HPLC-UV and HPLC-FLD Fingerprinting for the Detection and Quantitation of Adulterations in the Prevention of Coffee Frauds

Abstract

Globalization has produced a total change of scenario in food industry producing a tough competence to occupy the market share, instigating the reduction of costs by usage of fraudulent practices derived from food adulteration. These practices are performed by substitution of most valuable components for other with less commercial value and/or lower health beneficial properties supposing an economic fraud and a potential health problem. Coffees are sometimes the target of this kind of fraudulent practices due to the high demand of the product where manufacturers adulterate coffee with wheat, corn, and other grains, seeds and plants. In this work, simultaneous non-targeted HPLC-UV and HPLC-FLD fingerprinting methods were developed to achieve the classification and authentication of different instant coffee, and chicory samples using multivariate chemometric methodologies such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS). Both HPLC-UV and HPLC-FLD fingerprints, proved to be excellent chemical descriptors for the discrimination of chicory samples against instant coffee and decaffeinate coffee by PLS-DA. However, better results were obtained with HPLC-UV fingerprints when coffee was discriminated from decaffeinated coffee (94.4% classification rate respect to 83.3% for HPLC-FLD fingerprints). Besides, both methodologies were able to detect and quantify adulterant levels in coffee and decaffeinated samples adulterated with chicory exhibiting good regression linearity (R2≥0.996), and low calibration (0.7-2.1%) and prediction (2.4-3.5%) errors. Overall, both non-targeted HPLC-UV and HPLC-FLD showed to be effective, simple, and trustable to accomplish the characterization, classification and authentication of instant coffee and chicory samples being potential methodologies to prevent food frauds