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Updated: Jan 14, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Published on: March 14, 2013

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多条件机器学习模型用于理解保留机制和预测超临界流体染色学/质谱学中的保留时间

Omidreza Heravizadeh1, Kohta Nakatani2, Noriyuki Tomiyasu1

  • 1Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan.

Analytica chimica acta
|January 12, 2026
PubMed
概括

这项研究通过整合系统描述器,为超临界流体染色学 (SFC) 开发了准确的保留时间预测模型. 这些模型阐明了SFC保留机制,帮助方法开发和化合物识别.

关键词:
机器学习模型的机器学习模型质谱测量质量谱测量定量结构保留关系的数量结构保留机制的保留机制是什么保留时间预测预测.超临界流体色谱学 超临界流体色谱学

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