Physicochemical characterization of elephant apple (Dillenia indica L.) fruit and its mass and volume modeling using computer vision

Abstract

The elephant apple (Dillenia indica L.) is an underutilized fruit and a potential source of many nutrients. The variations in the mass and volume of fruits are highly dependent on their physical properties. This relation is used to design precise post-harvest machinery and analyze fruit quality. The work aims to study the physicochemical properties and predict the correlation of mass and volume with various physical properties measured by image processing techniques through single and multivariable regression models such as linear, quadratic, rational, and exponential, respectively. The total phenolic content, flavonoid, and antioxidant activity of elephant apple powder are 1.2 mg GAE/g, 353.58 mg QE/g, and 5.74 mg GAEAC/g, respectively. Among single-variable models, the length-based rational model was best suited for predicting the mass and volume of elephant apples with high R2 values of 0.930 and 0.924, respectively. For mass and volume of combined physical properties, rational and exponential models showed the best fit with the R2 values of 0.935 and 0.950 with RMSE values of 18.196 and 15.638. Thus, the correlation between the mass and volume of elephant apples with their physical characteristics resulted in a quicker, more accurate, and more precise grading system.