Development of Predictive Mass and Volume Models for African Star Apple Fruits

Abstract

Physical properties provide a vivid description of characteristics which can be used in classifying crops. Accurate prediction of mass and volume, among other important physical properties, is crucial in the design of systems for sorting, grading and packaging of fruits and vegetables. This study, therefore, focused on determining some physical attributes of African star apple and developing mathematical models to estimate the mass and volume of the fruits based on selected geometrical attributes. A total of 150 fresh African star apples were used in this study. The physical properties determined include mass, volume, axial dimensions, mean diameters and the projected areas along the three mutually-perpendicular axes. Regression analysis was carried out to develop the predictive mathematical models. The average length, width and thickness of African star apple fruits were 45.01±2.28, 43.88±2.52, and 44.22±2.30 mm, while the projected areas ranged from 1094.33 to 1951.187 mm². The average mass and volume were 57.36±7.46 g and 54.75±7.34 cm³, respectively. Models based on individual axial dimensions showed moderate correlation (R² = 0.514-0.632), with length-based models being the best among single-variable models. The longitudinal projected area models demonstrated a more predictive capability amongst all projected area models (R² = 0.607). The most accurate prediction was achieved by the mass model based on volume (R² = 0.9902), indicating that mass can be accurately predicted using volume measurements. These predictive mathematical models will facilitate the development of automated sorting and grading equipment, ultimately reducing processing costs and improving efficiency in African star apple value chain operations.