质谱学报2018，Vol.39Issue(5)：513-523,11.DOI:10.7538/zpxb.2017.0181
数据预处理技术和机器学习方法在质子转移反应质谱中的应用
Review of Data Pre-processing Techniques and Machine Learning in PTR-MS
摘要
Abstract
Proton transfer reaction mass spectrometry (PTR-MS) is an analytical technique developed for the detection of volatile organic compounds (VOCs) .It offers many advantages for VOCs analysis, such us ultra-low detection limits, very short response, no sample preparation, real-time analysis, etc.It has been applied in atmospheric chemistry environmental chemistry, food and biomedical.With the expansion of applications of PTR-MS and the increase of sample types, how to analyze the features from complexdata and find out the inherent rules have put forward higher requirements on the processing ability of the algorithm.Therefore, this paper discussed the data preprocessing techniques and machine learning methods.Firstly, we summarized the data preprocessing methods with PTR-MS features.The data generated by the instrument cannot be directly used for statistical analysis, otherwise it will bring great error.Therefore, data pre-processing is an essential step.It includes several steps, such as denoising, normalization, and concentration calculation.The purpose of preprocessing is to get data matrix for subsequent analysis.Next, we focused on the use of machine learning methods for data analysis in PTR-MS, and the advantages of this techniques would be demonstrated as well as the drawbacks.The machine learning method can be divided into two parts.Usually unsupervised methods are common choices for initial data analysis.For further analysis and a priori knowledge, a supervised analysis would be a better way.These methods use this knowledge to learn rules and patterns related to classes in the data, and then use these rules and patterns to predict classes in newly acquired data sets.The main goal of all surveillance techniques is to find the relationship between the predictor (VOC) matrix and the response vector.In general, the combination of the unsupervised and supervised methods is a good idea.PTR-MS is a soft ionization technique, however, the presence of a few fragments will still cause great difficulties in spectral analysis, especially for unknown mixtures, which is the main reason why spectral analysis of PTR-MS differs from other mass spectrometry methods.Perhaps, the data fusion of different platform instruments and different samples will be a good way to solve this problem.关键词
质子转移反应质谱 (PTR-MS)/挥发性有机物 (VOCs)/数据预处理/机器学习Key words
proton transfer reaction mass spectrometry (PTR-MS)/volatile organic compounds (VOCs)/data pre-processing/machine learning分类
化学化工引用本文复制引用
孙运,陈一冰,褚美娟,蒋学慧,汪曣,郭冰清..数据预处理技术和机器学习方法在质子转移反应质谱中的应用[J].质谱学报,2018,39(5):513-523,11.基金项目
国家重大科学仪器设备开发专项:质子转移反应质谱仪器研制及应用示范(2013YQ090875) （2013YQ090875）
天津市应用基础与前沿技术研究计划:用于环境监测的离子漏斗-质子转移反应离子源质谱研究(15JCYBJC23300)资助 （15JCYBJC23300）
