Modeling food matrix effects on chemical reactivity

Challenges and perspectives

Edoardo Capuano*, Teresa Oliviero, Martinus A.J.S. van Boekel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

Abstract

The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.

Original languageEnglish
Pages (from-to)2814-2828
JournalCritical Reviews in Food Science and Nutrition
Volume58
Issue number16
DOIs
Publication statusPublished - 2 Nov 2018

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Chemical reactivity
food matrix
Food
chemical reactions
artificial intelligence
mechanistic models
Chemical reactions
thermodynamics
Chemical Phenomena
Physical Phenomena
physical properties
physicochemical properties
chemical composition
Data Mining
kinetics
Thermodynamics
Chemical properties
Data mining
Macros
Learning systems

Keywords

  • activity coefficients
  • fingerprinting
  • Food matrix
  • kinetics
  • modeling
  • thermodynamics

Cite this

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title = "Modeling food matrix effects on chemical reactivity: Challenges and perspectives",
abstract = "The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.",
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Modeling food matrix effects on chemical reactivity : Challenges and perspectives. / Capuano, Edoardo; Oliviero, Teresa; van Boekel, Martinus A.J.S.

In: Critical Reviews in Food Science and Nutrition, Vol. 58, No. 16, 02.11.2018, p. 2814-2828.

Research output: Contribution to journalArticleAcademicpeer-review

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T2 - Challenges and perspectives

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AU - van Boekel, Martinus A.J.S.

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AB - The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.

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