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.

在这项研究中，研究了一种基于金纳米颗粒（AuNPs）聚集体同时进行超痕量比色法测定二甲双胍（MET）和西他列汀（STG）的简单而快速的方法。分别使用透射电子显微镜（TEM）和动态光散射（DLS）监测添加药物之前（Zipmet）前后合成的AuNPs的形态和尺寸分布。通过添加药物，吸收峰从520nm转移到650nm。比色法与偏最小二乘（PLS）作为多元校准方法，以及神经网络时间序列被应用于同时估计MET和STG。与MET和STG相关的混合物测试集的平均回收率和均方根误差（RMSE）的百分比分别为99.96、1.1301和99.77、1.0106，分别。另一方面，回归系数（R²）对应于人工神经网络（ANN）的训练，验证和测试集对于这两个组件而言都接近一个。最终，通过方差分析（ANOVA）测试，将该提议的方法与一种称为高效液相色谱（HPLC）的参考技术进行了比较，两者之间没有显着差异。