The multiverse of volatolomic: apply PCA or/and PLSDA to outperform our data set

Volatolomics, the comprehensive analysis of volatile organic compounds (VOCs), has emerged as a powerful approach in many fields ranging from clinical diagnostics and food safety to environmental monitoring. Chromatographic techniques such as gas chromatography–mass spectrometry (GC-MS) and two-dimensional gas chromatography (GC×GC) generate increasingly complex datasets that require advanced statistical tools for interpretation. Among these, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) are two multivariate methods often employed to enhance important information from these datasets. This presentation provides a comparative overview of PCA and PLS-DA as applied to volatolomic data, focusing on their respective strengths, limitations, and practical applications. Real dataset obtained in the field of forensic analyses and pest management will illustrate how PCA, an unsupervised method, is effective for initial data exploration, pattern recognition, and outlier detection, while PLS-DA, a supervised method, enables predictive modeling and identification of key VOC markers linked to specific classes. We demonstrate how these tools can be used complementarily, offering a practical workflow for exploratory analysis, classification, and identification of key volatile markers. This comparative analysis underscores the importance of choosing the appropriate multivariate technique based on study objectives. When combined, PCA and PLS-DA provide a robust strategy for the analysis and interpretation of high-dimensional chromatographic volatolomic data. Best practices in model validation and result interpretation will also be discussed, ensuring reproducibility and reliability across a broad range of separation science applications.