[Research and application of quality standard for standard decoction of Chrysanthemi Flos].

Standard decoction of traditional Chinese medicine (TCM) is prepared by standardized process, and can be used as references to evaluate the quality of dosage forms such as decoction and dispensing granules. In order to determine the quality evaluation method for standard decoction of Chrysanthemi Flos and investigate its application, 10 batches of white chrysanthemum of Hangzhou were collected to prepare the standard decoction of white chrysanthemum of Hangzhou with standardized process parameters. Parameters such as traits, relative density, pH value, extraction ratio, transfer rate and fingerprint were selected as the indexes for quality evaluation. The established quality evaluation method for standard decoction of Chrysanthemi Flos was applied in the detection of two types of commercial Chrysanthemum dispensing granules. The results showed that the standard decoction of Chrysanthemi Flos was a clear yellow-brown aqueous solution; the relative density was 1.007-1.011; the pH value was between 5.37-5.56; the average extraction ratio was 23.6%, ranging from 19.93% to 29.69%; the average transfer ratewas 56.2% in terms of chlorogenic acid, 57.4% in terms of luteoloside and 30.6% in terms of 3,5-O-dicaffeoylquinic acid. Fingerprint similarity was between 0.864-0.989.The method showed good precision, stability and repeatability in fingerprint analysis, indicating reliable and representative results for standard decoction of Chrysanthemi Flos, and it can be used to evaluate and standardize other dosage forms.

Linear Fuzzy Information-Granule-Based Fuzzy C-Means Algorithm for Clustering Time Series.

This article aims to design a trend-oriented-granulation-based fuzzy C -means (FCM) algorithm that can cluster a group of time series at an abstract (granular) level. To achieve a better trend-oriented granulation of a time series, l1 trend filtering is firstly carried out to result in segments which are then optimized by the proposed segment merging algorithm. By constructing a linear fuzzy information granule (LFIG) on each segment, a granular time series which well reflects the linear trend characteristic of the original time series is produced. With the novel designed distance that can well measure the trend similarity of two LFIGs, the distance between two granular time series is calculated by the modified dynamic time warping (DTW) algorithm. Based on this distance, the LFIG-based FCM algorithm is developed for clustering time series. In this algorithm, cluster prototypes are iteratively updated by the specifically designed granule splitting and merging algorithm, which allows the lengths of prototypes to change in the process of iteration. This overcomes the serious drawback of the existing approaches, where the lengths of prototypes cannot be changed. Experimental studies demonstrate the superior performance of the proposed algorithm in clustering time series with different shapes or trends.