Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia , via Campi 103, 41125 Modena, Italy.
Centro Interdipartimentale Grandi Strumenti, Università degli Studi di Modena e Reggio Emilia , via Campi 213/A, 41125 Modena, Italy.
J Agric Food Chem. 2017 Jul 5;65(26):5421-5426. doi: 10.1021/acs.jafc.7b01927. Epub 2017 Jun 22.
The aim of this work is to evaluate the possibility of using 2D-NMR for the construction of classification models for balsamic vinegars of Modena. The goal was to obtain an indirect indicator of authenticity and a quality control tool. The spectral data were analyzed by chemometric methods, aiming to discriminate the samples in relation to their origin. Application of general discriminant analysis (GDA) revealed a good discrimination; the two obtained models explained 83.9% and 97.3% of the total variance with a predictive capacity of 98.6% and 98.4%, respectively. The signals of 5-HMF, β-glucose, 2,3-butanediol, 6-acetyl glucose, and different aliphatic signals of sugars were the most significant variables. These results are very promising for giving an important contribution in quality control and characterization of such very valuable foods.
本工作旨在评估二维核磁（2D-NMR）用于构建摩德纳香醋分类模型的可能性。目标是获得真实性的间接指标和质量控制工具。通过化学计量学方法对光谱数据进行分析，旨在根据产地对样品进行区分。应用通用判别分析（GDA）表明具有良好的判别能力；两个获得的模型分别解释了 83.9%和 97.3%的总方差，预测能力分别为 98.6%和 98.4%。5-HMF、β-葡萄糖、2,3-丁二醇、6-乙酰葡萄糖和不同糖的脂肪族信号是最重要的变量。这些结果非常有希望为这类非常有价值的食品的质量控制和特性分析提供重要贡献。
