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桌面 volatilomics 超级充电: 如何基于机器学习的实验设计有助于优化非目标的GC-IMS气相代谢

Hadi Parastar¹, Philipp Weller²

¹Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran; Institute for Instrumental Analytics and Bioanalytics, Mannheim University of Applied Sciences, 68163, Mannheim, Germany.

|February 21, 2024

概括

优化气相色谱-离子移动谱法 (GC-IMS) 用于风味分析是具有挑战性的,因为非线性行为. 这项研究结合了实验设计 (DOE) 和机器学习 (ML) 以有效地建模GC-IMS条件,以进行增强的挥发性分析.

科学领域:

分析化学分析化学
化学测量化学测量化学测量
数据科学数据科学数据科学

背景情况:

气色谱-离子流动性光谱 (GC-IMS) 对于有针对性和非有针对性的分析至关重要.
在GC-IMS中,非线性行为和复杂的离子化学阻碍了最佳条件的确定.
机器学习 (ML) 为优化GC-IMS实验参数提供了一个有希望的解决方案.

研究的目的:

开发一种混合策略,结合实验设计 (DOE) 和ML来优化GC-IMS条件.
将这一策略应用于非向的挥发性/风味分析,以沙弗朗挥发物为案例研究.
为了比较线性 (MLR) 和非线性 (BR-ANN) ML模型的性能,用于预测GC-IMS响应.

主要方法:

使用可旋转的局限中央复合设计 (CCD) 来识别五个关键GC-IMS因素.
多重线性回归 (MLR) 和贝叶斯规范化人工神经网络 (BR-ANN) 模型被用于预测.
用德林格的可取性函数来整合多个响应变量 (峰值区域和检测到的峰数).

主要成果:

MLR有效地建模了GC-IMS因子与检测到的峰数 (PNs) 之间的关系,表明了线性行为.
BR-ANN更适合捕捉在总峰值区域 (PA) 中观察到的非线性行为.
混合DOE-ML方法,特别是MLR,在建模GC-IMS因素以实现综合响应方面提供了令人满意的性能.

结论:

混合DOE和ML方法成功地优化了GC-IMS条件,用于非目标的挥发性分析.
选择ML模型 (线性与非线性) 取决于特定的响应变量的行为.
这一综合战略提高了GC-IMS在风味和挥发性研究中的效率和可靠性.

关键词:

化学测量化学测量化学测量实验的设计实验的设计这就是Flavoromics.气色谱-离子移动性光谱学机器学习是机器学习.挥发力学是一种挥发力学.

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