化学
色谱法
检出限
分析化学(期刊)
戊烷
二维色谱法
火焰离子化检测器
气相色谱法
异戊烷
分析物
规范化(社会学)
己烷
二维气体
生物化学
有机化学
社会学
人类学
催化作用
作者
Lina Mikaliunaite,Paige E. Sudol,Caitlin N. Cain,Robert E. Synovec
标识
DOI:10.1016/j.chroma.2021.462358
摘要
A baseline correction method is developed for comprehensive two-dimensional (2D) chromatography (GC × GC) with flame-ionization detection (FID) using dynamic pressure gradient modulation (DPGM). The DPGM-GC × GC-FID utilized porous layer open tubular (PLOT) columns in both dimensions to focus on light hydrocarbon separations. Since DPGM is nominally a stop-flow modulation technique, a rhythmic baseline disturbance is observed in the FID signal that cycles with the modulation period (PM). This baseline disturbance needs to be corrected to optimize trace analysis. The baseline correction method has three steps: collection of a background "blank" chromatogram and multiplying it by an optimized normalization factor, subtraction of the normalization-optimized background chromatogram from a sample chromatogram, and application of Savitzky-Golay smoothing. An alkane standard solution, containing pentane, hexane and heptane was used for method development, producing linear calibration curves (r2 > 0.991) over a broad concentration range (7.8 ppm – 4000 ppm). Further, the limit-of-detection (LOD) and limit-of-quantification (LOQ) were determined for pentane (LOD = 2.5 ppm, LOQ = 8.2 ppm), hexane (LOD = 0.9 ppm, LOQ = 3.0 ppm), and heptane (LOD = 1.9 ppm, LOQ = 6.4 ppm). A natural gas sample separation illustrated method applicability, whereby the DPGM produced a signal enhancement (SE) of 30 for isopentane, where SE is defined as the height of the tallest 2D peak in the modulated chromatogram for the analyte divided by the height of the unmodulated 1D peak. The 30-fold SE resulted in about a 10-fold improvement in the signal-to-noise ratio (S/N) for isopentane. Additional versatility of the baseline correction method for more complicated samples was demonstrated for an unleaded gasoline sample, which enabled the detection (and visual appearance) of trace components.
科研通智能强力驱动
Strongly Powered by AbleSci AI