Quantitative Determination of Gold Immunochromatographic Assay based on Least Squares Support Vector Machine

The least squares support vector machine(LSSVM)was applied to build the fitting model for quantitative determination of nano-gold immunochromatographic assay(GICA)strip based on the reflective optical detection.The genetic algorithm(GA) was used to solve the optimization problem of the LSSVM model between the characteristic parameters and the sample concentration.In the statistical data of the alpha-fetoprotein(AFP)GICA strip test samples,the sample relative mean square error was 12.2%.The experimental results indicated that the least squares support vector machine model which optimizing by the genetic algorithm performed well,and proved to be appropriate in quantitative determination of GICA strip.