Comparison of principal component and partial least square regression method in NIRS data analysis for cocoa bean quality assessment

Abstract The quality of cocoa beans can be determined in various ways, and two of them are: (i) manual observation via splitting cocoa beans in order to determine the degree of fermentation and observe the defect; (ii) chemical analyses for determining the fat and moisture content; with the latter is known as a time-consuming process. The NIRS instrument is a kind of a non-destructive measurement that can predict rapidly the quality of cocoa beans. This study aims to simulate a mathematical model for the prediction of moisture- and fat-content using a NIRS instrument. These results were subsequently analyzed with two types of multivariate regression analysis: Principal Component Regression (PCR) and Partial least Square Regression (PLSR) and the results shown based on two methods were then compared. The PCR method delivered a higher determination coefficient in moisture analysis compared to PLSR. On the other hand, a greater determination coefficient was delivered by PLSR in terms of fat analysis compared to the value obtained via PCR. The root means square error of the PCR method was lower than that of PLSR. It can be concluded that the PLSR method is more suitable for fat content prediction.