Date of Award
Spring 5-22-2021
Document Type
Honors Project
University Scholars Director
Dr. Christine Chaney
First Advisor/Committee Member
Dr. Karisa Pierce
Keywords
environmental monitoring, chemometrics
Abstract
Polycyclic aromatic hydrocarbons (PAHs) constitute a diverse class of highly toxic, ubiquitous environmental pollutants, and are thus of high interest in environmental monitoring and regulation. In this study, biliary samples of English soles Parophrys vetulus and smallmouth bass Micropterus dolomieu from Seattle waterways, and chum salmon Onchorynchus keta from the north Pacific Ocean were analyzed by high performance liquid chromatography with fluorescence detection (HPLC-FLD) to gauge PAH exposure. Samples were profiled in three broad molecular weight categories, to capture naphthalene-like (NAPH), phenanthrene-like (PHEN), and benzo[a]pyrene-like (BAP) metabolites. While quantification was not achieved for the chum salmon, the semi-quantitative measurements of biliary PAH metabolites in English soles and smallmouth bass revealed differences in exposure between the two species. The fish also exhibited generally lower levels of BAP than NAPH and PHEN. Principal component analysis (PCA) of the bile data was able to capture differences in chromatogram profiles between all three species for each PAH metabolite group. Finally, the PAH metabolite concentrations of the smallmouth bass were modeled and predicted using partial least-squares (PLS) regression models applied to their HPLC-FLD chromatogram data. The leave-one-out cross validation models were able to make fairly accurate predictions of BAP (R2 = 0.9483) and PHEN (R2 = 0.9394) concentrations but performed slightly worse with the NAPH data (R2 = 0.8944). These results indicate the potential for automated chemometric screening of bile data to determine PAH contamination in fish.
Streaming Media
Recommended Citation
Padilla, Joshua C., "Modeling biliary polycyclic aromatic hydrocarbon metabolites in fish using high performance liquid chromatography with fluorescence detection, principal component analysis, and partial least-squares analysis" (2021). Honors Projects. 121.
https://digitalcommons.spu.edu/honorsprojects/121
Copyright Status
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Additional Rights Information
Copyright held by author.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
A project submitted in partial fulfillment of the requirements of the University Scholars Honors Program.