In this study, the intrinsic surface-enhanced Raman spectroscopy (SERS)-based approach coupled with chemometric analysis was adopted to establish the biochemical fingerprint of SARS-CoV-2 infected human fluids: saliva and nasopharyngeal swabs. The numerical methods, partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC), facilitated the spectroscopic identification of the viral-specific molecules, molecular changes, and distinct physiological signatures of pathetically altered fluids. Next, we developed the reliable classification model for fast identification and differentiation of negative CoV(−) and positive CoV(+) groups. The PLS-DA calibration model was described by a great statistical value—RMSEC and RMSECV below 0.3 and R²_cal at the level of ~0.7 for both type of body fluids. The calculated diagnostic parameters for SVMC and PLS-DA at the stage of preparation of calibration model and classification of external samples simulating real diagnostic conditions evinced high accuracy, sensitivity, and specificity for saliva specimens. Here, we outlined the significant role of neopterin as the biomarker in the prediction of COVID-19 infection from nasopharyngeal swab. We also observed the increased content of nucleic acids of DNA/RNA and proteins such as ferritin as well as specific immunoglobulins. The developed SERS for SARS-CoV-2 approach allows: (i) fast, simple and non-invasive collection of analyzed specimens; (ii) fast response with the time of analysis below 15 min, and (iii) sensitive and reliable SERS-based screening of COVID-19 disease.

The detection of freely circulating cancer cells (CTCs) is one of the greatest challenges of modern medical diagnostics. For several years, there has been increased attention on the use of surface-enhanced Raman spectroscopy (SERS) for the detection of CTCs. SERS is a non-destructive, accurate and precise technique, and the use of special SERS platforms even enables the amplification of weak signals from biological objects. In the current study, we demonstrate the unique arrangement of the SERS technique combined with the deposition of CTCs cells on the surface of the SERS platform via a dielectrophoretic effect. The appropriate frequencies of an alternating electric field and a selected shape of the electric field can result in the efficient deposition of CTCs on the SERS platform. The geometry of the microfluidic chip, the type of the cancer cells and the positive dielectrophoretic phenomenon resulted in the trapping of CTCs on the surface of the SERS platform. We presented results for two type of breast cancer cells, MCF-7 and MDA-MB-231, deposited from the 0.1 PBS solution. The limit of detection (LOD) is 20 cells/mL, which reflects the clinical potential and usefulness of the developed approach. We also provide a proof-of-concept for these CTCs deposited on the SERS platform from blood plasma.

In the human body, tumor cell occurrence can be indirectly monitored using the L-selectin concentration in the blood, since selectin ligands are present on the surface of tumor cells, and with tumor progression, a decrease in L-selectin levels can be expected and observed. In this study, we present a selective DNA-based surface-enhanced Raman spectroscopy (SERS) assay for the detection and determination of L-selectin in biological samples. Two calibration curves (linear in the 40–190 ng mL⁻¹ region and exponential in the 40–500 ng mL⁻¹ region) are fitted to the obtained SERS experimental data, i.e., the ratio of I₇₃₂/I₁₃₃₄ band intensities (LOQ = 46 ng mL⁻¹). Calculated determination coefficients are found to be R² = 0.997 for the linear region of the calibration curve and R² = 0.977 for the exponential region. Moreover, we demonstrate very good selectivity of the assay even in the presence of P- and E-selectin in a sample containing L-selectin. With our SERS assay, the L-selectin concentration in biological samples can be estimated directly from the calibration curves.

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

We present herein a label-free method based on surface-enhanced Raman spectroscopy (SERS) that was performed using a silicon-based SERS platform. The differentiation between primary and secondary brain tumors SERS data was completed partial least squares (PLS) method with a very high 85% of accuracy (in only first and second factors), whereas calculated using principal component analysis (PCA) method gives 74% (in two consecutive components). Additionally, due to the fact that the interleukin-10 (IL-10) cytokine receptor may act in cancer as both an immunosuppressive and an immunostimulant factor, the correlation between the tumor grading and the cytokine receptor are presented. Obtained data indicate that the function of IL-10 cytokine receptor subunit alpha most probably depends or is closely related to the tumor stage.

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall’s chemical composition; thus, the compound’s distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.

