Assessing saffron (Crocus sativus L.) adulteration with plant-derived adulterants by diffuse reflectance infrared Fourier transform spectroscopy coupled with chemometrics

Saffron, the dried red stigmas of the plant Crocus sativus L., is well-known as one of the most important and expensive spices worldwide. It is thus highly susceptible to fraudulent practices that employ, among others, plant-derived adulterants. This study presents an application of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and chemometric techniques for evaluating adulteration of saffron with six characteristic adulterants of plant origin, i.e. C. sativus stamens, calendula, safflower, turmeric, buddleja, and gardenia. The proposed method involved a three-step process for the detection of adulteration as well as for the identification and quantification of adulterants. Partial least squares discriminant analysis (PLS-DA) was applied to perform authentication of saffron based on mid-infrared fingerprints (4000–600cm−1), resulting in 99% correct classification of pure saffron and saffron adulterated at 5–20% (w/w) levels. Adulterant identification in positive samples was performed with high sensitivity and specificity by a six-class PLS-DA model, with spectroscopic data from the region 2000–600cm−1. Subsequently, partial least squares (PLS) regression models were built for the quantification of each adulterant. By using synergy interval PLS (siPLS) for variable selection, models with improved performance were developed, with detection limits ranging from 1.0% to 3.1% (w/w). The results obtained illustrate that this strategy based on DRIFTS has the potential to complement existing methodologies for the rapid and cost-effective assessment of typical saffron frauds. [Display omitted] •An integrated approach for rapid saffron authentication is proposed.•DRIFTS entails detection of adulteration with six adulterants of plant origin.•Identification of the adulterant used at levels as low as 5% (w/w).•Reliable quantification by partial least squares regression and variable selection.•Limits of detection are estimated for each adulterant in the range of 1.0–3.1% (w/w).