Flavor Retention and Release from Beverages: A Kinetic and Thermodynamic Perspective
Author(s) -
Ali Ammari,
Karin Schroën
Publication year - 2018
Publication title -
journal of agricultural and food chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.203
H-Index - 297
eISSN - 1520-5118
pISSN - 0021-8561
DOI - 10.1021/acs.jafc.8b04459
Subject(s) - flavor , chemistry , emulsion , matrix (chemical analysis) , chromatography , food science , organic chemistry
For the investigation of retention and release of flavor components, various methods are available, which are mostly used on a case-to-case basis depending on the raw material. These effects that originate from kinetics and thermodynamics could be put in a much wider perspective if these fields were taken as a starting point of investigation in combination with rigorous data analysis. In this Review, we give an overview of experimental techniques and data analysis methods, and predictive methods using mass transfer techniques are also discussed in detail. We use this as a foundation to discuss the interactions between volatile flavors and the matrix of liquid foods/beverages. Lipids present in the form of an emulsion are the strongest volatile retainers due to the lipophilic nature of most of the volatile flavors. Proteins also have flavor retention properties, whereas carbohydrates hardly have a retention effect in beverages. Smaller components, such as sugars and salts, can change the water activity, thereby facilitating flavor release. Alternatively, salts can also indirectly affect binding sites of proteins leading to release (e.g., NaCl and Na 2 SO 4 ) or retention (NaCSN and Cl 3 CCOONa) of flavors. Furthermore, the effects of temperature and pH are discussed. The Review concludes with a critical section on determination of parameters relevant to flavor release. We highlight the importance of accurate determination of low concentrations when using linearization methods and also show that there is an intrinsic preference for nonlinear regression methods that are much less sensitive to measurement error.
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