Publication Date
12-2025
Date of Final Oral Examination (Defense)
10-17-2025
Type of Culminating Activity
Dissertation
Degree Title
Doctor of Philosophy in Computing
Department
Computer Science
Supervisory Committee Chair
Timothy Andersen, Ph.D.
Supervisory Committee Member
Owen M. McDougal, Ph.D.
Supervisory Committee Member
Edoardo Serra, Ph.D.
Abstract
Traditional wet chemistry methods for milk protein analysis are accurate but time-consuming and unsuitable for real-time monitoring. Infrared (IR) spectroscopy offers a rapid, non-destructive alternative; however, the complex, overlapping signals in spectral data require chemometric approaches to deconvolute and extract meaningful information. This dissertation develops a chemometric framework that integrates IR spectroscopy with machine learning for rapid, accurate quantification of milk proteins.
The research is guided by Process Analytical Technology (PAT) principles; emphasizing real-time monitoring, chemometric analysis, and process control, but applied retrospectively to laboratory and industrial datasets. The framework achieves high predictive accuracy through systematic investigation of intelligent calibration sample selection, spectral preprocessing, wavenumber selection, and model optimization.
Chapter 2 demonstrates an optimized SVR chemometric model for MIR-based whey protein analysis (β-lactoglobulin R^2_P = 0.928; α-lactalbumin R^2_P = 0.927). Chapter 3 introduces automated preprocessing algorithms using Bayesian optimization, achieving R^2_P values above 0.90 for all evaluated milk components, including true protein, fat, lactose, and total solids, using the MIR dataset from Agropur. Chapter 4 validates the framework using an industrial NIR dataset from Company A (casein R^2_P = 0.994; true protein R^2_P = 0.997). Although the primary focus of this dissertation is on proteins, the framework also demonstrates strong performance for other components, including lactose, total solids, and fat.
The primary contributions are: (1) automated spectral preprocessing to eliminate subjective parameter selection; (2) dataset-specific automated wavenumber selection; and (3) development of a hybrid ensemble modeling architecture.
DOI
https://doi.org/10.18122/td.2442.boisestate
Recommended Citation
Babatunde, Habeeb Abolaji, "Qualitative and Quantitative Analysis of Milk Protein Using Chemometrics Coupled with Infrared Spectroscopy" (2025). Boise State University Theses and Dissertations. 2442.
https://doi.org/10.18122/td.2442.boisestate