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Prediction of benzo[ a ]pyrene content of smoked sausage using back‐propagation artificial neural network
Author(s) -
Chen Yan,
Cai Kezhou,
Tu Zehui,
Nie Wen,
Ji Tuo,
Hu Bing,
Chen Conggui,
Jiang Shaotong
Publication year - 2018
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.8801
Subject(s) - artificial neural network , pyrene , benzo(a)pyrene , backpropagation , food science , chemistry , artificial intelligence , network structure , biological system , computer science , machine learning , biology , organic chemistry
Abstract BACKGROUND Benzo[ a ]pyrene (BaP), a potent mutagen and carcinogen, is reported to be present in processed meat products and, in particular, in smoked meat. However, few methods exist for predictive determination of the BaP content of smoked meats such as sausage. In this study, an artificial neural network (ANN) model based on the back‐propagation (BP) algorithm was used to predict the BaP content of smoked sausage. RESULTS The results showed that the BP network based on the Levenberg–Marquardt algorithm was the best suited for creating a nonlinear map between the input and output parameters. The optimal network structure was 3‐7‐1 and the learning rate was 0.6. This BP‐ANN model allowed for accurate predictions, with the correlation coefficients ( R ) for the experimentally determined training, validation, test and global data sets being 0.94, 0.96, 0.95 and 0.95 respectively. The validation performance was 0.013, suggesting that the proposed BP‐ANN may be used to predictively detect the BaP content of smoked meat products. CONCLUSION An effective predictive model was constructed for estimation of the BaP content of smoked sausage using ANN modeling techniques, which shows potential to predict the BaP content in smoked sausage. © 2017 Society of Chemical Industry