解读协同作用:揭示渐变增强回归模型,用于多变量定量纯和片剂剂形式的皮奥格利塔,阿洛格利普丁和格利梅皮里德
Mahmoud M Elkhoudary1, Aya A Marie1,2, Sherin F Hammad2
1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Horus University-Egypt, New Damietta, 34517, Egypt.
BMC chemistry
|November 29, 2024
概括
极端梯度提升 (XG提升) 精确确定使用紫外线光谱测试的抗糖尿病药物混合物. 这种方法的性能优于部分最小方形,人工神经网络和支持向量回归对于皮奥格利塔,阿洛格利普丁和格利梅皮里德的分析.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 药品分析 药品分析
背景情况:
- 对抗糖尿病药物混合物的准确量化对于制药质量控制至关重要.
- 紫外线光谱与多变量校准相结合,为分析复杂的药物配方提供了强大的方法.
- 评估先进的算法对于提高分析方法性能至关重要.
研究的目的:
- 为了比较四种多变量方法的性能:部分最小平方 (PLS),人工神经网络 (ANN),支持向量回归 (SVR) 和极端梯度增强 (XG Boost).
- 用紫外线光谱测量来确定药物配方中的皮奥格利塔 (PIO),阿洛格利普丁 (ALG) 和格利梅皮里德 (GLM) 的抗糖尿病混合物.
- 确定分析这些化合物的最准确和最可靠的方法.
主要方法:
- 采用紫外线光谱测量来获取光谱数据.
- 使用变量选择和预处理技术 (平均集中,直角散射校正,主要成分分析) 来优化光谱数据.
- 应用了PLS,ANN,SVR和XG Boost算法进行多变量校准和预测.
- 模型性能使用根平均平方预测误差 (RMSEP) 和标准偏差 (SD) 在独立测试集上进行了评估.
主要成果:
- 在所有分析的化合物中,XG Boost与PLS,ANN和SVR相比表现优越.
- 对于PIO (RMSEP=0.100,SD=0.369),ALG (RMSEP=0.001,SD=0.005) 和GLM (RMSEP=0.001,SD=0.018) 来说,XG Boost实现了最低的RMSEP和SD值.
- 该算法有效处理复杂和重叠的光谱数据,提高计算速度和准确性.
结论:
- XG Boost是通过紫外线光谱测量来确定药物配方中的PIO,ALG和GLM的最可靠和最准确的方法.
- 该算法的管理复杂光谱特征的能力使其非常适合分析复杂的混合物.
- 这项研究验证了XG Boost作为药物分析中的强大工具,提供了更高的精度和效率.
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