Total luminescence spectroscopy for differentiating between brandies and wine distillates

• Published in: Czech Journal of Food Sciences Vol. 27; no. 6; pp. 425 - 432
• Main Authors: Tóthová, Jana, Sádecká, Jana, Májek, Pavel
• Format: Journal Article
• Language: English; Czech; Slovak
• Published: Prague Czech Academy of Agricultural Sciences (CAAS) 01.01.2009; Czech Academy of Agricultural Sciences
• Subjects: Beverages; Brandy; Classification; Cluster analysis; Differentiation; Discrimination; Distillates; Emission analysis; Emission spectra; Error correction; Excitation spectra; Fluorescence; Fluorescence spectroscopy; Information processing; Luminescence; Multivariate analysis; multivariate data analysis; Principal components analysis; Similarity; Spectral emissivity; Spectroscopy; Spectrum analysis; Wavelength; Wine; wine distillate
• ISSN: 1212-1800; 1805-9317
• DOI: 10.17221/125/2008-CJFS

In this study, the differentiation was investigated between brandy and wine distillate samples by fluorescence spectroscopy in combination with multivariate analysis. The samples corresponding to eight brandies from three producers and sixteen wine distillates from five producers were acquired in the local supermarkets. Total luminescence spectra of diluted and undiluted samples were recorded. In order to extract reliable information from the data sets, two multivariate analysis methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were applied separately on the excitation and emission spectra. The best differentiation was achieved using the emission spectra (400–470 nm) recorded at the excitation wavelength of 340 nm, or the excitation spectra (240–380 nm) recorded at the emission wavelength of 450 nm. The similarity map defined by the PC1 and PC2 of the PCA performed on the excitation spectra accounted for 94.9% of the total variance (PC1 90.3%, PC2 4.6%) and allowed a good discrimination between the beverages. Although the PCA similarity map defined by the PC1 (84.2%) and PC2 (13.0%) performed on the emission spectra did not lead to a clear discrimination between the beverages, a general trend pointing out the brandies and wine distillates was observed on the map. HCA performed on the excitation spectra provided a better differentiation between the two classes, without any classification error, while HCA performed on the emission spectra allowed 95.8% correct classification.