Mass modeling of kinnow mandarin based on some physical attributes

Abstract

AbstractThe correlation between the physical properties of fruits such as their dimensions, projected areas, volume, and mass may assist in predicting fruit quality along with the development of post‐harvest machinery. Thus, the present study aims to predict the mass of kinnow mandarin (Citrus reticulata L.) fruit as a function of its axial dimensions, projected areas, and volume using linear and nonlinear mathematical models (quadratic, power, and s‐curve). Further, the mass models were presented under three different classifications: dimension based, projected area based, and volume based. The effect of size grading was also evaluated and compared with the data of ungraded fruits. Results showed that mass modeling based on dimensions and volume of ungraded fruits was more appropriate compared to individual grades. The quadratic model based on geometric mean diameter (R2 = 0.956) and ellipsoid volume (R2 = 0.955) are recommended for predicting the mass of ungraded fruits with maximal accuracy.Practical applicationMass based fruit grading is one of the important aspects of packaging as it not only reduces the wastage of handling and transportation resources by optimizing packaging formations but also enhances the marketability of commodity. Consumers generally prefer the fruits of uniform size, weight, and shape. Grading of horticultural produce is usually based on its appearance, size, and weight. The automatic fruit grading techniques generally use mass as a grading parameter due to its accuracy and effectiveness of the operation. The available kinnow grading systems primarily grade the fruits based on their dimensional attributes. Hence, the study was aimed at mass modeling of kinnow mandarin based on the selected engineering attributes such that results might be helpful to develop an accurate automatic grading system for grading based on the combined approach of size and mass. This study provides information about relationships between fruit mass and axial dimensions, projected areas, and volume, which are useful for the development of mass, and size based kinnow grading systems.