Department of Chemistry, Faculty of Science, Yazd University, Yazd 89195-741, Iran.
Anal Biochem. 2011 Mar 15;410(2):289-95. doi: 10.1016/j.ab.2010.11.007. Epub 2010 Nov 13.
A chemometric-assisted kinetic spectrophotometric method has been developed for simultaneous determination of ascorbic acid (AA), uric acid (UA), and dopamine (DA). This method relies on the difference in the kinetic rates of the reactions of analytes with a common oxidizing agent, tris(1,10-phenanthroline) and iron(III) complex (ferritin, Fe(phen)(3)) at pH 4.4. The changes in absorbance were monitored spectrophotometrically. The data obtained from the experiments were processed by chemometric methods of artificial neural network (ANN) and partial least squares (PLS). Acceptable techniques of prediction set, randomization t test, cross-validation, and Y randomization were applied for the selection of the best chemometric method. The results showed that feedforward artificial neural network (FFANN) is more efficient than the other chemometric methods. The parameters affecting the experimental conditions were optimized, and it was found that under optimal conditions Beer's law is followed in the concentration ranges of 4.3-74.1, 4.3-78.3, and 2.0-33.0 μM for AA, UA, and DA, respectively. The proposed method was successfully applied to the determination of analytes in serum and urine samples.
一种基于化学计量学辅助的动力学分光光度法已被开发出来，用于同时测定抗坏血酸（AA）、尿酸（UA）和多巴胺（DA）。该方法依赖于分析物与共同氧化剂三（1,10-菲啰啉）和铁（III）配合物（铁蛋白，[Fe（phen）（3）]（3+））在 pH 4.4 下反应速率的差异。通过分光光度法监测吸光度的变化。通过人工神经网络（ANN）和偏最小二乘法（PLS）等化学计量方法对实验数据进行处理。采用预测集、随机化 t 检验、交叉验证和 Y 随机化等可接受的预测技术来选择最佳的化学计量方法。结果表明，前馈人工神经网络（FFANN）比其他化学计量方法更有效。优化了影响实验条件的参数，结果发现，在最佳条件下，AA、UA 和 DA 的浓度范围分别为 4.3-74.1、4.3-78.3 和 2.0-33.0 μM 时，遵循比尔定律。该方法已成功应用于血清和尿液样本中分析物的测定。
