H NMR and multi-technique data fusion as metabolomic tool for the classification of golden rums by multivariate statistical analysis.

H NMR spectroscopy combined with chemometrics was applied for the first time for golden rum classification based on several factors as fermentation barrel, raw material, distillation method and aging. Principal component analysis (PCA) was used to assess the overall structure, and partial least square discriminant analysis (PLS-DA) was carried out for the analytical discrimination of rums. Additionally, data-fusion of H NMR and chromatographic techniques (gas and liquid chromatography) coupled to mass spectrometry was applied to provide more accurate knowledge about rums. This approach provided a classification of samples with lower error rate than the one obtained by the use of a single technique (spectroscopic or chromatographic). The results showed that H NMR spectroscopy is an appropriate technique for the suitable classification of >95.5% of the samples. When data fusion methodology of spectroscopic and spectrometric data was performed, the prediction efficiency can reach 100% of the samples.