A water probe for direct pH measurement of individual particles via micro-Raman spectroscopy.

The acidity of atmospheric aerosols influences fundamental physicochemical processes that affect climate and human health. We recently developed a novel and facile water-probe-based method for directly measuring of the pH for micrometer-size droplets, providing a promising technique to better understand aerosol acidity in the atmosphere. The complex chemical composition of fine particles in the ambient air, however, poses certain challenges to using a water-probe for pH measurement, including interference from interactions between compositions and the influence of similar compositions on water structure. To explore the universality of our method, it was employed to measure the pH of ammonium, nitrate, carbonate, sulfate, and chloride particles. The pH of particles covering a broad range (0-14) were accurately determined, thereby demonstrating that our method can be generally applied, even to alkaline particles. Furthermore, a standard spectral library was developed by integrating the standard spectra of common hydrated ions extracted through the water-probe. The library can be employed to identify particle composition and overcome the spectral overlap problem resulting from similar effects. Using the spectral library, all ions were identified and their concentrations were determined, in turn allowing successful pH measurement of multicomponent (ammonium-sulfate-nitrate-chloride) particles. Insights into the synergistic effect of Cl-, NO3-, and NH4+ depletion obtained with our approach revealed the interplay between pH and volatile partitioning. Given the ubiquity of component partitioning and pH variation in particles, the water probe may provide a new perspective on the underlying mechanisms of aerosol aging and aerosol-cloud interaction.

Vibrational spectroscopy of interstellar PAHs: 1-cyanonaphthalene and 2-cyanonaphthalene.

In this work, we present a comprehensive experimental and theoretical study of the vibrational spectra of PAH molecules recently detected in the interstellar medium: 1-cyanonaphthalene and 2-cyanonaphthalene. The room temperature IR spectra of 1- and 2-cyanonaphthalene in the region 100-3100 cm-1 and their vibrational Raman spectra in the region 35-3100 cm-1 are reported here for the first time. A detailed spectral analysis is carried out using quantum chemical calculations employing the DFT methodology. Anharmonic corrections using the VPT2 method yield excellent agreement with the experimental spectra. A re-investigation of the vibrational spectrum of the parent molecule: naphthalene validates the experimental and theoretical methods used. A consistent set of assignments is reported for the fundamental bands of 1- and 2-cyanonapththalene. The experimental and theoretical data presented here would be useful inputs for modelling the role of cyanonaphthalene in astrophysical processes.

Glutathione and its structural modifications recognized by Raman Optical Activity and Circularly Polarized Luminescence.

Raman Optical Activity combined with Circularly Polarized Luminescence (ROA-CPL) was used in the spectral recognition of glutathione peptide (GSH) and its model post-translational modifications (PTMs). We demonstrate the potential of ROA spectroscopy and CPL probes (EuCl3, Na3[Eu(DPA)3], NaEuEDTA) in the study of unmodified peptide, i.e. GSH, and its derivatives, i.e. glutathione oxidized (GSSG), S-acetylglutathione (GSAc) and S-nitrosoglutathione (GSNO). ROA spectral features of GSH, GSSG, and GSAc were determined along with thier changes upon the different pH conditions. Apart from the ROA, induced CPL signals of Eu(III) probes also proved to be sensitive to the structural modifications of GSH-based model PTMs, enabling their spectral recognition, especially by the NaEuEDTA probe.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm.

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Plasmon-mediated photocatalytic conversion in Au or Ag nanorod aggregates by surface-enhanced Raman spectroscopy.

Plasmonic nanoparticles (NPs) hold considerable potential as photocatalysts owing to their robust light-matter interactions across diverse electromagnetic wavelengths, which significantly influence the photophysical characteristics of the adjacent molecular entities. Despite the widespread use of noble-metal NPs in surface-enhanced Raman scattering (SERS) applications, little is known about the kinetics of nanoparticle aggregation and how it affects their configurations. This study investigates the plasmon-driven photochemical conversion of 4-nitrobenzenethiol (NBT) to 4,4'-dimercaptoazobenzene (DMAB) on Au and Ag nanorods (NRs) through SERS. Significantly, photoconversion phenomena were observed on Ag NRs but not on Au NRs upon laser excitation at 633 nm. Finite-difference time-domain simulations revealed the presence of stronger electromagnetic fields on Ag NRs than on Au NRs. The aspect ratios and gaps between individual NPs in dimer configurations were determined to elucidate their effects on electromagnetic fields. The Ag NR dimer with an end-to-end configuration, an aspect ratio of 3.3, and a 1-nm gap exhibited the highest enhancement factor of 1.05 × 1012. Our results demonstrate that the primary contribution from diverse configurations in NR aggregates is the end-to-end configuration. The proposed NP design with adjustable parameters is expected to advance research in plasmonics, sensing, and wireless communications. These findings also contribute to the understanding of plasmon-driven photochemical processes in metallic nanostructures.

