In this paper is reported a proof of concept study to evaluate the usage of a public metadata base about beers to guide chemical interpretation of volatile organic compounds (VOC) profiling. 1,569,641 consumers’ evaluations were collected from Untappd® platform and used to define a property of interest according to beer preference. 14 brands of beers from lager family were divided in two groups, first one containing samples with low consumers’ ratings and the second with brands that exhibited high evaluations. VOC profiles were extracted by headspace solid phase microextraction (HS-SPME) and analyzed using comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC × GC-MS). To correlate the VOC profile and consumers’ preference, unfolded-partial least squares discriminant analysis (U-PLS-DA) with orthogonal signal correction (OSC) were employed. The mathematical model successfully classified all the beer samples. Furthermore, a template match protocol identified 31 compounds related to consumers’ preference. This proof of concept paper revealed the potential usage of public metadata bases for comprehensive chemical interpretation of VOC profiling in foodomics.

本文报道了一项概念验证研究，旨在评估啤酒公共元数据库的使用情况，以指导挥发性有机化合物 (VOC) 分析的化学解释。从 Untappd® 平台收集了 1,569,641 名消费者的评价，并用于根据啤酒偏好定义感兴趣的属性。啤酒家族的 14 个品牌啤酒被分为两组，第一组包含消费者评分较低的样品，第二组包含消费者评分较高的品牌。通过顶空固相微萃取 (HS-SPME) 提取 VOC 谱，并使用综合二维气相色谱与质谱联用 (GC × GC-MS) 进行分析。为了将 VOC 概况与消费者偏好关联起来，采用了带有正交信号校正 (OSC) 的展开偏最小二乘判别分析 (U-PLS-DA)。数学模型成功地对所有啤酒样品进行了分类。此外，模板匹配协议还确定了 31 种与消费者偏好相关的化合物。这篇概念验证论文揭示了公共元数据库在食品组学中对 VOC 分析进行全面化学解释的潜在用途。