Artificial Neural Networks for Rapid and Low-Cost Assessment of Color Quality of Date Syrup–Buttermilk Beverages

The visual quality of beverages is a major factor affecting consumers’ perception, quality evaluation, and market acceptance. Traditional colorimetric analysis is accurate but requires specialized equipment, time-consuming sample preparation, and substantial financial and time investment. The objective of this study was to develop a rapid, inexpensive, and accurate alternative method to predict the main color attributes of a date syrup–buttermilk beverage during processing and storage using an artificial neural network (ANN) approach. A multilayer perceptron ANN was developed using a back propagation algorithm. The ANN included three input variables (concentration of date syrup, storage cooling temperature, and storage time), one hidden layer with twenty neurons, and nine output color attributes (lightness, redness/greenness, yellowness/blueness, hue angle, Chroma, total color difference, browning index, whiteness index, and yellow index). To compare the effectiveness of the ANN model for the prediction of color attributes, the multiple linear regression (MLR) models were developed using the same inputs and the same training dataset. Experimental results indicated that all processing variables and their interactions had a significant effect on the color attributes of the beverage (p < 0.001). The trained ANN model exhibited excellent prediction capacity during the validation phase with high coefficients of determination (R2 range was between 0.9974 and 0.9997) with lower root mean squared error than MLR. Moreover, sensitivity analysis indicated date syrup concentration as the most influential factor on the final color profile. The developed ANN model provides an effective approach for the offline prediction of color quality during processing and storage under laboratory conditions. Although the integration of the ANN model with inline sensors may offer opportunities for future intelligent quality-control applications, real-time implementation and industrial deployment were not evaluated in the present study.