RESUMO
In order to standardize the quality control of traditional Chinese medicine（TCM） dispensing granules， the Chinese Pharmacopoeia Commission has promulgated and implemented 200 national drug standards for TCM dispensing granules， but there are still varieties of TCM dispensing granules without unified standards. Many provinces have actively invested in the formulation of provincial standards for TCM dispensing granules to make up for the gaps in standards for varieties of traditional Chinese medicine dispensing granules other than the national standards. By the end of July 2022， 29 provincial-level administrative regions have successively promulgated and implemented a total of 5 602 provincial standards for TCM dispensing granules， involving more than 400 varieties. In order to better understand the formulation and characteristics of provincial standards， this study took 105 provincial standards that have been promulgated and implemented in Henan province as an example， and comprehensively analyzed the formulation and characteristics through quality control indicators such as dry extract rate of raw materials， contents of index components and their transfer rates， specifications and so on. The formulation and characteristics of the same TCM dispensing granules in the provincial standards of different provinces were further analyzed， in order to provide reference for the formulation of provincial standards of TCM dispensing granules and the implementation of national standards.
RESUMO
OBJECTIVE： To establish the elimination method of outliers based on Grubbs rule and MATLAB language， and to evaluate the effects of it on drug bitterness evaluation. METHODS： Referring to Grubbs rule， the automatic cyclic outliers elimination method based on MATLAB language was established. Totally 20 volunteers were included in single oral taste test （Tetrapanax papyrifer） and multiple oral taste test （10 kinds of medicinal material as T. papyrifer， Changium smyrnioides， Poria cocos， etc.）. Seven sensors were selected for electronic tongue test （Clematis armandii）. The data of bitterness evaluation in above tests （oral taste test as bitterness value， electronic tongue test as response value of sensors） were used as the data source. Five researchers were selected and adopted table-by-table elimination method based on Grubbs rule （method one）， Excel software elimination method based on Grubbs rule （method two） and automatic cyclic outliers elimination method based on Grubbs rule and MATLAB language （method three） to judge and eliminate the outliers. The effects of above three methods were evaluated with the removal time and error rate of outliers as indexes. RESULTS： There were two outliers in the data of bitterness evaluation in single oral taste test； the elimination time of the three methods were（745.400 0±25.904 4），（288.333 3±31.253 1）and（0.000 3±0.000 0）s， respectively； error rates were 20.0％， 0 and 0， respectively. There were six outliers in the data of bitterness evaluation in multiple oral taste test； the elimination time of three methods were （3 693.107 7±75.023 3）， （1 494.761 4±53.826 9）， （0.005 2±0.000 0）s， respectively； error rates were 10.0％， 4.0％， 0， respectively. There were three outliers in the data of bitterness evaluation in electronic tongue test； the elimination time of three methods were （2 992.673 3±84.117 6）， （1 276.367 1±55.024 5）， （0.002 3±0.000 0）s， respectively； error rates were 5.7％， 2.9％， 0， respectively. The elimination results of the three methods were consistent. The elimination time of method two was significantly shorter than that of method one （P＜0.01）； the elimination time of method three was significantly shorter than those of method one and method two （P＜0.01）. There was no significant difference in error rate of 3 methods （P＞0.05）. CONCLUSIONS： The automatic cyclic elimination method of outliers based on Grubbs rule and MATLAB language can significantly shorten the elimination time of outliers in data of drug bitterness evaluation， improve the efficiency of data processing， and is suitable for drug bitterness evaluation.