Simultaneous kinetic spectrophotometric determination of Cu(II), Co(II) and Ni(II) using partial least squares (PLS) regression
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چکیده
منابع مشابه
Spectrophotometric Simultaneous Kinetic Determination of Iodide and Iodate Using Partial Least-Squares Calibration Method in a Single Kinetic Run
A rapid, sensitive and versatile kinetic method is presented for the simultaneous spectrophotometric determination of iodide and iodate by partial least-squares regression (PLS) using original and derivate data named as absorbance and rate data. The method is based on the catalytic effect of the cited anions on the reaction rate between Ce(IV) and As(III) in 2 mol l?1 sulfuric acid medium. The ...
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Simultaneous determination of difloxacin and enrofloxacin using two chemometric methods, partial least squares regression (PLS) and direct orthogonal signal correction-partial least squares regression (DOSC-PLS) is described in this paper. The simultaneous determination of these drugs is difficult due to spectral interferences. The calibration graphs were linear in the range of 1-10 μg mL-1 and...
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A partial least squares (PLS) calibration model was developed for the simultaneous spectrophotometricdetermination of Pb (ΙΙ), Cu (ΙΙ) and Ni (ΙΙ) using xylenol orange as a chromogenic reagent. The parameterscontrolling behavior of the system were investigated and optimum conditions were selected. The calibrationgraphs were linear in the ranges of 0.0–9.091, 0.0–2.719 and 0.0–2.381 ppm for lead...
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Number of latents The same number of factors will be extracted for PLS responses as for PLS factors. The researcher must specify how many latents to extract (in SPSS the default is 5). There is no one criterion for deciding how many latents to employ. Common alternatives are: 1. Cross-validating the model with increasing numbers of factors, then choosing the number with minimum prediction error...
متن کاملPartial Least Squares (PLS) Regression
Pls regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. It is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent variables (i.e., predictors). It originated in the social sciences (specifically economy, Herman Wold 1966) but became popular first in chemomet...
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ژورنال
عنوان ژورنال: Open Chemistry
سال: 2009
ISSN: 2391-5420
DOI: 10.2478/s11532-009-0030-2