Affiliation:
1. Department of Processing and Food Engineering, College of Agricultural Engineering & Technology Punjab Agricultural University Ludhiana Punjab India
2. Department of Food Engineering National Institute of Food Technology Entrepreneurship and Management (Institute of National Importance, Under MoFPI, Govt. of India) Sonipat Haryana India
Abstract
AbstractThe present study explores the infusion of active compounds (ascorbic acid and calcium lactate) into sliced button mushrooms (Agaricus bisporus) to increase the nutritional value and reduce the browning effect of sliced mushrooms using the vacuum impregnation (VI) technique. The aim was to functionalize the vacuum‐infused sliced mushrooms and evaluate the physicochemical properties of button mushrooms for diversifying food use. The central composite design was implemented to determine the optimized condition for the process with four independent factors, that is, immersion time (IT) 30–90 min, solution temperature (ST) 35–55°C, solution concentration (SC) 4%–12%, and vacuum pressure (VP) 50–170 mbar. The optimum VI processes obtained were ST‐40°C, SC‐8%, VP‐140 mbar, and IT‐65 min with a desirability function of 0.77. Statistically, two models (response surface methodology [RSM] and artificial neural network [ANN]) were employed to compare the better performance for the prediction of VI operational process parameters. The RSM model showed a better prediction of VI process parameters than the ANN model, with a higher R2 value (0.9228 vs. 0.8160) and lower root mean square error value (1.4004 vs. 2.1751), χ2 (2.4491 vs. 5.2762), mean absolute error (1.1177 vs. 1.1611), and absolute average deviation (4.3532 vs. 5.6746) for water loss. A similar pattern was observed for solute gain, ascorbic acid, titratable acidity, color change, firmness, and pH. Therefore, the VI process was found to be an effective method for enhancing the nutritional properties of sliced mushrooms. These findings concluded that the RSM model is more efficient for better prediction with good accuracy of the VI process than the ANN model.