Dynamic comprehensive quality assessment of post-harvest grape in different transportation chains using SAHP-CatBoost machine learning

文献类型： 外文期刊

第一作者： Chen, Qian

作者： Chen, Qian;Li, Jiali;Qian, Jianping;Feng, Jianying;Qian, Jianping;Feng, Jianying

作者机构：

关键词： Post-harvest grape; comprehensive quality assessment; self-adaptive AHP; CatBoost model; categorical feature; machine learning

期刊名称：FOOD QUALITY AND SAFETY （ 影响因子：5.6； 五年影响因子：6.2 ）

ISSN： 2399-1399

年卷期： 2024 年 8 卷

页码：

收录情况： SCI

摘要： Objectives Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value. Due to high environmental sensitivity, the post-harvest quality of fresh fruit will undergo complex changes in the supply chain, with various dynamic quality-related features. It is difficult to efficiently and accurately extract comprehensive quality feature of post-harvest fruits from high-dimensional monitoring data with heterogeneous characteristics (numerical and categorical). Therefore, we proposed a dynamic comprehensive quality assessment method based on self-adaptive analytic hierarchy process (SAHP) integrated with the CatBoost model.Materials and Methods By adaptive weight optimization, the SAHP was utilized to analyze the multi-source quality information and obtain the quantized fusion value, as an output sample of CatBoost machine learning. Then, using heterogeneous monitoring data as input, the CatBoost model was directly trained through unbiased boosting with categorical features for dynamic assessment of overall quality status.Results Three quality index monitoring data sets for 'Jufeng' grape in different transportation chains (normal temperature, cold insulation, and cold chain) were individually constructed as the research samples. Furthermore, compared to other machine learning methods, the SAHP-CatBoost had more accurate results in comprehensive quality feature extraction. In actual transportation chains, the mean absolute error, mean absolute percentage error, and root mean squared error of dynamic comprehensive assessment were limited to 0.0044, 1.012%, and 0.0078, respectively.Conclusions The proposed method is efficient in handling heterogeneous monitoring data and extracting comprehensive quality information of post-harvest grape as a robust shelf-life indicator. It can reasonably guide post-harvest quality management to reduce food loss and improve economic benefits.

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