The aim of this work was to clarify the influence of the properties (firmness and fat content) of a solid processed model cheese on in vivo aroma release while considering the role of the in‐mouth process during both mastication and post‐swallowing steps, and the hydrophobicity of aroma compounds, on a large number of well characterized subjects. In vivo aroma release was studied on 44 subjects who freely consumed six processed model cheeses flavoured with the same concentration of nonan‐2‐one and ethyl propanoate. Globally, an increase in firmness induced an increase in chewing duration, amount of saliva incorporated into the food bolus, total amount of aroma released and rate of release. The kinetics of release clearly differed between the two aroma compounds. Ethyl propanoate presented a higher release rate for firmer cheese and was more released during the mastication step, whereas nonan‐2‐one was more released during the post‐swallowing step and more persistent in the mouth, due to its higher hydrophobicity. Consuming cheeses with a higher fat content led to a higher amount of product remaining in the mouth after swallowing, a lower amount of nonan‐2‐one released and a longer persistence of nonan‐2‐one in the breath. The results could be helpful to better understand the relative influence of the parameters related to products and subjects in order to reformulate foods with good sensory acceptability. Copyright © 2012 John Wiley & Sons, Ltd.

The chewing process transforms food into bolus for a safe swallow. It is known that humans adapt their chewing behavior to food product characteristics. This study aimed at identifying individual chewing strategies of healthy consumers and determining the respective consequences on bolus properties. For that purpose, the chewing activity of 50 subjects was recorded during consumption of five model cheeses. Boluses were collected at the swallowing threshold for rheological analyses. We found that 30% of subjects showed only slight adaptation of chewing activity to product characteristics and thus produced boluses with different rheological properties. Among the 70% of subjects who adapted their chewing behavior, 57% adapted their behavior via chewing time and 40% adapted their behavior via chewing time and muscular contraction amplitude. Among the bolus rheological parameters, only consistency was not influenced by chewing strategies. Hence, it seemed to be a determinant factor of the swallowing threshold for these products. PRACTICAL APPLICATIONS Flavor compounds are released during the oral processing of food. It is thus important to understand how consumers form a bolus and to analyze the consequences of chewing behaviors on bolus properties at the swallowing threshold, at the end of the oral stage of food, to clarify the role of mastication in the release of stimuli responsible for perception. Chewing strategies and bolus properties are some of the key factors of flavor release and product acceptability that is of interest for food manufacturers who can use these factors as additional parameters for new solid product design.

Direct‐injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e‐noses) in classification tasks are briefly reviewed, with an emphasis on food‐related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR‐MS), and many results obtained using the powerful PTR‐time of flight‐MS (PTR‐ToF‐MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR‐ToF‐MS. A supervised multivariate data analysis based on partial least squares regression‐discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR‐MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.