Evaluation of the Use of Machine Learning to Predict Selected Mechanical Properties of Red Currant Fruit (Ribes rubrum L.) Ozonized during Storage

Abstract

The study examined selected biometric and mechanical properties of fruits of three varieties of red currant (Ribes rubrum L.) from organic cultivation. The influence of the harvest date of red currant fruits, their storage time, and the use of ozone at a concentration of 10 ppm for 15 and 30 min on the water content, volume, and density, as well as the destructive force and the apparent modulus of elasticity, were determined. Fruits harvested at harvest maturity were characterized by a much larger volume and lower water content compared to fruits harvested seven days earlier. The ozonation process, regardless of the harvest date, resulted in a reduction in volume, density, and humidity. After 15 days of storage, the fruits of the tested varieties showed a decrease in the average water content from 86.15% to 83.79%. The tests showed a decrease in the destructive force and the apparent modulus of elasticity, the average value of which for fresh fruit was 76.98 ± 21.0 kPa, and after 15 days of storage, it decreased to 56.34 ± 15.96 kPa. The relationships between fruit-related parameters, harvesting, and storage conditions and fruit strength characteristics were modeled with the use of neural networks and support vector machines. These relationships are complex and nonlinear, and therefore, machine learning is usually more relevant than the traditional methods of modeling. For evaluation of the performance of the models, statistical parameters such as the coefficient of correlation (R), root-mean-squared error (RMSE), and generalization ability coefficient (GA) were used. The best models for the prediction of an apparent modulus of elasticity were developed with the use of ANNs. These models can be used in practice because the correlation between expected and predicted values was in the range 0.78–0.82, RMSE was in the range 13.38–14.71, and generalization ability was excellent. A significantly lower accuracy was achieved for models with a destructive force as the output parameter (R ≤ 0.6).