Dynamic comprehensive quality assessment of postharvest grape in different transportation chains using SAHP–CatBoost machine learning  

作　　者：Qian Chen Jiali Li Jianying Feng Jianping Qian 

机构地区：[1]Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing,China [2]College of Information and Electrical Engineering,China Agricultural University,Beijing,China

出　　处：《Food Quality and Safety》2024年第2期440-450,共11页食品品质与安全研究（英文版）

基　　金：funded by the National Natural Science Foundation of China(No.31971808);the Central Publicinterest Scientifc Institution Basal Research Fund(No.CAASZDRW202107),China.

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 diffcult to effciently 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 effcient 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 benefts.

关 键 词：Post-harvest grape comprehensive quality assessment self-adaptive AHP CatBoost model categorical feature machine learning 

分 类 号：S663.1[农业科学—果树学]