Starting with an experiment to authenticate walnut oils based on GC-MS analysis of the volatolome, this paper aims to demonstrate the relevance of a two-step alignment-based strategy for the systematic research of VOC markers. The first step of the treatment consists of roughly reducing the time shifts with efficient, known warping techniques like COW (Correlation Optimized Warping). The second step relies on an accurate peak apex alignment in order to refine residual local misalignments and to enable further systematic marker research through univariate or multivariate data treatments. This two-step strategy was implemented on 117 GC-MS analyses of the volatolome of three vegetable oils with very similar composition. During the analysis campaign, the GC-MS system was intentionally subjected to instrumental drifts in order to generate realistic signal shifts. The first part of this study aims to assess the efficiency of the warping-based strategy in terms of signal alignment and sample discrimination. Whereas no distinction between the three oils was possible with unaligned raw GC-MS data, the application of COW enabled a significant but insufficient improvement of both reduction of temporal drifts and between-group separation with 79% of samples being well-classified according to Linear Discriminant Analysis (LDA). Applying the peak apex alignment procedure to COW-treated signals resulted in a suitable correction of the remaining local distortions and improved the proportion of well-classified samples in LDA to 100%. The second part of this study assesses the robustness of the discriminant markers highlighted in this approach by: (i) discussing the relevance of the best markers involved in the LDA model, where a close review of literature confirmed the consistency for two of them, and (ii) validating highlighted makers by retrieving the set of the 23 markers previously determined by manual processing among those automatically found. The third part shows the potential of the systematic approach for untargeted detection of 184 highly significant relevant markers from the oil volatolome. Finally, the fourth part presents a comparison of our hybrid alignment strategy with two reference alignment methods (iCoshift and STW) in order to assess quality alignment of the GC-MS data and to show the three methods' abilities to detect discriminant markers.

中文翻译：

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基于 GC-MS 信号系统对齐的挥发性组学中的标记发现：在食品认证中的应用

从基于挥发性有机物的 GC-MS 分析鉴定核桃油的实验开始，本文旨在证明基于两步比对的策略对 VOC 标记物的系统研究的相关性。处理的第一步包括使用有效的已知翘曲技术（如 COW（相关优化翘曲））大致减少时间偏移。第二步依赖于准确的峰顶对齐，以细化残留的局部错位，并通过单变量或多变量数据处理进行进一步的系统标记研究。这种两步策略是在对组成非常相似的三种植物油的挥发份进行 117 次 GC-MS 分析时实施的。在分析活动期间，GC-MS 系统有意进行仪器漂移，以产生真实的信号偏移。本研究的第一部分旨在评估基于扭曲的策略在信号对齐和样本区分方面的效率。虽然在未对齐的原始 GC-MS 数据下无法区分三种油，但 COW 的应用能够显着但不充分地改善时间漂移的减少和组间分离，79% 的样品根据线性分类得到了很好的分类判别分析 (LDA)。将峰顶点对齐程序应用于 COW 处理的信号导致对剩余局部失真的适当校正，并将 LDA 中分类良好的样本比例提高到 100%。本研究的第二部分通过以下方式评估该方法中突出显示的判别标记的稳健性：(i) 讨论 LDA 模型中涉及的最佳标记的相关性，其中对文献的仔细审查证实了其中两个的一致性，以及(ii) 通过在自动找到的标记中检索先前由手动处理确定的 23 个标记的集合来验证突出显示的标记。第三部分展示了系统方法在非靶向检测石油挥发份中 184 个非常重要的相关标记方面的潜力。最后，第四部分将我们的混合比对策略与两种参考比对方法（iCoshift 和 STW）进行比较，以评估 GC-MS 数据的质量比对并展示三种方法检测判别标记的能力。