Agricultural and Food Engineering Department, of Process Parameters for Development of Probiotic-GABA Enriched Nutri Bar by Response Surface Methodology (RSM) and Modelling Using Artificial Neural Networks (ANNs): Characterization and Sensory Evaluation by Fuzzy Logic Analysis.

The aim of the research was to optimize the process conditions for development of nutri bar enriched with spray dried probiotic strain Lactococcus lactis SKL 13 and γ-aminobutyric acid (GABA) using response surface methodology (RSM) and the artificial neural network (ANN) algorithms. Further, the physicochemical (such as probiotic count, GABA, hardness, water activity, and free fatty acid content), functional and sensory properties of the developed bars were evaluated using fuzzy logic analysis. The optimized process parameters, such as heating temperature and treatment time of 125.6 °C and 26.4 min, respectively, were obtained using RSM for the preparation of the nutri bar. While modelling, the ANN showed better predictive ability than RSM as the coefficient of determination (R) values of the quality attributes were higher in the case of ANN (0.9502-0.9994) than RSM (0.9296-0.9992). The developed bar had a good nutritional profile with a probiotic count of 7.53 log CFU/g and GABA content of 55.78 mg/100 g. The hardness of the bar samples significantly reduced from 72.92 to 63.87 N after the addition of spray dried microcapsules without having significant differences (p > 0.05) in cohesiveness, adhesiveness, and springiness compared to control samples. The probiotic-GABA enriched bar samples exhibited improved functional properties, but the X-ray diffraction patterns showed their weaker structure compared to the samples without probiotics and GABA. Moreover, the fuzzy logic evaluation showed that the optimized bar enriched with probiotic-GABA had superior sensory characteristics compared to the control samples.