细菌菌株的高级光谱建模:使用拉索规范化和基线优化进行MARS-PLS2方法
Sughra Sarwar1, Tahir Mehmood1, Mudassir Iqbal1
1School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|January 24, 2026
概括
这项研究引入了用于红外光谱分析的集成MARS-PLS2-Lasso框架,改善了细菌中的生物化学性质预测. 波形基线校正方法显著提高了用于光谱诊断的模型准确性和可解释性.
科学领域:
- 化学测量和光谱学 化学测量和光谱学
- 生物分析光谱学
- 机器学习用于生物化学分析
背景情况:
- 红外光谱数据提出了诸如基线扭曲,高维度和非线性相关性等挑战,阻碍了准确的生物化学性质预测.
- 现有的方法很难有效地解决这些固有的数据特征,以进行可靠的细菌分析.
研究的目的:
- 开发和验证一个综合框架 (MARS-PLS2-Lasso),用于增强红外光谱数据的化学度模型.
- 用光谱分析提高预测细菌生物化学性质的准确性和可解释性.
- 为了确定关键的光谱区域和生化成分,这对于细菌的分化至关重要.
主要方法:
- 实施了一个集成的MARS-PLS2-Lasso框架,结合了多变量自适应回归支柱 (MARS),部分最小平方2 (PLS2) 和拉索规范化.
- 评估了四种基线校正方法,确定波浪式方法 (sym8,第5级) 是光谱变化和降噪的最佳方法.
- 使用MARS进行非线性关系,PLS2用于隐性变量提取,Lasso用于稀疏性和模型复杂性降低.
主要成果:
- 波形基线校正实现了高预测准确度 (RMSE=0.2846-0.6857,MAE=0.2371-0.5445,MSE=0.0810-0.4705) 的细菌谱.
- 确定了六个关键的功能区域,对应于C-Cl,C-O,C=C,C=O,C-H和O-H/N-H拉伸振动.
- 马尔斯-PLS2-拉索模型显示了高预测准确度,降低了噪音,并提高了光谱生物化学相互作用的解释性.
结论:
- 集成的MARS-PLS2-Lasso框架为高维红外光谱建模提供了一个强大的,可解释的和化学一致的方法.
- 波形基线校正对于在细菌分析中预处理红外光谱非常有效.
- 这项研究为细菌建模,光谱诊断和生物分析应用提供了有前途的方法.
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