Nondestructive authentication of the regional and geographical origin of cocoa beans by using a handheld NIR spectrometer and multivariate algorithm

Abstract

Traceability in the cocoa bean trade is vital to ensuring quality. In this study, a handheld near-infrared (NIR) spectrometer was attempted for rapid and nondestructive regional and geographical classification of cocoa beans from different locations. Cocoa bean samples collected from seven cocoa-producing regions in Ghana (Eastern, Ashanti, Volta, Western South, Western North, Central, and Brong Ahafo) and four cocoa-producing countries in Africa (Uganda, Ivory Coast, Nigeria, and Ghana) were used. Among the preprocessing techniques employed, multiplicative scatter correction (MSC) performed better. The correct classification rate for the seven cocoa-producing regions in Ghana was 100% for LDA and SVM models in the training set and testing set. For classification of cocoa beans based on the country of origin, LDA and SVM also gave 100% classification rate both in the training set and testing set. The results give strong indications that hand-held spectroscopy coupled with chemometrics could be employed to provide the quick, accurate, and nondestructive classification of cocoa beans according to different locations. This technique could improve the work of quality control inspectors both from industry and regulatory perspectives for effective and quick detection of cocoa bean fraud.