目的 采用表面改性技術優(yōu)化葛根芩連方（Gegen Qinlian Compound，GQC）浸膏粉綜合性能，篩選最佳改性劑及改性工藝，為制劑開發(fā)提供指導。方法 利用球磨機（ball mill，BM）和噴霧干燥（spray drying，SD）包覆改性技術，選用二氧化硅（SiO₂）、乙基纖維素（ethylcellulose，EC）、硬脂酸鎂（magnesium stearate，ST）3種改性劑，制備GQC改性浸膏粉（分別編號為GQC/BM-SiO₂、GQC/BM-EC、GQC/BM-ST和GQC/SD-SiO₂、GQC/SD-EC、GQC/SD-ST）。測定改性前后GQC浸膏粉的吸濕率（H）、含水量（HR）、休止角（α）、松密度（D_a）、振實密度（D_c）、豪斯納比（IH）、卡爾指數（IC）、間隙率（I_e）、中值徑（D₅₀）、粒徑分布寬度（span）、粒徑范圍（width）及比表面積（SSA）總計12個二級指標，繪制物理指紋圖譜，進行相似度分析。將各二級指標轉換為5個一級指標，采用熵權-變異系數法確定權重，計算浸膏粉綜合性能評分，篩選最優(yōu)方案，掃描電子顯微鏡（scanning electron microscope，SEM）觀察粒子表面形態(tài)結構變化。主成分分析（principal component analysis，PCA）法評價二級指標貢獻率，偏最小二乘分析（partial least squares analysis，PLSA）法分析關鍵指標H、SSA的相關性。結果 獲得GQC浸膏粉及其6組改性浸膏粉（GQC/BM-SiO₂、GQC/BM-EC、GQC/BM-ST和GQC/SD-SiO₂、GQC/SD-EC、GQC/SD-ST），繪制物理指紋圖譜并計算相似度。BM改性時，與未改性GQC浸膏粉相比，GQC/BM-SiO₂、GQC/BM-ST、GQC/BM-EC的相似度分別為0.953、0.920、0.969，改性不明顯；SD改性時，與未改性GQC浸膏粉相比，GQC/SD-SiO₂、GQC/SD-ST、GQC/SD-EC的相似度分別為0.477、0.449、0.439，改性效果好。一級指標權重系數為流動性0.104 8，積聚性0.134 1，壓縮性0.111 0，穩(wěn)定性0.133 1，均勻性0.517 0。BM組中，GQC/BM-ST綜合評分最高，為50.54，提高了18.92%；SD組改性后綜合評分均值為62.65，提高了47.41%，GQC/SD-ST效果最佳，為63.21。SEM顯示，硬脂酸鎂改性劑均勻包覆浸膏粉表面，粒子表面光滑，大小均勻，圓整度高。PCA顯示，IH、D₅₀、span、width、SSA貢獻率大。PLSA顯示，H與α、IC、width、IH、D₅₀、span、SSA關聯顯著，與粉體密度相關；SSA與D₅₀、width、span關聯顯著，粒徑及跨距影響最大。結論 表面改性技術可提高GQC浸膏粉綜合性能，SD改性效果較好，硬脂酸鎂改性劑評分最高，為制劑開發(fā)提供參考。

Objective To optimize the comprehensive properties of Gegen Qinlian Compound (GQC, 葛根芩連方) extract powder using surface modification technology, screen the best modifier and process, and provide guidance for formulation development. Methods The ball mill (BM) and spray drying (SD) coating modification techniques were used, and three modifiers, silicon dioxide (SiO₂), ethyl cellulose (EC) and magnesium stearate (ST), were selected. The GQC modified extract powders were prepared and designated GQC/BM-SiO₂, GQC/BM-EC, GQC/BM-ST and GQC/SD-SiO₂, GQC/SD-EC, GQC/SD-ST. All powders were measured secondary indicators, including H, HR, α, D_a, D_c, IH, IC, I_e, D₅₀, span, width and SSA, draw physical fingerprints, and conduct similarity analysis. Convert the secondary indicators into 5 primary indicators, determine the weights using the entropy weight coefficient of variation method, calculate the comprehensive performance score, screen for the optimal solution, and observe the changes in particle surface morphology and structure using scanning electron microscopy (SEM). Principal component analysis (PCA) evaluates the contribution rate of secondary indicators, while partial least squares analysis (PLSA) analyzes the correlation between key indicators H and SSA. Results GQC and six groups of modified powders (GQC/BM-SiO₂, GQC/BM-EC, GQC/BM-ST and GQC/SD-SiO₂, GQC/SD-EC, GQC/SD-ST) were obtained, and physical fingerprints were drawn and similarity was calculated. The similarity between GQC extract powder and GQC/BM-SiO₂, GQC/BM-ST, GQC/BM-EC were 0.953, 0.920, and 0.969 respectively, indicating negligible modification, GQC extract powder compared with GQC/SD-SiO₂, GQC/SD-ST, QC/SD-EC the similarity were 0.477, 0.449, and 0.439, respectively, indicating significant modification effect. The weight coefficients of the first level indicators are: liquidity 0.104 8, accumulation 0.134 1, compressibility 0.111 0, stability 0.133 1, and uniformity 0.517 0. The comprehensive score of magnesium stearate modifier in the BM group was the highest at 50.54, an increase of 18.92%. The mean value was 62.65 an increase of 47.41%, After SD modification, the best modification effect of GQC/SD-ST was 63.21. SEM shows that ST uniformly coats the surface of the extract powder, with smooth particle surfaces, uniform sizes, and high roundness. PCA analysis shows that IH, D₅₀, span, width, and SSA contribute significantly. PLSA analysis showed a significant correlation between H and α, IC, width, IH, D₅₀, span, SSA, which were related to powder density, SSA is significantly associated with D₅₀, width, and span, with particle size and span having the greatest impact. Conclusion Surface modification technology can improve the comprehensive performance of GQC extract powder. SD modification has a better effect, and ST modifier has the highest score, providing reference for formulation development.

R283.6

江蘇省“青藍工程”優(yōu)秀教學團隊資助項目（2024）；江蘇省“青藍工程”優(yōu)秀青年骨干教師資助項目（2022）；泰州市科技支撐計劃（社會發(fā)展）項目（TS202425）；泰州市科技支撐計劃（社會發(fā)展）項目（SSF20230030）；江蘇省大學生創(chuàng)新創(chuàng)業(yè)訓練計劃項目（202413981004Y）