Abstract:
In the present study, the multi-component lactoperoxidase system (LPS) is used for improving milk safety and
requires thiocyanate (SCN− ) as a substrate for the generation of antimicrobial hypothiocyanite (OSCN− ). The
influence of four independent variables for activation of lactoperoxidase system on the improving the quality of
raw goat milk were investigated and optimized using an artificial neural network and response surface meth odology on the growth of total coliform count and bacterial count. The two models’ predictive capabilities were
compared in terms of root mean square error, mean absolute error, standard error of prediction, absolute average
deviation, and coefficient of determination based on the validation data set. The results showed that properly
trained artificial neural network model is more accurate in prediction than the RSM model. The optimum
conditions were a temperature of 25 ◦C, storage time of 10 hr, NaSCN of 30 ppm and hydrogen peroxide of 18
ppm. For these conditions, an experimental total coliform count of 4.51 × 102
cfu/mL and total bacteria count of
5.44× 104
cfu/mLwas obtained, which was in reasonable agreement with the predicted content.The results
indicate that the model is in substantial agreement with current research, and activating the LP System can
extend the storage period of goat milkfor up to10hr when stored at 25 ◦C.The results revealed no significant
differences in milk composition (protein content, fat content, lactose content, total solids, moisture content and
ash content) were observed among activated and control goat milk samples.
