Advancing hemoglobinopathy screening with Raman spectroscopy and machine learning

Sara Abbasi; Mehdi Feizpour; Ilse Weets; Qing Liu; Hugo Thienpont; Francesco Ferranti; Heidi Ottevaere

doi:10.1117/12.3014432

20 June 2024 Advancing hemoglobinopathy screening with Raman spectroscopy and machine learning

Sara Abbasi, Mehdi Feizpour, Ilse Weets, Qing Liu, Hugo Thienpont, Francesco Ferranti, Heidi Ottevaere

Proceedings Volume 13006, Biomedical Spectroscopy, Microscopy, and Imaging III; 1300604 (2024) https://doi.org/10.1117/12.3014432
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Hemoglobinopathies are among the most common inherited diseases worldwide, affecting approximately 7% of the global population. Despite advances in the standardization and harmonization of methods for HbA1c determination, an increasing number of hemoglobinopathies cause false HbA1c results. One of the common techniques for screening hemoglobinopathies is through high-performance liquid chromatography (HPLC) separation, followed by UV–VIS detection. Although UV–VIS can quantify the hemoglobin fractions, it is unable to identify them. In this study, we use Raman spectroscopy to study the fingerprint spectra of hemoglobin fractions based on which the fractions can be identified. To evaluate the potential of Raman spectroscopy in identifying these fractions, we utilize a range of commercially available hemoglobin fractions, including fetal hemoglobin. We automate the classification process with machine learning approaches such as support vector machines (SVM), fully connected neural networks (NN), k-Nearest Neighbors (KNN), Decision Trees (DT), and Bernoulli Naive Bayes (BNB). These models are fine-tuned and optimized to classify the hemoglobin fractions and achieve test accuracies of 98.2% and 98.5%, respectively. Our research highlights the potential of Raman spectroscopy as an identification tool when combined with HPLC.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sara Abbasi, Mehdi Feizpour, Ilse Weets, Qing Liu, Hugo Thienpont, Francesco Ferranti, and Heidi Ottevaere "Advancing hemoglobinopathy screening with Raman spectroscopy and machine learning", Proc. SPIE 13006, Biomedical Spectroscopy, Microscopy, and Imaging III, 1300604 (20 June 2024); https://doi.org/10.1117/12.3014432

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