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Liquid–air partition coefficients (Henry's law constants, HLCs) of eight flavour compounds (volatile organic compounds, VOCs) were determined in water, over a temperature range of 4 °C to 85 °C. The HLCs were derived by using nitrogen to strip a dilute solution of a VOC and then determining the decrease in concentration of the VOC in real-time in the stripped gas using proton-transfer-reaction time-of-flight mass-spectrometry (PTR-ToF-MS). This approach provided HLCs of improved accuracy (small 95% standard deviation) over a large temperature range, especially for low volatility VOCs (HLC > 2 mol/(m 3 Pa)). The outstanding features of this approach are: (i) it is applicable for VOCs over a large range of volatility; (ii) it can be used over a wide temperature range (4 °C to 85 °C); (iii) it is automated (high-throughput); (iv) it does not require calibration or knowledge of the initial concentration of the analyte; and (v) the experimental temperature can be controlled very precisely (Δ T better than ±0.1 °C). The eight flavour compounds analysed in water were: (E)-β-damascenone, 2,3-butanedione, 2-ethyl-3,5-dimethylpyrazine, 2-methylfuran, 3-methylbutanal, acetaldehyde, ethyl-3-methyl butanoate and guaiacol. Based on the measured HLCs at five fixed temperatures (4 °C, 25 °C, 45 °C, 65 °C and 85 °C), accurate non-linear analytical expressions for the temperature dependence of HLCs were derived, which were then used to calculate thermodynamic constants.

The characterization of aroma mobility within foods is an important challenge for a better understanding of aroma release in relation to product structure and perception but is difficult to achieve. An experimental device, based on the diffusion cell concept, was used to determine the apparent diffusivity of aroma compounds within complex food products using gaseous measurements. The originality of our approach was based on a mechanistic description of the system and on the early integration of modeling, leading to an apparatus especially adapted for complex matrices and easy to use (direct analysis and no sample storage). The impacts of the operating conditions and of the data treatment analysis on the accuracy of the determination of apparent diffusion properties were evaluated for three aroma compounds in model agar gels. The comparison of these diffusion results with data found in the literature demonstrated the reliability and the robustness of the system. Its suitability for the characterization of mobility properties of aroma compounds in real food matrices was achieved using dairy gels.

International audience; Correspondence to be sent to: Ioan Cristian Trelea, UMR782 Gé nie et Microbiologie des Procé dé s Alimentaires, AgroParisTech, INRA, 1 av. Lucien Bré tignè res, Abstract 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 buta-noate, 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.

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.

International audience; On the basis of a first mechanistic model predicting aroma release in the oropharynx during food consumption, the aim of the present work was to improve its accuracy and to use it to identify the main mechanisms responsible for in-mouth aroma release. Comparison between predicted release kinetics and the ones measured by APCI-MS in the nasal cavity of subjects eating flavoured yogurt highlighted the reasonably accurate time predictions of the relative aroma concentration in the nasal cavity and the model ability to simulate successive swallowing events as well as partial velopharyngeal closure. Parameters identified as the most influent for in-vivo aroma release were related to either physico-chemistry (equilibrium air/product partition coefficient, mass transfer coefficient in the product), physiology (the volume of the nasal cavity, breath airflow rate) or interaction between both (the amount of residual product in the pharynx, air/product contact area).

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%).

International audience

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 °C. The nonlinear regression based on several series of measurements appears to be the method of choice.