Optimization of Calibration Strategies for the Quantification of Volatile Compounds in Virgin Olive Oil

The quantification of volatile compounds in virgin olive oil poses several analytical challenges due to the existence of different concentrations, chemical families, and the possible matrix effect. Accurate quantification, using adequate methodological calibration and statistical procedures, is essential for obtaining reliable results. The aim of this work was to develop and validate an analytical-statistical approach for the quantification of volatile compounds in virgin olive oil. Therefore, several analytical parameters were determined for four calibrations. The ordinary least square (OLS) linear adjustment was selected over the weighted least square due to the homoscedasticity of the variable errors. Additionally, standard addition (AC) and AC with an internal standard (IS) exhibited greater variability, whereas external matrix-matched calibration (EC) was identified as the most reliable approach for quantifying volatile compounds in virgin olive oil. The employment of an IS did not improve the performance of the method in any case. Thus, based on the statistical results, the OLS linear adjustment with EC was selected as the best statistical-analytical approach for quantifying volatiles in olive oil matrices. The volatiles of nine virgin olive oil samples were quantified, applying different methodological calibrations, and no differences were detected, underscoring EC as a superior alternative.

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Publication Details

Journal
Foods
Published
2025-10-08
DOI
https://doi.org/10.3390/foods14193439
Citations
1
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
0.75

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article

Optimization of Calibration Strategies for the Quantification of Volatile Compounds in Virgin Olive Oil

Diego L. García‐González, María Barbero-López, M. T. Morales, Ramón Aparicio-Ruı́z et al.
1 citations
Foods
Spectroscopy and Chemometric Analyses
0.75
article

Optimization of Calibration Strategies for the Quantification of Volatile Compounds in Virgin Olive Oil

Diego L. García‐González, María Barbero-López, M. T. Morales, Ramón Aparicio-Ruı́z, Enrique Jacobo Díaz-Montaña
article en
1 citations

Abstract

The quantification of volatile compounds in virgin olive oil poses several analytical challenges due to the existence of different concentrations, chemical families, and the possible matrix effect. Accurate quantification, using adequate methodological calibration and statistical procedures, is essential for obtaining reliable results. The aim of this work was to develop and validate an analytical-statistical approach for the quantification of volatile compounds in virgin olive oil. Therefore, several analytical parameters were determined for four calibrations. The ordinary least square (OLS) linear adjustment was selected over the weighted least square due to the homoscedasticity of the variable errors. Additionally, standard addition (AC) and AC with an internal standard (IS) exhibited greater variability, whereas external matrix-matched calibration (EC) was identified as the most reliable approach for quantifying volatile compounds in virgin olive oil. The employment of an IS did not improve the performance of the method in any case. Thus, based on the statistical results, the OLS linear adjustment with EC was selected as the best statistical-analytical approach for quantifying volatiles in olive oil matrices. The volatiles of nine virgin olive oil samples were quantified, applying different methodological calibrations, and no differences were detected, underscoring EC as a superior alternative.

FoodsVol. 14(19)
Instituto de la Grasa (ES), Universidad de Sevilla (ES)
European Regional Development Fund, Agencia Estatal de Investigación
Openalex Percentile: Top 25%
Spectroscopy and Chemometric Analyses
0.75
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Optimization of Calibration Strategies for the Quantification of Volatile Compounds in Virgin Olive Oil — Diego L. García‐González, María Barbero-López, et al. · Foods (2025) | TGRS Research Map | TGRS