| The majority of consumers prefer hanging batter-fried food because of its distinct flavor,golden color,and crispy texture.Unfortunately,this kind of food still has some problems such as a high oil content and lackluster color and flavor,which is contrary to the safe and delicious diet that people pursue.Traditional batter-fried meal known as "crispy meat" is prepared by battering and frying methods and has a distinct flavor and aroma.Consequently,this study was carried out to produce a battered food with good flavor and taste by utilizing a single-factor test,response surface test,and kinetic model to enrich the flavor,improve the taste,and decrease the fat content of the batter-fried food.First,it was determined how different starch kinds in the batter’s constituents affected its physicochemical qualities and its quality traits.Analyzing how the sensory score is affected by the addition of starch,maltodextrin,and HPMC to the paste’s constituents.The sensory score was used as the response value in the response surface test to establish the ideal combination of components for the crispy meat hanging paste.Second,the relationship between the frying oil and the crispy meat quality traits was examined,and the best oil was chosen.Third,the impact of the major processing step on the quality traits of crispy meat throughout processing was investigated.Finally,the storage performance of crispy meat was examined under ideal processing circumstances,and a model for predicting shelf life was created as a result.The results findings close a research gap in crispy meat products and offer theoretical support and practical advice for the commercial manufacturing of crispy meat and even hanging batterfried food.The following are the key conclusions:(1)The batter hanging rate,water holding capacity,coagulation,and rheological characteristics are all impacted by single starch and composite starch,respectively.With the highest hanging rate,the least coagulation,the most stable batter,and the highest color,flavor substance content,and sensory score of the produced crispy meat,the addition of sweet potato/cassava starch had the greatest effect.Additionally,different complex starch ratios and additions as well as various improver additions had a substantial impact on the sensory evaluations of crispy meat(P<0.05).The greatest sensory score for crispy meat was 8.79 when the sweet potato/cassava starch was 4:5 and the additions of composite starch,HPMC and maltodextrin were 22.42%,6.1%,and 0.22%.In a validation test,a sensory score of 8.72 was obtained for the crispy meat,with a relative error of just 0.80%,demonstrating that the response surface model can accurately forecast the ideal amount of each ingredient in the formulation of the crispy meat batter.(2)Rapeseed oil,sunflower oil,peanut oil,corn oil,and soybean oil have different effects on the quality characteristics of crispy meat.Rapeseed oil gave the meat a crispier crust,but it also had a higher oil content and more oxidation.Less oil content and oxidation were present in crispy meat fried in sunflower oil,but the color and crispness were subpar.The degree of oxidation of the crispy meat that had been fried in soybean oil,corn oil,and peanut oil varied little.However,the crisp meat fired in soybean oil had the best sensory rating,the crisp meat fried in peanut oil had a higher oil content and poor color,in corn oil had a poorer level of shell crispness.According to GC-MC analysis,the five different kinds of crispy meat that were fried in edible oil had significantly different volatile flavor substance compositions and volatile flavor substance types.According to the Pearson correlation analysis,crispy meat color,shell crispness,and fat content were all related to the color and degree of oxidation of the edible oil,while crispy meat oxidation was related to the fatty acid composition of the oil.Additional factor and cluster studies revealed that using soybean oil for frying produced the crispiest meat overall.(3)Different frying temperatures(140°C,150°C,160°C,170°C,and 180°C)and times(80 s,100 s,120 s,140 s,and 160 s)had a substantial impact on the crispy meat quality traits.The a*,b*,crust crispness,fat content,TBARS,and carbonyl content of crispy meat rose continuously as frying temperature and time increased,whereas L* and moisture content showed a decreasing trend.The sensory scores tended to rise and then fall,when the frying conditions were 160°C and 120 s the sensory scores were the best.Additionally,the volatile flavor components of the crispy meat were affected by the various frying conditions.This is primarily illustrated by the fact that increasing the temperature and duration of cooking results in a rise in the concentration of aldehydes and heterocyclic compounds and a fall in the concentration of hydrocarbon compounds.Additional correlation analysis,factor analysis,and cluster analysis revealed that the frying conditions of 160°C and 100 s produced the crispy meat that scored the best overall for crispiness.(4)At various storage temperatures,crispy meat’s physicochemical characteristics and microbial safety changed at varying rates.The p H of crispy meat tended to fluctuate as storage time rose,with the AV,TVB-N,and TBARS rising and the sensory score steadily falling.When stored at 4°C for 25 days and 0°C for 35 days,the total number of colonies also grew with longer storage times and exceeded the national limit.Crispy meat had a 25-day and35-day shelf life at 4°C and 0°C,respectively.The primary quality indicator was decided to be total colony count.The main kinetic model was chosen as the response model by calculating the regression coefficients,and the total bacterial colony—which changed more quickly—was used as the measurement indicator for prediction.The storage life of crispy meat at-18°C was finally predicted by the Arrhenius equation to be 252.16 days.The validation test revealed that the model was suitable for forecasting the shelf life of crispy meat and provided some theoretical groundwork for the intelligent prediction of other hung batter fried foods.The relative error was found to be 7.07%. |
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