國家衛生研究院 NHRI:Item 3990099045/15207
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    题名: Machine learning for predicting chemical migration from food packaging materials to foods
    作者: Wang, SS;Lin, P;Wang, CC;Lin, YC;Tung, CW
    贡献者: Institute of Biotechnology and Pharmaceutical Research;National Institute of Environmental Health Sciences;NHRI Graduate Student Program
    摘要: Food contact chemicals (FCCs) can migrate from packaging materials to food posing an issue of exposure to FCCs of toxicity concern. Compared to costly experiments, computational methods can be utilized to assess the migration potentials for various migration scenarios for further experimental investigation that can potentially accelerate the migration assessment. This study developed a nonlinear machine learning method utilizing chemical properties, material type, food type and temperature to predict chemical migration from package to food. Nine nonlinear algorithms were evaluated for their prediction performance. The ensemble model leveraging multiple algorithms provides state-of-the-art performance that is much better than previous linear regression models. The developed prediction models were subsequently applied to profile the migration potential of FCCs of high toxicity concern. The models are expected to be useful for accelerating the assessment of migration of FCCs from package to foods.
    日期: 2023-08
    關聯: Food and Chemical Toxicology. 2023 Aug;178:Article number 113942.
    Link to: http://dx.doi.org/10.1016/j.fct.2023.113942
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0278-6915&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:001043950100001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85165173610
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