Analysis of ternary mixtures with a single dynamic microbial sensor and chemometrics using a nonlinear multivariate calibration.

An amperometric biosensor based on immobilized bacterial cells of Alcaligenes eutrophus KT02 and an oxygen electrode was integrated in a flow-through system. Because microorganisms metabolize various organic analytes in a specific manner, the sensor shows for different pure analytes distinct time-dependent oxygen consumption rates that can be treated as characteristic patterns. This behavior is conserved also when the biosensor is exposed to a mixture of these organic analytes; the sensor with a particular type of microorganisms responds with a total signal. The respiration curves as time-dependent amplitudes were subdivided into several time channels. This procedure creates an additional data dimension and makes the single sensor "dynamic". Using multivariate calibration models with only one single biosensor, simultaneous quantitative analysis of ternary mixtures of acetate, L-lactate, and succinate was realized. A nonlinear algorithm that compensated for conceivable interactions of the analytes was superior to a partial least-squares algorithm. Each analyte was predicted more precisely by the nonlinear approach resulting in root-mean-square errors of prediction of 0.20 mg/L for acetate, 0.43 mg/L for L-lactate, and 0.73 mg/L for succinate.