Abstract

Robust Independent Modelling of Class Analogy (RSIMCA) was applied to classify over 2,800 Passaic River sediment samples into seven groups or not assigned to any group after an initial screening of a 3,255 sample dataset. This multivariate statistical output was compressed from seven latent dimensions into two interpretable dimensions using t-Distributed Stochastic Neighbor Embedding (t-SNE) graphics. Polytopic Vector Analysis (PVA) was then used to identify distinct source end-members based on PCDD/F characteristics of the classified samples. Among several advantages, the integrated chemometrics approach 1) applies emerging data visualization tools in this “Big Data” era to retain the fidelity of high-dimensional data attributes of a chemical dataset spanning over two decades of sample collection; 2) employs a classification technique undisturbed by compositional outliers yet tracks those for subsequent investigations; 3) provides an intuitive reduced-dimensional data visualization map for the PVA mixing polytope solution; 4) fills a data gap in the contextual inventory of PCDD/F source dynamics in a complex river system; and 5) serves as a backdrop for further forensics investigations of the finer structure of less dominant point sources and potential upland source end-members in sediments. This tiered chemometrics strategy provides a strong weight-of-evidence approach to the interpretation of sediment data.

摘要

在初步筛选3,255个样本数据集之后，采用了稳健的类比独立建模（RSIMCA）将2,800多个Passaic River沉积物样本分为7组或未分配给任何组。使用t-分布随机邻域嵌入（t-SNE）图形，将该多元统计输出从七个潜在维压缩为两个可解释维。然后，基于分类样本的PCDD / F特性，使用多主题向量分析（PVA）来识别不同的来源最终成员。集成化学计量学方法具有以下几个优点：1）在“大数据”时代应用新兴的数据可视化工具，以保留跨越二十多年样本收集的化学数据集的高维数据属性的保真度；2）采用不受组成成分异常影响的分类技术，但仍跟踪那些以进行后续研究；3）为PVA混合多面体解决方案提供直观的降维数据可视化图；4）填补了复杂河流系统中PCDD / F源动态上下文清单中的数据缺口；5）为进一步的法证研究提供了背景，以进一步调查沉积物中不太占优势的点源和潜在的山地源最终成员的精细结构。这种分层的化学计量学策略为解释沉积物数据提供了强有力的证据权重方法。4）填补了复杂河流系统中PCDD / F源动态上下文清单中的数据缺口；5）为进一步的法证研究提供了背景，以进一步调查沉积物中不太占优势的点源和潜在的山地源最终成员的精细结构。这种分层的化学计量学策略为解释沉积物数据提供了强有力的证据权重方法。4）填补了复杂河流系统中PCDD / F源动态上下文清单中的数据缺口；5）为进一步的法证研究提供了背景，以进一步调查沉积物中不太占优势的点源和潜在的山地源最终成员的精细结构。这种分层的化学计量学策略为解释沉积物数据提供了强有力的证据权重方法。