AI summary
75% confidenceThis paper introduces an AI-enhanced electrochemical impedance spectroscopy platform for real-time monitoring of yeast cell concentration, providing high temporal resolution and label-free measurement. The platform uses broadband impedance measurements and engineered features to predict optical density with high precision. The system enables real-time monitoring with sub-millisecond latency and minimal memory footprint.
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Abstract
Quantifying microbial growth with high temporal resolution remains essential yet challenging due to limitations of optical, manual, and biochemical methods. Here, we introduce an AI-enhanced electrochemical impedance spectroscopy platform for real-time, label-free monitoring of Saccharomyces cerevisiae growth. Broadband impedance measurements (1 Hz-100 kHz) were collected from yeast cultures across log-phase development. Engineered features-derived from impedance magnitude and phase-captured dielectric and conductive shifts associated with cell proliferation, membrane polarization, and ionic redistribution. A Gaussian Process Regression model trained on these features predicted optical density (OD600) with high precision (RMSE = 0.79 min; R 2 = 0.9996; r = 0.9998), and achieved 100% classification accuracy when discretized into 15-min growth intervals. The system operated with sub-millisecond latency and minimal memory footprint, enabling embedded deployment. Benchmarking against conventional methods revealed superior throughput, automation potential, and independence from labeling or turbidity-based optics. This AI-driven platform forms the core of a real-time digital twin for yeast culture monitoring, capable of predictive tracking and adaptive control. By fusing electrochemical biosensing with machine learning, our method offers a scalable and robust solution for intelligent fermentation and bioprocess optimization.