Label-free Surface-enhanced Raman Spectroscopy Research Articles

Surface-Enhanced Raman Spectroscopy Detection for Fenthion Pesticides Based on Gold Molecularly Imprinted Polymer Solid-State Substrates.

Current label-free surface-enhanced Raman spectroscopy (SERS) assay for the detection and analysis of organophosphorus pesticides has achieved initial success, but the application still faces constraints of substrate portability and specificity. To this end, this paper demonstrates a method for portable, rapid, and specific detection of low concentrations of fenthion pesticides based on a solid substrate of gold nanoparticle monolayers combined with molecularly imprinted polymers (MIPs). The nano-monolayers were transferred to the surface of mercapto-silicon wafers by interfacial self-assembly technique to form a stable connection with S-Au bonds and, at the same time, prevent nanoparticles from dropping off during the surfactant removal process. Then, the fenthion MIPs were directly generated on the surface of the monolayer film by spin-coating with a pre-polymerization solution and ultraviolet-induced polymerization. Tests showed that the molecular imprint was able to accurately bind to fenthion, but not other molecules, in a mixture of structural analogs, achieving a low concentration detection of 10-8 mol/L. The composite substrate maintained a signal uniformity of a relative standard deviation (RSD) = 7.05% and a batch-to-batch reproducibility of RSD = 10.40%, making it a potential pathway for the extended application of SERS technology.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific “metabolic fingerprints” of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are “in-house” protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable.

Clinical feasibility of a label-free SERS assay for therapeutic drug monitoring of methotrexate

We present a therapeutic drug monitoring (TDM) feasibility study using a label-free surface-enhanced Raman spectroscopy (SERS)-based assay for quantifying methotrexate (MTX) from samples collected from children with acute lymphoblastic leukemia undergoing high-dose MTX therapy. We show that, when combined with an appropriate sample preparation (solid phase extraction) and multivariate data analysis (partial least squares regression), the SERS assay demonstrated a good correlation with a reference chromatographic method (r = 0.889, p < 0.005). We also found that the SERS-based approach underestimated MTX concentration, with a 10% bias calculated, but both methods showed similar variability (RSD of 8.4% for HPLC and 11.6% for SERS). The presented results are strong evidence that SERS can be used for TDM of MTX, and this work brings us one step closer to implementing a SERS-based assay in clinics.

Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease

Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) analysis of blood-based biomarkers is a convenient alternative to rapidly obtain spectral information from biofluids. However, despite the rapid acquisition of spectral information from biofluids, it is challenging to distinguish spectral features of biomarkers due to interference from biofluidic components. Here, we introduce a deep learning-assisted, SERS-based platform for separate analysis of blood-based amyloid β (1–42) and metabolites, enabling the diagnosis of Alzheimer's disease. SERS substrates consisting of Au nanowire arrays are fabricated and functionalized in two characteristic ways to compare the validity of different Alzheimer's disease biomarkers measured on our SERS system. The 6E10 antibody is immobilized for the capture of amyloid β (1–42) and analysis of its oligomerization process, while various self-assembled monolayers are attached for different dipole interactions with blood-based metabolites. Ultimately, SERS spectra of blood plasma of Alzheimer's disease patients and human controls are measured on the substrates and classified via advanced deep learning techniques that automatically extract informative features to learn generalizable representations. Accuracies up to 99.5% are achieved for metabolite-based analyses, which are verified with an explainable artificial intelligence technique that identifies key spectral features used for classification and for deducing significant biomarkers.

Advances in uremic toxin detection and monitoring in the management of chronic kidney disease progression to end-stage renal disease.

