目的在质量源于设计(quality by design,QbD)理念的指导下优化丹参川芎嗪注射液(Salviae Miltiorrhizae and Ligustrazine Hydrochloride Injection,SMLHI)的水提工艺。方法首先通过筛选实验设计确定加热温度、保温时间、药材规格和溶...目的在质量源于设计(quality by design,QbD)理念的指导下优化丹参川芎嗪注射液(Salviae Miltiorrhizae and Ligustrazine Hydrochloride Injection,SMLHI)的水提工艺。方法首先通过筛选实验设计确定加热温度、保温时间、药材规格和溶剂倍量为丹参水提工艺的关键工艺参数。随后为了提高实验效率,采用基于裂区的D-最优实验设计优化SMLHI的水提工艺。为了尽可能多的提取实验数据潜在信息,采用了响应曲面法和人工神经网络法2种方法建立关键工艺参数和关键质量属性之间的回归模型。最后,选择较优的模型进一步采用基于满意度函数的多指标优化算法综合考察提取液各关键质量属性,确定水提工艺的最佳操作条件。结果基于裂区的D-最优实验设计可以有效提高实验效率,同时发现人工神经网络模型较响应曲面法模型具有更好的拟合能力和预测能力。以人工神经网络模型为基础进行多指标优化,最终确定SMLHI水提工艺的最佳操作条件为加热温度150℃,保温时间84 min,药材规格11 cm,溶剂用量为10倍。结论利用基于裂区的D-最优实验设计法优化了SMLHI水提工艺,为传统的中药制药工艺研究过程中面临的工艺参数改动受限、更改成本高昂的情况提供合理的实验设计方案,提高研究效率,降低研究成本,对不同试验规模下中药制药工艺的过程研究具有较大参考价值,为中药的工艺二次开发提供了可供参考的新方法。展开更多
Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually used as one of the attracti...Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually used as one of the attractive design criterion for selecting fractional factorial split-plot design. In this paper, we are interested in the theoretical construction methods of the optimal fractional factorial split-plot designs under the general minimum lower order confounding criterion. We present the theoretical construction methods of optimal fractional factorial split-plot designs under general minimum lower order confounding criterion under several conditions.展开更多
This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to im...This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to implement due to the improvement in computing via MCMC sampling. We described the Bayesian methodology by introducing the Bayesian framework, and explaining Markov Chain Monte Carlo (MCMC) sampling. The Metropolis-Hastings within Gibbs sampling was used to draw dependent samples from the full conditional distributions which were explained. In mixture experiments with process variables, the response depends not only on the proportions of the mixture components but also on the effects of the process variables. In many such mixture-process variable experiments, constraints such as time or cost prohibit the selection of treatments completely at random. In these situations, restrictions on the randomisation force the level combinations of one group of factors to be fixed and the combinations of the other group of factors are run. Then a new level of the first-factor group is set and combinations of the other factors are run. We discussed the computational algorithm for the Stochastic Search Variable Selection (SSVS) in linear mixed models. We extended the computational algorithm of SSVS to fit models from split-plot mixture design by introducing the algorithm of the Stochastic Search Variable Selection for Split-plot Design (SSVS-SPD). The motivation of this extension is that we have two different levels of the experimental units, one for the whole plots and the other for subplots in the split-plot mixture design.展开更多
Fractional factorial split-plot （FFSP） designs are useful in practical experiments. When the num- bers of levels of the factors are not all equal in an experiment, mixed-level design is selected. This paper investig...Fractional factorial split-plot （FFSP） designs are useful in practical experiments. When the num- bers of levels of the factors are not all equal in an experiment, mixed-level design is selected. This paper investigates the conditions of a resolution III or IV FFSP design with both two-level and eight-level factors to have various clear effects, including two types of main effects and three types of two-factor interaction compo- nents.展开更多
文摘目的在质量源于设计(quality by design,QbD)理念的指导下优化丹参川芎嗪注射液(Salviae Miltiorrhizae and Ligustrazine Hydrochloride Injection,SMLHI)的水提工艺。方法首先通过筛选实验设计确定加热温度、保温时间、药材规格和溶剂倍量为丹参水提工艺的关键工艺参数。随后为了提高实验效率,采用基于裂区的D-最优实验设计优化SMLHI的水提工艺。为了尽可能多的提取实验数据潜在信息,采用了响应曲面法和人工神经网络法2种方法建立关键工艺参数和关键质量属性之间的回归模型。最后,选择较优的模型进一步采用基于满意度函数的多指标优化算法综合考察提取液各关键质量属性,确定水提工艺的最佳操作条件。结果基于裂区的D-最优实验设计可以有效提高实验效率,同时发现人工神经网络模型较响应曲面法模型具有更好的拟合能力和预测能力。以人工神经网络模型为基础进行多指标优化,最终确定SMLHI水提工艺的最佳操作条件为加热温度150℃,保温时间84 min,药材规格11 cm,溶剂用量为10倍。结论利用基于裂区的D-最优实验设计法优化了SMLHI水提工艺,为传统的中药制药工艺研究过程中面临的工艺参数改动受限、更改成本高昂的情况提供合理的实验设计方案,提高研究效率,降低研究成本,对不同试验规模下中药制药工艺的过程研究具有较大参考价值,为中药的工艺二次开发提供了可供参考的新方法。
文摘Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually used as one of the attractive design criterion for selecting fractional factorial split-plot design. In this paper, we are interested in the theoretical construction methods of the optimal fractional factorial split-plot designs under the general minimum lower order confounding criterion. We present the theoretical construction methods of optimal fractional factorial split-plot designs under general minimum lower order confounding criterion under several conditions.
文摘This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to implement due to the improvement in computing via MCMC sampling. We described the Bayesian methodology by introducing the Bayesian framework, and explaining Markov Chain Monte Carlo (MCMC) sampling. The Metropolis-Hastings within Gibbs sampling was used to draw dependent samples from the full conditional distributions which were explained. In mixture experiments with process variables, the response depends not only on the proportions of the mixture components but also on the effects of the process variables. In many such mixture-process variable experiments, constraints such as time or cost prohibit the selection of treatments completely at random. In these situations, restrictions on the randomisation force the level combinations of one group of factors to be fixed and the combinations of the other group of factors are run. Then a new level of the first-factor group is set and combinations of the other factors are run. We discussed the computational algorithm for the Stochastic Search Variable Selection (SSVS) in linear mixed models. We extended the computational algorithm of SSVS to fit models from split-plot mixture design by introducing the algorithm of the Stochastic Search Variable Selection for Split-plot Design (SSVS-SPD). The motivation of this extension is that we have two different levels of the experimental units, one for the whole plots and the other for subplots in the split-plot mixture design.
基金Supported by the National Natural Science Foundation of China(Nos.10901092,11171165,11171188)Shandong Provincial Scientific Research Reward Foundation for Excellent Young and Middle-aged Scientists(BS2011SF006)Program for Scientific Research Innovation Team in Colleges and Universities of Shandong Province
文摘Fractional factorial split-plot （FFSP） designs are useful in practical experiments. When the num- bers of levels of the factors are not all equal in an experiment, mixed-level design is selected. This paper investigates the conditions of a resolution III or IV FFSP design with both two-level and eight-level factors to have various clear effects, including two types of main effects and three types of two-factor interaction compo- nents.
