Flavor-protein interactions for four plant proteins with ketones and esters.

The interaction between flavors and proteins results in a reduced headspace concentration of the flavor, affecting flavor perception. We analyzed the retention of a series of esters and ketones with different chain lengths (C4, C6, C8, and C10) by protein isolates of yellow pea, soy, fava bean, and chickpea, with whey as a reference. An increase in protein concentration led to a decrease in flavor compound in the headspace as measured with atmospheric pressure chemical ionization time-of-flight mass spectroscopy (APCI-TOF-MS). Flavor retention was described with a flavor-partitioning model. It was found that flavor retention could be well predicted with the octanol-water partitioning coefficient and by fitting the hydrophobic interaction parameter. Hydrophobic interactions were highest for chickpea, followed by pea, fava bean, whey, and soy. However, the obtained predictive model was less appropriate for methyl decanoate, possibly due to its solubility. The obtained models and fitted parameters are relevant when designing flavored products with high protein concentrations.

Dynamic flavor release from chewing gum: Mechanisms of release.

Dynamic flavor release curves from chewing gum were measured using an Artificial Mouth coupled to the AFFIRM®. A flavor distribution model for chewing gum is proposed, where flavor is present as droplets in both the hydrophilic (water-soluble) and the hydrophobic (water insoluble) parts of the chewing gum and as molecularly dissolved in the hydrophobic part of the gum. During mastication, the flavor droplets in the water-soluble phase are released and responsible for an initial burst release. The flavor droplets captured in the gum-base are pushed towards the interface by mastication and are responsible for the subsequent release. The flavor molecules dissolved in the gum-base, released by diffusion, are only responsible for the release at very long time scales. It was found that the oil-water partition constant is an important parameter to explain the flavor release, where hydrophobic components show slower and longer release, while more hydrophilic components show more burst release.

A predictive model for flavor partitioning and protein-flavor interactions in fat-free dairy protein solutions.

Flavor perception is directly related to the concentration of aroma compounds in the headspace above a food matrix before and during consumption. With the knowledge of flavor partition coefficients, the distribution of aroma compounds within the food matrix and towards the headspace can be calculated. In this study static headspace measurements and modelling are combined to predict flavor partitioning of a wide range of flavor compounds above fat-free dairy protein mixture solutions. AFFIRM® (based on Atmospheric Pressure Chemical Ionization-Mass Spectrometry) was used to measure the static headspace concentrations of 9 flavor compounds (3 esters, 3 aldehydes and 3 alcohols) above protein solutions with different concentrations and ratios of sodium caseinate and whey protein isolate. Proteins had a small pushing out effect, leading to increased release of hydrophilic flavor compounds. This effect was negligible for more hydrophobic compounds, where clear retention was observed. An increased total protein concentration and higher whey to casein ratio increased the retention for all flavor compounds. Within the same chemical class, the retention increased with chain length. The experimental data was interpreted with a model describing flavor partitioning in protein solutions (Harrison & Hills, 1997), thereby enabling to extract protein-flavor binding constants. A clear power law was found between the protein-flavor binding constant and log P (octanol-water partition coefficient). Assuming solely non-specific hydrophobic interactions gave satisfying partitioning predictions for the esters and alcohols. For aldehydes specific chemical interactions with proteins turned out to be significant. This rendered a binding constant for whey protein that is 5 times higher than for caseinate in case of esters and alcohols, and 3 times higher in case of aldehydes. The model can accurately predict equilibrium flavor partitioning in dairy protein mixtures with only the knowledge of the octanol-water partition coefficients of the flavor compounds, and the composition of the protein mixture.

Novel Methodology for Measuring Temperature-Dependent Henry's Constants of Flavor Molecules.

A new methodology is presented to measure water-air partition coefficients (Henry's constants) of volatiles, using APCI-MS. Significant advantages over other Henry's constant determination methods include the short measurement and sample preparation time and the possibility for simultaneous measurement of multiple volatiles. The methodology is validated by obtaining good agreement with reliable literature values for a series of 2-ketones. The methodology is further explored for eight key volatiles typically found in citrus fruits, including the temperature dependence of the Henry's constant. Using these data can improve estimates of flavor losses during processing and volatile release during consumption.

Real-time flavor analysis: optimization of a proton-transfer-mass spectrometer and comparison with an atmospheric pressure chemical ionization mass spectrometer with an MS-nose interface.

Two techniques are recognized for the real-time analysis of flavors during eating and drinking, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), and proton transfer reaction mass spectrometry (PTR-MS). APCI-MS was developed for the analysis of flavors and fragrances, whereas PTR-MS was originally developed and optimized for the analysis of atmospheric pollutants. Here, the suitability of the two techniques for real-time flavor analysis is compared, using a varied range of common flavor compounds. An Ionicon PTR-MS was first optimized and then its performance critically compared with that of APCI-MS. Performance was gauged using the capacity for soft ionization, dynamic linear range, and limit of detection. Optimization of the PTR-MS increased the average sensitivity by a factor of more than 3. However, even with this increase in sensitivity, the Limit of Detection was typically 10 times higher and the Dynamic Linear Range ten times narrower than that of the APCI-MS.

Modeling the kinetics of flavour release during drinking.

Novel mathematical models for flavour release during drinking are described, based on the physiology of breathing and swallowing. Surprisingly, we conclude that most flavour molecules arriving in the nose are extracted from liquid left in the throat, after swallowing. The models are fit to real time flavour release data obtained using APCI-mass spectrometry. Before modelling, raw data are corrected for the effects of varying airflow rate, using the signal from acetone in exhaled air. A simple equilibrium batch extraction model correctly describes flavour release during the first breaths after swallowing a flavoured liquid. It shows that for eight volatiles, whose in vitro air-water partition coefficients vary by a factor of 500, the apparent in vivo air-saliva partition coefficients vary only by a factor of five. To interpret the kinetics of flavour release longer after swallowing, diffusion of flavour into the throat lining is included. This is done using a three-layer model for mass transfer in the throat. An analytical solution of this model gives good fits to typical data. These models de-couple the physiological and physico-chemical aspects of flavour release, clarifying the effect of behaviour on in-vivo flavour release.