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Bahram Hemmateenejad and Sadegh Karimi Construction of stable multivariate calibration models using unsupervised segmented principal component regression Journal of Chemometrics 25

Version of Record online: 8 APR 2011 | DOI: 10.1002/cem.1390

A segmentation approach based on unsupervised pattern recognition was proposed to identify the most informative spectral region and then to construct a stable multivariate calibration model by PCR. The instrument channels were clustered into different segments via Kohonen self-organization map and are then subjected to PCA. The derived PCs are used as input variables for an ILS regression model. The proposed method could model both simulated and experimental data sets with prediction errors lower than conventional PLS and PCR methods.

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