OBJECTIVE To optimize the water extraction process of Flos Lonicerae-Fructus Forsythuae and determine the range of water extraction process parameters.METHODS The active components were screened by network pharmacology,and the indica-tor ingredients were determined in combination with the quality markers under the relevant terms of Chinese Pharmacopoeia 2020 edition and the literature.Take extraction yield and the extraction rate of the indicative component as the critical quality attributes of the water extraction process to screen critical process parameters.The mathematical model was established by Box-Behnken experimental design to investigate the interaction between CQAs and CPPs and build the design space of the water extraction process of Flos Lonicerae-Fruc-tus Forsythuae.RESULTS The extraction percentages of phenolic acids,forsythoside A and forsythin were screened as the index components;specifications of medicinal slices,extraction time and water addition were the key process parameters.Based on the estab-lishment and optimization of the design space,the optimum water extraction process was obtained as follows:the medicinal slice of Lian-Qiao was broken into 0.8-1.2 cm,adding 12 times the amount of water in the first and extract for 30-50 min,10 times the a-mount of water in the second and extract for 25-30 min.CONCLUSION The verification results show that the measured value ob-tained by using the design space method to optimize the water extraction process is close to the predicted value,indicating that the method is stable and reliable,which can provide ideas for its further process development and quality control for the couple medicines of Flos Lonicerae-Fructus Forsythuae.

ObjectiveTo study the changing law of appearance color and physicochemical properties of Angelicae Sinensis Radix Carbonisata（ASRC） during the processing by color space method combined with statistical analysis， so as to provide reference for determining the processing endpoint and evaluating the quality of the decoction pieces. MethodsTaking processing time（4， 8， 12， 16 min） and temperature（180， 200， 220， 240 ℃） as factors， ASRC decoction pieces with different processing degrees were prepared in a completely randomized design. Then， the brightness value（L^*）， red-green value（a^*）， yellow-blue value（b^*）， and total chromaticity value （E^*ab） of the decoction pieces were determined by spectrophotometer， the color difference value（ΔE） was calculated， and the data of colorimetric values were analyzed by discriminant analysis. At the same time， the pH， charcoal adsorption， and contents of tannins， 5-hydroxymethylfurfural（5-HMF）， tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide of ASRC with different processing degrees were determined by pH meter， ultraviolet and visible spectrophotometry and ultra-high performance liquid chromatography（UPLC）. Principal component analysis（PCA） was used to analyze the data of physicochemical indexes， after determining the processing technology of ASRC， the canonical discriminant function was established to distinguish the decoction pieces with different processing degrees， and leave-one-out cross validation was conducted. Finally， Pearson correlation analysis was used to explore the correlation between various physicochemical indexes and chromaticity values. ResultsWith the prolongation of the processing time， L^*， a^*， b^* and E^*ab all showed a decreasing trend， and the established discriminant model based on color parameters was able to distinguish ASRC with different processing degrees. The pH showed an increasing trend with the prolongation of processing time， and the charcoal adsorption， and the contents of tannins， 5-HMF， and tryptophan all showed an increasing and then decreasing trend. Among them， the charcoal adsorption， contents of tannin and 5-HMF reached their maximum values successively after processing for 8-12 min. While the contents of chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide decreased with the increase of processing time， with a decrease of 60%-80% at 8 min of processing. Therefore， the optimal processing time should be determined to be 8-12 min. PCA could clearly distinguish ASRC with different processing degrees， while temperature had no significant effect on the processing degree. The 12 batches of process validation results（10 min， 180-240 ℃） showed that except for 3 batches identified as class Ⅱ light charcoal， all other batches were identified as class Ⅲ standard charcoal， and the chromaticity values of each batch of ASRC were within the reference range of class Ⅱ-Ⅲ sample chromaticity values. The correlation analysis showed that the chromaticity values were negatively correlated with pH and charcoal adsorption， and positively correlated with contents of tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H， and ligustilide. And both pH and charcoal adsorption were negatively correlated with the contents of the above components， but the charcoal adsorption was positively correlated with the content of 5-HMF. ConclusionThe chromaticity values and the contents of various physicochemical indicators of ASRC undergo significant changes with the prolongation of processing time， and there is a general correlation between chromaticity values and various physicochemical indicators. Based on the changes in color and physicochemical indicators， the optimal processing time for ASRC is determined to be 8-12 min. This study reveals the dynamic changes of the relevant indexes in the processing of ASRC， which can provide a reference for the discrimination of the processing degree and the quantitative study of the processing endpoint.