The surface-enhanced Raman scattering (SERS) has been widely tested for its usefulness in microbiological studies, providing many information-rich spectra which are a kind of ‘whole-organism fingerprint’ and enabling identification of bacterial species. Here we show, previously not considered, the comprehensive SERS-chemometric analysis of five bacterial pathogens, namely Neisseria gonorrhoeae, Mycoplasma hominis, Mycoplasma genitalium, Ureaplasma urealyticum, and Haemophilus ducreyi, all being responsible for sexually transmitted diseases (STDs). In the designed biosensor, the direct, intrinsic format of the spectroscopic analysis was adopted for the SERS-based screening of gonorrhea and chlamydiosis due to vibrational analysis of men’s urethra swabs. Our experiments demonstrated that the applied method enables identification the individual species of the Neisseria genus with high accuracy. In order to differentiate the sexually transmitted pathogens and to classify the clinical samples of male urethra swabs, three multivariate methods were used. In the external validation the created models correctly classified the men’s urethra swabs with prediction accuracy reaching 89% for SIMCA and 100% for PLS-DA. As a result, the developed protocol enables:

(i) simple and non-invasive analysis of clinical samples (the collection of urethra swabs specimens could be carried out at different points of care, such as doctor’s office),

(ii) fast analysis (<15 min),

(iii) culture-free identification, and

(iv) sensitive and reliable SERS-based diagnosis of STD.

The simplicity of the developed detection procedure, supported by high sensitivity, reproducibility, and specificity, open a new path in the improvement of the point-of-care applications.

Recently, Porphyromonas gingivalis, the keystone pathogen implicated in the development of gum disease (periodontitis), was detected in the brains of Alzheimer’s disease patients, opening up a fascinating possibility that it is also involved in the pathobiology of this neurodegenerative illness. To verify this hypothesis, an unbiased, specific, and sensitive method to detect this pathogen in biological specimens is needed. To this end, our interdisciplinary studies demonstrate that P. gingivalis can be easily identified by surface-enhanced Raman scattering (SERS). Moreover, based on SERS measurements, P. gingivalis can be distinguished from another common periodontal pathogen, Aggregatibacter actinomycetemcomitans, and also from ubiquitous oral Streptococcus spp. The results were confirmed by principal component analysis (PCA). Furthermore, we have shown that different P. gingivalis and A. actinomycetemcomitans strains can easily adsorb to silver-coated magnetic nanoparticles (Fe₂O₃@AgNPs). Thus, it is possible to magnetically separate investigated bacteria from other components of a specimen using the microfluidic chip. To obtain additional enhancement of the Raman signal, the NPs adsorbed to bacterial cells were magnetically attracted to the Si/Ag SERS platform. Afterward, the SERS spectra could be recorded. Such a time-saving procedure can be very helpful in rapid medical diagnostics and thus in starting the appropriate pharmacological therapy to prevent the development of periodontitis and associated comorbidities, e.g., Alzheimerʼs disease.

Surface-enhanced Raman spectroscopy (SERS) is a research method in which a lack of cost-effective, versatile platforms with high enhancement factor (EF) is still a major obstacle to its widespread use. The platforms should be also easy to manufacture, stable in time (for weeks or even for months) and manufactured with a highly reproducible method.

We demonstrate SERS platforms based on silicon modified on the surface by laser ablation and covered with SERS-active metal. The substrates were fabricated by a femtosecond laser, thus the method is simple, very fast and creates highly uniform SERS platforms in a large number. The platform was tested with para-mercaptobenzoic acid (p-MBA) in terms of sensitivity and reproducibility. The calculated EF was at the level of 10⁸ and the standard deviation (SD) gives 7% for 10⁻⁶ M solution of p-MBA based on the intensity of the band at 1073 cm⁻¹. Optimized SERS substrate also exhibits excellent stability for up to six months.

We also give the proof-of-concept of using our platform and, for the first time, the SERS analysis of the most important human opportunistic fungal pathogen Candida spp. (Candida glabrata, Candida albicans SN148 and C. albicans BWP17). Finally, the chemometric analysis in the form of Principal Component Analysis (PCA) allowed to strain differentiation of Candida spp., and to distinguish the studied Candida species from Gram-positive bacterial samples with Staphylococcus aureus. Our results demonstrate that the proposed SERS platform is a perfect substrate for detection, identification and differentiation between fungal and bacterial pathogens using SERS technique.

According to EU summary report on zoonoses, zoonotic agents and food‐borne outbreaks in 2017, Campylobacter was the most commonly reported gastrointestinal bacterial pathogen in humans in the EU. Unfortunately, the standard methods for the detection of thermotolerant Campylobacter spp. in foods are time‐consuming. Additionally, the qualified staff is obligatory. For this reason, new methods of pathogens detection are needed. The present work demonstrates that surface‐enhanced Raman scattering (SERS) is a reliable and fast method for detection of Campylobacter spp. in food samples. The proposed method combines the SERS measurements performed on an Ag/Si substrate with two initial steps of the ISO standard procedure. Finally, the principal component analysis (PCA) allows for statistical classification of the studied bacteria. By applying the proposed ISO‐SERS‐PCA method in the case of Campylobacter bacteria the total detection time may be reduced from 7 to 8 days required by ISO method to 3 to 4 days in the case of SERS‐based approach.

