Jul 2, 2026, 4:30 pm @Main Room

GC-SOMSA - A tool for the monitoring of fermentation processes

Lucia Corrà1, Gina Zeh2, Tilman Sauerwald2, Laura Capelli1, Michael Czerny2 1Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy, 2Fraunhofer Institute for Process Engineering and Packaging, Giggenhauser Strasse 35, D-85354 Freising, Germany Fermentation process monitoring remains a critical challenge in food manufacturing [1]. Metal Oxide Semiconductor (MOX) sensor-based systems represent a promising low-cost approach for continuous headspace monitoring, but their limited selectivity and sensitivity require careful application-specific tuning to deliver traceable measurements [2]. The GC-SOMSA (Gas Chromatography – Selective Odorant Measurement by Sensor Array) addresses this by coupling a gas chromatograph simultaneously to a mass spectrometer, an olfactory detection port, and a sensor chamber, enabling direct correlation between volatile compounds and sensor responses, with MS as an analytical confirmation platform for systematic sensor evaluation [3]. In this work, GC-SOMSA was applied to dough fermentation as a case study. Headspace was sampled under temperature-controlled conditions across 3 independent fermentation runs using SPME with a polydimethylsiloxane (PDMS) fiber, at 50-minute intervals over 4 h 10 min, extending into the over-fermentation regime. Compounds were separated on a Free Fatty Acid Phase (FFAP) column and identified by MS against the NIST library, with retention times confirmed using liquid standards. The volatile profile was consistent with previously published data [4], validating the approach. Sensor response was quantified as peak height, defined as peak maximum minus baseline at onset. Among the identified compounds, 2-phenylethanol exhibited a consistent and progressive increase across all fermentation runs, eliciting a measurable MOX sensor response, and was selected as a candidate volatile marker for fermentation progression. GC-SOMSA proved effective for VOC marker identification and sensor evaluation, providing the experimental basis for application-specific e-nose design, including sensor selection and pre-concentrator integration. In a broader perspective, continuous sensor response curves combined with sensory evaluation of the baked product as a labelling strategy will provide the dataset required to develop AI-driven predictive models for real-time fermentation status prediction. Reference List: 1. Yin, M., Tian, J., Zhu, D., Wang, Y., & Jiang, J. (2024). A data-driven distributed process monitoring method for industry manufacturing systems. Transactions of the Institute of Measurement and Control, 46(7), 1296-1316. 2. Leidinger, M., Rieger, M., Sauerwald, T., Alépée, C., & Schütze, A. (2016). Integrated pre-concentrator gas sensor microsystem for ppb level benzene detection. Sensors and Actuators B: Chemical, 236, 988-996. 3. Koehne, M., Penagos Carrascal, O. T., Czerny, M., Zeh, G., & Sauerwald, T. (2025). A versatile development platform for odor monitoring systems. Journal of Sensors and Sensor Systems, 14(1), 75-88. 4. Czerny, M., & Schieberle, P. (2002). Important aroma compounds in freshly ground wholemeal and white wheat flour identification and quantitative changes during sourdough fermentation. Journal of Agricultural and Food Chemistry, 50(23), 6835-6840.

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