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 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.
- Alvarez C, Perez E, Cros E, Lares M, Assemat S, Boulanger R, Davrieux F (2012) The use of near infrared spectroscopy to determine the fat, caffeine, theobromine and (-)-epicatechin contents in unfermented and sun-dried beans of criollo cocoa. J Near Infrared Spectrosc 20:307–315CrossRefGoogle Scholar
- AOAC (1995) Official methods of analysis, 16th edn. Association of Official Analytical Chemists, WashingtonGoogle Scholar
- Copikova J, Novotna M, Smidova I, Synytsya A, Cerna M (2003) Application of near infrared spectroscopy in chocolate analysis. Chemcké Listy 97:571–575Google Scholar
- Dagnew MD, Crowe TG, Schoenau JJ (2004) Measurement of nutrients in Saskatchewan hog manures using near-infrared spectroscopy. Can Biosyst Eng 46:33–37Google Scholar
- ISO (2016). ISO 3310-1 Test sieves—technical requirements and testing—part 1 test sieves of metal wire clothGoogle Scholar
- Kaffka KJ, Norris KH, Kulcsar F, Draskovits I (1982) Attempts to determine fat, protein and carbohydrate content in cocoa powder by the NIR technique. Acta Aliment 11:271–288Google Scholar
- Martens H, Naes T (1989) Multivariate calibration. Wiley, ChichesterGoogle Scholar
- Osborne BG, Fearn T (1986) Near infrared spectroscopy in food analysis. Wiley, New YorkGoogle Scholar
- Osborne BG, Fearn T, Hindle PH (1993) Pratical NIR spectroscopy: with applications in food and beverage analysis. Longman Scientific & Technical, Harlow, pp 227Google Scholar
- Reis N, França AS, Oliveira LS (2013) Discrimination between roasted coffee, roasted corn and coffee husks by diffuse reflectance infrared fourier transform spectroscopy. Food Sci Technol 50:715–722Google Scholar