Eftekhari Ali, Parastar Hadi
Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran.
Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran.
J Chromatogr A. 2016 Sep 30;1466:155-65. doi: 10.1016/j.chroma.2016.09.016. Epub 2016 Sep 12.
The present contribution is devoted to develop multivariate analytical figures of merit (AFOMs) as a new metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS). In this regard, new definition of sensitivity (SEN) is extended to GC×GC-MS data and then, other multivariate AFOMs including analytical SEN (γ), selectivity (SEL) and limit of detection (LOD) are calculated. Also, two frequently used second- and third-order calibration algorithms of multivariate curve resolution-alternating least squares (MCR-ALS) as representative of multi-set methods and parallel factor analysis (PARAFAC) as representative of multi-way methods are discussed to exploit pure component profiles and to calculate multivariate AFOMs. Different GC×GC-MS data sets with different number of components along with various levels of artifacts are simulated and analyzed. Noise, elution time shifts in both chromatographic dimensions, peak overlap and interferences are considered as the main artifacts in this work. Additionally, a new strategy is developed to estimate the noise level using variance-covariance matrix of residuals which is very important to calculate multivariate AFOMs. Finally, determination of polycyclic aromatic hydrocarbons (PAHs) in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC-MS is considered as real case to confirm applicability of the proposed metric in real samples. It should be pointed out that the proposed strategy in this work can be used for other types of comprehensive two-dimensional chromatographic (CTDC) techniques like comprehensive two dimensional liquid chromatography (LC×LC).
本论文致力于开发多变量分析品质因数（AFOMs），作为一种使用全二维气相色谱 - 质谱联用（GC×GC - MS）评估定量测量的新指标。在此方面，灵敏度（SEN）的新定义被扩展到GC×GC - MS数据，然后计算其他多变量AFOMs，包括分析灵敏度（γ）、选择性（SEL）和检测限（LOD）。此外，还讨论了多变量曲线分辨率交替最小二乘法（MCR - ALS）这一常用的二阶校准算法（作为多集方法的代表）和平行因子分析（PARAFAC）这一常用的三阶校准算法（作为多向方法的代表），以利用纯组分谱图并计算多变量AFOMs。模拟并分析了具有不同组分数目以及不同程度伪峰的不同GC×GC - MS数据集。噪声、两个色谱维度上的洗脱时间偏移、峰重叠和干扰被视为这项工作中的主要伪峰。此外，还开发了一种使用残差方差 - 协方差矩阵估计噪声水平的新策略，这对于计算多变量AFOMs非常重要。最后，将通过GC×GC - MS分析重质燃料油（HFO）芳烃馏分中的多环芳烃（PAHs）作为实际案例，以确认所提出的指标在实际样品中的适用性。需要指出的是，本工作中提出的策略可用于其他类型的全二维色谱（CTDC）技术，如全二维液相色谱（LC×LC）。
