A liquid handling system for the automated acquisition of data for training, validating and testing calibration models

Abstract

Multivariate calibration of a single sensor for many mixed analytes is useful, but generating, validating and testing the calibration models requires large data sets which can be time consuming to collect, particularly when the number of analytes is large. In this paper, a solution to this problem is presented, in the form of an automated liquid handling system capable of producing mixtures and triggering an external analytical instrument to analyse them. As a case study, the electrochemical technique of dual pulse staircase voltammetry (DPSV) was used to collect data for three analytes (glucose, fructose and ethanol) mixed in varying concentrations. Artificial neural networks (ANNs), optimised using genetic algorithms were used to create the best possible multivariate calibration model. The liquid handling system performed 1668 experiments used in the study in approximately 60 h, compared to over 2 weeks that would be required to collect perform the experiments manually. The best calibration models produced from the data bettered those produced from previous manually collected data [1,2] for all the analytes concerned.