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Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil

A fast and easy methodology to estimate total polyphenol content in extra virgin olive oil was developed by applying the chemometric multiblock method sequential and orthogonalized partial least squares (SO-PLS) in order to combine front-face emission fluorescence spectra (270 nm excitation waveleng...

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Autores principales: Hernández-Sánchez, Natalia, Lleó, Lourdes, Diezma, Belén, Correa, Eva Cristina, Sastre, Blanca, Roger, Jean-Michel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623568/
https://www.ncbi.nlm.nih.gov/pubmed/34828837
http://dx.doi.org/10.3390/foods10112556
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author Hernández-Sánchez, Natalia
Lleó, Lourdes
Diezma, Belén
Correa, Eva Cristina
Sastre, Blanca
Roger, Jean-Michel
author_facet Hernández-Sánchez, Natalia
Lleó, Lourdes
Diezma, Belén
Correa, Eva Cristina
Sastre, Blanca
Roger, Jean-Michel
author_sort Hernández-Sánchez, Natalia
collection PubMed
description A fast and easy methodology to estimate total polyphenol content in extra virgin olive oil was developed by applying the chemometric multiblock method sequential and orthogonalized partial least squares (SO-PLS) in order to combine front-face emission fluorescence spectra (270 nm excitation wavelength) and absorbance spectra. The hypothesis of this work stated that inner-filter effects in fluorescence spectra that would reduce the estimation performance of a single block model could be overcome by incorporating the absorbance spectral information of the compounds causing them. Different spectral preprocessing algorithms were applied. Double cross-validation with 50 iterations was implemented to improve the robustness of the obtained results. The PLSR model on the single block of fluorescence raw spectra achieved an RMSEP of 177.11 mg·kg(−1) as the median value, and the complexity of the model was high, as the median value of latent variables (LVs) was eight. Multiblock SO-PLS models with pretreated fluorescence and absorbance spectra provided better performance, although artefacts could be introduced by transformation. The combination of fluorescence and absorbance raw data decreased the RMSEP median to 134.45 mg·kg(−1). Moreover, the complexity of the model was greatly reduced, which contributed to an increase in robustness. The median value of LVs was three for fluorescence data and only one for absorbance data. Validation of the methodology could be addressed by further work considering a higher number of samples and a detailed composition of polyphenols.
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spelling pubmed-86235682021-11-27 Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil Hernández-Sánchez, Natalia Lleó, Lourdes Diezma, Belén Correa, Eva Cristina Sastre, Blanca Roger, Jean-Michel Foods Article A fast and easy methodology to estimate total polyphenol content in extra virgin olive oil was developed by applying the chemometric multiblock method sequential and orthogonalized partial least squares (SO-PLS) in order to combine front-face emission fluorescence spectra (270 nm excitation wavelength) and absorbance spectra. The hypothesis of this work stated that inner-filter effects in fluorescence spectra that would reduce the estimation performance of a single block model could be overcome by incorporating the absorbance spectral information of the compounds causing them. Different spectral preprocessing algorithms were applied. Double cross-validation with 50 iterations was implemented to improve the robustness of the obtained results. The PLSR model on the single block of fluorescence raw spectra achieved an RMSEP of 177.11 mg·kg(−1) as the median value, and the complexity of the model was high, as the median value of latent variables (LVs) was eight. Multiblock SO-PLS models with pretreated fluorescence and absorbance spectra provided better performance, although artefacts could be introduced by transformation. The combination of fluorescence and absorbance raw data decreased the RMSEP median to 134.45 mg·kg(−1). Moreover, the complexity of the model was greatly reduced, which contributed to an increase in robustness. The median value of LVs was three for fluorescence data and only one for absorbance data. Validation of the methodology could be addressed by further work considering a higher number of samples and a detailed composition of polyphenols. MDPI 2021-10-23 /pmc/articles/PMC8623568/ /pubmed/34828837 http://dx.doi.org/10.3390/foods10112556 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hernández-Sánchez, Natalia
Lleó, Lourdes
Diezma, Belén
Correa, Eva Cristina
Sastre, Blanca
Roger, Jean-Michel
Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil
title Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil
title_full Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil
title_fullStr Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil
title_full_unstemmed Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil
title_short Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil
title_sort multiblock analysis applied to fluorescence and absorbance spectra to estimate total polyphenol content in extra virgin olive oil
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623568/
https://www.ncbi.nlm.nih.gov/pubmed/34828837
http://dx.doi.org/10.3390/foods10112556
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