Patients with end-stage kidney disease (ESKD) rely on dialysis to remove toxins and stay alive. However, hemodialysis alone is insufficient to completely remove all/major uremic toxins, resulting in the accumulation of specific toxins over time. The complexity of uremic toxins and their varying clearance rates across different dialysis modalities poses significant challenges, and innovative approaches such as microfluidics, biomarker discovery, and point-of-care testing are being investigated. This review explores recent advances in the qualitative and quantitative analysis of uremic toxins and highlights the use of innovative methods, particularly label-mediated and label-free surface-enhanced Raman spectroscopy, primarily for qualitative detection. The ability to analyze uremic toxins can optimize hemodialysis settings for more efficient toxin removal. Integration of multiple omics disciplines will also help identify biomarkers and understand the pathogenesis of ESKD, provide deeper understanding of uremic toxin profiling, and offer insights for improving hemodialysis programs. This review also highlights the importance of early detection and improved understanding of chronic kidney disease to improve patient outcomes.

Label-free SERS method with size-matched selectivity for analytes of varying sizes

Label-free Surface-enhanced Raman spectroscopy (SERS) methods have great potential for detecting analytes of various sizes and dimensions with high sensitivity and selectivity. To mitigate the impact of interfering substances, SERS-active substrates with size-matched selectivity were designed by coating Au nanoparticles (Au NPs) on ZrO2 multilayer nanofibers (Au NPs/fZrO2) and referencing with Au NPs embedded on ZrO2 nanobowl (Au NPs/pZrO2). The detection efficiency study covered four small pesticide molecules, two live SARS-CoV-2 virus variants (Alpha and Delta), and three interfering substances. The results demonstrate that the fibrous structure of Au NPs/fZrO2 could effectively wet the sample and detect larger target molecules, such as live SARS-CoV-2 virus, thus improving efficiency by reducing unwanted molecules in the SERS signal. This substrate also showed high discrimination between Alpha and Delta variants. Au NPs/fZrO2 demonstrated similar trace detection capabilities to Au NPs/pZrO2 for pesticide molecules and virus variants; however, it particularly exhibited stronger peak intensities and more unique SERS peaks assigned to the variants. Thus, differences in substrate morphology affect the generation of hotspots and the distance between analyte and hotspots. These findings hold promise for the development of SERS-based label-free analytical method for trace detection of a variety of virus particles.

Label-Free Single-Cell SERS Detection and Fluorescence Imaging of Molecular Responses to Endoplasmic Reticulum Stress under Electrical Stimulation.

The endoplasmic reticulum (ER) is one of the most important organelles in eukaryotic cells, in which most proteins and lipids are synthesized to regulate complex cellular processes. Generally, the excessive accumulation of unfolded or misfolded proteins can disturb ER homeostasis and induce endoplasmic reticulum stress (ERS). Howbeit, the molecular stress responses within ERS and metastatic behaviors of tumor cells during electrical stimulation (ES) are still poorly investigated and remain a challenge. In this study, by the combined use of fluorescence imaging, ER-targeting plasmonic nanoprobes were developed to trace molecular stress response profiling within the ER during a constant-voltage ES process at ∼1 V based on label-free surface-enhanced Raman spectroscopy (SERS). The excess accumulation of β-misfolded proteins was found after the ES, leading to breaking of the ER homeostasis and further inducing mitochondrial dysfunction. Notably, the excessive stress of ER under ES can destroy the calcium ion balance and induce significant upregulation of calreticulin expression. Importantly, the content ratio of two kinds of cadherin between E-cadherin and N-cadherin was gradually improved with the voltages boosted. Meanwhile, the epithelial adhesion factor expression was ascended with voltages amplified, leading to inhibiting tumor cell migration at low voltages or death under higher voltages (∼1 V). This study provides cellular insights into the ES approach for tumor therapy and also provides a simple and effective method for detecting molecular stress responses in endoplasmic reticulum stress.

