消毒剂
单核细胞增生李斯特菌
次氯酸钠
生物膜
过氧乙酸
随机森林
微生物学
化学
机器学习
计算机科学
生物
细菌
生物化学
遗传学
有机化学
过氧化氢
作者
Hongmin Zhen,Yumeng Hu,Ke Xiong,Mengmeng Li,Jin Wen
标识
DOI:10.1016/j.fbio.2024.104637
摘要
The present study evaluates and models the efficacy of three different disinfectants-sodium hypochlorite (SH), peracetic acid (PAA), and chlorine dioxide (ClO2) solution in inactivating biofilm formation of Listeria monocytogenes on stainless steel (SS) surfaces. The nonlinear survival curves of L. monocytogenes biofilm cells treated with different disinfectants were fitted using the Weibull model and four supervised machine learning models (Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting Decision Tree (GBDT)). RF model exhibited superior predictive ability compared to other methods (RMSE: 0.12, MAE: 0.10, MAPE: 2.38, and R2: 0.99). Both RF and ANN showed better predictive effectiveness than the Weibull model. This research quantifies and models the efficacy of three disinfectants on L. monocytogenes biofilm on SS surfaces, providing valuable insights into the disinfection process of equipment surfaces in food processing.
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