Effects of Temperature, Time, and pH on the Stability of Anthocyanin Extracts: Prediction of Total Anthocyanin Content Using Nonlinear Models

Abstract

In this study, different anthocyanin sources including grape skin, black carrot, and red cabbage were used to determine the effect of thermal treatment, different acidity levels, and time on the anthocyanin content and degradation. The total anthocyanin contents were modeled by neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) models. The red cabbage anthocyanin stabilities were higher than others. The anthocyanins degraded more rapidly at higher temperatures. The anthocyanin contents of samples decreased with the increase of pH from 3 to 7. Comparison of the models showed that the ANFIS model performed better than the ANN model for the estimation of total anthocyanin content in all samples. The lowest root mean square error (0.0457) and highest R (2) (0.9942) values were obtained for red cabbage and grape skin in the validation period with the ANFIS model, respectively. This study showed that both models can be utilized efficiently for the prediction of total anthocyanin content affected by temperature, time, and pH.