A Back Propagation Neural Network Model for Postharvest Blueberry Shelf-Life Prediction Based on Feature Selection and Dung Beetle Optimizer

Runze Zhang, Yujie Zhu (), Zhongshen Liu (), Guohong Feng, Pengfei Diao, Hongen Wang, Shenghong Fu, Shuo Lv and Chen Zhang
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Runze Zhang: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Yujie Zhu: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Zhongshen Liu: College of Biopharmaceuticals, Heilongjiang Province Agricultural Engineering Vocational College, Harbin 150088, China
Guohong Feng: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Pengfei Diao: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Hongen Wang: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Shenghong Fu: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Shuo Lv: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Chen Zhang: College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China

Agriculture, 2023, vol. 13, issue 9, 1-31

Abstract: (1) Background: Traditional kinetic-based shelf-life prediction models have low fitting accuracy and inaccurate prediction results for blueberries. Therefore, this study aimed to develop a blueberry shelf-life prediction method based on a back propagation neural network (BPNN) optimized by the dung beetle optimizer using an elite pool strategy and a Gaussian distribution estimation strategy (GDEDBO); (2) Methods: The “Liberty” blueberry cultivar was used as the research object, and 23 quality indicators, including color parameters, weight loss rate, decay rate, and texture parameters, were measured under storage temperatures of 0, 4, and 25 °C. Based on the maximum relevance minimum redundancy (MRMR) algorithm, seven key influencing factors of shelf life were selected as the input parameters of the model, and then the MRMR-GDEDBO-BPNN prediction model was established; (3) Results: the results showed that the model outperformed the baseline model at all three temperatures, with strong generalization ability, high prediction accuracy, and reliability; and (4) Conclusions: this study provided a theoretical basis for the shelf-life determination of blueberries under different storage temperatures and offered technical support for the prediction of remaining shelf life.

Keywords: blueberry; shelf-life forecast; maximum relevance minimum redundancy (MRMR); gaussian distribution estimation (GDE) strategy (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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