Detection of adulteration of cumin powder by front-face synchronous fluorescence spectroscopy: The influence of the natural variation of adulterants.

For the authentication of powdered spices, the possible impact of the natural variations of adulterants on classification or prediction models is always ignored, and research in this area is still quite scarce. This study takes the application of front-face synchronous fluorescence spectroscopy (FFSFS) to the rapid and non-destructive authentication of cumin powder adulterated with ground peanut shells and maize flour as an example to show that how the natural variations of adulterants affect model prediction. Using three samples of each adulterant, two modes of sample set were designed for comparison. Mode 1 used the first adulterant for calibration and the other two for external validation. Mode 2 employed two adulterants in training and the third for external validation. The classification of adulterants by principal component analysis coupled with linear discriminant analysis (PCA–LDA) showed that mode 2 had a higher prediction rate (82%) in external validation than mode 1 (63%). For quantification, prediction models were built by partial least square (PLS) regression, and were validated by both cross- and external validation. The result of mode 2 was better than that of mode 1, with the determination coefficients of prediction (R p 2) greater than 0.93, the root mean square error of prediction (RMSEP) < 4.2% and residual predictive deviation (RPD) at least 2.7. This study demonstrates the necessity of the consideration of the natural variations of adulterants when constructing a robust model for adulteration detection. [Display omitted] • Front-face fluorescent property of cumin powder is investigated. • Its emission is dissimilar to those of ground peanut shells and maize flour. • Flavonoids, phenolic acids and tryptophan contribute to the distinction. • Simultaneous detection of dual adulterants in cumin powder by PLS2. • Effect of natural variation of adulterants on prediction model is evaluated. [ABSTRACT FROM AUTHOR]