Rule-based algorithm for the identification of single and binary mixtures of cross-reacting neonicotinoids with microsphere-based immunoassays - Lemmink, Ids B.; Alewijn, Martin; Beij, Erik; Xu, Mang; Bovee, Toine F.H.; Peters, Jeroen; Salentijn, Gert IJ (Talanta (Oxford), 2026)
Microsphere-based immunoassays are widely used for high-throughput multi-analyte determination. However, as multiple structurally similar analytes often cross-react with the same antibody, identifying which exact (mixture of) cross-reacting analytes is present is not possible in such instances. The aim of this study was to combine the immunochemical signal of multiple microsphere-based immunoassays into a single set for cross-reacting analyte identification. For this, a rule-based analyte identification algorithm was developed. The algorithm was trained with only 6 samples per contaminant, measured in duplicate. For each training sample, the individual immunochemical signal on the microspheres (∼100/assay) were resampled and multiplied with a pre-determined pipetting uncertainty. Subsequently, these resampled results were used for curve fitting and the 5th, 50th and 95th percentile of the expected immunochemical signal between 10−3 and 103 ng mL−1 computed. The performance of the algorithm was assessed for the identification of seven different neonicotinoids by analyzing 48 test samples in tap water (7 blanks, and 14 single contaminations), and spinach extract (7 blanks, 14 single contaminations, and 6 binary mixtures) with 5 different antibodies. Of the 48 samples, 44 (92 %) were identified correctly. The performance of the algorithm is expected to further improve by increasing the number of antibodies, provided they have differential affinity. Furthermore, the algorithm can be easily expanded with additional cross-reacting analytes, or adapted to entirely different analyte classes. [Display omitted] •First analyte identification algorithm for use with microsphere-based immunoassays.•Minimizing required training samples by an innovative bootstrapping approach.•Proof-of-principle for blank, single, and binary mixtures of neonicotinoids in tap water and spinach extract.•Estimated identification accuracy of 92 % (n = 48).