Study of polycyclic aromatic hydrocarbons generated from fatty acids by a model system.

BACKGROUND: The characteristics of carcinogenic polycyclic aromatic hydrocarbons (PAHs) produced from various fatty acids, as important components of fats and oils, at high temperature are still little known. The reason is because the existing data are from experiments conducted in complex food systems. In this study, 12 PAHs produced from nine fatty acids, representing saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), were investigated in a model system heated at 98, 165 and 240 °C. RESULTS: SFAs can with difficulty be pyrolyzed to generate PAHs at 98 °C, but small amounts of PAHs were determined in MUFAs (44.97 µg kg-1 ) and PUFAs (177.73 µg kg-1 ). When the temperature reached 165 °C, there were totals of 27.59, 142.8 and 449.68 µg kg-1 PAHs assayed in SFAs, MUFAs and PUFAs, respectively. The amounts of PAHs generated from SFAs, MUFAs and PUFAs at 240 °C were higher when compared with those of the 165 °C group (P < 0.05). With an increase of heating temperature, the proportion of PAHs with four to five rings increased. Under the same heating conditions, the concentration of PAHs in fatty acids increased with an increase in the number of double bonds. CONCLUSIONS: More PAHs, especially carcinogenic ones with four to five rings, will be produced in fatty acids heated at higher temperature. The content of PAHs generated from fatty acids increased with an increase in the number of double bonds. This study will increase the understanding of the production characteristics of PAHs from various fatty acids under heating condition. © 2019 Society of Chemical Industry.

Prediction of benzo[a]pyrene content of smoked sausage using back-propagation artificial neural network.

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.