Limit of detection in qualitative analysis: Classification Analytical Signal approach

Abstract

This paper presents a new methodology for the estimation of the limit of detection and other related parameters in qualitative analysis. Estimation of the detection limit is important in special cases when a small amount of an illegal ingredient can significantly affect the quality of the product or the presence of even a small amount of an extraneous analyte is significant. The approach is based on a recently introduced concept of an analytical signal in qualitative analysis, called the Classification Analytical Signal (CAS). A detailed algorithm to calculate the detection capability for a one-class classification method Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) is described and illustrated with two real-world examples. Both examples consider cases where the detection of low analyte concentrations is important and corresponding samples should be considered as non-compliant. The first case presents adulteration of wheat flour with talcum powder, where the samples were specially prepared with various concentrations of the analyte. The second case uses a publicly available dataset devoted to the estimation of tetracycline in blood serum. The latter case is used to compare the limits of detection estimated for multivariate calibration with those presented for qualitative analysis.