Quality control of fragrances using Raman spectroscopy and multivariate analysis
| Autor: | Mauricio C. Santos, Ronei J. Poppi, Robson B. Godinho |
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| Rok vydání: | 2015 |
| Předmět: |
Multivariate analysis
business.industry Chemistry 010401 analytical chemistry Analytical chemistry Multivariate calibration Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Chemometrics symbols.namesake Standard error Quality (physics) Partial least squares regression symbols Classification methods General Materials Science Artificial intelligence 0210 nano-technology Raman spectroscopy business Spectroscopy |
| Zdroj: | Journal of Raman Spectroscopy. 47:579-584 |
| ISSN: | 0377-0486 |
| DOI: | 10.1002/jrs.4856 |
| Popis: | An analytical methodology using Raman spectroscopy and chemometrics was developed for direct, fast and non-destructive discrimination and prediction of the properties of fragrances according to their composition. The soft independent modeling of class analogies was used as a supervised classification method for fragrances classification, and partial least squares regression as a multivariate calibration method for the prediction of physicochemical properties of fragrances, such as density and refractive index. From 155 fragrance samples, the model exhibited a high success rate for all of the studied fragrance classes, with 100% correct classification. In the multivariate calibration model, adequate correlation was observed between the measured and partial least squares regression-predicted data for refractive index and density, with a relative standard error of prediction between 0.02% and 0.07%, respectively. This study demonstrates the wide applicability of the methodology for the discrimination, classification, and prediction of complex olfactory mixtures in quality control of fragrances. Copyright © 2015 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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