X-ray Fluorescence Techniques for Element Abundance Analysis in Wine

Determination of Metal Content by Inductively Coupled Plasma-Mass Spectrometry in Polish Red and White Wine Samples in Relation to Their Type, Origin, Grape Variety and Health Risk Assessment

The main objective of the research was to assess the influence of selected factors (type of wine, grape variety, origin, alcohol content and daily consumption) on the concentration levels of 26 elements in 53 Polish wine samples, also using chemometric analysis tools. Concentration of Al, As, B, Ba, Be, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Ni, Pb, Sb, Se, Sr, Ti, V, Zn and Zr was analyzed by ICP-MS, while concentration of Ca, Na, K and Mg was determined by ICP-OES. White wines were characterized by higher concentrations of Al, As, Be, Ca, Co, Cu, Fe, Hg, Li, Mg, Na, Pb, Sb, Ti, V, Zn and Zr (mean values: 0.075-86,403 μg·L-1 in white wines, 0.069-81,232 μg·L-1 in red wines). Red wines were characterized by higher concentrations of Ba, Cd, Cr, K, Mn, Se and Sr (mean values: 0.407-1,160,000 μg·L-1 in white wines, 0.448-1,521,363 μg·L-1 in red wines). The results obtained for the health risk assessment indices, including the Target Hazard Quotient (THQ, mean values per glass of wine: 2.097 × 10-5 (Cr)-0.041 (B) in all wines), indicate that the analyzed elements do not show a potential toxic effect resulting from wine consumption. The chemometric analysis confirmed that elements such as Li, Ti, Ca, Mn, Sr, Ba, Zn, Mg, Cu, Se and B were closely related to local conditions and soil properties, and the presence of Fe, Cr, V and Pb was related to contamination of the soil.

TXRF spectral information enhanced by multivariate analysis: A new strategy for food fingerprint

The increased costumers' request of safe and high-quality food products makes food traceability a priority for frauds identification and quality certification. Elemental profiling is one of the strategies used for food traceability, and TXRF spectroscopy is widely used in food analysis even if its potentialities have not been fully investigated. In this work, a new method for food traceability using directly TXRF spectra coupled with multivariate analyses, was tested. Twenty-four different beans' genotypes (Phaseolus vulgaris L.) grown onto two different sites have been studied. After the development of the method for beans' analysis, TXRF spectra were collected and processed with PCA combined with SNV and GLSW filter obtaining a perfect clustering of the seeds according to their geographical origin. Finally, using PLS-DA, beans were correctly classified demonstrating that TXRF spectra can be successfully used as fingerprint for food/seed traceability and that elemental quantification procedure is not necessary to this aim.

Application of benchtop total-reflection X-ray fluorescence spectrometry and chemometrics in classification of origin and type of Croatian wines

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A low-power benchtop TXRF system was used for analysis of 70 wine samples.

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The metal content of K, Ca, Fe, Cu, Zn, Mn, Sr, Rb, Ba, Pb, Ni, Cr and V was estimated by chemometric methods.

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Mn, K, Ni, Sr, Rb and Ba were the main variables used to differentiate by wine type and origin.

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LDA showed good detection and prediction abilities with selected elements.

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Classification of origin and type of Croatian wines by chemometric tools.

The contents of selected metals (K, Ca, Fe, Cu, Zn, Mn, Sr, Rb, Ba, Pb, Ni, Cr and V) in 70 wine samples from Continental and Adriatic part of Croatia and different types of wine (red and white) were determined by TXRF. The aim of this study was to compare the elemental composition of wines from two different regions and to determine the discriminant ability of each variable and to indicate which variables discriminate between the four categories considered.

Principal component analysis and cluster analysis showed that K, Mn, Ba and Ni can be considered as the most important characteristics to distinguish between Continental red and white wines, Rb, Ni and Ba for Continental red and Adriatic red wines while Sr is the only metal that completely distinguishes the samples of each category. Finally, linear discriminant analysis showed good recognition (100%) and prediction abilities (96.43%) using these selected elements.