Study On The Optimization Of Spray Drying Technology For The Preparation Of Winter Jujube Powder

Winter jujube(Ziziphus jujube Mill.)is a late-maturing fresh jujube variety with rich nutritional value and medicinal value,and is an important economic crop in China.Because of its high water content,short storage time and high risk of corruption and deterioration,the use of spray drying technology with fast drying speed,good product quality and simple operation to process winter jujube into winter jujube powder can extend the shelf life and improve the economic use value of winter jujube.However,in the process of spray drying,the powder is prone to wall sticking,hot melting and other phenomena to reduce the product quality.At present,the research on spray drying of winter jujube powder mainly focuses on parameter optimization and wall material selection,while the research on pretreatment of winter jujube before spray drying and optimization of spray drying parameters by computational fluid dynamics(CFD)numerical simulation is less.Therefore,this paper takes winter jujube as the raw material,uses spray drying technology to prepare Winter jujube powder,and determines the optimal physical pretreatment process parameters and enzymolysis process through four different physical pretreatment and double enzyme synergistic enzymolysis treatment of Winter jujube,and explores the influence of physicochemical properties and structural characteristics of Winter jujube powder before and after enzymolysis treatment.On this basis,the spray drying process was analyzed by CFD simulation,and the simulation results and the actual results were optimized by orthogonal test to determine a preparation process of spray dried winter jujube powder.It is expected to provide theoretical support for the comprehensive utilization of winter jujube and the development of winter jujube powder.The main findings are as follows:(1)The effects of different physical pretreatment methods on the quality of spray dried winter jujube powder were investigated.The results showed that ultrasonic pretreatment could significantly improve the powder collection rate,total phenol content and flavonoid content of winter jujube powder.Among them,ultrasonic 360 W and 20 min treatment had the best effect,increasing 31.87%,31.89% and 47.18% respectively.At the same time,ultrasonic pretreatment improved the yellow green color of winter jujube powder;In addition,different physical pretreatment methods significantly reduced the vitamin C content of winter jujube powder,with blanching pretreatment and steam pretreatment losing the most vitamin C content,respectively,by 24.88% and 29.58%.(2)The process of enzymatic hydrolysis with two enzymes and its effect on the physicochemical properties and structural characteristics of winter jujube powder were investigated.The results showed that when the conditions of enzymatic hydrolysis with two enzymes were 0.27% of PPase,0.49% of CMCase,38.75 min of enzymatic hydrolysis,49.55 ℃ of enzymatic hydrolysis temperature,the juice yield of winter jujube could reach 89.87%.In addition,the particle size of winter jujube powder was significantly reduced after the treatment of double enzymes and enzymatic hydrolysis,and the color was more yellow-green.The dissolution time,solubility and swelling properties improved with the reduction of particle size;The powder fluidity was significantly improved;In addition to vitamin C,the content of functional components decreased significantly,reducing sugar,flavonoids and total phenols increased significantly,and the powder concentration and antioxidant activity were significantly improved;There is no obvious change in molecular structure,crystal form and thermal stability.(3)Optimization of spray drying parameters based on CFD numerical simulation:The air velocity distribution,temperature distribution and particle trajectory distribution of spray drying process were simulated by CFD.Under the condition that the inlet air temperature is 120 ℃,the feed rate is 0.45 L/h,and the feed concentration is 10%,the simulation result is 91.78%,and the experimental result is 89.54%.The model prediction result is basically consistent with the experimental temperature measurement result,with the maximum average error of 1.12%.