Detection of micro-plasma-induced exosomes secretion in a fibroblast-melanoma co-culture model

BackgroundMelanoma is a highly aggressive tumor and a significant cause of skin cancer-related death. Timely diagnosis and treatment require identification of specific biomarkers in exosomes secreted by melanoma cells. In this study, label-free surface-enhanced Raman spectroscopy (SERS) method with size-matched selectivity was used to detect membrane proteins in exosomes released from a stimulated environment of fibroblasts (L929) co-cultured with melanoma cells (B16–F10). To promote normal secretion of exosomes, micro-plasma treatment was used to gently induce the co-cultured cells and slightly increase the stress level around the cells for subsequent detection using the SERS method. Results and discussionFirstly, changes in reactive oxygen species/reactive nitrogen species (ROS/RNS) concentrations in the cellular microenvironment and the viability and proliferation of healthy cells are assessed. Results showed that micro-plasma treatment increased extracellular ROS/RNS levels while modestly reducing cell proliferation without significantly affecting cell survival. Secondly, the particle size of secreted exosomes isolated from the culture medium of L929, B16–F10, and co-cultured cells with different micro-plasma treatment time did not increase significantly under single-cell conditions at short treatment time but might be changed under co-culture condition or longer treatment time. Third, for SERS signals related to membrane protein biomarkers, exosome markers CD9, CD63, and CD81 can be assigned to significant Raman shifts in the range of 943–1030 and 1304-1561 cm-1, while the characteristics SERS peaks of L929 and B16–F10 cells are most likely located at 1394/1404, 1271 and 1592 cm-1 respectively. Significance and noveltyTherefore, this micro-plasma-induced co-culture model provides a promising preclinical approach to understand the diagnostic potential of exosomes secreted by cutaneous melanoma/fibroblasts. Furthermore, the label-free SERS method with size-matched selectivity provides a novel approach to screen biomarkers in exosomes secreted by melanoma cells, aiming to reduce the use of labeling reagents and the processing time traditionally required.

Advancing Mycotoxin Detection: Multivariate Rapid Analysis on Corn Using Surface Enhanced Raman Spectroscopy (SERS).

Mycotoxin contamination on food and feed can have deleterious effect on human and animal health. Agricultural crops may contain one or more mycotoxin compounds; therefore, a good multiplex detection method is desirable to ensure food safety. In this study, we developed a rapid method using label-free surface-enhanced Raman spectroscopy (SERS) to simultaneously detect three common types of mycotoxins found on corn, namely aflatoxin B1 (AFB1), zearalenone (ZEN), and ochratoxin A (OTA). The intrinsic chemical fingerprint from each mycotoxin was characterized by their unique Raman spectra, enabling clear discrimination between them. The limit of detection (LOD) of AFB1, ZEN, and OTA on corn were 10 ppb (32 nM), 20 ppb (64 nM), and 100 ppb (248 nM), respectively. Multivariate statistical analysis was used to predict concentrations of AFB1, ZEN, and OTA up to 1.5 ppm (4.8 µM) based on the SERS spectra of known concentrations, resulting in a correlation coefficient of 0.74, 0.89, and 0.72, respectively. The sampling time was less than 30 min per sample. The application of label-free SERS and multivariate analysis is a promising method for rapid and simultaneous detection of mycotoxins in corn and may be extended to other types of mycotoxins and crops.

Diagnosis and staging of cervical cancer using label-free surface-enhanced Raman spectroscopy and BWRPCA-TLNN model

This paper presents a label-free and highly accurate classification serum analytical platform, which will be used to identify cervical cancer at different stages. In detail, the microarray chip fabricated based on the ordered Ag-AuNRs substrate was prepared to measure the surface-enhanced Raman scattering (SERS) spectra of serum of healthy subjects and cervical cancer patients (Ⅰ, Ⅱ, Ⅲ, and Ⅳ), and then a binary weight robust principal component analysis (BWRPCA)-two-layer nearest neighbor (TLNN) model was designed as the diagnosis and recognition model of SERS spectra. The results revealed that the microarray chip can realize rapid, sensitive, high-throughput detection of SERS spectra of serum. The BWRPCA-TLNN successfully differentiated the SERS spectra and accurately captured the key characteristics for classification. The established BWRPCA-TLNN model achieved an excellent classification accuracy of 91.2 %, a diagnostic sensitivity of over 84.0 % and a specificity of over 97.0 %. This exploratory work demonstrated that SERS combined with BWRPCA-TLNN as a diagnostic screening method has potential for improving cervical cancer detection and screening.

