Anthocyanin content prediction in frozen strawberry puree

Rapid color degradation during processing and storage is a limitation when using strawberry puree (SP). This work aimed to use image analysis coupled with two machine learning algorithms: ordinary least squares (OLS) artificial neural networks (ANNs), predict anthocyanin content (AC) in frozen SP its at –18°C for 120 days. When applying the OLS regression model, unsatisfactory AC prediction values were obtained due multicollinearity. In contrast, good of ANNs model was observed by comparing predicted versus experimentally (coefficient determination, R2 = 0.977).