基于CRITIC赋权的BBD-ANN-Pareto耦合模型优化酒香附炮制工艺及鲁棒操作区间研究OA
Optimization of processing technology and study on robust operation interval of wine-processed Cyperi Rhizoma based on a CRITIC-weighted BBD-ANN-Pareto coupling model
目的 建立基于 CRITIC 法赋权结合 Box-Behnken 设计-响应面法(Box-Benhnken design-response surface methodology,BBD-RSM)与人工神经网络(artificial neural network,ANN)的酒香附Cyperi Rhizoma多指标综合权重优化工艺,基于模型预测界定工艺鲁棒操作区间,为参数固化与质量一致性控制提供参考.方法 以香附烯酮、α-香附酮、总黄酮和总挥发油含量为综合评价指标,应用CRITIC法客观赋权并计算综合评价值(overall desirability,OD).在单因素试验基础上,以炮制温度、炮制时间、闷润时间、投药量为自变量,采用BBD进行4因素3水平试验并建立回归模型.同时构建ANN预测模型,并采用Garson算法对因素相对贡献度进行解析;结合帕累托非支配解集(Pareto non-dominated solution set)对工艺参数空间进行筛选,获得鲁棒操作区间(模型预测).结果 CRITIC法确定香附烯酮、α-香附酮、总挥发油及总黄酮的权重分别为0.274 9、0.255 4、0.253 4、0.216 3.确定的酒香附最佳工艺条件为炮制温度140 ℃、炮制时间19 min、闷润时间6.9 h、投药量32 g/L.验证试验测得OD均值为0.655 1,RSD为2.97％,与模型预测值(0.653 3)接近.基于ANN预测与Pareto非支配解集界定的鲁棒参数区间为炮制温度140～150 ℃、炮制时间18～22min、闷润时间6.5～7.5h、投药量30～35 g/L;模型提示在该区间内参数波动±5％时,OD可保持相对稳定.结论 构建的BBD-ANN-Pareto耦合模型具有良好的预测能力和稳定性,可为酒香附炮制工艺优化及质量一致性控制提供参考.
Objective To establish a multi-index weighted optimization strategy for wine-processed Xiangfu(Cyperi Rhizoma)by integrating CRITIC weighting,Box-Behnken design-response surface methodology(BBD-RSM),and an artificial neural network(ANN),and define a robust operating space based on model prediction,thereby providing a reference for process parameter fixation and quality consistency control.Methods The contents of cyperotundone,α-cyperone,total flavonoids,and total volatile oil were used as comprehensive evaluation indices.Objective weights were assigned using the CRITIC method,and an overall desirability(OD)value was calculated.Based on single-factor experiments,a four-factor,three-level Box-Behnken design(BBD)was conducted with processing temperature,processing time,moistening time,and dosage as independent variables,and a regression model was established.An ANN prediction model was then developed,and the Garson algorithm was applied to interpret the relative contributions of factors.In addition,the Pareto non-dominated solution set was used to screen the process-parameter space to obtain a robust operating interval(model-predicted).Results The CRITIC-derived weights for cyperotundone,α-cyperone,total volatile oil,and total flavonoids were 0.274 9,0.255 4,0.253 4,and 0.216 3,respectively.The optimal processing conditions were determined as follows:processing temperature 140℃,processing time 19 min,moistening time 6.9 h,and dosage 32 g/L.The OD obtained from validation experiments was 0.655 1,RSD was 2.97％,which was close to the predicted value(0.653 3).The robust parameter interval defined based on ANN prediction and the Pareto non-dominated solution set was as follows:processing temperature 140-150℃,processing time 18-22 min,moistening time 6.5-7.5 h,and dosage 30-35 g/L.The model suggested that OD could remain relatively stable under±5％parameter fluctuations within this interval.Conclusion The constructed BBD-ANN-Pareto coupling model demonstrated good predictive ability and stability,which may provide a reference for optimizing the processing technology and ensuring the quality consistency of wine-processed Cyperi Rhizoma.
李沛森;路昕瑶;安和;彭红妍;郭龙;郑玉光;张丹
河北中医药大学药学院,河北省中药炮制技术创新中心,河北石家庄 050200河北中医药大学药学院,河北省中药炮制技术创新中心,河北石家庄 050200河北中医药大学药学院,河北省中药炮制技术创新中心,河北石家庄 050200河北中医药大学药学院,河北省中药炮制技术创新中心,河北石家庄 050200河北中医药大学药学院,河北省中药炮制技术创新中心,河北石家庄 050200||河北省中医药定量化研究与应用技术创新中心,河北石家庄 050200河北中医药大学药学院,河北省中药炮制技术创新中心,河北石家庄 050200||河北化工医药职业技术学院制药工程学院,河北石家庄 050000河北中医药大学药学院,河北省中药炮制技术创新中心,河北石家庄 050200||河北省中医药定量化研究与应用技术创新中心,河北石家庄 050200
医药卫生
酒香附炮制工艺Box-Behnken设计CRITIC权重赋值法人工神经网络Pareto非支配解集香附烯酮α-香附酮总黄酮总挥发油鲁棒操作区间
wine-processed Cyperi Rhizomaprocessing technologyBox-Behnken designCRITIC weight assignment methodartificial neural networkPareto non-dominated solution setcyperotundoneα-cyperonetotal flavonoidstotal volatile oilrobust operation interval
《中草药》 2026 (12)
4619-4630,12
河北省重点研发计划项目(23372503D)河北省自然科学基金中医药联合基金重点项目(H2025423029)国家中医药管理局科研项目(gzy-kjs-2023-029)国家中医药管理局科研项目(gzy-kjs-2025-015)河北省省级科技计划项目(252W2501D)河北中医药大学研究生创新资助项目(XCXZZSS2025019)
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