Simultaneous ultra-trace quantitative colorimetric determination of antidiabetic drugs based on gold nanoparticles aggregation using multivariate calibration and neural network methods.

Abstract In this study, a simple and rapid method was investigated for the simultaneous ultra-trace colorimetric determination of Metformin (MET) and Sitagliptin (STG) based on the aggregation of gold nanoparticles (AuNPs). The Morphology and size distribution of synthesized AuNPs before and after adding drug (Zipmet) were monitored using transmission electron microscopy (TEM) and dynamic light scattering (DLS), respectively. By adding a drug, the absorption peak was shifted from 520 to 650 nm. The colorimetric method along with partial least squares (PLS) as a multivariate calibration method, as well as neural network time series were applied to estimate MET and STG simultaneously. The percentage of the mean recovery and root mean square error (RMSE) of the test set of mixtures related to the MET and STG were obtained 99.96, 1.1301 and 99.77, 1.0106, respectively. On the other hand, the regression coefficient (R2) of the training, validation, and test sets corresponding to the artificial neural network (ANN) were close to one for both components. Eventually, the proposed method was compared with a reference technique named high-performance liquid chromatography (HPLC) by analysis of variance (ANOVA) test and there was no significant difference between them.