OBJECTIVE:To optimize the extraction technology of garlic oil. METHODS:Using extraction rate of garlic oil as index,based on single factor test,Box-Behnken response surface method was used to optimize conditions of steam distillation method for the extraction of garlic as fermentation time,solid to liquid ratio,fermentation temperature and the verification test were made for the optimized technology. RESULTS:The optimal extraction technology was as follows as fermentation time of 4.5 h,solid to liquid ratio of 1:7,fermentation temperature of 55 ℃. The average extraction rate of garlic oil in verification test was 0.32%(RSD=1.43%,n=3);the relative error between the measured value and predicted value was 0.06%. CONCLUSIONS:Box-Behnken response surface method is simple,reasonable and feasible to optimize the extraction technology of garlic oil,which can provide a scientific basis for industrial production.

OBJECTIVE：To provide referenc e for the construction and software development of knowledge base for rational use of TCM decoction pieces. METHODS ：By reviewing the literatures on rational drug use software and TCM decoction pieces in recent years ，the clinical characteristics of rational drug use of TCM decoction pieces as well as the characteristics and shortcomings of existing rational drug use software in the detection of rational drug use of TCM decoction pieces were analyzed ， and the core contents and difficulties in the construction of knowledge base of rational drug use software of TCM decoction pieces were summarized. RESULTS & CONCLUSIONS ：Clinical application of TCM decoction pieces was mainly based on “syndrome differentiation”，which reflected the unity of dialectics ，treatment，prescription selection and medication. Therefore ，the consideration of the rationality of clinical use of TCM decoction pieces could not blindly imitate the evaluation method of chemical medicine. Current rational drug use software was not based on the theoretical system of traditional Chinese medicine ，and it was not comprehensive and mature in the aspect of rational drug use review of TCM decoction pieces ，and lacks the knowledge base that could meet the requirements of rational use of TCM decoction pieces. Therefore ，it is necessary to construct a set of knowledge base which can meet the evaluation requirements of “consistency of principle ，method and prescription use ”of TCM decoction pieces under the guidance of TCM theoretical system. Its contents include that patient information collection ，construction of knowledge base related to diseases and syndromes ，selection of processed products of TCM dec oction pieces ，addition andsubtraction of clinical symptoms ，selection taboo of varieties of TCM decoction pieces ， compatibility taboo ， combined application of Chinese patent medicine or chemical medicine ， dosage of TCM decoction pieces ， total dosage and tastquantity of each prescription ， special de coction drugs ， medication methods and administration frequency ，etc. There are still some difficulties in the development of rational drug use software of TCM decoction pieces ，such as the construction of disease and syndrome related knowledge base and the difficulty in judging the rationality of clinical symptom addition and subtraction.

ObjectiveTo investigate the feasibility of applying electronic nose technology to rapidly identify Bletillae Rhizoma and its approximate decoction pieces. MethodA total of 134 batches of Bletillae Rhizoma and its approximate decoction pieces， including 45 batches of Bletillae Rhizoma， 30 batches of Gastrodiae Rhizoma， 30 batches of Polygonati Odorati Rhizoma and 29 batches of Bletillae Ochraceae Rhizoma， were collected as test samples. The olfactory sensory data of the samples were collected by PEN3 electronic nose as the independent variable（X）. Based on the identification results of the 2020 edition of Chinese Pharmacopoeia and local standards， as well as the high performance liquid chromatography（HPLC） fingerprint and original purchase information of 134 batches of the decoction pieces， the benchmark data Y of the identification model were obtained， and four chemometric methods of principal component analysis-discriminant analysis（PCA-DA）， partial least squares-discriminant analysis（PLS-DA）， least square-support vector machine（LS-SVM） and K-nearest neighbor（KNN） were used to establish the binary identification model for 45 batches of Bletillae Rhizoma and 89 batches of non-Bletillae Rhizoma and the quadratic identification model of the four kinds of decoction pieces， that is， Y=F（X）. ResultAfter leave-one-out cross validation， the positive discrimination rates of the above four models were 97.01%， 97.01%， 98.51% and 97.01% in the binary identification， and 97.76%， 89.55%， 98.51% and 97.01% in the quadratic identification， respectively. The highest positive discrimination rate could reach 98.51% for the binary and quadratic identification models， and LS-SVM algorithm is both the optimal one， the most suitable kernel functions were chosen as radial basis function and linear kernel function， respectively. The optimal models discriminated well with no unclassified samples. ConclusionElectronic nose technology can accurately and rapidly identify Bletillae Rhizoma and its approximate decoction pieces， which can provide new ideas and methods for rapid quality evaluation of other decoction pieces.

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.

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.