Identification of the geographical origins of pomelos using multielement fingerprinting

Tracing the Origins of Blueberries by Their Mineral Element Contents and 87Sr/86Sr Ratios

This study investigated the feasibility of using the mineral element contents and ⁸⁷Sr/⁸⁶Sr ratios of blueberries to trace their origins. The contents of 28 mineral elements, including K, Al, and Mg, were determined in 104 blueberry samples from three blueberry-producing areas in Guizhou Province, China. Also determined were both the ⁸⁷Sr/⁸⁶Sr ratios in 48 blueberry samples as well as the type of soil in which the blueberries were grown. Cluster analysis of ⁸⁷Sr/⁸⁶Sr ratios, stepwise discriminant analysis of mineral element contents, and combined discriminant analyses of ⁸⁷Sr/⁸⁶Sr ratios and mineral element contents were done. The results show that ten elements (Ca, Cr, Cs, Mg, Mn, P, Rb, Sb, Th, and Y) were selected by linear discriminant analysis, which could be used to establish the provenance traceability model of blueberries in Guizhou. The original accuracy of linear discriminant analysis was 89.4%, and the accuracy of cross-validation was 83.6%. When ⁸⁷Sr/⁸⁶Sr ratios were used for tracing, the accuracies of both the original discrimination and the cross-validation were 81.3% as determined by linear discriminant analysis, and the accuracy rate of origin discrimination was 81.25% by cluster analysis. The results of combined discrimination were the best: the accuracy of the original discrimination was 95.8%, and the accuracy of cross-validation was 93.8%. Mineral element contents and ⁸⁷Sr/⁸⁶Sr ratios can be used to trace the origins of blueberries, and combining them can significantly improve the discrimination accuracy. Fisher linear discriminant analysis had the greatest accuracy followed by cluster analysis, while principal component analysis was relatively poor in the research of Guizhou blueberry origin traceability.

Discrimination of geographical origin and species of China's cattle bones based on multi-element analyses by inductively coupled plasma mass spectrometry

Consumers have an increasing concern in the provenance of the foods they consume. Methods for discriminating geographical origins and species of cattle bone product are essential to provide veracious information for consumers and avoid the adulteration and inferior problems. In this study, 50 element contents of a total of 143 cattle bone samples from eight producing regions in China, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Element contents were used as chemical indicators to discriminate species and geographical origins of cattle bone samples by multivariate data analysis, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). The K-fold cross validation accuracy for species and geographical origin discrimination was 99.3% and 94.5%, respectively. This study reveals that multi-element analysis accompanied by LDA is an effective technique to ensure the information reliability of cattle bone samples, and this strategy may be a potential tool for standardizing market.

Ionome signatures discriminates the geographical origin of jackfruits (Artocarpus heterophyllus Lam.)

Jackfruits are nutritionally rich fruit crop indigenous to the humid tropics, known by their place of origin. In the present study, using multielemental profiling of fruit samples, we demonstrated the discrimination of different jack fruit germplasm based on their geographical origin in India. The concentration of 24 elements in soil and fruit were determined by inductively coupled plasma mass spectrometry (ICP-MS). ANOVA revealed the significant difference of these 24 elements amongst the geographical locations both in soils and fruits. The correlation between soil and fruit ionome indicated the major influence of germplasm and other locational factors on the acquisition and accumulation of fruit multi elemental characteristics with minimal contribution of soil elements. Among the multivariate analysis tools, linear discriminant analysis (LDA) of fruit multi elemental fingerprint was found to be an efficient tool for discrimination of geographical origin of Indian jackfruits.

Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China

This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions (p < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01–1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.

Multivariate analysis to discriminate the origin of sesame seeds by multi-element analysis inductively coupled plasma-mass spectrometry

In this study, inductively coupled plasma-mass spectrometry (ICP-MS) was used to determine the concentration of 15 elements (Mg, Al, K, Ca, Cr, Mn, Co, Ni, Cu, Zn, Rb, Sr, Cd, Ba, and Pb) of sesame seeds. Multivariate analysis was then performed to discriminate the origin of sesame seeds. Korean (48), Chinese (44), and Indian (21) samples were used to develop the calibration model. Another 10 samples were used to validate this model. All elements were significantly different (p<0.05) among the samples from three countries, and all elements were subjected to both principal component analysis (PCA) and discriminant analysis. The concentrations of multi-element showed a trend of clustering according to the origin of samples based on PCA. They showed a discrimination rate of 92.0% in the discriminant analysis. The results demonstrated that a combination of ICP-MS multi-element determination and multivariate analysis could be used to discriminate the sesame seed origin.

A nondestructive approach for discrimination of the origin of sesame seeds using ED-XRF and NIR spectrometry with chemometrics

An energy dispersive X-ray fluorescence (ED-XRF) spectrometer and a near infrared (NIR) spectrometer combined with chemometrics were applied for origin discrimination of 48 Korean, 44 Chinese, and 21 Indian sesame seed samples used for development of a discriminant calibration model. Multi-elemental ED-XRF analysis based on Mg, Al, Si, P, S, Cl, K, Ca, Mn, Fe, and Cu was used for comparisons among origins. All elements, except for Fe, showed differences and 96.5% of seed samples were assigned to the correct origin using discriminant analysis based on chemical analytical results. NIR measurements were performed for spectral scanning. Classification of seeds using NIR discriminant analysis achieved 89.4% of seed samples assigned to the correct origin. Both ED-XRF and NIR are useful as nondestructive tools for discrimination of sesame seed origins.