Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses
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Effective and fast methods are important for distinguishing cocoa varieties in the field and in the processing industry. This work proposes the application of NIR spectroscopy as a potential analytical method to classify different varieties and predict the chemical composition of cocoa. Chemical composition and colour features were determined by traditional methods and then related with the spectral information by partial least-squares regression. Several mathematical pre-processing methods including first and second derivatives, standard normal variate and multiplicative scatter correction were applied to study the influence of spectral variations. The results of chemical composition analysis and colourimetric measurements show significant differences between varieties. NIR spectra of samples exhibited characteristic profiles for each variety and principal component analysis showed different varieties in according to spectral features.
KeywordsChemical composition Chocolate Principal component analysis Cocoa beans NIR spectroscopy PLS regression
The authors gratefully acknowledge the financial support from the Coordination for the Improvement of Higher Education Personnel (CAPES) strategic research initiative under the Brazilian Ministry of Education, Project Number 23038.019085/2009-14. This research was supported by Sao Paulo Research Foundation (FAPESP), Young Researchers Award, Grant Number 2015/24351-2. Professor Elisa Yoko Hirooka is a CNPq research fellow.
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