Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis
Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis
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DOI：
10.1021/acs.analchem.7b00741
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发表时间：
2017-06-20
影响因子：
7.4
通讯作者：
Sancho, Juan V.
中科院分区：
文献类型：
--
作者：
Bijlsma, Lubertus;Bade, Richard;Sancho, Juan V.
通过离子迁移率高分辨率质谱法获得的碰撞截面（CCS）值的使用增加了第三个维度（保留时间和精确质量），以帮助识别化合物。然而，其效用受到目前可用的实验CCS值数量的限制。这项工作表明了人工神经网络（ANN）的潜力，用于预测的CCS值的农药。预测，基于8个软件选择的分子描述符，优化使用CCS值的205个小分子和验证使用一组131种农药。质子化分子的所有CCS值的95%的相对误差在6%以内，导致中值相对误差小于2%。为了证明CCS预测的潜力，该策略应用于菠菜样品。它显著提高了对可疑和非目标农药的初步鉴定的信心。
The use of collision cross-section (CCS) values obtained by ion mobility high-resolution mass spectrometry has added a third dimension (alongside retention time and exact mass) to aid in the identification of compounds. However, its utility is limited by the number of experimental CCS values currently available. This work demonstrates the potential of artificial neural networks (ANNs) for the prediction of CCS values of pesticides. The predictor, based on eight software-chosen molecular descriptors, was optimized using CCS values of 205 small molecules and validated using a set of 131 pesticides. The relative error was within 6% for 95% of all CCS values for protonated molecules, resulting in a median relative error less than 2%. In order to demonstrate the potential of CCS prediction, the strategy was applied to spinach samples. It notably improved the confidence in the tentative identification of suspect and nontarget pesticides.