Duality based trilinear decomposition of second order data set

Second-order calibration methods are gaining widespread acceptance among the analytical community mainly because of second order advantage. Accurate prediction of concentration of the analyte of interest even in the presence of unknown interferents is possible under enough knowledge about the system under investigation. In calibration the most commonly used model for second order data is the trilinear model. The trilinear model is considered to be the most restricted model of all three-way models, and is the only one that allows a unique solution [1]. Duality is very interesting property in science which has introduced by R. C. Henry for multivariate receptor modeling and the generalization of it has introduced by R. Rajko that is, proving that there exists natural duality using minimal constrains, is given for universally using it in SMCR which is based on singular value decomposition [2]. Constructing “a universal calibration model” for predicting an analyte in different mixtures with different composition, is a challenging subject in the field of analytical chemistry and pharmaceutical analysis.Introduction of Duality Based Trilinear Decomposition of second order data (DBTD), is the main point of this work. The method is able to quantify the analyte in the different mixture with different composition based on duality concept.In order to evaluate the performance of the proposed method, real example was used beside the simulation. Deterimination of Tryptophan in wheat sample has done with satisfying result.