Non-targeted analysis is nowadays applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. Conventional processing strategies for GC-MS data include baseline correction, feature detection, and retention time alignment before multivariate modeling. These techniques can be prone to errors and therefore time-consuming manual corrections are generally necessary. We introduce here a novel fully automated approach to non-targeted GC-MS data processing. This new approach avoids feature extraction and retention time alignment. Supervised machine learning on decomposed tensors of segmented chromatographic raw data signal is used to rank regions in the chromatograms contributing to differentiation between sample classes. The performance of this novel data analysis approach is demonstrated on three published datasets.

中文翻译：

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用于非靶向 GC-MS 数据分析的自动监督学习管道

非靶向分析如今应用于分析化学的许多不同领域，例如代谢组学、环境和食品分析。GC-MS 数据的常规处理策略包括在多变量建模之前进行基线校正、特征检测和保留时间对齐。这些技术容易出错，因此通常需要耗时的手动更正。我们在此介绍一种全新的全自动非靶向 GC-MS 数据处理方法。这种新方法避免了特征提取和保留时间对齐。对分段色谱原始数据信号的分解张量进行监督机器学习，用于对色谱图中有助于区分样品类别的区域进行排序。