基于稀疏模型的气相色谱-质谱数据解析及其在严重重叠峰解析中的应用

提出一种气相色谱-质谱（GC-MS）数据解析算法。以色谱峰顶点处的质谱作为待测谱,在谱库中检索一定量相关参考谱,求解关于各纯组分色谱响应值的方程。质谱检索采取分步策略,先利用质谱碎片规律建立高速索引进行粗选,然后使用强峰高概率出峰准则和耐挤压性准则排除无关质谱。为求解色谱响应值方程,提出基于稀疏模型的回归算法,相比传统算法,稀疏算法利于提取待测谱的主要结构,避免"过拟合"。实验结果表明,该质谱检索算法具有较高的精度和规模较小的剩余参考谱集,而所提稀疏模型算法可有效解析严重重叠峰。该算法可作为GC-MS重叠峰解析,特别是严重重叠峰解析的一种有效解决方案。

Gas chromatography-mass spectrometry data analysis algorithm based on sparse model and its application in resolving severely overlapped peaks

A Gas chromatography-mass spectrometry (GC-MS) data analysis algorithm is proposed. The mass spectrum at the top of the chromatographic peak is the spectrum to be solved. A certain amount of related reference spectra is retrieved from the spectral library, then, the equation of the chromatographic response value of each pure component is solved. A step by step strategy is used for the mass spectra retrieval. Firstly, an efficient indexing technology is used for rough selection, then, the "strong peak out with high probability" and the "extrusion" rules are used to exclude more unrelated mass spectra. A regression algorithm based on a sparse model is proposed to solve the equation of the chromatographic response value. Compared with the traditional algorithm, this algorithm can extract the main structure of the spectrum to be solved, and avoid over-fitting. The experimental results show that the proposed algorithm has a higher accuracy and smaller residual reference spectrum set, and the sparse model achieves satisfactory experimental results in the analysis of severely overlapped peaks. The proposed method provides an effective solution for resolving overlapped peaks, especially severely overlapped peaks, in GC-MS data.