The circulating tumor cells (CTCs) isolation and characterization has a great potential for non-invasive biopsy. In the present research, the surface–enhanced Raman spectroscopy (SERS)-based assay utilizing magnetic nanoparticles and solid SERS-active support integrated in the external field assisted microfluidic device was designed for efficient isolation of CTCs from blood samples. Magnetic nanospheres (Fe₂O₃) were coated with SERS-active metal and then modified with p-mercaptobenzoic acid (p-MBA) which works simultaneously as a Raman reporter and linker to an antiepithelial-cell-adhesion-molecule (anti-EpCAM) antibodies. The newly developed laser-induced SERS-active silicon substrate with a very strong enhancement factor (up to 10⁸) and high stability and reproducibility provide the additional extra-enhancement in the sandwich plasmonic configuration of immune assay which finally leads to increase the efficiency of detection. The sensitive immune recognition of cancer cells is assisted by the introducing of the controllable external magnetic field into the microfluidic chip. Moreover, the integration of the SERS-active platform and p-MBA-labeled immuno-Ag@Fe₂O₃ nanostructures with microfluidic device offers less sample and analytes demand, precise operation, increase reproducibly of spectral responses, and enables miniaturization and portability of the presented approach. In this work, we have also investigated the effect of varying expression of the EpCAM established by the Western Blot method supported by immunochemistry on the efficiency of CTCs’ detection with the developed SERS method. We used four target cancer cell lines with relatively high (human metastatic prostate adenocarcinoma cells (LNCaP)), medium (human metastatic prostate adenocarcinoma cells (LNCaP)), weak (human metastatic prostate adenocarcinoma cells (LNCaP)), and no EpCAM expressions (cervical cancer cells (HeLa)) to estimate the limits of detection based on constructed calibration curves. Finally, blood samples from lung cancer patients were used to validate the efficiency of the developed method in clinical trials.

The surface-enhanced Raman spectroscopy (SERS) is a method known for its effectiveness in detecting and identifying microorganisms, that was employed to differentiate various bacterial strains both at genus and species level. In this work, we have examined five species belonging to Streptococcus genus, namely S. pneumoniae, S. suis, S. pseudopneumoniae, S. oralis, and S. mitis. Additionally, we conducted SERS experiments on ten S. pneumoniae strains, representing different capsular types. In all of cases we obtained unique SERS signals being spectroscopic fingerprints of bacterial strains tested. Moreover, the principal component analysis (PCA) was performed in order to prove that the spectra of all studied strains can be well separated into five (in case of streptococcal strains) or ten (in case of pneumococcal serotypes) groups. In both investigated situations, the separation at the level of 95% was achieved, proving that SERS-PCA-based method can be used for reliable and fast identification of different strains belonging to the Streptococcus genus, including encapsulated pneumococcal isolates.

Surface-enhanced Raman spectroscopy (SERS) is a vibrational method successfully applied in analytical chemistry, molecular biology and medical diagnostics. In this article, we demonstrate the combination of the negative dielectrophoretic (nDEP) phenomenon and a flexible surface-enhanced Raman platform for quick isolation (3 min), concentration and label-free identification of bacteria. The platform ensures a strong enhancement factor, high stability and reproducibility for the SERS response of analyzed samples. By introducing radial dielectrophoretic forces directed at the SERS platform, we can efficiently execute bacterial cell separation, concentration and deposition onto the SERS-active surface, which simultaneously works as a counter electrode and thus enables such hybrid DEP-SERS device vibration-based detection. Additionally, we show the ability of our DEP-SERS system to perform rapid, cultivation-free, direct detection of bacteria in urine and apple juice samples. The device provides new opportunities for the detection of pathogens.

Selectin ligands are present on the surface of tumor cells, for this reason lowering the L‐selectin level in the blood and lymph can indicate presence of the tumor. Therefore the selectin level in the plasma are potential targets for anticancer therapy. We demonstrate the surface enhanced Raman spectroscopy (SERS)‐based sensor for the determination of L‐selectin level in biological samples that can be used in medical diagnosis. The combination of SERS with the method of multivariate analysis as principle component analysis (PCA) allows to strengthen the presented data analysis. The loadings of PCA permit to indicate those vibration modes, that are the most important for the assumed identification (bands at 1574, 1450, 1292 cm⁻¹). Two bands at 1286 and 1580 cm⁻¹ were selected for the determination of the calibration curve (bands intensities I₁₂₈₆/I₁₅₈₀ ratio). The L‐selectin level of biological samples can be read, directly from the calibration curve. The presented sensor is a sensitive tool with good specificity and selectivity of L‐selectin, even in the case of coexistence of P‐ and E‐selectin.

