南方医科大学学报

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (10): 2092-2103.doi: 10.12122/j.issn.1673-4254.2025.10.05

• • 上一篇    

基于决明子提取物谱效关系GA-BP神经网络模型的谱效评分及质量评价

鄢海燕1(), 王恒1,2, 邹纯才1()   

  1. 1.皖南医学院药学院，安徽 芜湖 241002
    2.安庆一一六医院，安徽 安庆 246003
  • 收稿日期:2025-04-28 出版日期:2025-10-20 发布日期:2025-10-24
  • 通讯作者: 邹纯才 E-mail:yhy0801@126.com;zouchc@163.com
  • 作者简介:鄢海燕，教授，E-mail: yhy0801@126.com
  • 基金资助:
    安徽高校省级自然科学研究重大项目(KJ2016SD60)

A GA-BP neural network model based on spectrum-effect relationship for assessing spectrum-effect score and quality evaluation of Cassia seeds extract

Haiyan YAN1(), Heng WANG1,2, Chuncai ZOU1()   

  1. 1.School of Pharmacy, Wannan Medical College, Wuhu 241002, China
    2.Anqing 116 Hostipal, Anqing 246003, China
  • Received:2025-04-28 Online:2025-10-20 Published:2025-10-24
  • Contact: Chuncai ZOU E-mail:yhy0801@126.com;zouchc@163.com

RichHTML

0

PDF

15

摘要：

目的 构建决明子提取物谱效关系的GA-BP神经网络模型，探索谱效评分用于中药质量控制新方法。 方法 建立决明子提取物（0.1、0.2、0.4 g/mL）指纹图谱；采用5-Fu制备小鼠肝损伤模型，测定给予不同剂量（0.4、0.8、1.6 g/kg）决明子提取物的小鼠体质量、肝脏指数及血清ALT、AST、肝组织MPO、SOD、T-AOC等药效学指标并利用AHP-EWM计算综合药效；构建指纹图谱与综合药效的GA-BP神经网络模型，获取相应预测综合药效。利用灰色关联度法建立指纹图谱与实测综合药效、预测综合药效的谱效关系并进行Gaussian拟合分析。利用指纹图谱相对峰面积和谱效关联度计算谱效评分，Z比分数法检验数据可靠性，确定谱效评分限度范围并对验证样品进行质量评价。 结果 GA-BP神经网络模型的综合药效预测值与实测值均非常接近，误差小于0.2。实测综合药效与预测综合药效的谱效关系数据经Gaussian拟合表明，SEE和RMSE值均接近于0，R-square和Adjusted R-square值均大于0.95，实测综合药效与预测综合药效的谱效关系高度拟合。对谱效评分进行计算及Z比分数法检验，确定谱效评分限度为6.16~7.30。验证用各组的预测结果与实验结果相符，且在谱效评分限度范围内，Z比分数法检验结果表明数据可靠。 结论 GA-BP神经网络模型能较好地预测综合药效，建立的谱效评分方法可用于样品的质量评价。

关键词: GA-BP神经网络, 决明子提取物, 谱效关系

Abstract:

Objective To construct a GA-BP neural network model based on the spectrum-effect relationship of Cassia seeds extract and test its performance for quality control of Cassia seeds using spectrum-effect score. Methods The HPLC fingerprints of Cassia seeds extract (0.1, 0.2, and 0.4 g/mL) were established. In a mouse model of 5-Fu-induced liver injury treated with 0.4, 0.8, and 1.6 g/kg of Cassia seeds extract, the pharmacodynamics parameters were measured to calculate the comprehensive efficacy using AHP-EWM. A GA-BP neural network model between the fingerprints and comprehensive efficacy was constructed, and the corresponding predicted comprehensive efficacy was obtained. The spectrum-effect relationship between the fingerprints and the measured and predicted comprehensive efficacy was established using grey correlation method followed by Gaussian fitting analysis. The spectral efficiency score was calculated using the relative peak area of the fingerprints and the correlation degree of the spectral efficiency. The reliability of the data was tested using the Z-ratio score method. The limit range of the spectral efficiency score was determined and the quality of the verification samples was evaluated. Results The error between the predicted value using the GA-BP neural network model and the measured value of the comprehensive efficacy was less than 0.2. Gaussian fitting analysis showed good fitting between the spectrum-effect relationship data of the measured and predicted comprehensive efficacy. The limit of the spectral efficiency score was 6.16-7.30. The prediction results for each verification group were consistent with the experimental results and within the limit of spectral efficiency score, and the results of Z-ratio score analysis demonstrated good data reliability. Conclusion The GA-BP neural network model can effectively predict the comprehensive efficacy of Cassia seeds extract, and the established spectrum-effect scoring method can be used for quality evaluation of samples.

Key words: GA-BP neural network, Cassia seeds extract, spectrum-effect relationship