Mechanistic mathematical model for in vivo aroma release during eating of semiliquid foods.

The paper describes a mechanistic mathematical model for aroma release in the oropharynx to the nasal cavity during food consumption. The model is based on the physiology of the swallowing process and is validated with atmospheric pressure chemical ionization coupled with mass spectrometry measurements of aroma concentration in the nasal cavity of subjects eating flavored yogurt. The study is conducted on 3 aroma compounds representative for strawberry flavor (ethyl acetate, ethyl butanoate, and ethyl hexanoate) and 3 panelists. The model provides reasonably accurate time predictions of the relative aroma concentration in the nasal cavity and is able to simulate successive swallowing events as well as imperfect velopharyngeal closure. The most influent parameters are found to be the amount of the residual product in the pharynx and its contact area with the air flux, the volume of the nasal cavity, the equilibrium air/product partition coefficient of the volatile compound, the breath airflow rate, as well as the mass transfer coefficient of the aroma compound in the product, and the amount of product in the mouth. This work constitutes a first step toward computer-aided product formulation by allowing calculation of retronasal aroma intensity as a function of transfer and volatility properties of aroma compounds in food matrices and anatomophysiological characteristics of consumers.

Ethyl hexanoate transfer modeling in carrageenan matrices for determination of diffusion and partition properties.

Aroma compound properties in food matrices, such as volatility and diffusivity, have to be determined to understand the effect of composition and structure on aroma release and perception. This work illustrates the use of mass transfer modeling to identify diffusion and partition properties of ethyl hexanoate in water and in carrageenan matrices with various degrees of structure. The comparison of results obtained with a diffusive model to those obtained with a convective model highlights the importance of considering the appropriate transfer mechanism. Modeling of the preliminary experimental steps ensures correct estimation of the conditions for the main aroma release step. The obtained values of partition and diffusion coefficients are in agreement with those found in the literature (either experimentally determined or predicted by theoretical equations) and demonstrate that the structure level of carrageenan matrices has little influence on diffusion properties of ethyl hexanoate (less than 20%).

Complex viscosity induced by protein composition variation influences the aroma release of flavored stirred yogurt.

Dairy protein composition is known to influence the structure and the texture characteristics of yogurt. The objective of the present work was therefore to investigate the impact of protein composition, at a constant protein level, on the physicochemical properties of 4% fat flavored stirred yogurt and, more specifically, on the rheological properties, the microstructure, and the aroma release. The results showed that caseinate-enriched yogurt generally presented changes in their microstructure network and had a higher complex viscosity than whey protein-enriched yogurt. To a lesser extent, the release of the majority of aroma compounds was lower in caseinate-enriched yogurt. It was therefore possible to quantify physicochemical interactions between aroma compounds and proteins. The influence of gel structure on the flavor release was observed and was in agreement with sensory characteristics previously studied for these products.

Processing gas chromatographic data and confidence interval calculation for partition coefficients determined by the phase ratio variation method.

The phase ratio variation (PRV) method is widely used for the determination of partition coefficient values (dimensionless Henry's law constants) by headspace gas chromatography. Traditional data processing by linear regression has several drawbacks: potential bias introduced by linearization, absence of quality indicator of the resulting value and, in case of replicate determinations, poor utilisation of the existing measurements leading to unnecessarily large confidence intervals. The paper compares existing PRV data processing methods (linear and nonlinear regression, parametric) and derives confidence intervals for the resulting partition coefficient values. The possibility of using several series of measurements to derive a single partition coefficient value with tighter and more reliable confidence intervals is presented for all three processing methods. The methods are tested on published literature data and new experimental data for 12 volatile organic compounds in water at 25 degrees C. The nonlinear regression based on several series of measurements appears to be the method of choice.