Physical characterization and development of mathematical models for predicting mass and area of oil palm fruits

Abstract

AbstractEngineering properties play an important role in designing any post‐harvest machinery or process. Oil palm fruits are nonuniform in size and shape, hence to develop an appropriate handling, conveying, and extraction method, detailed knowledge of engineering properties of oil palm fruits is required. Further, distribution of oil palm fruits within the bunch is also nonuniform, varied with fruit mass/size which affects the distribution pattern of oil within the bunch. Hence, to represent the complete bunch while sampling, fruit mass needs to be taken into consideration. However, measuring fruit mass for individual fruit may not be feasible. Hence the present study is targeted to measure the engineering properties of oil palm fruits and development of various mathematical models to predict the mass, surface area and projected area of oil palm fruits based on their engineering properties. Oil palm fruits (N = 1200) of the Tenera variety were collected from 10 bunches and engineering properties of oil palm fruits were measured. The data collected for engineering properties of oil palm fruits revealed that a wide range for each engineering property was reported for oil palm fruits, and hence it can be concluded that oil palm fruits are diversified in terms of their engineering properties. A correlation study was conducted and correlation coefficient for each parameter was recorded. Mass, surface area and project area modeling was done. The four models, linear, quadratic, S‐curve and power models were considered and best‐fitted model was selected based on fitting characteristics of the model.Practical applicationsEngineering properties of oil palm fruits measured in this study play a vital role in designing industrial level post‐harvest machinery for oil palm bunch processing and oil extraction. Models developed for predicting mass, surface area and projected area of oil palm fruits reported fairly accepted validation results. Hence post‐harvest machinery/process developed based on desired engineering property predicted with these models can work efficiently. Grading of oil palm fruits based on mass is now possible at industrial level with the help of grading machinery developed using these model data.