Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise
Food emulsions with high amounts of unsaturated fats, such as mayonnaise, are prone to lipid oxidation. In the food industry, typically accelerated shelf life tests are applied to assess the oxidative stability of different formulations. Here, the appearance of aldehydes at the so-called onset time,...
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MDPI AG
2021-02-01
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Series: | Antioxidants |
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author | Donny W. H. Merkx Andries Swager Ewoud J. J. van Velzen John P. M. van Duynhoven Marie Hennebelle |
author_facet | Donny W. H. Merkx Andries Swager Ewoud J. J. van Velzen John P. M. van Duynhoven Marie Hennebelle |
author_sort | Donny W. H. Merkx |
collection | DOAJ |
description | Food emulsions with high amounts of unsaturated fats, such as mayonnaise, are prone to lipid oxidation. In the food industry, typically accelerated shelf life tests are applied to assess the oxidative stability of different formulations. Here, the appearance of aldehydes at the so-called onset time, typically weeks, is considered a measure for oxidative stability of food emulsions, such as mayonnaise. To enable earlier assessment of compromised shelf-life, a predictive model for volatile off-flavor generation is developed. The model is based on the formation kinetics of hydroperoxides, which are early oxidation products and precursors of volatile aldehydes, responsible for off-flavor. Under accelerated shelf-life conditions (50 °C), hydroperoxide (LOOH) concentration over time shows a sigmoidal curvature followed by an acceleration phase that occurs at a LOOH-concentration between 38–50 mmol/kg, here interpreted as a critical LOOH concentration (CC<sub>LOOH</sub>). We hypothesize that the time at which CC<sub>LOOH</sub> was reached is related to the onset of aldehyde generation and that the characterization of the LOOH-generation curvature could be based on reaction kinetics in the first days. These hypotheses are tested using semi-empirical models to describe the autocatalytic character of hydroperoxide formation in combination with the CC<sub>LOOH</sub>. The Foubert function is selected as best describing the LOOH-curvature and is hence used to accurately predict onset of aldehyde generation, in most cases within several days of shelf-life. Furthermore, we find that the defining parameters of this model could be used to recognize antioxidant mechanisms at play. |
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id | doaj.art-06534ae616f047c080714b8a9007435c |
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issn | 2076-3921 |
language | English |
last_indexed | 2024-03-09T00:52:07Z |
publishDate | 2021-02-01 |
publisher | MDPI AG |
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series | Antioxidants |
spelling | doaj.art-06534ae616f047c080714b8a9007435c2023-12-11T17:07:07ZengMDPI AGAntioxidants2076-39212021-02-0110228710.3390/antiox10020287Quantitative and Predictive Modelling of Lipid Oxidation in MayonnaiseDonny W. H. Merkx0Andries Swager1Ewoud J. J. van Velzen2John P. M. van Duynhoven3Marie Hennebelle4Unilever Food Innovation Centre, Bronland 14, 6708 WH Wageningen, The NetherlandsLaboratory of Food Chemistry, Wageningen University & Research, Bornse Weilanden 9, 6708 WG Wageningen, The NetherlandsUnilever Food Innovation Centre, Bronland 14, 6708 WH Wageningen, The NetherlandsUnilever Food Innovation Centre, Bronland 14, 6708 WH Wageningen, The NetherlandsLaboratory of Food Chemistry, Wageningen University & Research, Bornse Weilanden 9, 6708 WG Wageningen, The NetherlandsFood emulsions with high amounts of unsaturated fats, such as mayonnaise, are prone to lipid oxidation. In the food industry, typically accelerated shelf life tests are applied to assess the oxidative stability of different formulations. Here, the appearance of aldehydes at the so-called onset time, typically weeks, is considered a measure for oxidative stability of food emulsions, such as mayonnaise. To enable earlier assessment of compromised shelf-life, a predictive model for volatile off-flavor generation is developed. The model is based on the formation kinetics of hydroperoxides, which are early oxidation products and precursors of volatile aldehydes, responsible for off-flavor. Under accelerated shelf-life conditions (50 °C), hydroperoxide (LOOH) concentration over time shows a sigmoidal curvature followed by an acceleration phase that occurs at a LOOH-concentration between 38–50 mmol/kg, here interpreted as a critical LOOH concentration (CC<sub>LOOH</sub>). We hypothesize that the time at which CC<sub>LOOH</sub> was reached is related to the onset of aldehyde generation and that the characterization of the LOOH-generation curvature could be based on reaction kinetics in the first days. These hypotheses are tested using semi-empirical models to describe the autocatalytic character of hydroperoxide formation in combination with the CC<sub>LOOH</sub>. The Foubert function is selected as best describing the LOOH-curvature and is hence used to accurately predict onset of aldehyde generation, in most cases within several days of shelf-life. Furthermore, we find that the defining parameters of this model could be used to recognize antioxidant mechanisms at play.https://www.mdpi.com/2076-3921/10/2/287hydroperoxidesaldehydeson-set timeoil-in-water emulsionaccelerated shelf-life<sup>1</sup>H NMR |
spellingShingle | Donny W. H. Merkx Andries Swager Ewoud J. J. van Velzen John P. M. van Duynhoven Marie Hennebelle Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise Antioxidants hydroperoxides aldehydes on-set time oil-in-water emulsion accelerated shelf-life <sup>1</sup>H NMR |
title | Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise |
title_full | Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise |
title_fullStr | Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise |
title_full_unstemmed | Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise |
title_short | Quantitative and Predictive Modelling of Lipid Oxidation in Mayonnaise |
title_sort | quantitative and predictive modelling of lipid oxidation in mayonnaise |
topic | hydroperoxides aldehydes on-set time oil-in-water emulsion accelerated shelf-life <sup>1</sup>H NMR |
url | https://www.mdpi.com/2076-3921/10/2/287 |
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