Department of Chemistry and Physics, Research Centre CIAIMBITAL, Agrifood Campus of International Excellence, ceiA3, University of Almería, Ctra. Sacramento, s/n, 04120, Almería, Spain.
Department of Chemistry and Physics, Research Centre CIAIMBITAL, Agrifood Campus of International Excellence, ceiA3, University of Almería, Ctra. Sacramento, s/n, 04120, Almería, Spain.
Food Chem. 2020 Jul 1;317:126363. doi: 10.1016/j.foodchem.2020.126363. Epub 2020 Feb 5.
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.
首次应用核磁共振波谱结合化学计量学方法，基于发酵桶、原料、蒸馏方法和陈酿等因素对金朗姆酒进行分类。主成分分析（PCA）用于评估整体结构，偏最小二乘判别分析（PLS-DA）用于朗姆酒的分析判别。此外，还应用了核磁共振波谱与气相和液相色谱-质谱联用的色谱技术的数据融合，以提供关于朗姆酒的更准确知识。与使用单一技术（光谱或色谱）相比，该方法提供了分类样品的错误率更低。结果表明，核磁共振波谱是一种合适的技术，可将>95.5%的样品进行适当分类。当执行光谱和光谱数据的融合方法时，预测效率可达到 100%的样品。
