Research On The Evaluation Method Of Partial Texture Properties Of Fluid Food Based On On Tribology

With the increasing risk of obesity and cardiovascular diseases caused by high-fat and highcalorie diets in recent years,low-fat foods have become an urgent demand of consumers.In order to solve the problems of poor texture properties of low-fat foods and the inadequacy of their scientific assessment methods,it is necessary to clarify the physical mechanism of foods texture and to understand their perception mechanism in brain.Therefore,fluid foods frictional properties and their influencing factors are investigated,and the neural activities during the process of perceiving texture differences foods are also analyzed by using EEG signals.This study will provide a better technical reference for the development of low-fat foodsFirst,the particle size,texture profile analysis(TPA)parameters and coefficients of friction of commercially salad sauces with different fat contents were characterized,and the effects of fat contents,particle size,TPA properties,saliva and loads on their frictional properties were analyzed.The results showed that the particle size of salad sauces tended to increase with decreasing fat content,and there is a significant correlation between the particle size and their coefficients of friction,and the larger particle size would lead to an increase in their coefficients of friction,which would affect their smoothness finally.While the weak correlation between the TPA parameters of salad sauces and their friction coefficients indicated that the TPA method was not applicable to the assessment of smoothness of salad sauces.What’s more,fat can reduce the coefficients of friction of salad sauces in the presence of saliva effectively,and the appropriate load is more favorable to characterize the difference in smoothness of salad sauces.Secondly,twenty-five fluid foods were compounded based on drinking water,skim milk powder,milk protein,milk fat,maltodextrin,carboxymethyl cellulose and microcrystalline cellulose,and their particle sizes,rheological properties and frictional properties were characterized.A neural network prediction model of frictional properties of fluid foods was developed with the ingredient content,viscosity and particle size as input quantities.The results showed that fat can promote lubrication of fluid foods and the optimal effect is achieved at the addition amount of 4 g/100 ml.Besides,milk protein and maltodextrin can significantly reduce the friction of fluid foods in the boundary and mixed zones due to the rolling ball mechanism and viscosity increase.In addition,the correlations between the prediction model of BP neural network for coefficients of friction of fluid foods under different lubrication states are all over0.95.Finally,sensory evaluation of three fluid foods with different frictional properties was carried out and the EEG signals of the subjects were measured with the aid of food bolus injection and EEG signal labeling device to analyze the effects of different smoothness fluid foods on the neural activity of the brain.The results showed that there is a significant correlation between the frictional properties and their smoothness,and the fluid foods with lower friction has a better smoothness.What’s more,the variation of amplitude and latency on P3 component is related to the ease of fluid foods perception,and the higher amplitude and longer latency of P3 evoked by fluid foods with lower friction coefficients and higher smoothness indicate a more complex perception and recognition process.In last,there is a significant positive correlation between the intensity of neural activity in the oral perception region of primary sensory area and the coefficients of friction,which means that fluid foods with higher coefficients of friction and lower smoothness are able to evoke higher potential amplitudes in the somatosensory areas.These research results will provide better technical support for the development of low-fat fluid foods.