Abstract In this study we examine the application of a factor analysis technique for datasets recorded by the chemical ionization instrument Proton Transfer Reaction - Time-of-Flight - Mass Spectrometer (PTR-ToF-MS). Numerical simulations were carried out to test and optimize the performance of the factorization method Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). The simulations demonstrated that the choice of initial estimates for the factor analysis are crucial for the outcome of the factorization. For this purpose we describe a new method based on the Pearson correlation coefficient which, compared to the other tested methods, yields the best factorization results. Overlapping contributions of different substances to a certain mass bin and large uncertainties in the data deteriorate the performance of the factor analysis. The deconvolution technique was also applied to data obtained with a PTR-ToF-MS employed at a smog chamber. The response from single substances (fragmentation patterns) such as α-pinene, α-pinene oxide and pinonaldehyde as well as a more complex experiment involving the ozonolysis of α-pinene are investigated. For the single substances, the factorization method points to the presence of impurities which could be differentiated from the masses affiliated with the actual substances. The α-pinene ozonolysis experiment was analysed with the factorization technique and yielded four factors, one representing the decay of the reactant and three different products. The matrix factorization technique appeared to be a valuable tool allowing for a detailed analysis of complex mass spectra without the need of any prior information.

中文翻译：

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化学电离实验的因子分析：使用 PTR-ToF-MS 对 α-蒎烯进行臭氧分解的数值模拟和实验案例研究

摘要 在这项研究中，我们研究了因子分析技术对化学电离仪器质子转移反应 - 飞行时间 - 质谱仪 (PTR-ToF-MS) 记录的数据集的应用。进行了数值模拟以测试和优化分解方法多元曲线分辨率 - 交替最小二乘法 (MCR-ALS) 的性能。模拟表明，因子分析的初始估计值的选择对于因子分解的结果至关重要。为此，我们描述了一种基于 Pearson 相关系数的新方法，与其他测试方法相比，该方法可产生最佳的分解结果。不同物质对某个质量区间的重叠贡献和数据中的大不确定性会降低因子分析的性能。去卷积技术还应用于在烟雾室中使用 PTR-ToF-MS 获得的数据。研究了单一物质（碎片模式）的响应，例如 α-蒎烯、α-蒎烯氧化物和松香醛，以及涉及 α-蒎烯臭氧分解的更复杂实验。对于单一物质，因式分解法指出存在的杂质可以与实际物质的质量区分开来。α-蒎烯臭氧分解实验用分解技术进行分析，得到四个因子，一个代表反应物的衰变和三种不同的产物。矩阵分解技术似乎是一种有价值的工具，可以在不需要任何先验信息的情况下对复杂的质谱进行详细分析。