College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan 750004, China.
Ningxia Food Testing Institute, Yinchuan 750004, China.
J Chromatogr A. 2019 Sep 13;1601:300-309. doi: 10.1016/j.chroma.2019.04.065. Epub 2019 Apr 24.
Gas chromatography-mass spectrometry (GCMS) has been extensively used in complex sample analysis for the high-throughput characterization of volatile and semivolatile compounds. However, the accurate extraction of compound information remains challenging. Here, we present a combined algorithm strategy for GCMS data analysis to accurately screen metabolites across groups. First, chromatographic peaks in a total ion chromatogram (TIC) are extracted by using a Gaussian smoothing strategy and aligned on the basis of their mass spectra by a dynamic programing algorithm. The aligned TIC peaks are then registered into a component list table by applying a nearest-neighbor clustering algorithm. Significantly expressed TIC peaks among groups are screened through statistical analysis, such as ANOVA. Second, a chemometric method of multivariate curve resolution-alternating least squares for the peak resolution of the screened TIC peaks is utilized to retrieve the chromatographic and mass spectral profiles of coeluted components. The developed strategy is employed for the analysis of standard and complex plant sample datasets. Results indicate that our methodology is comparable with several state-of-the-art methods that are widely used in GC-MS-based metabolomics.
气相色谱-质谱联用（GCMS）技术已广泛应用于复杂样品分析中，用于高通量鉴定挥发性和半挥发性化合物。然而，化合物信息的准确提取仍然具有挑战性。在此，我们提出了一种用于 GCMS 数据分析的组合算法策略，以准确筛选组间的代谢物。首先，采用高斯平滑策略提取总离子色谱（TIC）中的色谱峰，并通过动态编程算法根据其质谱图进行对齐。然后，通过应用最近邻聚类算法，将对齐的 TIC 峰注册到成分列表表中。通过统计分析（如 ANOVA）筛选组间有显著差异表达的 TIC 峰。其次，利用多元曲线分辨-交替最小二乘法对筛选出的 TIC 峰进行峰解析的化学计量学方法，以检索共洗脱成分的色谱和质谱谱图。该策略用于分析标准和复杂植物样品数据集。结果表明，我们的方法与几种广泛应用于基于 GC-MS 的代谢组学的最先进方法具有可比性。
