Research Group Environmental Organic Chemistry and Technology (EnVOC), Department of Sustainable Organic Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University , Coupure Links 653, B-9000 Ghent, Belgium.
Anal Chem. 2015 Feb 17;87(4):2170-7. doi: 10.1021/ac503426k. Epub 2015 Jan 26.
Modern high-resolution mass spectrometry (HRMS) enables full-spectrum trace level analysis of emerging environmental organic contaminants. This raises the opportunity for post-acquisition suspect screening when no reference standards are a priori available. When setting up a conventional screening identification train based on successively different identification criteria including mass error and isotope fit, the false negative rate typically accumulates upon advancing through the decision tree. The challenge is thus to elaborate a well-balanced screening, in which the different criteria are equally stringent, leading to a controllable number of false negatives. Presented is a novel suspect screening approach using liquid-chromatography Orbitrap HRMS. Based on a multivariate statistical model, the screening takes into account the accurate mass error of the mono isotopic ion and up to three isotopes, isotope ratios, and a peak/noise filter. As such, for the first time, controlling the overall false negative rate of the screening algorithm to a desired level (5% in this study) is achieved. Simultaneously, a well-balanced identification decision is guaranteed taking the different identification criteria as a whole in a holistic statistical approach. Taking into account 1, 2, and 3 isotopes decreases the false positive rate from 22, 2.8 to <0.3%, but the cost of increasing the median limits of identification from 200, 2000 to 2062 ng L(-1), respectively, should also be considered. As proof of concept, 7 biologically treated wastewaters were screened toward 77 suspect pharmaceuticals resulting in the indicative identification of 25 suspects. Subsequently obtained reference standards allowed confirmation for 19 out of these 25 pharmaceutical contaminants.
现代高分辨率质谱（HRMS）能够对新兴环境有机污染物进行全谱痕量分析。这为在没有先验参考标准的情况下进行采集后可疑物筛查提供了机会。在建立基于不同识别标准（包括质量误差和同位素拟合）的传统筛选鉴定方案时，随着决策树的推进，假阴性率通常会累积。因此，挑战在于精心设计一个平衡的筛选方案，其中不同的标准同样严格，从而导致可控制数量的假阴性。本文提出了一种使用液相色谱-轨道阱 HRMS 的新型可疑物筛选方法。该筛选方法基于多元统计模型，考虑了单同位素离子的精确质量误差以及多达三个同位素、同位素比和峰/噪声滤波器。因此，首次实现了将筛选算法的总假阴性率控制在预期水平（本研究中为 5%）。同时，通过整体统计方法将不同的识别标准作为一个整体进行综合考虑，从而保证了平衡的识别决策。考虑 1、2 和 3 个同位素可将假阳性率从 22%、2.8%降至<0.3%，但需要考虑的是，识别的中位数限值分别从 200、2000 增加至 2062ng/L。作为概念验证，对 7 种经过生物处理的废水进行了 77 种可疑药物的筛查，结果指示性鉴定出 25 种可疑物。随后获得的参考标准允许对这 25 种药物污染物中的 19 种进行确认。
