Identification of Walnut from Different Regions of China by Statistical Methods Based on the Determination of Multi-element Contents

Objective To identify the walnut from major producing provinces in China and provide some basic data and theoretical basis for the protection of geographical indication.

Method The contents of 35 elements in 128 walnut samples from eight major producing provinces of China were determined by inductively coupled plasma mass spectrometry (ICP-MS), the discriminant model was established by one-way analysis of variance, principal component analysis (PCA) and linear discriminant analysis (LDA) to identify walnut from different areas.

Result It was found that the contents of Fe, Zn, Cu and Ni were the most abundant nutrient elements in walnut, and the contents of heavy metal (Pb, Cd and As) and rare earth elements were in relatively low level. One-way analysis of variance indicated that there were significant differences in the elemental composition of walnut samples from different regions (P < 0.05). The PCA showed that Fe, Ti, Rb, B, Ba, Cu, Zn, Ba, Mo, Al, Pb and rare earth elements were inferred to be the characteristic elements of walnut samples from different regions, and these elements could explain 64.33% of the total variance. LDA was applied to construct the classification model of walnuts according to their geographical origins, and the accuracy was as high as 99.2%. LDA was also applied to construct the model of identifying the walnut with geographical indication from that without geographical indication, the accuracy was 95.7%.

Conclusion Through the determination of multi-element contents in walnut combined with principal component analysis (PCA) and linear discriminant analysis (LDA), the walnut from different regions can be identified successfully.