基于熵权法多指标优化栀子中环烯醚萜类成分的提取工艺

FU Qing,XIAO Xue-feng,ZHOU Yu-tong,YANG Bing,YUE Yu-ming,CHEN Jian-yu,SHAN Qi,ZHOU Fu-jun

Chinese Journal of New Drugs（2022）

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Abstract

目的:联用熵权法(entropy weight method)与正交实验设计(orthogonal experimental design)法,多指标筛选栀子中环烯醚萜类成分的最佳提取工艺.方法:基于栀子HPLC指纹图谱构建的基础上,采用HPLC法测定栀子中去乙酰车叶草苷酸甲酯、京尼平龙胆双糖苷、栀子苷、西红花苷Ⅰ、西红花苷Ⅱ的含量,并以此5种指标成分的质量分数和浸膏率为评价指标,联用熵权法和正交实验设计法对栀子中环烯醚萜类成分的提取工艺参数进行优化.结果:根据综合评价评分(M)结果,确定了栀子中环烯醚萜类成分的最佳提取工艺参数为10:8:8倍量的水,回流提取3次,每次2 h.3批平行验证M评分分别为98.07,98.56,98.77,RSD为0.36％.结论:联用熵权法和正交实验设计法对栀子中环烯醚萜类成分的提取工艺参数进行优化,科学可行,简单易于操作,节约成本,重复性和稳定性好.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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