MID-FTIR-PLS Chemometric Analysis of Cr(VI) from Aqueous Solutions Using a Polymer Inclusion Membrane-Based Sensor.
Membranes (Basel)
; 13(8)2023 Aug 18.
Article
em En
| MEDLINE
| ID: mdl-37623801
ABSTRACT
A partial least squares (PLS) quantitative chemometric method based on the analysis of the mid-Fourier transform infrared spectroscopy (MID-FTIR) spectrum of polymer inclusion membranes (PIMs) used for the extraction of Cr(VI) from aqueous media is developed. The system previously optimized considering the variables membrane composition, extraction time, and pH, is characterized in terms of its adsorption isotherm, distribution coefficient, extraction percent, and enrichment factor. A Langmuir-type adsorption behavior with KL = 2199 cm3/mmol, qmax = 0.188 mmol/g, and 0 < RL < 1 indicates that metal adsorption is favorable. The characterization of the extraction reaction is performed as well, showing a 11 Cr(VI)Aliquat 336 ratio, in agreement with solvent extraction data. The principal component analysis (PCA) of the PIMs reveals a complex pattern, which is satisfactorily simplified and related to Cr(VI) concentrations through the use of a variable selection method (iPLS) in which the bands in the ranges 3451-3500 cm-1 and 3751-3800 cm-1 are chosen. The final PLS model, including the 100 wavelengths selected by iPLS and 10 latent variables, shows excellent parameter values with root mean square error of calibration (RMSEC) of 3.73115, root mean square error of cross-validation (RMSECV) of 6.82685, bias of -1.91847 × 10-13, cross-validation (CV) bias of 0.185947, R2 Cal of 0.98145, R2 CV of 0.940902, recovery% of 104.02 ± 4.12 (α = 0.05), sensitivity% of 0.001547 ppb, analytical sensitivity (γ) of 3.8 ppb, γ-1 0.6 ppb-1, selectivity of 0.0155, linear range of 5.8-100 ppb, limit of detection (LD) of 1.9 ppb, and limit of quantitation (LQ) of 5.8 ppb. The developed PIM sensor is easy to implement as it requires few manipulations and a reduced number of chemical compounds in comparison to other similar reported systems.
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Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article