Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses

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 com...

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Bibliographic Details
Published in:Journal of food science and technology 2018-07, Vol.55 (7), p.2457-2466
Main Authors: Barbin, Douglas Fernandes, Maciel, Leonardo Fonseca, Bazoni, Carlos Henrique Vidigal, Ribeiro, Margareth da Silva, Carvalho, Rosemary Duarte Sales, Bispo, Eliete da Silva, Miranda, Maria da Pureza Spínola, Hirooka, Elisa Yoko
Format: Article
Language:English
Subjects:
Beans
Chemical composition
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Classification
Cocoa
Cocoa beans
Food Science
Infrared analysis
Infrared spectroscopy
Mathematical analysis
Near infrared radiation
Nutrition
Original
Original Article
Principal components analysis
Processing industry
Regression analysis
Spectra
Spectroscopic analysis
Spectrum analysis
Statistical analysis
Statistical methods
Studies
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Summary: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.
ISSN:0022-1155
0975-8402
DOI:10.1007/s13197-018-3163-5