T-2毒素微胶囊是制备虾毒饵料研究该毒素在对虾食品链中传递规律的前提技术,从T-2毒素微胶囊毒饵料的性状、包埋率及对虾利用率3个方面对其进行评价。采用微胶囊技术,以复合蛋白制剂为壁材,20 ng T-2毒素和对虾饲料为混合芯材,采用均质...T-2毒素微胶囊是制备虾毒饵料研究该毒素在对虾食品链中传递规律的前提技术,从T-2毒素微胶囊毒饵料的性状、包埋率及对虾利用率3个方面对其进行评价。采用微胶囊技术,以复合蛋白制剂为壁材,20 ng T-2毒素和对虾饲料为混合芯材,采用均质和物理加热法使复合蛋白制剂包裹混合心材,再用恒温干燥法使其硬化,形成颗粒状对虾T-2毒素微胶囊毒饵料,建立了T-2毒素微胶囊毒饵料的制备工艺。采用LC-MS/MS技术检测T-2毒素,普通毒饵料、微胶囊毒饵料抽提水样中T-2的毒素含量分别为2.661、0.654 ng/mL,微胶囊毒饵料的包埋率达96.73%,其利用率是普通毒饵料的1.7倍。研究表明T-2毒素微胶囊毒饵料可以解决对虾口服难溶于水的T-2毒素的问题,并提高了T-2毒素的利用率,为研究T-2毒素对对虾的作用提供技术支撑。展开更多
We consider an extension to Sequential Probability Ratio Tests for when we have uncertain costs, but also opportunity to learn about these in an adaptive manner. In doing so we demonstrate the effects that allowing un...We consider an extension to Sequential Probability Ratio Tests for when we have uncertain costs, but also opportunity to learn about these in an adaptive manner. In doing so we demonstrate the effects that allowing uncertainty has on observation cost, and the costs associated with Type I and Type II error. The value of information relating to modelled uncertainties is derived and the case of statistical dependence between the parameter affecting decision outcome and the parameter affecting unknown cost is also examined. Numerical examples of the derived theory are provided, along with a simulation comparing this adaptive learning framework to the classical one.展开更多
文摘T-2毒素微胶囊是制备虾毒饵料研究该毒素在对虾食品链中传递规律的前提技术,从T-2毒素微胶囊毒饵料的性状、包埋率及对虾利用率3个方面对其进行评价。采用微胶囊技术,以复合蛋白制剂为壁材,20 ng T-2毒素和对虾饲料为混合芯材,采用均质和物理加热法使复合蛋白制剂包裹混合心材,再用恒温干燥法使其硬化,形成颗粒状对虾T-2毒素微胶囊毒饵料,建立了T-2毒素微胶囊毒饵料的制备工艺。采用LC-MS/MS技术检测T-2毒素,普通毒饵料、微胶囊毒饵料抽提水样中T-2的毒素含量分别为2.661、0.654 ng/mL,微胶囊毒饵料的包埋率达96.73%,其利用率是普通毒饵料的1.7倍。研究表明T-2毒素微胶囊毒饵料可以解决对虾口服难溶于水的T-2毒素的问题,并提高了T-2毒素的利用率,为研究T-2毒素对对虾的作用提供技术支撑。
文摘We consider an extension to Sequential Probability Ratio Tests for when we have uncertain costs, but also opportunity to learn about these in an adaptive manner. In doing so we demonstrate the effects that allowing uncertainty has on observation cost, and the costs associated with Type I and Type II error. The value of information relating to modelled uncertainties is derived and the case of statistical dependence between the parameter affecting decision outcome and the parameter affecting unknown cost is also examined. Numerical examples of the derived theory are provided, along with a simulation comparing this adaptive learning framework to the classical one.