SERS aptasensor for simultaneous detection of ochratoxin A and zearalenone utilizing a rigid enhanced substrate (ITO/AuNPs/GO) combined with Au@AgNPs.

The contamination of mycotoxins poses a serious threat to global food security, hence the urgent need for simultaneous detection of multiple mycotoxins. Herein, two SERS nanoprobes were synthesized by embedded SERS tags (4-mercaptopyridine, 4MPy; 4-mercaptobenzonitrile, TBN) into the Au and Ag core-shell structure, and each was coupled with the aptamers specific to ochratoxin A (OTA) and zearalenone (ZEN). Meanwhile, a rigid enhanced substrate Indium tin oxide glass/AuNPs/Graphene oxide (ITO/AuNPs/GO) was combined with aptamer functionalized Au@AgNPs via π-π stacking interactions between the aptamer and GO to construct a surface-enhanced Raman spectroscopy (SERS) aptasensor, thereby inducing a SERS enhancement effect for the effective and swift simultaneous detection of both OTA and ZEN. The presence of OTA and ZEN caused signal probes dissociation, resulting in an inverse correlation between Raman signal intensity (1005 cm-1 and 2227 cm-1) and the concentrations of OTA and ZEN, respectively. The SERS aptasensor exhibited wide linear detection ranges of 0.001-20 ng/mL for OTA and 0.1-100 ng/mL for ZEN, with low detection limits (LOD) of 0.94 pg/mL for OTA and 59 pg/mL for ZEN. Furthermore, the developed SERS aptasensor demonstrated feasible applicability in the detection of OTA and ZEN in maize, showcasing its substantial potential for practical implementation.

Rapid detection of fertilizer information based on Raman spectroscopy and machine learning.

The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843-0.9990 and a root mean square error in the range of 0.0486-0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.

Examination of primary aromatic amines content in polylactic acid straws and migration into food simulants using SERS with LC-MS.

Polylactic acid (PLA) straws hold eco-friendly potential; however, residual diisocyanates used to enhance the mechanical strength can generate carcinogenic primary aromatic amines (PAAs), posing health risks. Herein, we present a rapid, comprehensive strategy to detecting PAAs in 18 brands of food-grade PLA straws and assessing their migration into diverse food simulants. Surface-enhanced Raman spectroscopy was conducted to rapidly screen straws for PAAs. Subsequently, qualitative determination of migrating PAAs into various food simulants (4 % acetic acid, 10 % ethanol, 50 % ethanol) occurred at 70 °C for 2 h using liquid chromatography-mass spectrometry. Three PAAs including 4,4'-methylenedianiline, 2,4'-methylenedianiline, and 2,4-diaminotoluene were detected in all straws. Specifically, 2,4-diaminotoluene in 50 % ethanol exceeded specific migration limit of 2 µg/kg, raising safety concerns. Notably, PAAs migration to 10 % and 50 % ethanol surpassed that to 4 % acetic acid within a short 2-hour period. Moreover, PLA straws underwent varying degrees of shape changes before and after migration. Straws with poly(butylene succinate) resisted deformation compared to those without, indicating enhanced heat resistance, while poly(butyleneadipate-co-terephthalate) improved hydrolysis resistance. Importantly, swelling study unveiled swelling effect wasn't the primary factor contributing to the increased PAAs migration in ethanol food simulant, as there was no significant disparity in swelling degrees across different food simulants. FT-IR and DSC analysis revealed higher PAAs content in 50 % ethanol were due to highly concentrated polar ethanol disrupting hydrogen bonds and van der Waal forces holding PLA molecules together. Overall, minimizing contact between PLA straws and alcoholic foods is crucial to avoid potential safety risks posed by PAAs.

Cascade internal electric field dominated carbon nitride decorated with gold nanoparticles as SERS substrate for thiram assay.

