Robust calibration with respect to background variation
Robust calibration with respect to background variation
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DOI：
10.1366/0003702011952848
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发表时间：
2001-07-01
影响因子：
3.5
通讯作者：
Brown, SD
中科院分区：
文献类型：
--
作者：
Mittermayr, CR;Tan, HW;Brown, SD
应用小波系数的线性回归方法对背景变化较大的光谱数据进行了稳健校正，并用合成和真实的数据进行了验证。蒙特卡洛研究调查的方法在两种情况下，在预测集的背景变化是相同的校准集和变化是不同的性能。多元线性回归小波系数被证明是有竞争力的，在第一种情况下，在第二种情况下，相对于偏最小二乘（PLS）校准上级。对真实的近红外（NIR）数据的结果证实了模拟研究。作为对小波系数回归的研究，这是小波系数回归的第一个应用研究，展示了如何利用小波的消失矩特性来减少变化背景的影响。作为一种背景校正方法，该方法避免了在估计过程中引入的误差。此外，这里提出的策略也可以应用于各种其他分析技术收集的数据。
The application of linear regression on wavelet coefficients for robust calibration of spectral data with highly variable background was successfully demonstrated with synthetic and real data. A Monte Carlo study was made to investigate the performance of the methods in both the cases where the background variation in the prediction set was the same as in the calibration set and where the variation was different. Multivariate linear regression on wavelet coefficients proved to be competitive in the first case and superior in the second case with respect to partial least squares (PLS) calibration. Results on real near-infrared (NIR) data confirmed the simulation study. As a study of regression on wavelet coefficients, this is the first application study of regression on wavelet coefficients that shows how the wavelet's property of vanishing moments can be used for reducing the effects of varying background. As a background correction method, the proposed approach avoided errors introduced in the estimation process. In addition, the strategy proposed here can be applied to data collected by various other analytical techniques as well.