磷脂脂肪酸分析中内标物的使用及数据处理建模

Using of internal standard and modeling the data processing of phospholipid fatty acids

摘要:

【目的】评估未知样品内标进样量的参考范围，优化数据处理方法。【方法】采用C19为内标，不同进样量进行分析；编写数据分析程序优化数据处理模型。【结果】随着内标进样量的增加，检测到的PLFA数量和响应值等指标均有增加，但进样量超过16 nmol/g时反而下降；25组数据显示采用响应值计算PLFA含量比百分比值计算所得含量略高；采用Dot Net C#语言编写数据分析模型。【结论】初步确定了未知样品内标进样量的参考范围，得出最优处理方法；采用响应值计算PLFA含量可避免由于百分比值缺失带来的误差；校准系数的引入可减少仪器参数条件改变等因素带来的误差；通过编写谱图数据分析程序获得一定自动化操作，提高了数据分析效率和准确性。

Abstract:

[Objective] To estimate the reference range of internal standard amount of unknown samples and optimize the method of data processing. [Methods] The different amount use of Methyl Nonadecanoate as internal standard was studied and discussed. The data analysis model of fatty acids was established to optimize the data processing of PLFA. [Results] The number and response values of detected PLFA were advanced with the increased of amount of internal standard, but both reduced when the amount over 16 nmol/g. Twenty-five sets of PLFA data showed that the PLFA contents calculated by response value were higher than by percentage value. A data analysis model of PLFA was established using Dot Net C# language. [Conclusion] The reference range of internal standard amount of unknown samples was preliminary estimated and optimized. Calculation of PLFA content by response value can avoid the error caused by the lack of percentage value. The use of calibration coefficient can reduce the system error. Data processing was automated by the data analysis mode. It improves the efficiency and accuracy of data analysis.