Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

Abstract

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 chromatographymass 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 byproducts 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.

Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs

“…Association graphs have been traditionally used as a knowledge discovery tool [8] to discover and visualize association rules between variables, known and latent, in high-dimensional and large-scale data analysis. More recently, the idea of using graph-based visualization of associations between microbes in the microbiome was introduced in [9], and a graph-based visualization of chemical contaminants that co-occur in the raw instrument signal was introduced in [10]. In this work, we build upon the visualization in [9], [10] to introduce the idea of quantifying co-occurrence associations between ISMs in a graph setting.…”

“…More recently, the idea of using graph-based visualization of associations between microbes in the microbiome was introduced in [9], and a graph-based visualization of chemical contaminants that co-occur in the raw instrument signal was introduced in [10]. In this work, we build upon the visualization in [9], [10] to introduce the idea of quantifying co-occurrence associations between ISMs in a graph setting. The key motivation is to discover ISMs that co-occur across different subject groups to identify dominant ISMs in different locations and time, and then present these ISM associations in a graph-setting for expert interpretation.…”

Bioremediation of Soil Pollution by Polychlorinated Biphenyls in Solid Waste Dismantling

“…In a related study, McCarthy et al used a combination of statistical, signal processing, and graph-based informatics techniques to assess variability in polychlorinated biphenyl (PCB) data [3], providing graph-based visualizations that linked quantitative contamination Two complementary approaches to research. In addition, a peak-fitting technique based on L2 error minimization is used to autonomously calculate the amount present per PCB.…”

Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring

“…This work harnesses pre-processing techniques from recent work presented in McCarthy et al to interface with popular machine learning architectures to provide autonomously annotated signature profiles of different diverse geographic locations that are well known to have PCB contamination [ 2 ]. Our previous work analyzes individual MRM signals using signal processing techniques to identify and quantify the relative abundance of a certain congener present within the sample.…”