Modified robust continuum regression by net analyte signal to improve prediction performance for data with outliers

作者：Zhang Xiao Yu; Li Qing Bo; Zhang Guang Jun^*

来源：Chemometrics and Intelligent Laboratory Systems, 2011, 107(2): 333-342.

DOI：10.1016/j.chemolab.2011.05.003

• Modified robust continuum
• regression (mRCR)
• Net analyte signal (NAS)
• Outlying observation
• Spectrometric quantization
• Glucose concentration

Contaminated data exist in diverse situations, even in high quality surveys and experiments. If classical statistic models are blindly applied to data containing outliers, the results can be misleading at best. In this paper, a modified robust continuum regression (mRCR) method is proposed to improve prediction performance for data with outliers. The mRCR method constructs projection pursuit directions by using projection matrix for computing the net analyte signal (NAS) of the target analyte. This paper examines applications to the determination of glucose concentration by near-infrared (NIR) spectrometry, including aqueous solution with glucose experiment, plasma experiment in vitro, oral glucose tolerance test (OGTT) in vivo, to illustrate the advantages of mRCR for various kinds of outliers depending on the way of contamination. The results indicate that the mRCR method is entirely robust with respect to any type of outlying observations, and it yields smaller prediction errors for normal samples than other calibration methods.

• 出版日期2011-7
• 单位北京航空航天大学