One of the biggest challenge for modern medicine is to make a discrimination among healthy and cancerous tissues. Therefore, nowadays big effort of scientist are devoted to find a new way for as fast as possible diagnosis with as much as possible accuracy in distinguishing healthy from cancerous tissues. That issues are probably the most important in the case of brain tumours, when the diagnosis time plays a great role. Herein we present the surface-enhanced Raman spectroscopy (SERS) together with the principal component analysis (PCA) used to identify the spectra of different brain specimens, healthy and tumour tissues homogenates. The presented analyses include three sets of brain tissues as control samples taken from healthy objects (one set consists of samples from four brain lobes and both hemispheres; eight samples) and the brain tumours from five patients (two Anaplastic Astrocytoma and three Glioblastoma samples). Results prove that tumour brain samples can be discriminated well from the healthy tissues by using only three main principal components, with 96 % of accuracy. The largest influence onto the calculated separation is attributed to the spectral regions corresponding in SERS spectra to vibrations of the L-Tryptophan (1450, 1278 cm⁻¹), protein (1300 cm⁻¹), phenylalanine and Amide-I (1005, 1654 cm⁻¹). Therefore, the presented method may open the way for the probable application as a very fast diagnosis tool alternative for conventionally used histopathology or even more as an intraoperative diagnostic tool during brain tumour surgery.

Isolation and detection of circulating tumor cells (CTCs) from human blood plays an important role in non- invasive screening of cancer evolution and in predictive therapeutic treatment. Here, we present the novel tool utilizing:

(i) the microfluidic device with

(ii) incorporated photovoltaic (PV) based SERS-active platform, and

(iii) shell-isolated nanoparticles (SHINs) for simultaneous separation and label-free analysis of circulating tumour cells CTCs in the blood specimens with high specificity and sensitivity.

The proposed microfluidic chip enables the efficient size – based inertial separation of circulating cancer cells from the whole blood samples. The SERS-active platform incorporated into the microfluidic device permits the label-free detection and identification of isolated cells through the insight into their molecular and biochemical structure. Additionally, the silver nanoparticles coated with an ultrathin shell of silica (Ag@SiO₂) was used to improve the detection accuracy and sensitivity of analysed tumor cells via taking advantages of shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS). The empirical analysis of SHINERS spectra revealed that there are some differences among studied (HeLa), renal cell carcinoma (Caki-1), and blood cells. Unique SHINERS features and differences in bands intensities between healthy and cancer cells might be associated with the variations in the quantity and quality of molecules such as lipid, protein, and DNA or their structure during the metastasis cancer formation. To demonstrate the statistical efficiency of the developed method and improve the differentiation for circulating tumors cells detection the principal component analysis (PCA) has been performed for all SHINERS data. PCA method has been applied to recognize the most significant differences in SHINERS data among the three analyzed cells: Caki-1, HeLa, and blood cells. The proposed approach challenges the current multi-steps CTCs detection methods in the terms of simplicity, sensitivity, invasiveness, destructivity, time and cost of analysis, and also prevents the defragmentation/damage of tumor cells and thus leads to improving the accuracy of analysis. The results of this research work show the potential of developed SERS based tool for the separation of tumor cells from whole blood samples in a simple and minimally invasive manner, their detection and molecular characterization using one single technology.

We show a new type of elastic surface-enhanced Raman spectroscopy (SERS) platform made of poly(ethylene terephthalate) (PET) covered with a layer of indium tin oxide (ITO). This composite is subjected to dielectric barrier discharge (DBD) that develops the active surface of the PET/ITO foil. To enhance the Raman signal, a modified composite was covered with a thin layer of silver using the physical vapor deposition (PVD) technique. The SERS platform was used for measurements of para-mercaptobenzoic acid (p-MBA) and popular pesticides, i.e., Thiram and Carbaryl. The detection and identification of pesticides on the surface of fruits and vegetables is a crucial issue due to extensive use of those chemical substances for plant fungicide and insecticide protection. Therefore, the developed PET/ITO/Ag SERS platform was dedicated to quantitative analysis of selected pesticides, i.e., Thiram and Carbaryl from fruits. The presented SERS platform exhibits excellent enhancement and reproducibility of the Raman signal, which enables the trace analysis of these pesticides in the range up to their maximum residues limit. Based on the constructed calibration curves, the pesticide concentrations from the skin of apples was estimated as 2.5 µg/mL and 0.012 µg/mL for Thiram and Carbaryl, respectively. Additionally, the PET/ITO/Ag SERS platform satisfies other spectroscopic properties required for trace pesticide analysis e.g., ease, cost-effective method of preparation, and specially designed physical properties, especially flexibility and transparency, that broaden the sampling versatility to irregular surfaces.

