Real-time measurement of short-chain fatty acids via microwave sensing: A pilot study

Abstract

BackgroundShort-chain fatty acids (SCFAs) - acetate, propionate, and butyrate - are key microbial metabolites that influence host metabolism, barrier function, and immune tone. Yet, in vivo measurements of SCFAs remain poorly characterized because stool measurements only capture a small, spatially averaged fraction of luminal production. PurposeTo evaluate whether label-free microwave sensing using a vector network analyzer (VNA) can differentiate SCFA identity, concentration, and their ratios via unique dielectric signatures. MethodsSodium acetate, sodium propionate, and sodium butyrate solutions (10 uM - 1M) were prepared by measuring their electrochemical properties (pH and mV). Using a 2-port VNA interfaced with a high-frequency copper plate (1 - 3 GHz), scattering and impedance parameters were measured for SCFAs and their ratios. A per-frequency ANOVA was employed to identify discriminatory frequency bands, and supervised machine learning (random forest) was employed to identify additional features differentiating SCFAs. ResultsSCFAs displayed distinct (ANOVA, p < 0.05) electrochemical signatures for both pH and mV. The 21 transmission spectrum revealed discriminatory frequencies with prominent bands at 1.38 - 1.40 GHz and 1.79 - 1.85 GHz. For SCFA ratios, the random forest model achieved up to 80.6% accuracy (k = 0.72) with S11 (77.7%) and S22 (69.9%) unwrapped phases observed as the top two important features contributing to the models performance. ConclusionVNA-based microwave sensing resolves SCFA-specific, frequency-dependent dielectric signatures across biologically relevant concentrations and classifies SCFA-like mixtures with high accuracy. These findings support microwave sensing as a foundation for real-time, non-invasive monitoring of gut microbial metabolism. IMPORTANCEShort-chain fatty acids (SCFAs) are central to host-microbe interactions, yet conventional stool assays provide only snapshots of microbial metabolism and poorly reflect real-time intestinal production of these microbial metabolites. SCFAs mediate intestinal barrier integrity, immune regulation, energy availability, and energy metabolism. Alterations in SCFA production and composition are increasingly recognized as biomarkers of gut microbial dysbiosis, with strong connections to conditions such as inflammatory bowel disease, metabolic syndrome, obesity, and neurodegenerative disorders. As such, the dynamic and quantitative assessment of SCFA profiles in vivo may provide valuable insight into gut health, disease progression, and therapeutic response. Despite large technological advances in the past decade, there is a limited understanding of in vivo SCFA dynamics within the gut, largely due to the inaccessibility and limitation of developing non-invasive diagnostic tools. Furthermore, the spatiotemporal resolution of microbial-derived metabolites in the gut is also very limited, with most current research methods relying on utilizing stool samples, which only contain a fraction of produced SCFA concentrations. In this pilot study, SCFA solutions of known concentrations and identity were measured and analyzed via their capacitance to use as an emerging diagnostic tool in clinical and/or athletic research and to further practice assessing gut microbial dynamics. We demonstrate that microwave sensing - a label-free, electrical approach - can distinguish among SCFAs and their concentrations by exploiting their dielectric differences and frequency-resolved behavior. Using a Vector Network Analyzer and a low-loss dielectric sensor, we identify reproducible GHz-scale bands that separate acetate, propionate, and butyrate. Results indicated that these metabolites can be differentiated via their S parameter (frequency response) electrical signals and pH values. Additionally, we classify physiological SCFA mixtures with strong performance.The study will be repeated by measuring various fluid media, starting with acidic solutions and testing with fluid mixes such as mock carbohydrate solutions that best mimic gut microbial conditions in hopes of developing a diagnostic tool (i.e., swallowable sensor) that can be used to monitor real-time human gut environments. This establishes a practical path toward dynamic, non-invasive readouts of gut microbial activity with potential applications in clinical monitoring.