Our findings underline the importance to further study their impact on humans. ## References  
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* [6] Department of Nutritional Sciences & Dietetics, International Hellenic University, 54248 Thessaloniki, Greece;
* [7] FoodOmicsGR_Research Infrastructure, Auth Node, CIRI-AUTH, 54248 Thessaloniki, Greece
* [8] Department of Chemistry, Aristotle University of Thessaloniki, 54248 Thessaloniki, Greece
* [9] Biomic_Auth, Bioanalysis and Omics Laboratory, CIRI-AUTH, 54248 Thessaloniki, Greece
* [10] Laboratory of Organic Chemistry, School of Chemical Engineering, Aristotle University of Thessaloniki, 54248 Thessaloniki, Greece
* [11] Natural Products Research Centre of Excellence (NatPro-AUTh), CIRI-AUTh, 54248 Thessaloniki, Greece
* [12] Correspondence: isampson@nuttr.teithe.gr  
Optimization of extraction conditions in analytical chemistry is traditionally performed following the one-factor-at-a-time (OFAT) approach while consulting the literature or previously acquired in-house knowledge. The OFAT approach, however, apart from being time and labour intensive, does not provide the practitioner with knowledge on factor interactions, and may even lead to false optima. On the contrary, the design of experiments approach (DOE) allows for more accurate optima detection, alongside the acquisition of process specific knowledge while performing a minimum number of experimental runs [1,2]. Optimization of Headspace Solid Phase Micro-Extraction (HS-SPME) coupled to Gas Chromatography Mass Spectrometry (GC-MS) has been performed in the past using DOE by a number of practitioners on a number of matrices. However, in most of the aforementioned approaches, such optimization is focused on specific compounds, or compound classes, known to be present and/or abundant in the corresponding matrices [3,4]. However, during untargeted analysis and marker discovery, such approach may apply bias on the optimization of the extraction process in favour of the selected compounds and classes, while it may suppress the optimization of unknowns that could provide valuable information, especially for classification purposes. Here, we apply a Box-Behnken Response Surface Methodology (RSM) design for the optimization of three HS-SPME factors on a pooled grape marc spirit sample that is representative of a large pool of samples. Following data processing, individual features are extracted from raw chromatograms under specific settings and individual models are fitted to each feature.