Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses.
Douglas Fernandes BarbinLeonardo Fonseca MacielCarlos Henrique Vidigal BazoniMargareth da Silva RibeiroRosemary Duarte Sales CarvalhoEliete da Silva BispoMaria da Pureza Spínola MirandaElisa Yoko HirookaPublished in: Journal of food science and technology (2018)
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.
Keyphrases
- optical coherence tomography
- photodynamic therapy
- machine learning
- dual energy
- high resolution
- fluorescent probe
- fluorescence imaging
- healthcare
- mass spectrometry
- magnetic resonance imaging
- liquid chromatography
- computed tomography
- magnetic resonance
- data analysis
- health information
- human health
- social media
- structure activity relationship
- contrast enhanced