A Surface-Enhanced Raman Spectroscopy-Based Biosensor for the Detection of Biological Macromolecules: The Case of the Lipopolysaccharide Endotoxin Molecules.

The development of sensitive methods for the detection of endotoxin molecules, such as lipopolysaccharides (LPS), is essential for food safety and health control. Conventional analytical methods used for LPS detection are based on the pyrogen test, plating and culture-based methods, and the limulus amoebocyte lysate method (LAL). Alternatively, the development of reliable biosensors for LPS detection would be highly desirable to solve some critical issues, such as high cost and a long turnaround time. In this work, we present a label-free Surface-Enhanced Raman Spectroscopy (SERS)-based method for LPS detection in its free form. The proposed method combines the benefits of plasmonic enhancement with the selectivity provided by a specific anti-lipid A antibody (Ab). A high-enhancing nanostructured silver substrate was coated with Ab. The presence of LPS was quantitatively monitored by analyzing the changes in the Ab spectra obtained in the absence and presence of LPS. A limit of detection (LOD) and quantification (LOQ) of 12 ng/mL and 41 ng/mL were estimated, respectively. Importantly, the proposed technology could be easily expanded for the determination of other biological macromolecules.

Machine Learning-Based Label-Free SERS Profiling of Exosomes for Accurate Fuzzy Diagnosis of Cancer and Dynamic Monitoring of Drug Therapeutic Processes.

Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.

Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis

Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically discovered when it is already advanced. In this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.

Detection of live SARS-CoV-2 virus and its variants by specially designed SERS-active substrates and spectroscopic analyses

A method using label-free surface enhanced Raman spectroscopy (SERS) based on substrate design is provided for an early detection and differentiation of spike glycoprotein mutation sites in live SARS-CoV-2 variants. Two SERS-active substrates, Au nanocavities (Au NCs) and Au NPs on porous ZrO2 (Au NPs/pZrO2), were used to identify specific peaks of A.3, Alpha, and Delta variants at different concentrations and demonstrated the ability to provide their SERS spectra with detection limits of 0.1–1.0% (or 104−5 copies/mL). Variant identification can be achieved by cross-examining reference spectra and analyzing the substrate-analyte relationship between the suitability of the analyte upon the hotspot(s) formed at high concentrations and the effective detection distance at low concentrations. Mutation sites on the S1 chain of the spike glycoprotein for each variant may be related and distinguishable. This method does not require sample preprocessing and therefore allows for fast screening, which is of high value for more comprehensive and specific studies to distinguish upcoming variants.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms.

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings. Graphical abstract.

Investigation of the Influence of Stress on Label-Free Bacterial Surface-Enhanced Raman Spectra.

Label-free surface-enhanced Raman spectroscopy (SERS) has been proposed as a promising bacterial detection technique. However, the quality of the collected bacterial spectra can be affected by the time between sample acquisition and the SERS measurement. This study evaluated how storage stress stimuli influence the label-free SERS spectra of Pseudomonas syringae samples stored in phosphate buffered saline. The results indicate that when faced with nutrient limitations and changes in osmatic pressure, samples at room temperature (25 °C) exhibit more significant spectral changes than those stored at cold temperature (4 °C). At higher temperatures, bacterial communities secrete extracellular biomolecules that induce programmed cell death and result in increases in the supernatant SERS signals. Surviving cells consume cellular components to support their metabolism, thus leading to measurable declines in cell SERS intensity. Two-dimensional correlation spectroscopy analysis suggests that cellular component signatures decline sequentially in the following order: proteins, nucleic acids, and lipids. Extracellular nucleic acids, proteins, and carbohydrates are secreted in turn. After subtracting the SERS changes resulting from storage, we evaluated bacterial response to viral infection. P. syringae SERS profile changes enable accurate bacteriophage Phi6 quantification over the range of 104-1010 PFU/mL. The results indicate that storage conditions impact bacterial label-free SERS signals and that such influences need to be accounted for and if possible avoided when detecting bacteria or evaluating bacterial response to stress stimuli.

