Citation: LI Zheng-Feng, XU Guang-Jin, WANG Jia-Jun, DU Guo-Rong, CAI Wen-Sheng, SHAO Xue-Guang. Outlier Detection for Multivariate Calibration in Near Infrared Spectroscopic Analysis by Model Diagnostics[J]. Chinese Journal of Analytical Chemistry, ;2016, 44(2): 305-309. doi: 10.11895/j.issn.0253-3820.150793 shu

Outlier Detection for Multivariate Calibration in Near Infrared Spectroscopic Analysis by Model Diagnostics

1.
¹ R&D Center, China Tobacco Yunnan Industrial Co. Ltd., Kunming 650231, China;
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² Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, China;
3.
³ College of Chemistry and Environmental Science, Kashgar University, Kashgar 844000, China

Corresponding author: SHAO Xue-Guang,
Received Date: 11 October 2015
Available Online: 28 October 2015
Fund Project: 本文系国家自然科学基金项目(No.21475068) (No.21475068)中国烟草总公司重大专项课题(No.Ts-03-20110020)资助 (No.Ts-03-20110020)

Outlier detection is an important task in multivariate calibration because the quality of a calibration model is determined by that of the calibration data. An outlier detection method is proposed for near infrared (NIR) spectral analysis. The method is based on the definition of outlier and the principle of partial least squares (PLS) regression, i.e., an outlier in a dataset behaves differently from the rest, and the prediction result of a PLS model is an accumulation of several independent latent variables. Therefore, the proposed method builds a PLS model with a calibration dataset, and then the contribution of each latent variable is investigated. Outliers can be detected by comparing these contributions. An NIR spectral dataset of orange juice samples is adopted for testing the method. Six outliers are detected in the calibration set. The root mean squared error of cross validation (RMSECV) becomes to 4.809 from 16.870 and the root mean squared error of prediction (RMSEP) becomes to 3.332 from 3.688 after the removal of the outliers. Compared with a robust regression method, the result of the proposed method seems more reasonable.
- Multivariate calibration,
- Outlier detection,
- Partial least squares,
- Near infrared spectroscopy,
- Quantitative analysis
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沈阳化工大学材料科学与工程学院沈阳 110142