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Spectrophotometric simultaneous determination of ceratine, creatinine, and uric acid in real samples by orthogonal signal correction-partial least squares regression

机译:通过正交信号校正-偏最小二乘回归分光光度法同时测定实际样品中的芥子碱,肌酐和尿酸

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摘要

This work describes a quantitative spectroscopic method for the analysis of ternary mixtures of ceratine (CER), creatinine (CRE), and uric acid (UA) using multivariate data models based upon ultraviolet spectroscopy. By multivariate calibration methods, such as partial least squares regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. In this study, the calibration model is based on absorption spectra in the 200-260 nm range for 36 different mixtures of CER, CRE, and UA. The unrelated information was removed by the orthogonal signal correction (OSC) method and the results were proved. Evaluation of the prediction errors for the prediction set reveals the OSC-treated data give substantially lower root mean square error of prediction (RMSEP) values than original data. The RMSEP for CER, CRE, and UA with OSC were 1.1686, 0.2195, and 0.3726, and without OSC were 1.9057, 0.3482, and 0.6164, respectively. This procedure allows the simultaneous determination of CER, CRE, and UA in synthetic and real samples.
机译:这项工作描述了一种基于紫外光谱的多元数据模型,用于定量分析ceratine(CER),肌酐(CRE)和尿酸(UA)的三元混合物的定量光谱方法。通过多变量校准方法(例如偏最小二乘回归),可以获得调整到在校准范围内使用的混合物的浓度值的模型。在这项研究中,校准模型基于CER,CRE和UA的36种不同混合物在200-260 nm范围内的吸收光谱。通过正交信号校正(OSC)方法去除了无关信息,并证明了结果。对预测集的预测误差的评估表明,经OSC处理的数据比原始数据给出的预测均方根误差(RMSEP)值要低得多。使用OSC的CER,CRE和UA的RMSEP分别为1.9057、0.3482和0.6164,分别为1.686、0.2195和0.3726。该程序可以同时测定合成样品和真实样品中的CER,CRE和UA。

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