ArticleAnalytical and bioanalytical chemistry2026
Evaluation and optimization of SPE pre-processing combined with V-ITEX-GC-MS versus direct V-ITEX-GC-MS for urinary volatilome profiling.
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Abstract
Volatile urinary metabolites offer valuable insights into nutritional status and metabolic function. However, the extraction of these metabolites remains analytically challenging due to the complexity and variability of the urine matrix. This study compares and optimizes two extraction techniques, solid-phase extraction followed by vacuum in-tube extraction (SPE+V-ITEX), and direct V-ITEX for the untargeted gas chromatography-mass spectrometry analysis of urinary volatiles. Using design of experiment (DoE) approaches, critical parameters were systematically optimized for both methods. The identified compounds were analyzed with respect to origin, extraction efficiency, and chemical class specificity. Method greenness was assessed using the AGREE and AGREEprep tools. Both SPE+V-ITEX and direct V-ITEX enabled broad metabolite coverage, yielding 1198 detected features, with sufficient repeatability, as 76% of evaluated responses showed RSD values below 20%. SPE+V-ITEX achieved higher total signal intensity for several compounds and compound groups (e.g., alcohols, caprolactam, hetero aromats), and both methods demonstrated sufficient repeatability. Temporal trends in pooled real samples confirmed both methods are applicable for metabolite profiling. Based on AGREE and AGREEprep assessments, V-ITEX emerged as a more sustainable and operationally efficient workflow, aligning closely with green analytical chemistry principles. Desorption flow, trap material, extraction temperature, and time significantly influenced direct V-ITEX performance. Compound-specific trends were observed, but no single method was optimal for all metabolite classes. These findings present direct V-ITEX as a robust, green, and efficient alternative to SPE+V-ITEX for urinary volatilomics and a method particularly well-suited for high-throughput analytical workflows with minimal manual steps.
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