[Sources of Uncertainty and Influencing Factors in the Analysis of Volatile Components in Blood Using Headspace Gas Chromatography].

内容摘要

To use uncertainty as an indicator to evaluate the main factors affecting data quality in the quantitative analysis of 12 volatile components in blood, including ethanol and toluene, and to assess the impact of different quality parameters, such as different hardware platforms on analytical results. Two established headspace gas chromatography platforms were used following the method specified in Examination Methods for Ethanol, Methanol, n-Propanol, Acetone, Isopropanol and n-Butanol in Blood and Urine (GB/T 42430-2023) for analysis. According to the requirements of Guidance on Quantifying Uncertainty in Chemical Analysis (CNAS-GL006：2019) and Evaluation and Expression of Uncertainty in Measurement (JJF 1059.1-2012), the uncertainty of the whole process of 12 volatile components quantitative analysis such as ethanol and toluene in blood was calculated. The differences of individual uncertainty components and the same uncertainty components across different hardware platforms were compared sequentially, and the results were verified by quantitative analysis of actual samples. There was no significant difference in the uncertainty components of quantitative analysis of 12 volatile components, whether it was a hardware platform composed of domestic or imported instruments. Among them, the relative standard uncertainty of type A introduced by repeatability tests and analysts ranged from 2.81×10-3 to 9.28×10-3; the type B relative combined standard uncertainties introduced by the standard solution and internal standard solution were 5.65×10-3 to 1.15×10-2, 4.85×10-3, respectively, the type B relative standard uncertainties introduced by the calibration curve and equipment were 1.45×10-2 to 2.47×10-2 and 5.00×10-3, respectively. The overall relative combined standard uncertainty of each component ranged from 1.74×10-2 to 3.07×10-2. In the analysis of 12 volatile components in blood, including ethanol and toluene, calibration curve fitting is the dominant source of uncertainty. Reasonable parallel operation can effectively control the uncertainty. The selection of different hardware platforms and other quality parameters does not significantly affect the quantitative results of 12 volatile components in blood. 目的: 以不确定度为指标，评估定量分析血液中乙醇、甲苯等12种挥发性成分的过程中影响数据质量的主要因素及不同的硬件平台等质量参数选择对分析结果的影响。方法: 使用建立的两种顶空-气相色谱平台，以《血液、尿液中乙醇、甲醇、正丙醇、丙酮、异丙醇和正丁醇检验》（GB/T 42430—2023）规定的方法开展分析，按照《化学分析中不确定度的评估指南》（CNAS-GL006:2019）及《测量不确定度评定与表示》（JJF 1059.1—2012）的要求计算血液中乙醇、甲苯等12种挥发性成分定量分析全过程的不确定度，依次比较各不确定度分量及同一不确定度分量在不同硬件平台等质量参数间的差异，并通过实际样品的定量分析应用进行验证。结果: 无论是国产还是进口仪器组成的硬件平台，12种挥发性成分定量分析的各不确定度分量差异均无统计学意义。其中，由重复性检测次数、检测人员引入的A类相对标准不确定度为2.81×10-3~9.28×10-3;由标准溶液、内标溶液引入的B类相对合成标准不确定度分别为5.65×10-3~1.15×10-2、4.85×10-3，由校准曲线和设备引入的B类相对标准不确定度分别为1.45×10-2~2.47×10-2、5.00×10-3;各成分总体相对合成标准不确定度为1.74×10-2~3.07×10-2。结论: 血液中乙醇、甲苯等12种挥发性成分的分析过程中，校准曲线拟合是最主要的不确定度来源，合理的平行操作可以有效控制该不确定度，不同的硬件平台等质量参数选择不显著影响血液中乙醇、甲苯等12种挥发性成分的定量结果。.