Nonparametric multivariate regression methods to determine dexamethasone concentration using ELISA measurements

Dexamethasone is a synthetic steroid used in veterinary medicine, but its presence in food must remain below certain limits to avoid risk to human health. HPLC methods determine dexamethasone concentration with a high level of accuracy but these methods are expensive and the field instrumentation is difficult to obtain. On the other hand, the classical ELISA method can be used to estimate dexamethasone concentration but it suffers from natural steroid interferences which result in false negatives and positives. In this article we propose the concept of multi-detection in order to estimate dexamethasone concentration from a vector of absorbances. The function which maps the absorbance vector into dexamethasone concentration is calculated by means of non-linear regression algorithms. Results demonstrate that the proposed methods perform much better than the classical ELISA method when interfering steroids are present. This implies that results of immunoassays with cross-reactivity could be treated with nonparametric multivariate regression methods to improve accuracy.