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Updated: Feb 10, 2026

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Automating Aggregate Quantification in Caenorhabditis elegans
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启用绿色分析化学:用于兽医药物的光谱测量量量化的自动化化学测量框架
Ayman M Algohary1, Sultanah M N Alhunayhin1, Belal Muneeb Kanaan1
1Department of Chemistry, College of Science Al-zulfi, Majmaah University, Al-Majmaah, Saudi Arabia.
Archiv der Pharmazie
|February 8, 2026
概括
这项研究引入了AutoRegress,这是一个自动化的机器学习框架,用于量化兽医中MetaMizole (MT), (PA) 和Diclofenac (DI). 它通过开发分析物特定模型,实现快速的绿色光谱光度分析.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 机器学习在化学中的应用
背景情况:
- 在兽医配方中同时量化甲醇 (MT), (PA) 和二 (DI) 是具有挑战性的,因为光谱重叠.
- 传统的光谱光度法受到光谱重叠的阻碍,往往需要不那么环保的色谱技术.
研究的目的:
- 开发一个新的自动化机器学习 (AutoML) 框架,AutoRegress,用于自动化化学测量模型开发.
- 为了使MT,PA和DI的快速,绿色和准确的同时量化,使用光谱测量.
主要方法:
- 自动回归框架自动选择回归算法和特征选择技术.
- 对各种化学测量模型的竞争性评价.
- 开发用于MT,PA和DI的分析物特定模型.
主要成果:
- 自动回归确定了最佳的,分析物特定的模型:MT的拉索回归,PA的非线性支向量回归 (SVR),DI的拉索回归.
- 开发的模型在一个独立的测试组中实现了高预测精度 (R2 > 0.988).
- 该框架成功地分解了复杂的光谱,使得快速和绿色的分析方法成为可能.
结论:
- 自动回归提供了一个可重复的平台,用于自动化化学测量的复杂模型选择.
- 该研究挑战了"一刀切"的方法,强调了分析物特定分析模型的重要性.
- 这种AutoML框架使先进的分析解决方案在药品分析中更容易获得常规质量控制.
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