Anthocyanin content prediction in frozen strawberry puree

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Laura García-Curiel
Jesús G. Pérez-Flores
Elizabeth Contreras-López
Emmanuel Pérez-Escalante
Aldahir Alberto Hernández-Hernández


anthocyanin content, color measurement, image analysis, machine learning, 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) and artificial neural networks (ANNs), to predict anthocyanin content (AC) in frozen SP during its storage at –18°C for 120 days. When applying the OLS regression model, unsatisfactory AC prediction values were obtained due to multicollinearity. In contrast, a good prediction of AC using ANNs model was observed by comparing AC in SP predicted by the model versus the experimentally obtained values (coefficient of determination, R2 = 0.977).

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