Determination of sugars in depilatory formulations: a green analytical method employing infrared detection and partial least squares regression.
Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Cascant M
- Garrigues S
- de la Guardia M
Abstract
A green analytical method was developed for the analysis of sugar-based depilatories. Three independent partial least squares (PLS) regression models were built for the direct determination of glucose, fructose and maltose without any sample pretreatment based on their attenuated total reflectance - Fourier transform infrared (ATR-FTIR) spectra. The models showed adequate prediction capabilities with root-mean-square-errors of prediction ranging from 7.04 to 12.55 mg sugar g(-1) sample. As a reference procedure, gradient liquid chromatography with on-line infrared detection, employing background correction based on cubic smoothing splines, was used. The analysis revealed changes in the sugar concentration due to the formulation process as compared to information on the ingredients provided by the manufacturers. Although fructose, glucose and sucrose were declared to be used for the production of depilatories, in the final products only fructose, glucose and maltose were determined. This fact was attributed to pH and temperature conditions employed during the production process as well as to the use of glucose syrup instead of crystalline glucose. The present ATR-FTIR-PLS method enables an accurate, cheap and fast determination without solvent consumption or toxic waste generation and offers therefore a green screening alternative to methods employing chromatographic techniques.
Datos de la publicación
- ISSN/ISSNe:
- 0039-9140, 1873-3573
- Tipo:
- Article
- Páginas:
- 1721-1729
- PubMed:
- 21872009
- Factor de Impacto:
- 1,432 SCImago ℠
Talanta ELSEVIER SCIENCE BV
Citas Recibidas en Web of Science: 12
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Filiaciones
Cita
Cascant M,KULIGOWSKI J,Garrigues S,de la Guardia M. Determination of sugars in depilatory formulations: a green analytical method employing infrared detection and partial least squares regression. Talanta. 2011. 85. (4):p. 1721-1729. IF:3,794. (1).
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