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K. Kim, J. M. Lee and I. B. Lee, “A Novel Multivariate Regression Approach Based on Kernel Partial Least Squares with Orthogonal Signal Correction,” Chemometrics and Intelligent Laboratory Systems, Vol. 79, No. 1, 2005, pp. 22-30. doi:10.1016/j.chemolab.2005.03.003

has been cited by the following article:

• TITLE: Near-Infrared Spectroscopy Coupled with Kernel Partial Least Squares-Discriminant Analysis for Rapid Screening Water Containing Malathion

  AUTHORS: Congying Gu, Bingren Xiang, Yilong Su, Jianping Xu

  KEYWORDS: Kernel Partial Least Squares-Discriminant Analysis; Near-Infrared Spectroscopy; Malathion; Water

  JOURNAL NAME: American Journal of Analytical Chemistry, Vol.4 No.3, March 27, 2013

  ABSTRACT: Near-infrared spectroscopy coupled with kernel partial least squares-discriminant analysis was used to rapidly screen water containing malathion. In the wavenumber of 4348 cm-1 to 9091 cm-1, the overall correct classification rate of kernel partial least squares-discriminant analysis was 100% for training set, and 100% for test set, with the lowest concentration detected malathion residues in water being 1 μg·ml-1. Kernel partial least squares-discriminant analysis was able to have a good performance in classifying data in nonlinear systems. It was inferred that Near-infrared spectroscopy coupled with the kernel partial least squares-discriminant analysis had a potential in rapid screening other pesticide residues in water.