How to search the experimental conditions that improve a Partial Least Squares calibration model. Application to a flow system with electrochemical detection for the determination of sulfonamides in milk

Abstract This paper deals with the selection of experimental conditions and how the signals obtained in these conditions influence the fitted Partial Least Squares calibration model. The multivariate signals come from a flow analysis system with amperometric detection when determining sulfadiazine, sulfamerazine and sulfamethazine in milk. The solution (carrier plus analyte) was pumped through the system to provide a continuous supply of analyte to the cell. The detector was programmed for a scan mode operation being the multivariate signal the hydrodynamic voltammogram. To obtain an analytical signal of enough analytical quality, the Net Analyte Signal and its standard deviation have been optimised by using an experimental design. The conflicting behaviour of the two responses has been solved by estimating the Pareto-optimal front. The multivariate signals recorded in the optimal conditions found have been calibrated by Partial Least Squares regression and their figures of merit validated according to the criteria established in European Decision 2002/657/EC. In relation to the permitted limit, 100 µg l− 1 in milk, for the total content of sulfonamides established in the Commission Regulation EC no. 281/96 the proposed method has a decision limit of 109.1 µg l− 1 and the capability of detection is 117.9 µg l− 1 for both probability of false non-compliance and of false compliance equal to 5%. A recovery of 86.5% ± 2.4% (n = 5) has been obtained.