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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Runze Zhang & Yujie Zhu & Zhongshen Liu & Guohong Feng & Pengfei Diao & Hongen Wang & Shenghong Fu & Shuo Lv & Chen Zhang, 2023. "A Back Propagation Neural Network Model for Postharvest Blueberry Shelf-Life Prediction Based on Feature Selection and Dung Beetle Optimizer," Agriculture, MDPI, vol. 13(9), pages 1-31, September.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1784-:d:1236208
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    References listed on IDEAS

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    1. Lian, Shiguo & Sun, Jinsheng & Wang, Zhiquan, 2005. "A block cipher based on a suitable use of the chaotic standard map," Chaos, Solitons & Fractals, Elsevier, vol. 26(1), pages 117-129.
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