Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Agricultural and Food Biotechnology (BITAL), University of Almería, Agrifood Campus of International Excellence, ceiA3, E-04120 Almería, Spain.
Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Agricultural and Food Biotechnology (BITAL), University of Almería, Agrifood Campus of International Excellence, ceiA3, E-04120 Almería, Spain; UCL Department of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, United Kingdom; UCL Department of Chemistry, 20 Gordon Street, London WC1H 0AJ, United Kingdom.
Talanta. 2018 Sep 1;187:348-356. doi: 10.1016/j.talanta.2018.05.025. Epub 2018 May 8.
In this study, targeted and untargeted analyses based on headspace solid phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME-GC-MS) method were developed for classifying 33 different commercial rums. Targeted analysis showed correlation of ethyl acetate and ethyl esters of carboxylic acids with aging when rums of the same brand were studied, but presented certain limitations when the comparison was carried out between different brands. To overcome these limitations, untargeted strategies based on unsupervised treatments, such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as supervised methods, such as linear discriminant analysis (LDA) were applied. HCA allowed distinguishing main groups (with and without additives), while the PCA method indicated 40 ions corresponding to 13 discriminant compounds as relevant chemical descriptors for the correct rum classification (PCA variance of 88%). The compounds were confirmed based on the combination of retention indexes and low and high-resolution mass spectrometry (HRMS). Using the obtained results, LDA was carried out for the analytical discrimination of the remaining rums based on manufacturing country, raw material type, distillation method, wood barrel type and aging period and 94%, 91%, 92%, 95% and 94% of rums, respectively, were correctly classified. The proposed methodology has led to a robust analytical strategy for the classification of rums as a function of different parameters depending on the rum production process.
在这项研究中，基于顶空固相微萃取结合气相色谱-质谱联用（HS-SPME-GC-MS）方法开发了靶向和非靶向分析，用于对 33 种不同的商业朗姆酒进行分类。靶向分析表明，在研究同一品牌的朗姆酒时，乙酸乙酯和羧酸乙酯与陈酿有关，但在比较不同品牌时存在一定的局限性。为了克服这些局限性，应用了基于无监督处理的非靶向策略，如层次聚类分析（HCA）和主成分分析（PCA），以及有监督的方法，如线性判别分析（LDA）。HCA 允许区分主要组（有和没有添加剂），而 PCA 方法表明 40 个离子对应 13 个判别化合物作为正确朗姆酒分类的相关化学描述符（PCA 方差为 88%）。通过保留指数和低分辨和高分辨质谱（HRMS）的组合对化合物进行了确认。根据获得的结果，基于制造国家、原料类型、蒸馏方法、木桶类型和陈酿期，利用 LDA 对剩余的朗姆酒进行了分析鉴别，分别有 94%、91%、92%、95%和 94%的朗姆酒得到正确分类。该方法为根据朗姆酒生产过程中的不同参数对朗姆酒进行分类提供了一种稳健的分析策略。
