Quality Prediction of Fruits using Artificial Neural Network and its Application in Food Science

The nutritional value of fruit has a significant impact on customer satisfaction. Fruit quality and costeffectiveness would enhance customer fulfillment and revenue, according to retailers. The whole supply chain requires rapid and simple techniques for assessing quality. The strawberry is a popular fruit that is valued for its distinctive taste as well as numerous health benefits, including antioxidants and helpful amino acids, which significantly improve the item’s total quality. Therefore, dividing strawberries into desired and undesired groups would help customers and startups decide if the strawberries they buy and sell are desired depending on important factors like color and form. To overcome that, Artificial Neural Networks (ANN) were used in the present research. When Multi-Linear Regression (MLR) was used, unacceptable prediction results were achieved for every variable. In contrast, it was discovered that the interior functionality attributes may be properly anticipated by applying ANNs. This was particularly true for the overall monomeric anthocyanin $(\mathrm{R} 2=0.943)$ and antioxidant potential. The current research demonstrated that utilizing color positions with ANNs may effectively assess strawberry quality.