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.
The automation of spectral classification tasks has made machine learning models essential analytical tools. However, the complexity of hyperparameter tuning limits the practical use, particularly for novices. This study applies these classifiers to identify bacteria using surface-enhanced Raman spectroscopy (SERS), offering a rapid and non-invasive alternative to the gold standard, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). An evolutionary algorithm was employed to optimize the hyperparameters of 10 machine learning models. We found the topperforming model for the classification of the SERS spectra of E. coli and S. pneumoniae water suspensions. This approach yielded a test accuracy of 95.8%, 100%, 100% when using the Bernoulli Naïve Bayes, Support Vector Machine, and Multilayer Perceptron models, respectively. This demonstrates the potential of self-optimizing machine learning models as accessible analytical tools for diverse classification tasks in biophotonics. This automated approach extends to identify various samples and data structures, not just pathogens’ spectra.
Hemoglobinopathies are the most common genetic disorders caused by a mutation in the genes encoding for one of the globin chains and leading to structural (hemoglobin [Hb] variants) or quantitative defects (thalassemias) in hemoglobin. Early diagnosis and characterization of hemoglobinopathies are essential to avoid severe hematological consequences in the offspring of healthy carriers of a mutation. Despite being extensively studied, hemoglobinopathies continue to provide a diagnostic challenge. Sickle-cell hemoglobin (HbS) is the most common and clinically significant hemoglobin variant among all Hb variants. To overcome the challenge of diagnosing Hb variants, we propose the use of Surface-Enhanced Raman Spectroscopy (SERS). SERS is a powerful label-free tool for providing fingerprint structural information of analyses. It can rapidly generate the spectral signature of samples. This study investigates the structural differences between HbS and normal Hb using gold nanopillar SERS substrates with a leaning effect. The SERS spectra of Hb variants showed subtle spectral differences between HbS and normal Hb located in the valine (975 cm-1) and glutamic acid (1547 cm-1) band, reflecting the amino acid substitution in the HbS β-globin chain. We also automated the identification of HbS and normal Hb with principal component analysis (PCA) combined with support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, leading to an accuracy of 98% and 96%, respectively. This study demonstrated that SERS can provide a fast, highly sensitive, noninvasive, and accurate detection module for the diagnosis of Sickle-cell disease and potentially other hemoglobinopathies.
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