RESUMO
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.
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.
RESUMO
Objective The quality evaluation of the Shuwei mixture was determined by the content of the six components of sinapine cyanide sulfonate, magnolol, honokiol, hesperidin, naringin and neohesperidin. Methods RP-HPLC method was used.The separation was performed on a Agilent ZORBAX Eclispse SB-C18column (4.6 mm×250 mm,5 mm),the mobile phase consisted of acetonitrile(A)-0.1% phosphoric acid with gradient elution at the flow rate of 1.0 mL·min-1.The detection wavelength was 326 nm ( sinapine cyanide sulfonate ), 294 nm ( magnolol, honokiol ) and 283 nm ( naringin, neohesperidin).The column temperature was kept at 30 ℃. Results The sinapine cyanide sulfonate, magnolol, honokiol, hesperidin,naringin and neohesperidin all had good linear relationship in the ranges of 0.049 6-1.24,0.048 2-1.205,0.060 5-1.512 5,0.187 2-4.68,0.131 6-3.29,0.197-4.925 μg.The average recoveries were 100.66%, 99.86%, 101.37%, 102.41%, 99.01%, 102.05%, respectively, RSD were 0.82%, 1.89%, 2.56 %, 0.74%, 1.54%, 0.99%, respectively. Conclusion The method is simple,accurate,reproducible and nice to the separation,and can be used for the quality evaluation of Shuwei mixture.
RESUMO
This study was ai med to observe the taste-masking effects of Neotame on bitter Chinese herbal ingredients. Five kinds of herbal ingredients, which include Scutellaria baicalensis Georgi, Cortex Phellodendri chinensis, Coptis chinensis Franch, Gentiana scabra Bunge, Andrographis paniculata, were selected to measure the bitterness degree of decoctions with berberine solution as the benchmark. The decreasing of bitterness degree was used as index. Healthy volunteers were recruited to taste and compare the changes of bitterness of decoctions with the taste-masking effects of Neotame. Different concentrations of Neotame were selected in the determination of the influence on changes of bitterness. The results showed that when the concentration of Neotame was at 0.012 5‰-0.4‰, taste-masking effects of Neotame on selected herbal decoctions were in a concentration-dependent fashion. When the concentration of Neotame was 0.4‰, the reduced bitterness of S. baicalensis Georgi and Cortex P. chinensis decoctions were 1.22 and 1.77, by 70.11% and 71.88%, respectively. Three highly-bitter herbal ingredients C. chinensis Franch, G. scabra Bunge and A . paniculata were also reduced in bitter taste by 49.12%, 50.87% and 38.39%, with the bitter reduced value (△I) of 1.78, 2.02 and 1.43, respectively. It was concluded that Neotame exerted taste masking potential on bitter herbal ingredients with different bitter degrees.