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 Alcaligen es eutrophus KT02 and an oxygen electrode was integrated in a now-through s ystem. Because microorganisms metabolize various organic analytes in a spec ific manner, the sensor shows for different pure analytes distinct time-dep endent oxygen consumption rates that can be treated as characteristic patte rns. This behavior is conserved also when the biosensor is exposed to a mix ture of these organic analytes; the sensor with a particular type of microo rganisms responds with a total signal. The respiration curves as time-depen dent 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, simu ltaneous quantitative analysis of ternary mixtures of acetate, L-lactate, a nd succinate was realized. A nonlinear algorithm that compensated for conce ivable interactions of the analytes was superior to a partial least-squares algorithm. Each analyte was predicted more precisely by the nonlinear appr oach resulting in root-mean-square errors of prediction of 0.20 mg/L for ac etate, 0.43 mg/L for L-lactate, and 0.73 mg/L for succinate.