Firmenich S.A., Rue de la Bergère 7, Meyrin 2, CH-1217 Geneva, Switzerland; Wageningen University, Food Process Engineering group, Bornse Weilanden 9, 6708 WG Wageningen, Netherlands.
Wageningen University, Food Process Engineering group, Bornse Weilanden 9, 6708 WG Wageningen, Netherlands.
Food Res Int. 2018 Jul;109:52-58. doi: 10.1016/j.foodres.2018.04.013. Epub 2018 Apr 16.
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.
风味感知直接与食物基质上方和食用过程中香气化合物的顶空浓度有关。有了风味分配系数的知识,就可以计算香气化合物在食物基质内和向顶空的分布。在这项研究中,静态顶空测量和建模相结合,以预测无脂乳蛋白混合物溶液中多种风味化合物的风味分配。使用 AFFIRM®(基于大气压化学电离-质谱法)测量了不同浓度和比例的酪蛋白酸钠和乳清蛋白分离物的蛋白质溶液上方 9 种风味化合物(3 种酯、3 种醛和 3 种醇)的静态顶空浓度。蛋白质具有较小的推挤作用,导致亲水性风味化合物释放增加。对于更疏水性的化合物,这种影响可以忽略不计,因为观察到明显的保留。总蛋白浓度增加和乳清蛋白对酪蛋白的比例增加,所有风味化合物的保留增加。在同一化学类中,保留随着链长的增加而增加。实验数据通过描述蛋白质溶液中风味分配的模型进行解释(Harrison 和 Hills,1997),从而能够提取蛋白质-风味结合常数。在蛋白质-风味结合常数和 log P(辛醇-水分配系数)之间发现了明显的幂律关系。仅假设非特异性疏水相互作用,对酯类和醇类的分配预测令人满意。对于醛类,与蛋白质的特异性化学相互作用非常重要。这使得乳清蛋白的结合常数对于酯类和醇类是酪蛋白的 5 倍,对于醛类是 3 倍。该模型仅通过风味化合物的辛醇-水分配系数和蛋白质混合物的组成,就可以准确预测乳制品蛋白质混合物中的平衡风味分配。