The detection and monitoring of circulating tumor cells (CTCs) in blood is an important strategy for early cancer evidence, analysis, monitoring of therapeutic response, and optimization of cancer therapy treatments. In this work, tailor-made membranes (MBSP) for surface-enhanced Raman spectroscopy (SERS)-based analysis, which permitted the separation and enrichment of CTCs from blood samples, were developed. A thin layer of SERS-active metals deposited on polymer mat enhanced the Raman signals of CTCs and provided further insight into CTCs molecular and biochemical composition. The SERS spectra of all studied cells—prostate cancer (PC3), cervical carcinoma (HeLa), and leucocytes as an example of healthy (normal) cell—revealed significant differences in both the band positions and/or their relative intensities. The multivariate statistical technique based on principal component analysis (PCA) was applied to identify the most significant differences (marker bands) in SERS data among the analyzed cells and to perform quantitative analysis of SERS data. Based on a developed PCA algorithm, the studied cell types were classified with an accuracy of 95% in 2D PCA to 98% in 3D PCA. These results clearly indicate the diagnostic efficiency for the discrimination between cancer and normal cells. In our approach, we exploited the one-step technology that exceeds most of the multi-stage CTCs analysis methods used and enables simultaneous filtration, enrichment, and identification of the tumor cells from blood specimens.

The surface-enhanced Raman spectroscopy (SERS)-based analysis of bacteria suffers from the lack of a standard SERS detection protocol (type of substrates, excitation frequencies, and sampling methodologies) that could be employed throughout laboratories to produce repeatable and valuable spectral information. In this work, we have examined several factors influencing the spectrum and signal enhancement during SERS studies conducted on both Gram-negative and Gram-positive bacterial species: Escherichia coli and Bacillus subtilis, respectively. These factors can be grouped into those which are related to the structure and types of plasmonic systems used during SERS measurements and those that are associated with the culturing conditions,  types of culture media, and method of biological sample preparation.

Surface-enhanced Raman scattering (SERS) has been intensively used recently as a highly sensitive, non-destructive, chemical specific, and label-free technique for a variety of studies. Here, we present a novel SERS substrate for:

(i) the standard ultra-trace analysis,

(ii) detection of whole microorganisms, and

(iii) spectroelectrochemical measurements.

The integration of electrochemistry and SERS spectroscopy is a powerful approach for in situ investigation of the structural changes of adsorbed molecules, their redox properties, and for studying the intermediates of the reactions. We have developed a conductive SERS platform based on photovoltaic materials (PV) covered with a thin layer of silver, especially useful in electrochemical SERS analysis. These substrates named Ag/PV presented in this study combine crucial spectroscopic features such as high sensitivity, reproducibility, specificity, and chemical/physical stability. The designed substrates permit the label-free identification and differentiation of cancer cells (renal carcinoma) and pathogens (Escherichia coli and Bacillus subtilis). In addition, the developed SERS platform was adopted as the working electrode in an electrochemical SERS approach for p-aminothiophenol (p-ATP) studies. The capability to monitor in real-time the electrochemical changes spectro-electro-chemically has great potential for broadening the application of SERS.

In this paper, we present novel type of Surface-enhanced Raman spectroscopy (SERS) platform, based on stainless steel wire mesh (SSWM) covered with thin silver layer. The stainless steel wire mesh, typically used in chemical engineering industry, is a cheap and versatile substrate for SERS platforms. SSWM consists of multiple steel wires with diameter of tens of micrometers, which gives periodical structure and high stiffness. Moreover, stainless steel provides great resistance towards organic and inorganic solvents and provides excellent heat dissipation. It is worth mentioning that continuous irradiation of the laser beam over the SERS substrate can be a source of significant increase in the local temperature of metallic nanostructures, which can lead to thermal degradation or fragmentation of the adsorbed analyte. Decomposition or fragmentation of the analysed sample usually causea a significant decrease in the intensity of recorded SERS bands, which either leads to false SERS responses or enables the analysis of spectral data. To our knowledge, we have developed for the first time the thermally resistant SERS platform. This type of SERS substrate, termed Ag/SSWM, exhibit high sensitivity (Enhancement Factor (EF) = 10⁶) and reproducibility (Relative Standard Deviation (RSD) of 6.4%) towards detection of p-mercaptobenzoic acid (p-MBA). Besides, Ag/SSWM allows the specific detection and differentiation between Gram-positive and Gram-negative bacterial species: Escherichia coli and Bacillus subtilis in label-free and reproducible manner. The unique properties of designed substrate overcome the limitations associated with photo- and thermal degradation of sensitive bacterial samples. Thus, a distinctive SERS analysis of all kinds of chemical and biological samples at high sensitivity and selectivity can be performed on the developed SERS-active substrate.

