Contribution to the Identification and Quantitation of Aroclor Mixtures by Least-Squares Analysis of Gas Chromatographic Data

Corbella, R.; Rodríguez‐Delgado, Miguel Ángel; Montelongo, F. J. García

doi:10.1093/chromsci/36.7.372

Cited by 1 publication

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“…This enables robust joint and compound-cognizant interpretation of target and nontarget peaks from the raw signal. 2) IDENTIFYING SOURCES -Analysis to identify sources can be done using linear regression models [9]- [13] or even positive matrix factorization (PMF) [10]- [13], [52]- [55]. These algorithms attempt to solve for the various percentages of sources through linear equations.…”

Section: ) Modeling Gc/ms/ms -Recent Computational Tech-mentioning

confidence: 99%

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

et al. 2020

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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.

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