The effectiveness of multi‐element fingerprints for identifying the geographical origin of wheat. (2nd March 2017)

• Record Type: Journal Article
• Authors: Liu, Hongyan; Wei, Yimin; Zhang, Yingquan; Wei, Shuai; Zhang, Senshen; Guo, Boli
• Is Part Of: International journal of food science & technology. Volume 52:Number 4(2017)
• Page Start: 1018
• Page End: 1025
• Publication Date: 2017-03-02
• Subjects: Genotype -- geographical origin -- harvest year -- multi‐element -- wheat; Food industry and trade -- Periodicals; 664
• DOI: 10.1111/ijfs.13366
• Languages: English
• ISSNs: 0950-5423

Abstract

Summary: Totally 270 wheat samples with ten genotypes of 2010/2011, 2011/2012 and 2012/2013 from three regions were collected, and the multi‐elemental compositions (Mg, Al, Ca, Mn, Fe, Cu, Zn, As, Sr, Mo, Cd, Ba, Pb) were analysed with high‐resolution inductively coupled plasma mass spectrometry (HR‐ICP‐MS). Multiway analysis of variance was employed to investigate the influences of region, genotype, harvest year and their interactions on all elements. The contribution rates of variances were computed, and the results showed that the elements of Mn, Sr, Mo and Cd were closely related to region explaining 34.2%, 39.6%, 35.0% and 78.8% of the total variation, respectively. The genotype contributed most for the variation of Ba, accounting for 27.3%, and the other elements were affected by the harvest year. Mn, Sr, Mo and Cd can be used for establishing the robust discriminant model with the correct classification rate of 98.5% to identify the geographical origin of wheat. Abstract : The effects of region, genotype, harvest and their interactions on 14 elements were investigated, only 4 elements were observed to be closely related to region, and discriminant model built by these 4 elements could realize 98.5% of correct classification rate for identifying wheat origin.