One of the potential applications of surface-enhanced Raman spectroscopy (SERS) is the detection of biological compounds and microorganisms. Here we demonstrate that SERS coupled with principal component analysis (PCA) serves as a perfect method for determining the taxonomic affiliation of bacteria at the strain level. We demonstrate for the first time that it is possible to distinguish different genoserogroups within a single species, Listeria monocytogenes, which is one of the most virulent foodborne pathogens and in some cases contact with which may be fatal. We also postulate that it is possible to detect additional proteins in the L. monocytogenes cell envelope, which provide resistance to benzalkonium chloride and cadmium. A better understanding of this infectious agent could help in selecting the appropriate pharmaceutical product for enhanced treatment.

An ultrasensitive surface-enhanced Raman scattering (SERS) immunoassay based on diatom biosilica with integrated gold nanoparticles (AuNPs) for the detection of interleukin 8 (IL-8) in blood plasma has been developed. The SERS sensing originates from unique features of the diatom frustules, which are capable of enhancing the localized surface-plasmon resonance of metal nanostructures. The SERS immune tags ware fabricated by functionalizing 70-nm Au nanoparticles with DTNB (i.e., 5,5′-dithiobis(2-nitrobenzoic acid)), which acted as a Raman reporter molecule, as well as the specific antibodies. These DTNB-labeled immune-AuNPs can form a sandwich structure with IL-8 antigens (infection marker) and the antibodies immobilized on the biosilica material. Our method showed an improved IL-8 detection limit in comparison to standard ELISA methods. The current detection limit for IL-8 using a conventional ELISA test is about 15.6 pg mL-1. The lower detection limit for IL-8 in blood plasma was estimated to be 6.2 pg mL-1. To the best of our knowledge, this is the first report on the recognition of IL-8 in human samples using a SERS-based method. This method clearly possesses high sensitivity to clinically relevant interleukin concentrations in body fluids. The average relative standard deviation of this method is less than 8 %, which is sufficient for analytical analysis and comparable to those of classical ELISA methods. This SERS immunoassay also exhibits high biological specificity for the detection of IL-8 antigens. The established SERS immunoassay offers a valuable platform for the ultrasensitive and highly specific detection of immune biomarkers in a clinical setting for medical diagnostics. Graphical Abstract The SERS-based immunoassay based on naturally generated photonic biosilica for the detection of interleukin 8 (IL-8) in human plasma samples.

This paper demonstrates that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast and reliable technique for detection and identification of dermatophyte fungi at both genus and species level. Dermatophyte infections are the most common mycotic diseases worldwide, affecting a quarter of the human population. Currently, there is no optimal method for detection and identification of fungal diseases, as each has certain limitations. Here, for the first time, we have achieved with a high accuracy, differentiation of dermatophytes representing three major genera, i.e. Trichophyton, Microsporum, and Epidermophyton. Two first principal components (PC), namely PC-1 and PC-2, gave together 97 % of total variance. Additionally, species-level identification within the Trichophyton genus has been performed. PC-1 and PC-2, which are the most diagnostically significant, explain 98 % of the variance in the data obtained from spectra of: Trichophyton rubrum, Trichophyton menatgrophytes, Trichophyton interdigitale and Trichophyton tonsurans. This study offers a new diagnostic approach for the identification of dermatophytes. Being fast, reliable and cost-effective, it has the potential to be incorporated in the clinical practice to improve diagnostics of medically important fungi.

SERS-active nanostructures incorporated into a microfluidic device have been developed for rapid and multiplex monitoring of selected Type 1 cytokine (interleukins: IL-6, IL-8, IL-18) levels in blood plasma. Multiple analyses have been performed by using nanoparticles, each coated with different Raman reporter molecules: 5,5′-dithio-bis(2-nitro-benzoic acid) (DTNB), fuchsin (FC), and p-mercatpobenzoic acid (p-MBA) and with specific antibodies. The multivariate statistical method, principal component analysis (PCA), was applied for segregation of three different antigen-antibody complexes encoded by three Raman reporters (FC, p-MBA, and DTNB) during simultaneous multiplexed detection approach. To the best of our knowledge, we have also presented, for the first time, a possibility for multiplexed quantification of three interleukins: IL-6, IL-8, and IL-18 in blood plasma samples using SERS technique. Our method improves the detection limit in comparison to standard ELISA methods. The low detection limits were estimated to be 2.3 pg·ml⁻¹, 6.5 pg·ml⁻¹, and 4.2 pg·ml⁻¹ in a parallel approach, and 3.8 pg·ml⁻¹, 7.5 pg·ml⁻¹, and 5.2 pg·ml⁻¹ in a simultaneous multiplexed method for IL-6, IL-8, and IL-18, respectively. This demonstrated the sensitivity and reproducibility desirable for analytical examinations.