The development of valid chemical enhancement strategy with charge transfer (CT) for semiconductors has great scientific significance in surface-enhanced Raman scattering (SERS) technology. Herein, a phosphorus doped crystalline/amorphous polymeric carbon nitride (PCPCN) is fabricated by a facile molten salt method, and is employed as a SERS substrate for the first time. Upon the synergies of phosphatization and molten salt etching, PCPCN owns a cascaded internal electric field (IEF) due to the formation of p-n homojunction (interface-IEF) and crystalline/amorphous homojunction (bulk-IEF). The interface-IEF and bulk-IEF could effectively suppress the recombination of charge carriers and promote electron transfer between PCPCN and target methylene blue (MB), respectively. The strong CT interaction endows PCPCN substrate with superior SERS activity with an enhancement factor (EF) of 5.53 × 105. Au nanoparticles (Au NPs) are subsequently decorated on PCPCN to introduce electromagnetic enhancement for a better SERS response. The Au/PCPCN substrate allows to reliably detect trace crystal violet, as well as the thiram residue on cherry tomato. This work offers an integrated solution to enhance CT efficiency based on collaborative homojunction and internal electric field, and may inspire the design of novel semiconductor-based SERS substrates.

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme.

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Super-Spectral-Resolution Raman spectroscopy using angle-tuning of a Fabry-Pérot etalon with application to diamond characterization.

Raman spectroscopy is an extremely powerful laser-based method for characterizing materials based on their unique inelastic scattering spectrum. Ultimately, the power of the technique is limited by the resolution of the spectrometer. Here we introduce a new method for achieving Super-Spectral-Resolution Raman Spectroscopy (SSR-RS), by angle-tuning a Fabry-Pérot (F-P) etalon filter that we incorporated in a micro-Raman setup. A monolithically coated F-P etalon structure, only 1.686 mm in thickness, was mounted onto an angle-tunable motorized stage, and Raman spectra were automatically acquired for many different angles of the etalon. Using a low-resolution grating of 150 g/mm by itself, without the F-P etalon, we obtained a best-case regular Raman spectral linewidth of 44 cm-1 for the characteristic Raman peak from a diamond sample. When we applied the SSR-RS technique to diamond, we obtained a super-spectral resolution peak that was 27x narrower, namely 1.63 cm-1, and a Raman shift of 1331.3 cm-1. To baseline SSR-RS, we applied the super-spectral-resolution method to extract the linewidth and peak wavelength of the laser excitation itself and obtained a laser linewidth of better than 0.014 cm-1, with a laser wavelength centered at 531.962 nm, close to the stated wavelength of 532 nm. This extracted laser linewidth is 3300x times narrower compared to its measured linewidth of 46 cm-1. Thus, our work suggests that SSR-RS can be very generally applied to greatly improve the resolution and precision of Raman instrumentation, and potentially lower the cost of obtaining high-resolution Raman spectroscopic capabilities.

Optimizing number of Raman spectra using an artificial neural network guided Monte Carlo simulation approach to analyze human cortical bone.

This study presents a novel methodology for optimizing the number of Raman spectra required per sample for human bone compositional analysis. The methodology integrates Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS). We demonstrate the robustness of ANN in enabling prediction of Raman spectroscopy-based bone quality properties even with limited spectral inputs. The ANN algorithms tailored to individual sex and age groups, which enhance the specificity and accuracy of predictions in bone quality properties. In addition, ANN guided MCS systematically explores the variability and uncertainty inherent in different sample sizes and spectral datasets, leading to the identification of an optimal number of spectra per sample for characterizing human bone tissues. The findings suggest that as low as 2 spectra are sufficient for biochemical analysis of bone, with R2 values between real and predicted values of v1/PO4/Amide I and ∼I1670/I1640 ratios, ranging from 0.60 to 0.89. Our results also suggest that up to 8 spectra could be optimal when balancing other factors. This optimized approach streamlines experimental workflows, reduces data and acquisition costs. Additionally, our study highlights the potential for advancing Raman spectroscopy in bone research through the innovative integration of ANN-guided probabilistic modeling techniques. This research could significantly contribute to the broader landscape of bone quality analyses by establishing a precedent for optimizing the number of Raman spectra with sophisticated computational tools. It also sets a novel platform for future optimization studies in Raman spectroscopy applications in biomedical field.

