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.
Surface-enhanced Raman Spectroscopy (SERS) is a powerful optical sensing technique widely used in fields like medicine, microbiology, and environmental analysis. Planar SERS substrates are preferred for their ease of integration into lab-on-chip systems and superior reproducibility. Substrate performance is assessed using metrics like enhancement factor, sensitivity, and reproducibility. Many experimental and post-processing factors can influence these metrics and their interpretations, with one of the most critical being the illumination area—essentially, the number of hotspots generating the signal. We investigated with Raman mapping the impact of the illumination area on five SERS substrates showing that a larger illumination area improves reproducibility on random structures, while it sacrifices resolution. Furthermore, a larger illumination area leads to more stable signals, particularly in irregular nanostructures, yielding higher sensitivity. In conclusion, choosing a SERS substrate should consider the trade-off between uniformity for resolution and larger illumination area for signal reproducibility.
This paper presents a comprehensive exploration of Surface-Enhanced Raman Spectroscopy (SERS) substrate design, leveraging Finite-Difference Time-Domain (FDTD) simulations for systematic parameter sweeping. Our investigation goes beyond enhancing SERS performance, addressing the crucial aspect of manufacturability with two-photon polymerization (2PP). By examining parameters such as height, pitch, and diameter, we aim to identify structures that not only exhibit high enhancement factors but also possess higher tolerances suitable for practical fabrication. Novel models are developed to visualize SERS hotspots based on nominal shape and AFM data, providing valuable insights for both substrate performance and manufacturability. This dual-focused strategy, integrating FDTD modeling with 2PP fabrication, offers an understanding for advancing SERS substrate design in both sensitivity and practicality.
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.
Raman spectroscopy is a powerful tool for analytical measurements in many applications. Traditional Raman spectroscopic analyses require bulky equipment, considerable time of signal acquisition and manual sampling of substances under test. In this paper, we take a step from bulky and manual consuming laboratory testing towards lab-on-chip (LOC) analyses. We miniaturize the Raman spectroscopic system by combining a free-form reflector based polymer LOC with a customized Raman probe. By using the confocal detection principle, we aim to enhance the detection of the Raman signals from the substance of interest due to the suppression of the background Raman signal from the polymer of the chip. Next to the LOC we miniaturize the external optical components, surrounding the reflector embedding optofluidic chip, and assemble these in a Raman probe. We evaluate the misalignment tolerance of internal optics (LOC) and external optics (Raman probe) by non-sequential ray tracing which shows that off-axis misalignment is around ±400μm and the maximum working distance of our Raman probe is 71mm. Using this probe, the system could be implemented as a portable reader unit containing the external optics, in which a low-cost, robust and mass manufacturable microfluidic LOC containing a freeform reflector is inserted, to enable confocal Raman spectroscopy measurements.
Traditionally, Raman spectroscopy is done in a specialized lab, with considerable requirements in terms of equipment, time and manual sampling of substances of interest. We present the modeling, the design and the fabrication process of a microfluidic device incorporation Raman spectroscopy, from which one enables confocal Raman measurements on-chip. The latter is fabricated using ultra precision diamond tooling and is tested in a proof-of-concept setup, by for example measuring Raman spectra of urea solutions with various concentrations. If one wants to analyze single cells instead of a sample solution, precautions need to be taken. Since Raman scattering is a weak process, the molecular fingerprint of flowing particles would be hard to measure. One method is to stably position the cell under test in the detection area during acquisition of the Raman scattering such that the acquisition time can be increased. Positioning of cells can be done through optical trapping and leads to an enhanced signal-to-noise ratio and thus a more reliable cell identification. Like Raman spectroscopy, optical trapping can also be miniaturized. We present the modeling, design process and fabrication of a mass-manufacturable polymer microfluidic device for dual fiber optical trapping using two counterpropagating singlemode beams. We use a novel fabrication process that consists of a premilling step and ultraprecision diamond tooling for the manufacturing of the molds and double-sided hot embossing for replication, resulting in a robust microfluidic chip for optical trapping. In a proof-of-concept demonstration, we characterize the trapping capabilities of the hot embossed chip.
Raman spectroscopy is a powerful optical and non-destructive technique and a well-known method for analysis purposes, especially to determine the molecular fingerprint of substances. Traditionally, such analyses are done in a specialized lab, with considerable requirements in terms of equipment, time and manual sampling of substances of interest. In this paper we take a step from bulky Raman spectroscopy laboratory analyses towards lab-on-chip (LOC) analyses. We present an optofluidic lab-on-chip for confocal Raman spectroscopy, which can be used for the analysis of liquids. The confocal detection suppresses the unwanted background from the polymer material out of which the chip is fabricated. We design the free-form optical reflector using non-sequential ray-tracing combined with a mathematical code to simulate the Raman scattering behavior of the substance under test. We prototype the device in Polymethyl methacrylate (PMMA) by means of ultraprecision diamond tooling. In a proof-of-concept demonstration, we first show the confocal behavior of our Raman lab-on-chip system by measuring the Raman spectrum of ethanol. In a next step, we compare the Raman spectra measured in our lab-on-chip with spectra measured with a commercial Raman spectrometer. Finally, to calibrate the system we perform Raman measurements on urea solutions with different concentrations. We achieve a detection limit that corresponds to a noise equivalent concentration of 20mM. Apart from strongly reducing the background perturbations, our confocal Raman spectroscopy system has other advantages as well. The reflector design is robust from a mechanical point of view and has the potential for mass-manufacturing using hot embossing or injection molding.
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