SERS uncovers the link between conformation of cytochrome c heme and mitochondrial membrane potential.

The balance between the mitochondrial respiratory chain activity and the cell's needs in ATP ensures optimal cellular function. Cytochrome c is an essential component of the electron transport chain (ETC), which regulates ETC activity, oxygen consumption, ATP synthesis and can initiate apoptosis. The impact of conformational changes in cytochrome c on its function is not understood for the lack of access to these changes in intact mitochondria. We have developed a novel sensor that uses unique properties of label-free surface-enhanced Raman spectroscopy (SERS) to identify conformational changes in heme of cytochrome c and to elucidate their role in functioning mitochondria. We have verified that molecule bond vibrations assessed by SERS are a reliable indicator of the heme conformation during changes in the inner mitochondrial membrane potential and ETC activity. We have demonstrated that cytochrome c heme reversibly switches between planar and ruffled conformations in response to the inner mitochondrial membrane potential (ΔΨ) and H+ concentration in the intermembrane space. This regulates the efficiency of the mitochondrial respiratory chain, thus, adjusting the mitochondrial respiration to the cell's consumption of ATP and the overall activity. We have found that under hypertensive conditions cytochrome c heme loses its sensitivity to ΔΨ that can affect the regulation of ETC activity. The ability of the proposed SERS-based sensor to track mitochondrial function opens broad perspectives in cell bioenergetics.

From the lab to the field: handheld surface enhanced Raman spectroscopy (SERS) detection of viral proteins.

Translating sensors from the lab benchtop to a readily available point-of-need setting is desirable for many fields, including medicine, agriculture, and industry. However, this transition generally suffers from loss of sensitivity, high background signals, and other issues which can impair reproducibility. Here we adapt a label-free surface-enhanced Raman spectroscopy (SERS) sensor for SARS-CoV-2 antigens from a lab-based assay to a handheld device. Utilizing a peptide capture molecule, which we previously employed for a surface-based assay, we optimize a simpler and more cost-efficient nanoparticle-based assay. This new assay allows for the direct detection of these viral antigens by SERS, now with the advantages of robustness and portability. We highlight considerations for nanoparticle modification conditions and warn against methods which can interfere with accurate detection. The comparison of these two assays will help guide further development of SERS-based sensors into devices that can be easily used in point-of-care settings, such as by emergency room nurses, farmers, or quality control technicians.

Machine Learning Assisted Real-Time Label-Free SERS Diagnoses of Malignant Pleural Effusion due to Lung Cancer.

More than half of all pleural effusions are due to malignancy of which lung cancer is the main cause. Pleural effusions can complicate the course of pneumonia, pulmonary tuberculosis, or underlying systemic disease. We explore the application of label-free surface-enhanced Raman spectroscopy (SERS) as a point of care (POC) diagnostic tool to identify if pleural effusions are due to lung cancer or to other causes (controls). Lung cancer samples showed specific SERS spectral signatures such as the position and intensity of the Raman band in different wave number region using a novel silver coated silicon nanopillar (SCSNP) as a SERS substrate. We report a classification accuracy of 85% along with a sensitivity and specificity of 87% and 83%, respectively, for the detection of lung cancer over control pleural fluid samples with a receiver operating characteristics (ROC) area under curve value of 0.93 using a PLS-DA binary classifier to distinguish between lung cancer over control subjects. We have also evaluated discriminative wavenumber bands responsible for the distinction between the two classes with the help of a variable importance in projection (VIP) score. We found that our label-free SERS platform was able to distinguish lung cancer from pleural effusions due to other causes (controls) with higher diagnostic accuracy.