Linking soil, water, and honey composition to assess the geographical origin of argentinean honey by multielemental and isotopic analyses.

The objective of this research was to investigate the development of a reliable fingerprint from elemental and isotopic signatures of Argentinean honey to assess its geographical provenance. Honey, soil, and water from three regions (Córdoba, Buenos Aires, and Entre Ríos) were collected. The multielemental composition was determined by ICP-MS. δ(13)C was measured by isotopic ratio mass spectrometry, whereas the (87)Sr/(86)Sr ratio was determined using thermal ionization mass spectrometry. The data were analyzed by chemometrics looking for the association between the elements, stable isotopes, and honey samples from the three studied areas. Honey samples were differentiated by classification trees and discriminant analysis using a combination of eight key variables (Rb, K/Rb, B, U, (87)Sr/(86)Sr, Na, La, and Zn) presenting differences among the studied regions. The application of canonical correlation analysis and generalized procrustes analysis showed 91.5% consensus between soil, water, and honey samples, in addition to clear differences between studied areas. To the authors' knowledge, this is the first report demonstrating the correspondence between soil, water, and honey samples using different statistical methods, showing that elemental and isotopic honey compositions are related to soil and water characteristics of the site of origin.

Elemental and isotopic fingerprint of Argentinean wheat. Matching soil, water, and crop composition to differentiate provenance.

The aim of this study was to investigate if elemental and isotopic signatures of Argentinean wheat can be used to develop a reliable fingerprint to assess its geographical provenance. For this pilot study we used wheat cultivated at three different regions (Buenos Aires, Córdoba, and Entre Ríos), together with matching soil and water. Elemental composition was determined by ICP-MS. δ(13)C and δ(15)N were measured by isotopic ratio mass spectrometry, while (87)Sr/(86)Sr ratio was determined using thermal ionization mass spectrometry. Wheat samples from three sampling sites were differentiated by the combination of 11 key variables (K/Rb, Ca/Sr, Ba, (87)Sr/(86)Sr, Co, Mo, Zn, Mn, Eu, δ(13)C, and Na), demonstrating differences among the three studied regions. The application of generalized Procrustes analysis showed 99.2% consensus between cultivation soil, irrigation water, and wheat samples, in addition to clear differences between studied areas. Furthermore, canonical correlation analysis showed significant correlation between the elemental and isotopic profiles of wheat and those corresponding to both soil and water (r(2) = 0.97, p < 0.001 and r(2) = 0.96, p < 0.001, respectively). To our knowledge, this is the first report demonstrating the correspondence between soil, water, and wheat samples using different statistical methods, showing that wheat elemental and isotopic compositions are mainly related to soil and irrigation water characteristics of the site of growth.

How much do soil and water contribute to the composition of meat? A case study: meat from three areas of Argentina.

The main goal of this study was to propose a reliable method to verify the geographical origin of meat, establishing the influence of soil and water on its isotopic and elemental composition. Thus, beef meat, soil, and water samples were collected from three major cattle-producing regions of Argentina (Buenos Aires, Córdoba, and Entre Ríos). Multielemental composition was determined on these three matrices by inductively coupled plasma mass spectrometry (ICP-MS), δ(13)C and δ(15)N by isotope-ratio mass spectrometry (IRMS), and the (87)Sr/(86)Sr ratio by thermal ionization mass spectrometry (TIMS). Soil and drinking water samples could be characterized and clearly differentiated by combining the isotopic ratios and elements, demonstrating differences in geology and climatic conditions of three regions. Similarly, meat originating at each sampling area was characterized and differentiated using only five key variables (Rb, Ca/Sr, δ(13)C, δ(15)N, and (87)Sr/(86)Sr). Generalized procrustes analysis (GPA), using the three studied matrices (soil, water, and meat) shows consensus between them and clear differences between studied areas. Furthermore, canonical correlation analysis (CCA) demonstrates significant correlation between the chemical-isotopic profile of meat with those corresponding to both soil and water (r(2) = 0.93, p < 0.001; and r(2) = 0.83, p < 0.001, respectively). So far, there are clear coincidences between the meat fingerprint and those from soil/water where cattle grew, presenting a good method to establish beef provenance. To the authors' knowledge this is the first report linking the influence of soil and water all together on the composition of beef, presenting the basis for the authentication of Argentinean beef, which could be extended to meat from different provenances.

Fingerprints for main varieties of argentinean wines: terroir differentiation by inorganic, organic, and stable isotopic analyses coupled to chemometrics.

Our main goal was to investigate if robust chemical fingerprints could be developed for three Argentinean red wines based on organic, inorganic, and isotopic patterns, in relation to the regional soil composition. Soils and wines from three regions (Mendoza, San Juan, and Córdoba) and three varieties (Cabernet Sauvignon, Malbec, and Syrah) were collected. The phenolic profile was determined by HPLC-MS/MS and multielemental composition by ICP-MS; (87)Sr/(86)Sr and δ(13)C were determined by TIMS and IRMS, respectively. Chemometrics allowed robust differentiation between regions, wine varieties, and the same variety from different regions. Among phenolic compounds, resveratrol concentration was the most useful marker for wine differentiation, whereas Mg, K/Rb, Ca/Sr, and (87)Sr/(86)Sr were the main inorganic and isotopic parameters selected. Generalized Procrustes analysis (GPA) using two studied matrices (wine and soil) shows consensus between them and clear differences between studied areas. Finally, we applied a canonical correlation analysis, demonstrating significant correlation (r = 0.99; p < 0.001) between soil and wine composition. To our knowledge this is the first report combining independent variables, constructing a fingerprint including elemental composition, isotopic, and polyphenol patterns to differentiate wines, matching part of this fingerprint with the soil provenance.