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Investigation of interpolation techniques for the reconstruction of the first dimension of comprehensive two-dimensional liquid chromatography-diode array detector data

机译:二维液相色谱-二极管阵列检测器数据一维重构的插值技术研究

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Simulated and experimental data were used to measure the effectiveness of common interpolation techniques during chromatographic alignment of comprehensive two-dimensional liquid chromatography-diode array detector (LC x LC-DAD) data. Interpolation was used to generate a sufficient number of data points in the sampled first chromatographic dimension to allow for alignment of retention times from different injections. Five different interpolation methods, linear interpolation followed by cross correlation, piecewise cubic Hermite interpolating polynomial, cubic spline, Fourier zero-filling, and Gaussian fitting, were investigated. The fully aligned chromatograms, in both the first and second chromatographic dimensions, were analyzed by parallel factor analysis to determine the relative area for each peak in each injection. A calibration curve was generated for the simulated data set. The standard error of prediction and percent relative standard deviation were calculated for the simulated peak for each technique. The Gaussian fitting interpolation technique resulted in the lowest standard error of prediction and average relative standard deviation for the simulated data. However, upon applying the interpolation techniques to the experimental data, most of the interpolation methods were not found to produce statistically different relative peak areas from each other. While most of the techniques were not statistically different, the performance was improved relative to the PARAFAC results obtained when analyzing the unaligned data.
机译:模拟和实验数据用于测量全面二维液相色谱-二极管阵列检测器(LC x LC-DAD)数据的色谱比对期间常用插值技术的有效性。插值用于在采样的第一色谱维中生成足够数量的数据点,以允许对不同进样的保留时间进行校准。研究了五种不同的插值方法:线性插值后为互相关,分段三次Hermite插值多项式,三次样条,傅立叶零填充和高斯拟合。通过平行因子分析对第一色谱图和第二色谱图上的完全对齐的色谱图进行分析,以确定每次进样中每个峰的相对面积。为模拟数据集生成了一条校准曲线。针对每种技术的模拟峰计算了预测的标准误差和相对标准偏差百分比。高斯拟合插值技术使模拟数据的预测标准误差最小,平均相对标准偏差最小。但是,将插值技术应用于实验数据后,大多数插值方法并未发现统计上彼此不同的相对峰面积。尽管大多数技术在统计上没有差异,但与分析未对齐数据时获得的PARAFAC结果相比,性能有所提高。

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