以3种不同包装材料(利乐砖、利乐枕和百利包)的常温牛奶为研究对象,采用顶空固相微萃取(Head Space Solid-Phase MicroExtraction,HS-SPME)对3种不同包材的常温奶进行挥发性风味物质测定,比较不同包材的常温奶中挥发性风味物质的差异。...以3种不同包装材料(利乐砖、利乐枕和百利包)的常温牛奶为研究对象,采用顶空固相微萃取(Head Space Solid-Phase MicroExtraction,HS-SPME)对3种不同包材的常温奶进行挥发性风味物质测定,比较不同包材的常温奶中挥发性风味物质的差异。结果表明,百利包材样品中检出最多的脂肪二级氧化产物醛、酮、醇,其中酮-醇化合物占比最大,占总挥发性化合物的73.9%。在描述性感官评价中,气味和饱满度的评分结果由高到低依次是利乐枕、利乐砖、百利包;所有感官评价人员对百利包样品的气味和饱满度(P<0.05)评分最低,而异味评分最高(P<0.05);可能与其无菌复合膜材料的氧气透过率较高有关,较高氧气透过率包材使牛奶中含有较多的溶解氧,易引起牛奶中的乳脂肪氧化而产生大量的酮-醇化合物,从而导致牛奶的风味发生变化。展开更多
The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The ...The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.展开更多
文摘以3种不同包装材料(利乐砖、利乐枕和百利包)的常温牛奶为研究对象,采用顶空固相微萃取(Head Space Solid-Phase MicroExtraction,HS-SPME)对3种不同包材的常温奶进行挥发性风味物质测定,比较不同包材的常温奶中挥发性风味物质的差异。结果表明,百利包材样品中检出最多的脂肪二级氧化产物醛、酮、醇,其中酮-醇化合物占比最大,占总挥发性化合物的73.9%。在描述性感官评价中,气味和饱满度的评分结果由高到低依次是利乐枕、利乐砖、百利包;所有感官评价人员对百利包样品的气味和饱满度(P<0.05)评分最低,而异味评分最高(P<0.05);可能与其无菌复合膜材料的氧气透过率较高有关,较高氧气透过率包材使牛奶中含有较多的溶解氧,易引起牛奶中的乳脂肪氧化而产生大量的酮-醇化合物,从而导致牛奶的风味发生变化。
基金The Natural Science Foundation of Jiangsu Province,China(No.BK20200470)China Postdoctoral Science Foundation(No.2021M691595)Innovation and Entrepreneurship Plan Talent Program of Jiangsu Province(No.AD99002).
文摘The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.