Predictions of apple mechanical damage volume using micro-CT measurements and support vector regression(SVR)

Abstract

Accurately calculating the damage volume and making clear the interconnected effects of the physical and chemical properties of apples on mechanical damage are crucial steps in reducing the possibility of apple damage. Tests have been conducted on apples at different maturity levels, including measuring the firmness, moisture content, water-soluble pectin (WSP) content, soluble solids content (SSC) of the flesh, and elastic modulus of the apple flesh and peel. Transient collisions were performed using a pendulum device to create damage zones under specific impact energies. Then, the X-ray micro-computed tomography (Micro-CT) was utilized to quantitatively analyse mechanical damage volumes, the effects of apple tissue characteristics and impact energy on the damage volume were analysed in detail. The results indicated that higher-maturity apples were more susceptible to mechanical damage, and Micro-CT measurements were more accurate when the impact energy ≥ 0.05 J, while the empirical formula showed greater deviation; the curvature radius at the impact point can be considered as a latent variable influencing the apple damage volume. Furthermore, a damage volume prediction model, based on bruise area calculated by the empirical formula, WSP content of the flesh, and elastic modulus of the apple flesh and peel, was established. With a testing dataset without anticipate in model training for verification, the developed model achieved a coefficient of determination of 0.9782, indicating that the model can predict damage volume effectively and reduce errors associated with the empirical formula, particularly at higher impact energies. The research can provide insights into potential applications in apple industry practices to reduce the mechanical damage.