Development and Validation of a Simple Analytical Method to Quantify Tocopherol Isoforms in Food Matrices by HPLC–UV–Vis

This study developed, validated, and tested a simple method for tocopherol analysis on five different food matrices (sunflower oil, mackerel fillets, almonds, spinach, and avocado pulp). Tocopherol extraction from foods was carried out by the Folch method and with n-hexane, and the identification and quantification of tocopherol isoforms (α, β, γ, and δ) was performed using normal-phase liquid chromatography with ultraviolet–visible detection (NP-HPLC–UV–Vis). The normal-phase column fully separated the four tocopherol isoforms in less than ten minutes. Linearity was shown to be excellent for the four isoforms in the assayed range (10–375 ppm, R2 > 0.99). Furthermore, the limits of detection (LOD) and quantification (LOQ) ranged from 0.32 to 0.63 ppm, and from 1.08 to 2.11 ppm, respectively. The intra-day and inter-day precision were assessed at different concentrations (10, 100, and 250 ppm) for each tocopherol isoform and they were within the range of acceptable values. Recovery rates were above 80% in most cases for all of the assayed food matrices, regardless of the extraction method (Folch solvents or n-hexane). α-Tocopherol was the main isoform found in all tested foods, and sunflower oil was the sample with the highest content, followed by almond, avocado pulp, mackerel fillet, and spinach. This method provides a convenient alternative for obtaining a complete profile of the four tocopherol isoforms in a variety of food matrices and for tracking the potential degradation kinetics of fortified foods during their processing and storage.

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Publication Details

Journal
Applied Sciences
Published
2024-09-27
DOI
https://doi.org/10.3390/app14198750
Citations
4
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.74

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article

Development and Validation of a Simple Analytical Method to Quantify Tocopherol Isoforms in Food Matrices by HPLC–UV–Vis

Miguel Ángel Rincón‐Cervera, María Fernanda Arias‐Santé, José Luis Guil‐Guerrero, Adriano Costa de Camargo et al.
4 citations
Applied Sciences
Advanced Chemical Sensor Technologies
0.74
article

Development and Validation of a Simple Analytical Method to Quantify Tocopherol Isoforms in Food Matrices by HPLC–UV–Vis

Miguel Ángel Rincón‐Cervera, María Fernanda Arias‐Santé, José Luis Guil‐Guerrero, Adriano Costa de Camargo, Sussi López-Puebla
article en
4 citations

Abstract

This study developed, validated, and tested a simple method for tocopherol analysis on five different food matrices (sunflower oil, mackerel fillets, almonds, spinach, and avocado pulp). Tocopherol extraction from foods was carried out by the Folch method and with n-hexane, and the identification and quantification of tocopherol isoforms (α, β, γ, and δ) was performed using normal-phase liquid chromatography with ultraviolet–visible detection (NP-HPLC–UV–Vis). The normal-phase column fully separated the four tocopherol isoforms in less than ten minutes. Linearity was shown to be excellent for the four isoforms in the assayed range (10–375 ppm, R2 > 0.99). Furthermore, the limits of detection (LOD) and quantification (LOQ) ranged from 0.32 to 0.63 ppm, and from 1.08 to 2.11 ppm, respectively. The intra-day and inter-day precision were assessed at different concentrations (10, 100, and 250 ppm) for each tocopherol isoform and they were within the range of acceptable values. Recovery rates were above 80% in most cases for all of the assayed food matrices, regardless of the extraction method (Folch solvents or n-hexane). α-Tocopherol was the main isoform found in all tested foods, and sunflower oil was the sample with the highest content, followed by almond, avocado pulp, mackerel fillet, and spinach. This method provides a convenient alternative for obtaining a complete profile of the four tocopherol isoforms in a variety of food matrices and for tracking the potential degradation kinetics of fortified foods during their processing and storage.

Applied SciencesVol. 14(19)
University of Almería (ES), University of Chile (CL)
Fondo Nacional de Desarrollo Científico y Tecnológico
Openalex Percentile: Top 32%
Advanced Chemical Sensor Technologies
0.74
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