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Updated: Feb 8, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
Partial least squares and random sample consensus in outlier detection
Jiangtao Peng1, Silong Peng, Yong Hu
1Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, PR China. jiangtao.peng@ia.ac.cn
Abstract:
A novel outlier detection method in partial least squares based on random sample consensus is proposed. The proposed algorithm repeatedly generates partial least squares solutions estimated from random samples and then tests each solution for the support from the complete dataset for consistency. A comparative study of the proposed method and leave-one-out cross validation in outlier detection on simulated data and near-infrared data of pharmaceutical tablets is presented. In addition, a comparison between the proposed method and PLS, RSIMPLS, PRM is provided. The obtained results demonstrate that the proposed method is highly efficient.
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