FT-Raman spectroscopy represents an environmentally friendly technique, suitable for the analysis of high-water content food matrices, like wines, due to its relatively weak water bending mode in the fingerprint region. Based on metabolomics applied to FT-Raman spectra, this study presents the classifications achieved for a sample set comprising 126 wines, originated from Romania and France, with respect to cultivar, geographical origin and vintage. Cultivar recognition was successfully performed among four varieties (Sauvignon, Riesling, Chardonnay, Pinot Gris) while subtle particularities exiting between the Chardonnay wines, coming from the two countries, because of terroir influences were pointed out. The obtained separations of 100% in both initial and cross-validation procedure for geographical differentiation between the two origin countries, as well as, among the three Romanian areas (Transylvania, Muntenia and Moldova) were also discussed. Apart of this, the limitations and the importance of choosing a meaningful data set, in terms of representativity for each classification criterion, are addressed in the present work.
Soil contamination by metals is a common problem encountered in many industrialized countries. In this work we present a new approach for heavy metals detection by using surface-enhanced Raman scattering (SERS) spectroscopy. Zn(II) can be clearly determined by SERS in contaminated soil by using 4-(2-pyridylazo) resorcinol (PAR) as chelating molecule for the metal ion. The SERS spectra of PAR, of its metal chelates and of the soil extract-PAR mixture were recorded using a hydroxylamine reduced silver colloid. An excellent match of the PAR-contaminated soil extracts SERS spectrum to the Zn(PAR)2 SERS spectrum can be observed, demonstrating the presence of Zn(II) in the soil probes. Density functional theory (DFT) based calculations were also performed for a reliable assignment of SERS spectra.
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