Flavour fingerprint for the differentiation of Grappa from other Italian distillates by GC-MS and chemometrics
Flavour fingerprint for the differentiation of Grappa from other Italian distillates by GC-MS and chemometrics
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DOI：
10.1016/j.foodcont.2019.05.028
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发表时间：
2019-11-01
期刊：
影响因子：
6
通讯作者：
Biancolillo, Alessandra
中科院分区：
文献类型：
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作者：
Giannetti, Vanessa;Mariani, Maurizio Boccacci;Biancolillo, Alessandra
优化了 HS-SPME/GC-MS 程序，以表征属于 Grappa GI 样品和其他馏出物的 82 种烈酒的芳香指纹。 “Grappa”是欧盟第 110/2008 号法规允许的地理标志 (GI)，仅适用于意大利制造的葡萄渣烈酒。将多变量化学计量技术应用于收集的色谱图，以便根据样品的挥发性成分数据提供的化学信息对样品进行分类。偏最小二乘判别分析 (PIS-DA) 显示样本的自然分组，突出了良好的分类结果，在两个馏分油类别之间提供了良好的分离，具有较高的预测精度（外部测试集的总分类率为 95%）。此外，还计算了 PLS-DA 模型的 VIP 分数，从而可以识别数量减少的与区分不同类别相关的挥发性化合物。还测试了按类别类比的软独立建模 (SIMCA)，以构建用于预测分类目的的类别模型。所提出的方法可能是评估 Grappa GI 样品真实性的强大工具，因此，它可以通过验证样品是否符合产品规格来帮助保护该产品免受可能的欺诈。
An HS-SPME/GC-MS procedure was optimised in order to characterize the aromatic fingerprint of 82 spirit drinks, belonging to Grappa GI samples and other distillates. "Grappa" is a geographical indication (GI) allowed by EC Regulation No 110/2008 only for Italian-made grape marc spirit. Multivariate chemometric techniques were applied to the collected chromatographic profiles in order to classify the samples on the basis of chemical information provided by their volatile composition data. Partial Least Squares-Discriminant Analysis (PIS-DA) showed natural grouping of samples highlighting good classification results, providing a good separation between the two distillate categories with high prediction accuracy (the total classification rate on the external test set was 95%). In addition, VIP scores of PLS-DA models were calculated, allowing to identify a reduced number of volatile compounds which are relevant for discrimination of the different categories. Also Soft Independent Modelling by Class Analogies (SIMCA) was tested to build class models for predictive classification purposes. The proposed approach may represent a powerful tool suitable to assess the authenticity of Grappa GI samples and, hence, it may help to protect this product from possible frauds by verifying whether samples comply with the product specification.