Fast quantitative detection of black pepper and cumin adulterations by near-infrared spectroscopy and multivariate modeling

Abstract

Spices are high value-added ingredients that are part of a complex supply chain susceptible to a variety of fraudulent actions which requires fast and efficient quality control. The study developed methods for detection of non-targeted and targeted adulteration in black pepper and cumin powder by addition of starch cassava and corn flour (5%–50%) using NIRs associated with chemometric models. The developed non-targeted models indicated greater ease of identification of chewing in cumin samples when compared to black pepper samples. In terms of the discrimination of the type of adulterant used the models were efficient for the two spices. Targeted models were developed using the MLR and PLS techniques where the results presented the high predictive capacity for the different types of adulterants, with a correlation coefficient above 0.90 and RMSE ranging from 2.2 to 7. The practical application of the models was tested in samples of powdered spices sold in supermarkets and street markets. A high percentage of adulterated samples were observed 62% and 79% for black pepper and cumin, respectively. The results demonstrate that adulteration in black pepper and cumin samples by addition of cassava starch and corn flour (or similar) are common, indicating the importance of this type of investigation.