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Comprehensive combinatory standard correction: A calibration method for handling instrumental drifts of gas chromatography-mass spectrometry systems

机译:全面的组合标准校正:处理气相色谱-质谱系统仪器漂移的校准方法

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摘要

The current work describes a new method, the comprehensive combinatory standard correction (CCSC), for the correction of instrumental signal drifts in GC-MS systems. The method consists in analyzing together with the products of interest a mixture of n selected internal standards, and in normalizing the peak area of each analyte by the sum of standard areas and then, select among the Sigma(n)(p=1) C-n(p) possible sums, the sum that enables the best product discrimination. The CCSC method was compared with classical techniques of data pre-processing like internal normalization (IN) or single standard correction (SSC) on their ability to correct raw data from the main drifts occurring in a dynamic headspace-gas chromatography-mass spectrometry system. Three edible oils with closely similar compositions in volatile compounds were analysed using a device which performance was modulated by using new or used dynamic headspace traps and GC-columns, and by modifying the tuning of the mass spectrometer. According to one-way ANOVA, the CCSC method increased the number of analytes discriminating the products (31 after CCSC versus 25 with raw data or after IN and 26 after SSC). Moreover, CCSC enabled a satisfactory discrimination of the products irrespective of the drifts. In a factorial discriminant analysis, 100% of the samples (n = 121) were well-classified after CCSC versus 45% for raw data, 90 and 93%, respectively after IN and SSC. (c) 2006 Elsevier B.V. All rights reserved.
机译:当前的工作描述了一种新方法,即综合组合标准校正(CCSC),用于校正GC-MS系统中仪器信号的漂移。该方法包括与感兴趣的产物一起分析n种选定内标的混合物,并通过标准面积之和归一化每种分析物的峰面积,然后在Sigma(n)(p = 1)Cn中进行选择。 (p)可能的总和,即能最好地区分产品的总和。将CCSC方法与经典的数据预处理技术(如内部归一化(IN)或单一标准校正(SSC))进行了比较,以从动态顶空-气相色谱-质谱系统中发生的主要漂移中校正原始数据。使用一种设备分析了三种挥发性成分组成极为接近的食用油,该设备的性能通过使用新的或使用过的动态顶空阱和GC色谱柱,以及通过修改质谱仪的调节来进行调节。根据单向方差分析,CCSC方法增加了区分产品的分析物数量(CCSC后为31,而原始数据为25或IN后为25,SSC后为26)。此外,CCSC能够对产品进行令人满意的区分,而与漂移无关。在阶乘判别分析中,CCSC后对100%的样本(n = 121)进行了很好的分类,而原始数据为45%,IN和SSC后分别为90%和93%。 (c)2006 Elsevier B.V.保留所有权利。

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