ORCID
https://orcid.org/0000-0003-1928-7913
Date of Award
Fall 2024
Language
English
Embargo Period
10-16-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
College/School/Department
Department of Chemistry
Program
Chemistry
First Advisor
Igor K. Lednev
Second Advisor
Alexander Shekthman
Third Advisor
Jeremy Feldblyum
Committee Members
Entesar Al-Hetlani
Keywords
Raman Spectroscopy, Machine Learning, Medical Diagnostics, PFAS Environmental Contamination, Species Authentication, Forensic Analysis
Subject Categories
Analytical Chemistry | Data Science | Multivariate Analysis
Abstract
There is an urgent need for rapid and on-site analytical methods to address various environmental and forensic challenges. This dissertation explores the application of Raman spectroscopy combined with machine learning for three critical areas: evaluating perfluoroalkyl or polyfluoroalkyl substances (PFAS) exposure in wildlife, authenticating tuna species in the seafood industry, identifying body fluids at crime scenes, and medical diagnostics.
First, Raman spectroscopy and support vector machine discriminant analysis (SVMDA) were employed to detect PFAS levels in the blood plasma of smallmouth bass from two different tributaries, rivers, or streams that flow into larger bodies of water. The method achieved 100% accuracy in differentiating between samples containing either low or high PFAS concentrations, highlighting its potential as a diagnostic tool for fish health.
Second, the capability of Raman spectroscopy to authenticate three commonly sold tuna species—yellowfin, bluefin, and escolar was assessed. The developed method accurately identified the species using partial least squares discriminant analysis (PLSDA), demonstrating its value as a quality control tool in the seafood industry to prevent fraudulent labeling.
Third, Raman spectroscopy combined with Random Forest machine learning was utilized to identify six main body fluids and distinguish them from 49 potential environmental interferences that may be discovered at crime scenes. The method achieved 100% accuracy in external validation, indicating its effectiveness as a rapid, non-destructive technique for body fluid detection, which is crucial for criminal investigations.
Lastly, the detection of Alzheimer’s disease (AD) was explored using a novel screening method combining Raman spectroscopy with deep learning, specifically Artificial Neural Networks (ANNs), to analyze blood plasma. The ANN model demonstrated high accuracy, sensitivity, and specificity in distinguishing between moderate AD patients and healthy controls. This integrative approach offers a promising, non-invasive, rapid, and accurate screening method for early AD detection, potentially enhancing diagnostic capabilities and paving the way for earlier and more effective therapeutic interventions.
Overall, this research showcases the versatility and efficacy of Raman spectroscopy and machine learning in providing reliable, accurate, and rapid analysis across different fields. Thus offering significant advancements in environmental monitoring, food authentication, and forensic science.
License
This work is licensed under the University at Albany Standard Author Agreement.
Recommended Citation
Perez Almodovar, Luis, "Innovative Solutions in Medical Diagnostics, Seafood Industry, Environmental, and Forensic applications through Integrating Raman Spectroscopy and Machine Learning" (2024). Electronic Theses & Dissertations (2024 - present). 54.
https://scholarsarchive.library.albany.edu/etd/54