Raman and resonance Raman spectroscopic study of red blood cells in patients diagnosed with malaria.

Raman spectroscopy was used to study erythrocytes collected from patients diagnosed with malaria at the University Hospital in Kraków and from healthy volunteers. A laser line with a wavelength of 442 nm was used to induce the Raman resonance of haem, while a laser with a wavelength of 785 nm was used for the normal Raman effect. The results were analysed using Principal Component Analysis. For the 442 nm laser line, analysis of the entire spectral range (3200 cm-1 to 300 cm-1) showed satisfactory separation of Raman spectra for healthy cells from infected cells, which was significantly improved in the 1500 cm-1-1200 cm-1 spectral range. For the 785 nm laser line, some separation was observed in each range studied, but the best results were achieved over the full spectral range. Plasmodium-derived nucleic acids and phosphodiester vibrations were observed at excitation lines of 442 nm and 785 nm, respectively.

The Effect of Organic Semiconductor Electron Affinity on Preventing Parasitic Oxidation Reactions Limiting Performance of n-Type Organic Electrochemical Transistors.

A key challenge in the development of organic mixed ionic-electronic conducting materials (OMIEC) for high performance electrochemical transistors is their stable performance in ambient. When operating in aqueous electrolyte, potential reactions of the electrochemically injected electrons with air and water could hinder their persistence, leading to a reduction in charge transport. Here, the impact of deepening the LUMO energy level of a series of electron-transporting semiconducting polymers is evaluated, and subsequently rendering the most common oxidation processes of electron polarons thermodynamically unfavorable, on organic electrochemical transistors (OECTs) performance. Employing time resolved spectroelectrochemistry with three analogous polymers having varying electron affinities (EA), it is found that an EA below the thermodynamic threshold for oxidation of its electron polarons by oxygen significantly improves electron transport and lifetime in air. A polymer with a sufficiently large EA and subsequent thermodynamically unfavorable oxidation of electron polarons is reported, which is used as the semiconducting layer in an OECT, in its neutral and N-DMBI doped form, resulting in an excellent and air-stable OECT performance. These results show a general design methodology to avoid detrimental parasitic reactions under ambient conditions, and the benefits that arise in electrical performance.

Multi-Biomarker Profiling for Precision Diagnosis of Lung Cancer.

Multi-biomarker analysis can enhance the accuracy of the single-biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi-biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi-biomarker analysis because of their long pre-processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi-biomarker profiling using a single drop of blood. For this, surface-enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi-biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20 µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi-biomarker analysis in overcoming the challenges posed by single-biomarker diagnostics. Furthermore, it markedly improves multi-biomarker-based analysis methods, illustrating its important impact on clinical diagnostics.

Metallo-Supramolecular Helicates as Surface-Enhanced Raman Scattering (SERS) Substrates with High Tailorability.

The exploration of novel functionalized supramolecular coordination complexes (SCCs) can enable new applications in domains that include purification and sensing. In this study, employing a coordination-driven self-assembly strategy, we designed and prepared a series of benzochalcogenodiazole-based metallohelicates as high-efficiency charge transfer surface-enhanced Raman scattering (SERS) substrates, expanding the range of applications for these metallohelicates. Through structural modifications, including the substitution of single heteroatoms on ligands, replacement of coordinating metals, and alteration of ligand framework linkages, the Raman performance of these metallohelicates as substrates were systematically optimized. Notably, the SERS enhancement factors (EFs) of the metallohelicate-based SERS substrates were significantly enhanced to levels as high as 1.03 × 107, which rivals the EFs of noble metals devoid of "hot spots". Additionally, the underlying Raman enhancement mechanisms of these metallohelicates have been investigated through a combination of control experiments and theoretical calculations. This study not only demonstrates the utility of metallohelicates as SERS substrates but also offers insights and materials for the development of high-efficiency new charge transfer SERS substrates.

Monitoring Hot Holes in Plasmonic Catalysis on Silver Nanoparticles by Using an Ion Label.