Surface-enhanced Raman spectroscopy (SERS) has been widely used in a variety of biomedical, analytical, forensic and environmental investigations due to its chemical specificity, label-free nature combined with high sensitivity. Here, we report a simple method for the fabrication of reproducible and reliable, well-defined, stable SERS substrates with uniform and giant Raman enhancement suitable for routine trace chemical analysis and detection of biological compounds in complex biological fluids. We prepared porous silicone (PS) surface by a galvanostatic anodic etch of crystalline silicon wafers. The electrochemical process generates a specific layer of PS: the thickness and porosity of a given layer is controlled by the current density, the duration of the etch cycle, and the composition of the etchant solution. These substrates presented high sensitivity to p-mercaptobenzoic acid (p-MBA) at a low concentration of 10^− 6 M and the enhancement factor of over 10⁸ was achieved. Such high enhancement is attributed to semiconducting silicon-induced and stabilized hot spots. The uniform distribution of SERS–active ‘hot-spots’ on the Au/Si surface results in high reproducibility towards detecting p-MBA at 40 different, randomly selected positions on a single substrate (RSD = 6.7 %) and on twenty different SERS substrates prepared under identical conditions (RSD = 8 %). Designed substrates allow the ultrahigh sensitive and specific detection of human such biofluids as blood, urine and cerebrospinal fluid (CSF) in a reliable, label-free, and reproducible manner. The SERS spectra of these fluids are rich in patient-specific information and can be useful in many analytical and biomedical applications. We have shown that our developed SERS substrates allow the nanomolar detection of neopterin (bacterial infections' marker) in cerebrospinal fluid samples. In order to test the performance of our SERS method in term of low detection limit (LOD), the calibration curve i.e. plot of SERS intensity of the marker band at 695 cm^− 1 versus the concentration of neopterin in CSF was constructed and used to calculate the neopterin concentration in clinical samples. The level of neopterin was significantly higher in CSF samples infected by Neisseria meningitidis, (54 nmol/L), compared to normal (control) group, (4.3 nmol/L).

The high sensitivity, selectivity and stability of obtained SERS-active substrates combined with simple, low-cost, and easy method of producing offer a promising tool for SERS-based analysis in clinical trials.

We show that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast, reliable, and easy method for detection and identification of food-borne bacteria, namely Salmonella spp., Listeria monocytogenes, and Cronobacter spp., in different types of food matrices (salmon, eggs, powdered infant formula milk, mixed herbs, respectively). The main aim of this work was to introduce the SERS technique into three ISO (6579:2002; 11290–1:1996/A1:2004; 22964:2006) standard procedures required for detection of these bacteria in food. Our study demonstrates that the SERS technique is effective in distinguishing very closely related bacteria within a genus grown on solid and liquid media. The advantages of the proposed ISO-SERS method for bacteria identification include simplicity and reduced time of analysis, from almost 144 h required by standard methods to 48 h for the SERS-based approach. Additionally, PCA allows one to perform statistical classification of studied bacteria and to identify the spectrum of an unknown sample. Calculated first and second principal components (PC-1, PC-2) account for 96, 98, and 90 % of total variance in the spectra and enable one to identify the Salmonella spp., L. monocytogenes, and Cronobacter spp., respectively. Moreover, the presented study demonstrates the excellent possibility for simultaneous detection of analyzed food-borne bacteria in one sample test (98 % of PC-1 and PC-2) with a goal of splitting the data set into three separated clusters corresponding to the three studied bacteria species. The studies described in this paper suggest that SERS represents an alternative to standard microorganism diagnostic procedures.

Three of the most common meningitis pathogens, Neisseria meningitidis, Haemophilus influenzae, and Streptococcus pneumoniae, have been successfully detected and identified in clinical cerebrospinal fluid (CSF) samples using a new class of a surface-enhanced Raman scattering (SERS) assay. Bacterial meningitis is a disease of the nervous system that is extremely serious and often fatal (an inflammation encompasses the lining around the brain and spinal cord). The approach presented in this study challenges the current SERS-based method of microorganism detection in terms of sensitivity and, more importantly, reveals a simple, quick (on a timescale of seconds), label-free detection of multiple components from very small volumes of clinical samples. This new SERS class of assay, based on the combination of two types of Au/Ag-coated, nuclepore track-etched polycarbonate membranes, allow simultaneous filtration of CSF and immobilization of CSF components, enhancing their Raman signals and enabling detection of the spectra of a single bacteria cell present in the analyzed CSF samples. The multivariate statistical method, principal component analysis (PCA), was applied:

(i) to extract the biochemical information from the recorded bacterial spectra,

(ii) to perform the statistical classification of analyzed microorganisms, and, finally,

(iii) to identify the spectrum of an unknown sample by comparing it to the library of known bacterial spectra.

