Advanced evaluation of strawberry quality, consumer preference, and cultivar discrimination through spectral imaging and neural networks

Abstract

Strawberries are among the most popular fruits, and meeting the rising demand for high-quality, flavorful varieties requires understanding consumer preferences. Accurately predicting these preferences, assessing quality, and preventing food fraud are crucial for breeders and sellers. This helps breeders develop superior cultivars and enables sellers to sort and market strawberries by taste and quality. This study explores the prediction of the quality and the acceptance of Dutch consumers of seventeen strawberry cultivars and their discrimination using VIS-NIR spectral imaging with a spectral range between 400 and 1000 nm and Artificial Neural Networks (ANNs), which was not done before. A total of 3564 samples were utilized. Three algorithms: Support Vector Machine, XGBoost, and a Multilayer Perceptron (MLP), were evaluated to predict quality parameters, consumer acceptance, and cultivar discrimination. MLP models showed the highest accuracy, with R² values of 0.85 for total soluble solids, 0.81 for titratable acidity, 0.76 for bite, and 0.78 for overall consumer acceptance. For cultivar discrimination, the MLP model achieved an F1 score of 0.84. These findings highlight the potential of ANNs in enhancing product quality assessment, preventing food fraud, and aligning products with consumer preferences in the food industry.