Isotopic and elemental profiling alongside with chemometric methods for vegetable differentiation.

In this study, three chemometric models for vegetables growing system (field versus greenhouse), geographical origin and species attribution using stable isotope (δ13C, δ18O, δ2H) and elemental fingerprints of 101 samples (54 squashes and 47 radishes) commercialized on Romanian market were developed. These models were constructed and validated through linear discriminant analysis. Initial validations of 94.4% and 83% were obtained for squash and radish growing systems, respectively, such that one squash and four radish samples declared to be grown in the field were attributed to the greenhouse group. For this purpose, the most powerful differentiation markers appeared to be Sn and δ13C for radishes, and Sn, Cu for squashes. Regarding the vegetable origin, four samples, initially considered to originate from Romania (95% for initial classification) were attributed to the foreign group in the cross-validation procedure (93.1%). Romanian radishes and squashes were characterized by a higher content of Na and Cu, respectively, compared with foreign samples, while the mean values for Zn, Sr, Zr and Co concentrations were found to be higher for the vegetables from abroad.

Isotopic and multielemental fingerprinting of organically and conventionally grown potatoes.

Two marker combinations were used for the differentiation of organically produced from conventionally produced potatoes and also for the geographical origin identification. Fifty-seven samples (from Romanian local producers or imported) were analysed from the stable isotopic (isotope ratio mass spectrometry) and elemental profile (inductively coupled plasma mass spectrometry) point of view. In order to assess the best marker combination, both isotopic and elemental experimental results were subject to chemometric analysis. The statistical tests performed were ANOVA test, Pearson correlation, principal component analysis and linear discriminant analysis (LDA). For a more comprehensive differentiation between organic vs. conventional potato samples, LDA was applied, and 94.7 % of original cases were correctly classified and the percentage obtained in cross-validation procedure was 91.2 %. Regarding the geographic origin classification, LDA provided an initial classification of 96.5 %, while for cross-validation the percentage was 87.7. LDA found δ15N, Cd, Ca, Cu and Zn as best discrimination markers between organically and conventionally grown potatoes. The strongest predictors for Romania vs. foreign geographical areas along LDA were seen to be Ca, P, Co, Ni and δ13C.