Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples.
Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples.
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信号处理方法解释空气中样品中的多氯联苯。
DOI：
10.1109/access.2020.3013108
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Hornbuckle KC
中科院分区：
文献类型：
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作者：
McCarthy RA;Gupta AS;Kubicek B;Awad AM;Martinez A;Marek RF;Hornbuckle KC
这项跨学科工作的主要贡献是一个强大的计算框架，可以自动发现和量化已知（目标）和潜在未知（非目标）有毒工业空气污染物之间以前未知的关联。在这项工作中，结合统计、信号处理和基于图形的信息学技术来评估多氯联苯 (PCB) 数据的变异性，以解释来自气相色谱-质谱 (GC/MS/MS) 数据集的原始仪器信号。具体来说，自适应信号处理文献中的最小均方技术被扩展到检测和分离原始仪器信号中的共洗脱（重叠）峰。提供基于图形的可视化，它连接了定量污染研究的两种互补方法：（i）峰值识别目标分析（将数据分析限制为少数众所周知的化合物）和（ii）与特定化合物无关的化学计量分析（统计大规模数据分析）。此外，采用基于 L2 误差最小化的峰值拟合技术来自主计算每个 PCB 的数量，归一化均方误差为 -18.4851 dB。通过主成分分析开发了已知和未知化合物之间关联的基于图形的可视化，并实现和比较了模糊 c 均值 (FCM) 和 k 均值聚类技术。使用 GC/MS/MS 对单个 PCB 进行分析的 150 个空气样本与仅对已知（目标）PCB 进行分析的传统仅目标技术进行了比较，从而比较了这些方法的效率。采用参数优化技术来评估 PCB 信号与 10 个潜在源信号的相对贡献，这些信号代表了 Aroclors 历史制造的遗留特征以及作为颜料和聚合物制造产品生产的现代 PCB 来源。许多样品中都发现了 Aroclors 1232、1254、1016 和 1221 以及非 Aroclor 3、3'、二氯联苯 (PCB 11)，作为描述从伊利诺伊州芝加哥收集的空气样品中 PCB 混合物的独特源信号。
The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatography-mass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal. A graph-based visualization is provided which bridges two complementary approaches to quantitative pollution studies: (i) peak-cognizant target analysis (limits data analysis to few well-known compounds) and (ii) chemometric analysis (statistical large-scale data analysis) that is agnostic of specific compounds. Further, peak fitting techniques based on L2 error minimization are employed to autonomously calculate the amount of each PCB present with a normalized mean square error of −18.4851 dB. Graph-based visualization of associations between known and unknown compounds are developed through principal component analysis and both fuzzy c-means (FCM) and k-means clustering techniques are implemented and compared. The efficiency of these methods are compared using 150 air samples analyzed for individual PCBs with GC/MS/MS against traditional target-only techniques that perform analysis across only the known (target) PCBs. Parameter optimization techniques are employed to evaluate the relative contribution of PCB signals against ten potential source signals representing legacy signatures from historical manufacture of Aroclors and modern sources of PCBs produced as by products of pigment and polymer manufacturing. Aroclors 1232, 1254, 1016, and 1221 as well as non-Aroclor 3, 3’, dichlorobiphenyl (PCB 11) were found in many of the samples as unique source signals that describe PCB mixtures in air samples collected from Chicago, IL.