IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p12291-d673991.html
   My bibliography  Save this article

Ensemble Learning Models for Food Safety Risk Prediction

Author

Listed:
  • Li-Ya Wu

    (Division of Risk Management, Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
    Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Sung-Shun Weng

    (Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.

Suggested Citation

  • Li-Ya Wu & Sung-Shun Weng, 2021. "Ensemble Learning Models for Food Safety Risk Prediction," Sustainability, MDPI, vol. 13(21), pages 1-26, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12291-:d:673991
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/12291/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/21/12291/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amirhosein Mosavi & Farzaneh Sajedi Hosseini & Bahram Choubin & Massoud Goodarzi & Adrienn A. Dineva & Elham Rafiei Sardooi, 2021. "Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 23-37, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kagiso Samuel More & Christian Wolkersdorfer, 2022. "Predicting and Forecasting Mine Water Parameters Using a Hybrid Intelligent System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2813-2826, June.
    2. Ahmad Jafarzadeh & Abbas Khashei-Siuki & Mohsen Pourreza-Bilondi, 2022. "Performance Assessment of Model Averaging Techniques to Reduce Structural Uncertainty of Groundwater Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 353-377, January.
    3. Duong Hai Ha & Phong Tung Nguyen & Romulus Costache & Nadhir Al-Ansari & Tran Phong & Huu Duy Nguyen & Mahdis Amiri & Rohit Sharma & Indra Prakash & Hiep Le & Hanh Bich Thi Nguyen & Binh Thai Pham, 2021. "Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4415-4433, October.
    4. Abinash Mohanta & Arpan Pradhan & Monalisa Mallick & K. C. Patra, 2021. "Assessment of Shear Stress Distribution in Meandering Compound Channels with Differential Roughness Through Various Artificial Intelligence Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4535-4559, October.
    5. You-Shyang Chen & Chien-Ku Lin & Chih-Min Lo & Su-Fen Chen & Qi-Jun Liao, 2021. "Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry," Mathematics, MDPI, vol. 9(20), pages 1-26, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12291-:d:673991. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.