Rationale for Using Multiple Regression Analysis with Complex Interferences

摘要: Non-specificities and interferences may become complex when they involve the analyte as well other interfering substances. These non-specificities are known analyte-dependent multi-interferent interferences. Multiple regression analysis has proven valuable in analysing this type of interference, but theoretical foundation for using multiple to study basic mechanisms interference not been explicitly demonstrated. Graph theory can depict model possible interactions. The relationship between analyte, interferents, response instrument these entities be approximated by a polymial order three, which includes partial derivatives cross-terms. relate different interactions found with graph model. Further, associated coefficients respective values three variables (analyte, interferent one, two) multiplied one another. One decide retain or discard coefficient variable, based on statistical significance coefficient. graphic then assembled framework mechanism established.