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Yiping Du
Researcher at East China University of Science and Technology
Publications - 131
Citations - 2542
Yiping Du is an academic researcher from East China University of Science and Technology. The author has contributed to research in topics: Partial least squares regression & Detection limit. The author has an hindex of 25, co-authored 122 publications receiving 2145 citations. Previous affiliations of Yiping Du include Shandong University of Technology & Central South University.
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The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics.
TL;DR: A chemometric analysis of the near-infrared spectra of olive-oil mixtures containing different adulterants revealed that the PCA developed models were able to classify unknown adulterated olive oil mixtures with almost 100% certainty.
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Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares
TL;DR: In this paper, a changeable size moving window partial least squares (CSMWPLS) and a searching combination moving window part-least-squares (SCMWPLs) are proposed to search for an optimized spectral interval and an optimized combination of spectral regions from informative regions obtained by a previously proposed spectral interval selection method.
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Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra
TL;DR: The results show that SCARS can select the least variables and supply the least RMSECV and latent variable number of the PLS model comparing with methods of Moving Window PLS, MCUVE and CARS.
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Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares
TL;DR: In this article, a chemometric method called searching combination moving window partial least squares (SCMWPLS) was employed to determine the concentrations of human serum albumin (HSA), γ-globulin, and glucose contained in the control serum IIB (CS IIB) solutions with various concentrations.
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Improvement of partial least squares models for in vitro and in vivo glucose quantifications by using near-infrared spectroscopy and searching combination moving window partial least squares
TL;DR: In this article, a search combination moving window partial least squares (SCMWPLS) algorithm was proposed to find the optimum spectral regions for developing efficient PLS models of glucose in the bovine serum samples and the human skin.