yin, Maosong;Huo, Longfei;Li, Nuo;Hu, Jinyou;Zhu, Hongliang;Zhu, Zhiqiang;Zhu, Zhiqiang

作者机构：

关键词： Table grape; Vibration damage; Packaging performance; Freshness prediction

期刊名称：FOOD CONTROL （ 影响因子：5.6； 五年影响因子：5.4 ）

ISSN： 0956-7135

年卷期： 2024 年 165 卷

页码：

收录情况： SCI

摘要： Vibrations during transportation inevitably lead to mechanical damage, endangering grape freshness and directly impacting their economic worth. While adequate packaging serves as a viable solution, current studies on packaging efficacy lack depth. Moreover, conventional methods for forecasting fruit freshness fail to accommodate the varying freshness levels of grapes across different packaging techniques. Consequently, a novel approach for predicting fruit freshness leveraging multi-sensing technology and machine learning algorithms is introduced. By reasonably evaluating packaging performance, the automation, intelligence, and accuracy of fruit freshness prediction are enhanced. Initially, critical control points in grape supply chain logistics were scrutinized using the HACCP method to identify key environmental parameters (vibration, temperature, and humidity) and their interaction with grape freshness. Subsequently, an environmental monitoring platform was devised for the grape supply chain, facilitating environmental surveillance under distinct packaging types (corrugated carton, foam box, plastic box, and inflatable package). Through a blend of environmental monitoring outcomes and physical-chemical indicators, the protective efficacy of diverse transport packaging was meticulously analyzed and appraised alongside finite element analysis. Notably, environmental data proved capable of characterizing grape freshness in lieu of quality data, with vibration metrics exhibiting strong correlations with quality metrics. Machine learning models were developed to predict grape freshness based on environmental cues, yielding prediction accuracies of 92.512% (SVM) and 94.334% (GA-ANN). The automated, non-destructive data acquisition and novel machine learning approaches offer a fresh avenue for evaluating packaging, predicting freshness, and managing food quality within grape logistics operations.

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