Artificial neural network and response surface methodology for modeling and optimization of activation of lactoperoxidase system
| Autor: | Yasin Ahmed, Abadr Adem, Ramachandran Kasirajan, ermias girma aklilu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Goat milk
Coefficient of determination Mean squared error 020209 energy Filtration and Separation 02 engineering and technology Catalysis Education chemistry.chemical_compound Chemical engineering 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering Food science Response surface methodology Response surface methodology (rsm) 0204 chemical engineering Lactose Water content Mathematics Fluid Flow and Transfer Processes Process Chemistry and Technology Lactoperoxidase artificial neural network (ANN) Total bacterial count Total dissolved solids Lactoperoxidase system Standard error chemistry Total coliform count TP155-156 Energy (miscellaneous) |
| Zdroj: | South African Journal of Chemical Engineering, Vol 37, Iss, Pp 12-22 (2021) |
| ISSN: | 1026-9185 |
| DOI: | 10.1016/j.sajce.2021.03.006 |
| Popis: | 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 methodology 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 × 102cfu/mL and total bacteria count of 5.44× 104cfu/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. |
| Databáze: | OpenAIRE |
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