Energetic carriers generated by localized surface plasmon resonance (LSPR) provide an efficient way to drive chemical reactions. However, their dynamics and impact on surface reactions remain unknown due to the challenge in observing hot holes. This makes it difficult to correlate the reduction and oxidation half-reactions involving hot electrons and holes, respectively. Here we detect hot holes in their chemical form, Ag(I), on a Ag surface using surface-enhanced Raman scattering (SERS) of SO32- as a hole-specific label. It allows us to determine the dynamic correlations of hot electrons and holes. We find that the equilibrium of holes is the key factor of the surface chemistry, and the wavelength-dependent plasmonic chemical anode refilling (PCAR) effect plays an important role, in addition to the LSPR, in promoting the electron transfer. This method paves the way for visualizing hot holes with nanoscale spatial resolution toward the rational design of a plasmonic catalytic platform.

Development of a cost-effective confocal Raman microscopy with high sensitivity.

Confocal Raman microscopy is a powerful technique for identifying materials and molecular species; however, the signal from Raman scattering is extremely weak. Typically, handheld Raman instruments are cost-effective but less sensitive, while high-end scientific-grade Raman instruments are highly sensitive but extremely expensive. This limits the widespread use of Raman technique in our daily life. To bridge this gap, we explored and developed a cost-effective yet highly sensitive confocal Raman microscopy system. The key components of the system include an excitation laser based on readily available laser diode, a lens-grating-lens type spectrometer with high throughput and image quality, and a sensitive detector based on a linear charge-coupled device (CCD) that can be cooled down to -30 °C. The developed compact Raman instrument can provide high-quality Raman spectra with good spectral resolution. The 3rd order 1450 cm-1 peak of Si (111) wafer shows a signal-to-noise ratio (SNR) better than 10:1, demonstrating high sensitivity comparable to high-end scientific-grade Raman instruments. We also tested a wide range of different samples (organic molecules, minerals and polymers) to demonstrate its universal application capability.

New screen-printed electrodes for Raman spectroelectrochemistry. Determination of p-aminosalicylic acid.

BACKGROUND: The availability of new surface enhanced Raman scattering (SERS) substrates is essential to develop quantitative analytical methods. Electrochemistry is an easy, fast and reproducible methodology to prepare SERS substrates on screen-printed electrodes (SPEs). RESULTS: This work proposes new SPEs based on a three-electrode system all made of silver. Using the same ink for the whole electrode system facilitates the fabrication process, reduces production costs, and leads to excellent analytical performance. The results showed that Raman enhancement depends strongly on the type of silver ink. To demonstrate the capabilities of the new electrodes developed, 4-aminosalicylic acid was determined in complex matrices and in the presence of strong interfering compounds such as salicylic acid and acetylsalicylic acid. The proposed analytical method is based on the electrochemical surface oxidation enhanced Raman scattering (EC-SOERS) strategy. AgCl nanocrystals are generated on the working electrode surface, which amplify the Raman signal of 4-aminosalicylic acid. Good figures of merit were obtained both in the absence and in the presence of the interfering compounds, achieving a correct estimation of a 4-aminosalicylic test sample in complex matrices. SIGNIFICANCE: The new SPEs have been demonstrated to be very sensitive and reproducible which, together to the high specificity of the Raman signal, makes this methodology very attractive for chemical analysis.

Innovative and versatile surface-enhanced Raman spectroscopy-inspired approaches for viral detection leading to clinical applications: A review.

The evolution of analytical techniques has opened the possibilities of accurate analyte detection through a straightforward method and short acquisition time, leading towards their applicability to identify medical conditions. Surface-enhanced Raman spectroscopy (SERS) has long been proven effective for rapid detection and relies on SERS spectra that are unique to each specific analyte. However, the complexity of viruses poses challenges to SERS and hinders further progress in its practical applications. The principle of SERS revolves around the interaction among substrate, analyte, and Raman laser, but most studies only emphasize the substrate, especially label-free methods, and the synergy among these factors is often ignored. Therefore, issues related to reproducibility and consistency of results, which are crucial for medical diagnosis and are the main highlights of this review, can be understood and largely addressed when considering these interactions. Viruses are composed of multiple surface components and can be detected by label-free SERS, but the presence of non-target molecules in clinical samples interferes with the detection process. Appropriate spectral data processing workflow also plays an important role in the interpretation of results. Furthermore, integrating machine learning into data processing can account for changes brought about by the presence of non-target molecules when analyzing spectral features to accurately group the data, for example, whether the sample corresponds to a positive or negative patient, and whether a virus variant or multiple viruses are present in the sample. Subsequently, advances in interdisciplinary fields can bring SERS closer to practical applications.