Application of Artificial Neural Network (ANN) and Partial Least-Squares Regression (PLSR) to Predict the Changes of Anthocyanins, Ascorbic Acid, Total Phenols, Flavonoids, and Antioxidant Activity during Storage of Red Bayberry Juice Based on Fractal Analysis and Red, Green, and Blue (RGB) Intensity Values

Abstract

Artificial neural network (ANN) and partial least-squares regression (PLSR) models were developed to predict the changes of anthocyanin (AC), ascorbic acid (AA), total phenols (TP), total flavonoid (TF), and DPPH radical scavenging activity (SA) in bayberry juice during storage based on fractal analysis (FA) and red, green, and blue (RGB) intensity values. The results show the root mean squared error (RMSE) of ANN-FA decreased 2.44 and 12.45% for AC (RMSE = 18.673 mg/100 mL, R(2) = 0.939) and AA (RMSE = 8.694 mg/100 mL, R(2) = 0.935) compared with PLSR-RGB, respectively. In addition, PLSR-FA (RMSE = 5.966%, R(2) = 0.958) showed a 12.01% decrease in the RMSE compared with PLSR-RGB for predicting SA. For the prediction of TP and TF, however, both models showed poor performances based on FA and RGB. Therefore, ANN and PLSR combined with FA may be a potential method for quality evaluation of bayberry juice during processing, storage, and distribution, but the selection of the most adequate model is of great importance to predict different nutritional components.