Storage behaviour of ohmic heated and ultrasonicated amla juice: AI mediated correlation between ascorbic acid content and color attributes

Artificial neural networking (ANN) based models are being increasingly and effectively used in the prediction and estimation of response variables during various food processing applications. We demonstrated the feasibility of a feed forward back propagation ANN model to estimate ascorbic acid content in fresh amla juice that was ohmic heating assisted vacuum evaporated (OHVC) with/without ultrasonication (US) from color attributes (L*, a*, b* and ΔE) during 4 weeks of ambient storage. There was a positive effect of synergistic processing of OHVC and US on the quality of stored amla juice with the fresh untreated sample witnessing ascorbic acid and total phenolic degradation of up to 60% and 14% higher than the OHVC + US treated samples respectively. At the end of the storage period, the color change was lowest in OHVC samples stored in glass containers (ΔE = 5.00) in comparison to untreated samples stored in PET containers (ΔE = 9.61). The results further suggested that coefficient of determination (R²) for the fitted ANN model was approximately 0.93 with a mean square error of 0.15, which was 20% higher and 66% lower than that obtained using multiple linear regression model, respectively. Furthermore, a number of training algorithms were tested out of which Levenberg–Marquardt algorithm showed the highest efficiency in model prediction. Overall, the quality in OHVC and US processed juice was retained better in comparison to control and the fitted ANN model effectively predicted ascorbic acid content from input color parameters. The results provided can be helpful in the non-destructive evaluation of ascorbic acid content in stored amla juice using AI mediated modelling.