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Abstract

The accurate detection and quantification of ethanol and acetic acid in biological systems remain a major challenge due to overlapping spectral signatures with water and other biomolecules. This thesis investigates the application of diffuse reflectance spectrophotometry in the short-wave infrared region, combined with bio-impedance analysis, to improve discrimination and quantification of ethanol and acetic acid in liquid and biological models. A comprehensive set of experiments was performed using water, human serum, artificial interstitial fluid, sheep blood, and intralipid as representative matrices, alongside gelatin phantoms and porcine skin to simulate tissue environments.

The optical analysis demonstrated strong predictive ability under controlled conditions. In water, polynomial regression achieved high determination coefficients with R² = 0.884 for ethanol and R² = 0.979 for acetic acid, with low errors (RMSE = 58.1 mg/dL and 1.49 mg/dL, respectively). In human serum, predictive accuracy remained robust (R² = 0.871 for ethanol, 0.898 for acetic acid), though errors increased due to protein interference. Artificial interstitial fluid showed greater variability, particularly for ethanol (R² = 0.790, RMSE = 78.3 mg/dL), while acetic acid maintained strong performance (R² = 0.885, RMSE = 3.5 mg/dL). In sheep blood, haemoglobin absorption and cellular scattering reduced predictive strength, with ethanol showing R² = 0.729 and acetic acid R² = 0.793. Intralipid, used as a scattering phantom, proved most challenging, with ethanol prediction deteriorating severely (R² = 0.447, RMSE = 123.5 mg/dL), while acetic acid retained moderate accuracy (R² = 0.799, RMSE = 4.6 mg/dL). Across all matrices, acetic acid consistently outperformed ethanol, reflecting the distinctiveness of its carbonyl absorption bands relative to overlapping O–H features.

Gelatin phantoms extended the investigation into tissue-analogue models with controlled scattering and absorption. Using partial least squares regression, ethanol achieved an R² of 0.967 with an RMSEP of 30.9 mg/dL, while acetic acid reached an R² of 0.950 with an RMSEP of 2.3 mg/dL. These results confirm that phantoms not only replicate tissue-like complexity but also support highly accurate prediction. Experiments on porcine skin further validated the approach in a true biological substrate, where both ethanol and acetic acid remained detectable with statistically significant correlations despite the increased scattering and heterogeneity of ex vivo tissue.

Complementary bio-impedance analysis was applied across all media and tissue models to characterise dielectric responses to ethanol and acetic acid. Principal component analysis revealed distinct clustering patterns associated with solute composition, confirming that impedance-based methods provide an orthogonal and supportive modality to optical measurements. The combined use of spectroscopy and bio-impedance improved separation of ethanol from acetic acid, highlighting the potential of integrated sensing platforms.

The results demonstrate that while diffuse reflectance spectroscopy alone achieves high accuracy in simple aqueous systems, predictive performance diminishes with increasing biological complexity. Nevertheless, statistically significant correlations were retained across all tested models, with acetic acid consistently yielding higher predictive fidelity. The integration of bio-impedance, gelatin phantoms, and porcine skin validation strengthened the robustness of the approach. Together, these findings provide a foundation for multimodal sensing technologies capable of quantifying ethanol and acetic acid in physiologically relevant environments, with implications for real-time intoxication monitoring and clinical diagnostics.

Publication Type:	Thesis (Doctoral)
Subjects:	R Medicine R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology > Department of Engineering School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

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