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多块分析应用于荧光和吸收光谱以估算特级初榨橄榄油中的总多酚含量。

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

作者信息

Hernández-Sánchez Natalia, Lleó Lourdes, Diezma Belén, Correa Eva Cristina, Sastre Blanca, Roger Jean-Michel

机构信息

Laboratorio de Propiedades Físicas y Técnicas Avanzadas en Agroalimentación (LPF_Tagralia), Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Av. Puerta de Hierro, 2-4, 28040 Madrid, Spain.

Applied Research Department, IMIDRA, Finca El Encín, Alcalá de Henares, 28805 Madrid, Spain.

出版信息

Foods. 2021 Oct 23;10(11):2556. doi: 10.3390/foods10112556.

DOI:10.3390/foods10112556
PMID:34828837
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8623568/
Abstract

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 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. 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.

摘要

通过应用化学计量学多块方法——顺序和正交化偏最小二乘法(SO-PLS),结合前表面发射荧光光谱(激发波长270nm)和吸光度光谱,开发了一种快速简便的方法来估算特级初榨橄榄油中的总多酚含量。这项工作的假设是,通过纳入导致荧光光谱中内滤效应的化合物的吸光光谱信息,可以克服这些内滤效应,而这些效应会降低单块模型的估算性能。应用了不同的光谱预处理算法。实施了50次迭代的双重交叉验证,以提高所得结果的稳健性。单块荧光原始光谱上的PLSR模型的RMSEP中位数为177.11mg·kg,且模型复杂度较高,因为潜变量(LVs)的中位数为8。经过预处理的荧光和吸光度光谱的多块SO-PLS模型性能更好,尽管变换可能会引入伪像。荧光和吸光度原始数据的组合将RMSEP中位数降至134.45mg·kg。此外,模型的复杂度大大降低,这有助于提高稳健性。荧光数据的LVs中位数为3,吸光度数据的LVs中位数仅为1。考虑到更多数量的样品和多酚的详细组成,进一步的工作可以解决该方法的验证问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/1456b1beec2d/foods-10-02556-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/0476d2412991/foods-10-02556-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/93ebf8be0b18/foods-10-02556-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/c2729498fd5e/foods-10-02556-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/f03693374772/foods-10-02556-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/c5c9624131c3/foods-10-02556-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/fbc6768220bc/foods-10-02556-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/1456b1beec2d/foods-10-02556-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/0476d2412991/foods-10-02556-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/93ebf8be0b18/foods-10-02556-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/c2729498fd5e/foods-10-02556-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/f03693374772/foods-10-02556-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/c5c9624131c3/foods-10-02556-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/fbc6768220bc/foods-10-02556-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf2/8623568/1456b1beec2d/foods-10-02556-g007.jpg

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