在全面的二维气体染色学-质谱学中验证化合物识别的实用方法
Friscilla Hermatasia1, Apitchaya Sanchai1,2, Nuttanee Tungkijanansin1
1Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Analytical chemistry
|October 21, 2025
概括
一种新的曲线拟合方法准确计算了在没有参考标准的情况下在GC×GC-MS中用于化合物识别的保留指数. 这种方法验证了数千种化合物,提高了复杂化学分析中的数据可靠性.
科学领域:
- 分析化学 分析化学
- 染色体学 染色体学 是一种染色学.
- 质谱测量质量谱测量
背景情况:
- 在综合二维气色谱与质谱学 (GC×GC-MS) 结合的综合性二维气色谱中,化合物识别通常依赖于保留指数 (I).
- 准确计算保留指数对于可靠的化合物识别至关重要.
- 传统方法需要注射参考化合物,这可能是不切实际的.
研究的目的:
- 建立一个简单的曲线拟合方法来计算保留指数 (I),而不需要参考化合物注射.
- 应用此方法在GC×GC-MS中验证化合物识别数据.
- 评估已开发的化合物识别和重新注释方法的准确性和适用性.
主要方法:
- 开发了一个使用化合物名称,化温度和实验保留时间来计算1D和2D保留指数 (I_Cal) 的曲线拟合模型.
- 调查不同的数学模型,选择一个指数式方程与八个系数是最合适的.
- 通过将计算I (I_Cal) 与文献I (I_Lit) 对495种化合物进行比较来验证模型.
- 基于I_Lit和I_Cal之间的差异来验证化合物身份的确立标准 (Δ1I和Δ2I在±60单位内).
- 应用该方法分析了来自15个样本的1741个峰值,并从85个文献中选了1285个化合物.
主要成果:
- 选择的指数模型表现出高精度,平均R2值为0.9982 (1I) 和0.9957 (2I).
- 该模型实现了低误差:MAE为8.29/12.89和RMSE为11.63/16.48分别为1I和2I.
- 采样数据的应用确定了536种 (在995种中) 和534种 (在746种中) 的化合物,根据初步标准可能被错误识别.
- 该方法成功地从文献数据中选了1285种化合物,突出了其在重新评估现有结果中的实用性.
- 证明了61个峰值的重新编号,具有更正确的身份.
结论:
- 开发的曲线拟合方法为计算GC×GC-MS在没有参考标准的保留指数提供了强大的和高效的方法.
- 这种方法通过标记可能被错误识别的化合物,显著提高了化合物识别的可靠性.
- 该方法广泛适用于验证新数据集中的化合物身份,并重新评估已发布的GC × GC-MS结果.
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