Chemometric-based approach for economically motivated fraud detection in organic spices via NIR spectroscopy

Organic spices, recognized as high-value products, are at high risk of intentional adulteration (also called economically motivated adulteration). This highlights the importance of developing reliable methods to ensure the quality and authenticity of organic spices. The main aim of this study was to develop and optimize a reliable technique based on near-infrared (NIR) spectroscopy and chemometrics for rapid and accurate adulterant detection in multiple organic spices. Ground cardamon, cinnamon, cloves, coriander, mustard, and nutmeg spices were adulterated with corn starch in the range of 1–10 % (w/w). Principal component analysis (PCA) was initially performed to examine the spectral properties of pure spices and the adulterant (corn), followed by individual PCA analyses for each spice to explore spectral changes across different levels of adulteration. Partial least squares regression (PLSR) was used with different pre-processing techniques, alone and in combination, to improve adulteration prediction. With second derivative (SD) and multiplicative scatter correction (MSC) pre-processing, the best PLSR model showed excellent prediction performance in the external validation set with a coefficient of determination for prediction (R²p) of 0.95, a root mean square error of prediction (RMSEP) of 0.62 %, and a ratio of predictive to deviation (RPD) of 4.21, demonstrating NIR spectroscopy is a fast and accurate technique for adulteration detection in organic spices and could play a significant role in controlling food safety and preventing potential economic losses. • NIR spectroscopy detected adulteration in organic spices. • PCA identified spectral variance to distinguish pure and adulterated spices. • Spectral pre-processing enhanced the accuracy of the PLSR model. • A global prediction model was created to detect adulteration in six organic spices.