# AI-Assisted Impedance Biosensing of Yeast Cell Concentration

## TL;DR

This paper introduces an AI-powered electrochemical system that can monitor yeast growth in real time without labels, offering a faster and more precise alternative to traditional methods.

## Contribution

The novel contribution is an AI-enhanced impedance biosensing platform for real-time, label-free yeast cell concentration monitoring with high precision and low latency.

## Key findings

- The Gaussian Process Regression model achieved high precision in predicting optical density (OD600) with R2 = 0.9996.
- The system enabled 100% classification accuracy in 15-minute growth intervals with sub-millisecond latency.
- The method outperformed conventional techniques in throughput and automation potential.

## 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; R2 = 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.