The use of wavelength dispersive X-ray fluorescence and discriminant analysis in the identification of the elemental composition of cumin samples and the determination of the country of origin

• Published in: Food chemistry 2012-12, Vol.135 (4), p.2825-2831
• Main Authors: Hondrogiannis, E., Peterson, K., Zapf, C.M., Roy, W., Blackney, B., Dailey, K.
• Format: Article
• Language: English
• Subjects: ANOVA; Aroma and flavouring agent industries; Biological and medical sciences; Cumin; Cuminum - chemistry; Discriminant Analysis; Elements; Food industries; Fundamental and applied biological sciences. Psychology; Geographic origin; Geography; Spectrometry, X-Ray Emission - methods; Spice; Trace Elements - analysis; Wavelength dispersive X-ray fluorescence spectroscopy
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2012.07.003

► WDXRF was used to measure elemental composition of cumin spice. ► Cumin originated from China, India, Syria, and Turkey. ► Discriminant analysis classified the cumin samples by country of origin. Sixteen elements found in 33 cumin spice samples from China, India, Syria, and Turkey were analysed by wavelength dispersive X-ray fluorescence (WDXRF) spectroscopy using the commercially available Bruker – AXS S4 Explorer for the purpose of using the elements to discriminate among country of origin. Pellets were prepared of the samples and elemental concentrations calculated from calibration curves constructed using four National Institute of Standards and Technology (NIST) standards. A separate NIST tomato standard (1573a) was used as a validation check, while the WDXRF data for six of the cumin samples was further validated using inductively coupled plasma-atomic emission spectroscopy (ICP-AES). The elements measured included Ca, Mg, K, P, S, Al, Ba, Br, Cl, Fe, Na, Mn, Rb, Sr, Cu, and Zn and were detected in the range from an average mean of 4.3mgkg−1 for Ba up to 19223.8mgkg−1 for K. Analysis of variance (ANOVA) was used to determine which elemental concentrations were statistically different from one another, and discriminant analysis was used to classify the cumin samples by country of origin. Using only eight elements (Ca, Mg, K, Fe, Na, Mn, Sr, and Zn) we were able to differentiate among cumin samples from four different geographic origins. Validation of the model with the validation set yielded 87.50% accuracy. Successful discrimination with just eight elements will allow for higher throughput in the screening of cumin samples using WDXRF for origin verification in less time.