Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical features-colloquially termed a chemical fingerprint-that can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92-100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located similar to 3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.

化学法医分析的一个常见目标是选择一组诊断性化学特征（通俗地称为化学指纹），以预测新样本中某种来源的存在。然而，大多数已开发的化学指纹分析流程本质上是定性的。在此，我们报道一种定量机器学习流程。从五个化学来源收集了抓取样本（n = 51），这些来源包括农业径流、源头水、牲畜粪便、（城市郊区）径流和城市污水。使用支持向量分类来选择最能区分每个来源与其他所有来源的前10、25、50和100个化学特征。所有来源的交叉验证平衡准确率为92 - 100%（n = 1000次迭代）。当在从四条当地小溪收集的样本中筛选每个来源的诊断特征时，除了在一条小溪的两个下游位置的污水外，所有来源的存在概率都很低。经过进一步调查，一个污水处理设施位于距离最近样本位置上游约3公里处。此外，使用模拟的计算机混合样本，该流程能够区分某些来源在10000倍稀释时的存在与否。这些结果有力地表明，该流程能够选择化学特征的诊断子集，可用于定量预测环境中痕量水平下各种来源的存在与否。