| 基于机器学习的直接电离质谱爆炸物检测方法 |
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| 基金项目:国家重点研发计划(2018YFC0807404)；云南省重点研发计划(2018BC011)；广州市番禺区创新创业领军团队(2017-R01-5)；宁波大学王宽诚幸福基金资助 |
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| 中文摘要:直接电离质谱系统在现场快速检测中的应用日益广泛，主要用于爆炸物、毒品、食品添加剂等的检测。然而，直接电离质谱系统中质谱信号波动大且同一浓度样品峰强呈现对数正态分布，严重影响了检出限附近低浓度样品的检测准确性。该研究将乙酰水杨酸(115个样品)作为爆炸物模拟物，利用介质阻挡放电离子源与质谱系统，研究了基于机器学习的直接电离质谱数据预处理和分类算法，以提高低浓度样品的检测准确率。对两种浓度为1 ng/mL的常见爆炸物样本(三硝基甲苯和硝酸铵分别为110、90个)及空白对照样本(366个)开展了应用实验。结果表明，与传统提取离子流方法和高斯混合模型方法相比，采用随机森林算法可将F_score从0.74、0.89提升至0.96，显著提高了检测准确率，且单个样本数据分析时间远少于0.1 s，满足实时检测需求。 |
| 中文关键词:直接电离质谱 爆炸物 机器学习 快速检测 对数正态分布 |
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| Detection of Explosives by Direct Ionization Mass Spectrometry Based on Machine Learning Algorithm |
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| Abstract:It is well known that direct ionization mass spectrometry(MS) has the advantages of few pretreatments,rapidness,and convenience,which is widely used in the fields of rapid detection of various substances,such as explosives,drugs,food additives,etc.However,for the direct ionization mass spectrometry,there exist large fluctuations in the signal intensities which usually obey the lognormal distributions.As a result,how to identify these signals and classify the samples becomes a real challenge.The common analysis methods for mass spectrum data include the extracted ion current(EIC) and Gaussian mixture model(GMM).Both of these analysis methods make only use of the peak intensity information,but ignoring the peak shape features such as peak position,full width at half maximum(FWHM) and correlation information among multi peaks.In addition,the detection accuracies of these methods also depend on the preset thresholds,which is usually a tedious work to optimize.However,machine learning unlike EIC and GMM could take advantage of all the characteristics of mass spectrum signals to improve the detection performance without a preset threshold.In this work,acetylsalicylic acid(115 samples) was adopted as an analog for explosives in the mass spectrometry detection.In order to improve the detection accuracy especially for dielectric barrier discharge ionization(DBDI) source and mass spectrometer in detection of the samples with low concentrations,the data preprocessing and classification algorithms based on EIC,GMM and machine learning of the direct ionization mass spectrometry were studied.Moreover,2,4,6-trinitrotoluene and ammonium nitrate(110,90 samples,respectively) with a concentration of 1 ng/mL,and blank samples(366 samples) under the same detecting conditions were investigated and analyzed.Results showed that the signal intensities of either the blank samples or the explosive samples with the same concentration,all obey the lognormal distributions,spanning several orders of magnitude.As set forth,the detection results based on the EIC and GMM were found all sensitive to the preset thresholds.However,the random forest(RF) algorithm could be used to improve the classification accuracy,compared with EIC and GMM methods.And the F_score numbers for EIC,GMM and RF are 0.74,0.89 and 0.96,respectively.In addition,with the same analysis methods,MS/MS could significantly reduce the background interference and improve the detection accuracy,compared with the full scan mass spectrometry.In practice,it usually takes 3-6 seconds to acquire the mass spectrum data for each sample.In this work,the analysis time for each single sample data is much less than 0.1 second,which means the algorithm could be adopted to a real time detection.In conclusion,direct ionization mass spectrometry combined with machine learning could meet the needs for rapid,real time and accurate detection of explosives,which exhibits good application prospects. |
| Key Words:direct ionization mass spectrometry explosives machine learning rapid detection lognormal distributions |
| 引用本文：叶倩,洪欢欢,周峰,郭荣,李刚强,闻路红,陈腊.基于机器学习的直接电离质谱爆炸物检测方法[J].分析测试学报,2021,40(4):589-595. |
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