Simultaneous Detection of Multiple Adulterants in Cocoa Powder Using Fourier Transform Infrared Spectroscopy and Chemometrics

Abstract Economically motivated adulteration of cocoa powder poses significant risks to food safety, product quality, and consumer trust. Traditional detection methods, while accurate, are often time-consuming and resource intensive. This study presents a novel approach for the simultaneous detection and quantification of carob flour and cocoa shell adulterants in cocoa powder using Fourier transform infrared (FTIR) spectroscopy coupled with partial least-squares regression (PLSR) modeling. More than 600 spectra from 153 samples, including pure, single-adulterant, and mixed-adulterant cocoa powders, were analyzed. A three-stage model development strategy was employed, and the best model, using standard normal variate (SNV) transformation combined with Savitzky–Golay smoothing, achieved high predictive accuracy (CV-R2 = 0.946 ± 0.011) with low detection limits (2.28% for carob and 5.80% for shell). Variable importance in projection (VIP) analysis identified key spectral regions contributing to adulterant discrimination. This methodology demonstrates strong potential for scalable, nondestructive quality control in the cocoa industry and offers a framework for multiadulterant detection in other complex food matrices.