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采用气相色谱-质谱联用仪(GC×GC-TOF-MS)和多变量分析对56种意大利苹果酒中的半挥发性化合物进行表征。

Characterization of semi-volatile compounds in 56 Italian ciders using GC×GC-TOF-MS and multivariate analysis.

作者信息

Orecchio Ciro, Bedini Andrea, Romagnoli Monica, Pantò Sebastiano, Alladio Eugenio, Pazzi Marco

机构信息

Department of Chemistry, University of Turin, Via P. Giuria 7, 10125, Torino, Italy.

Founder and member, Associazione Pommelier e Assaggiatori Sidro, A.P.A.S., Torino, Italy.

出版信息

Heliyon. 2024 Aug 4;10(15):e35687. doi: 10.1016/j.heliyon.2024.e35687. eCollection 2024 Aug 15.

Abstract

Fifty-six samples of differently produced commercial Italian ciders were analysed for semi-volatile organic compounds (SVOCs) profiling, using comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-TOF-MS) technique for the very first time. To properly support the compositional investigation of this emerging beverage, a chemometric approach through Principal Component Analysis (PCA) was employed. This revealed a sample distribution in agreement with results of the sensory tasting panel performed on such ciders, highlighting an excellent correlation between variables and perceived odorants. In particular, the positions of peculiar and anomalous objects in the Principal Components (PCs) space are explicitly evaluated, exploring the associated loadings (i.e., the importance of the identified chemical compounds), paying attention to their biochemical origin along the cider-making process and their impact on the sample olfactory analysis. Besides this, the t-distributed Stochastic Neighbor Embedding (t-SNE) method was shown to be an efficient tool for gathering pear ciders from the other samples (apple ciders), better than PCA. This study stands for the first survey on Italian commercial craft cider, and its results are aimed to be a milestone for its characterization and to start and promote cider culture in this country.

摘要

首次使用全二维气相色谱-质谱联用(GC×GC-TOF-MS)技术,对56个不同生产工艺的意大利商业苹果酒样品进行了半挥发性有机化合物(SVOCs)剖析。为了妥善支持对这种新兴饮料的成分研究,采用了主成分分析(PCA)的化学计量学方法。这揭示了样品分布与对此类苹果酒进行的感官品尝小组的结果一致,突出了变量与感知气味物质之间的良好相关性。特别地,明确评估了主成分(PCs)空间中特殊和异常对象的位置,探索相关载荷(即已鉴定化合物的重要性),同时关注它们在苹果酒酿造过程中的生化来源及其对样品嗅觉分析的影响。除此之外,t分布随机邻域嵌入(t-SNE)方法被证明是一种比PCA更有效的工具,可将梨苹果酒与其他样品(苹果酒)区分开来。本研究是对意大利商业精酿苹果酒的首次调查,其结果旨在成为其特征描述的一个里程碑,并在该国开启和推广苹果酒文化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d651/11336988/447a94f80fba/ga1.jpg

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