Abstract
Delaunay triangulation (DT) has been applied in analytical chemistry as a local multivariate calibration method for analysis of near-infrared (NIR) data. The method performs feature extraction from the spectra of calibration samples by using principal component analysis (PCA) at first, forming a mesh of simplexes, and then carries out a prediction based on the position of the unknown sample in the principal component (PC) space. In this study, for improving the prediction accuracy, concentration information was considered in the formation of the simplex meshes, i.e., the meshes were constructed with the score vectors of PLS modeling, in stead of the PCs of PCA. An application of the proposed method in prediction of cetane number (CN) in diesel fuels from NIR spectroscopy is investigated. Better results are obtained compared with conventional DT method, therefore the proposed DT method maybe used as a new local calibration method.
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