Application of artificial neural network to predict benzo[a]pyrene based on multiple quality of smoked sausage

Wei, Xing; Liu, Xingyun; Xu, Chaoyang; Farid, Muhammad Salman; Cai, Kezhou; Zhou, Hui; Chen, Conggui; Xu, Baocai

doi:10.1016/j.lwt.2022.113571

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Different superscript letters (a-e) indicate significant differences between groups (P < 0.05). 79 which were lower than the results of this study. In a recent project, the benzo(a)pyrene content of smoked sausages was predicted using a BP-ANN model, the values of training R, validation R, testing R and all R being, respectively, 0.94, 0.96, 0.95 and 0.95, with a validation performance of 0.013, and it was concluded that the BP-ANN model had a higher prediction accuracy than the nonlinear regression model.…”

Section: Ann Analysiscontrasting

confidence: 87%

“…It has already been reported that with the aid of BP‐ANN, it was possible to accurately anticipate how fresh packaged meat products would be by using the color shift of the film, the values of R for the model being 0.94–0.98 52 . Moreover, the link between benzo(a)pyrene concentration and various masses was modeled using BP‐ANN, where the values of validation R and testing R for the model were 0.9510 and 0.9264, as reported by Xing et al ., 79 which were lower than the results of this study. In a recent project, the benzo(a)pyrene content of smoked sausages was predicted using a BP‐ANN model, the values of training R , validation R , testing R and all R being, respectively, 0.94, 0.96, 0.95 and 0.95, with a validation performance of 0.013, and it was concluded that the BP‐ANN model had a higher prediction accuracy than the nonlinear regression model 40 …”

Section: Resultsmentioning

confidence: 81%

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Formation and prediction of heterocyclic amines and N‐nitrosamines in smoked sausages using back propagation artificial neural network

Zhou,

Zhai,

Yao

et al. 2024

J Sci Food Agric

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BACKGROUNDHeterocyclic amines (HAs) and N‐nitrosamines (NAs) are formed easily during the thermal processing of food, and epidemiological studies have demonstrated that consuming HAs and NAs increases the risk of cancer. However, there are few studies on the application of back propagation artificial neural network (BP‐ANN) models to simultaneously predict the content of HAs and NAs in sausages. This study aimed to investigate the effects of cooking time and temperature, smoking time and temperature, and fat‐to‐lean ratio on the formation of HAs and NAs in smoked sausages, and to predict their total content based on the BP‐ANN model.RESULTSWith an increase in processing time, processing temperature and fat ratio, the content of HAs and NAs in smoked sausages increased significantly, while the content of HA precursors and nitrite residues decreased significantly. The optimal network topology of the BP‐ANN model was 5–11–2, the correlation coefficient values for training, validation, testing and all datasets were 0.99228, 0.99785, 0.99520 and 0.99369, respectively, and the mean squared error value of the best validation performance was 0.11326. The bias factor and the accuracy factor were within acceptable limits, and the predicted values approximated the true values, indicating that the model has good predictive performance.CONCLUSIONThe contents of HAs and NAs in smoked sausages were significantly influenced by the cooking conditions, smoking conditions and fat ratio. The BP‐ANN model has high application value in predicting the contents of HAs and NAs in sausages, which provides a theoretical basis for the suppression of carcinogen formation. © 2024 Society of Chemical Industry.

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Section: Ann Analysiscontrasting

confidence: 87%