PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

Matoa fruit is a tropical fruit with a native background of Southeast Asia that is very promising in terms of specific flavour and nutrient properties, yet is under a poor usage by the groups because of the lack of post-harvest studies. There are two types of mass modelling discussed in this paper to facilitate post-harvest handling, sorting, and grading: purple and red Matoa. The average mass, primary diameter, Surface Area (SA), and sphericity of purple Matoa were 42.86 g, 54.22 mm, 7133 mm², and 0.85, in comparison to 17.55 g, 40.95 mm, 3610 mm², and 0.78 of red Matoa, respectively. It is worth noting that the dimensions, SA, and volume have been used as independent parameters to come up with regression models. The quadratic model proved to have the best predictive potential. The most suitable predictor of mass was the equivalent mean diameter (De) with a R² = 0.962 and Standard Error of Estimate (SEE) = 0.689. In line with this, the quadratic model was very good in explaining the case of SA, where R² = 0.960 and SEE = 0.714. Likewise, the model of the volume of an ellipsoid had a high predictive accuracy (R² = 0.960, SEE = 0.709). The results indicate that quadratic models are reliable in forecasting the mass of Matoa fruit, which can be used to design effective automated grading systems. This research would help to commercialise Matoa fruit in a sustainable way by removing the labour-intensive operations and increasing the value of the fruit in commercial and industrial use. Future research may focus on scalability and integration with machine vision systems.