Multivariate analysis for the classification of chocolate according to its percentage of Cocoa by using Terahertz Time-Domain Spectroscopy (THz-TDS)

Abstract

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.