Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

The study investigated the selected mechanical properties of fresh and stored large cranberries. analyses focused on changes in energy requirement up to breaking point aimed identify apparent elasticity index fruit cranberry varieties relating harvest time, water content, as well storage duration conditions. After 25 days storage, were found with a decrease mean acidity, from 1.56 g?100 g?1 1.42 g?1, 89.71% 87.95%. findings showed energy; there was also change modulus elasticity, its value 0.431 ± 0.07 MPa, after it decreased 0.271 0.08 MPa. relationships between varieties, temperature, x, y, z dimensions fruits, their parameters modeled use multiple linear regression, artificial neural networks, support vector machines. Machine learning techniques outperformed regression.