The three meningitis pathogens, namely, N. meningitidis, H. influenzae, and S. pneumoniae, were detected and identified simultaneously using a label-free SERS method. This method of detection produces consistent results faster and cheaper than traditional laboratory techniques and demonstrates the powerful potential of SERS technique in medical applications. Additionally, the present study was undertaken to evaluate the CSF neopterin level in patients with diagnosed meningococcal meningitis. The results of this study confirmed that bacterial meningitis caused by N. meningitidis, H. influenzae, and S. pneumoniae is associated with elevated cerebrospinal fluid neopterin levels compared with control CSF samples. The neopterin concentration can be used to predict meningitis, but cannot be applied to qualify the species of bacteria inducing the meningitis infection.

This paper demonstrates that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast and reliable technique for the detection and identification of human fungal pathogens, such as Trichophyton rubrum, Candida krusei, Scopulariopsis brumptii, and Aspergillus flavus. Fungal infections have become one of the leading infectious causes of morbidity and mortality among hospitalized patients and/or immunocompromised hosts. Hence, there is a strong need for the development of new technologies allowing for fast and reliable diagnosis of fungal diseases. Our study shows that the SERS technique effectively distinguishes between selected common fungal pathogens and thus offers taxonomic affiliation of fungi within several minutes. Additionally, the PCA analysis allows performing statistical classification of fungal pathogens studied and identifying the fungal spectrum directly from a clinical sample. Calculated two principal components (PCs) (PC-1, PC-2) are the most diagnostically significant, explain 97 % of the variability and enable, with very high probability, discrimination between the four mentioned fungal species. Moreover, the results of this study demonstrate the excellent possibility for the identification of fungi from human skin samples. The research presented in this paper offers an alternative for conventional fungal diagnostics and paves the way for the development of a new, fast, robust, and cost-effective diagnostic test for the detection and identification of fungal pathogens.

A highly efficient recognition unit based on surface-enhanced Raman spectroscopy (SERS) was developed as a promising, fast, and sensitive tool for detection of meningococcal meningitis, which is an extremely serious and often fatal disease of the nervous system (an inflammation of the lining around the brain and spinal cord). The results of this study confirmed that there were specific differences in SERS spectra between cerebrospinal fluid (CSF) samples infected by Neisseria meningitidis and the normal CSF, suggesting a potential role for neopterin in meningococcal meningitis detection and screening applications. To estimate the best performance of neopterin as a marker of bacterial infection, principal component analysis (PCA) was performed in a selected region (640–720 cm⁻¹) where the most prominent SERS peak at 695 cm⁻¹ arising from neopterin was observed. The calculated specificity of 95 % and sensitivity of 98 % clearly indicate the effective diagnostic efficiency for differentiation between infected and control samples. Additionally, the limit of detection (LOD) of neopterin in CSF clinical samples was estimated. The level of neopterin was significantly higher in CSF samples infected by N. meningitidis (48 nmol/L), compared to the normal (control) group (4.3 nmol/L). Additionally, this work presents a new type of SERS-active nanostructure, based on polymer mats, that allows simultaneous filtration, immobilization, and enhancement of the Raman signal, enabling detection of spectra from single bacterial cells of N. meningitidis present in CSF samples. This provides a new possibility for fast and easy detection of bacteria in CSF and other clinical body fluids on a time scale of seconds. This method of detection produces consistent results faster and cheaper than traditional laboratory techniques, demonstrates the powerful potential of SERS for detection of disease, and shows the viability of future development in healthcare applications.

One of potential applications of nano- and microscale polymer fibers is SERS-active platforms for the detection of biological compounds and microorganisms. This paper demonstrates the polymer mat obtained with Forcespinning technique used to detect the bacteria from blood plasma. Forcespinning is a new method of manufacturing of polymer fibers which can be applied to variety of polymer materials, e.g. polyethylene, nylon, PA6 and others. The method is based on the centrifugal force to draw fiber from molten polymer, which allows tuning the diameter of the fiber from tens of nanometers up to micrometers. Wide range of diameters makes the forcespun polymer mat an excellent material to filter bacteria from fluids (e.g. blood plasma, water). Covering the mat with Au:Ag alloy turns it into a SERS platform able to immobilize, detect, and identify bacteria. We provide proof-of-concept, showing detection of S. aureus, P. aeruginosa, and S. Typhimurium from blood plasma.

A stable and efficient surface-enhanced Raman scattering (SERS) substrate for neurotransmitter and cholinergic neurotransmission precursor detection was obtained by silver nanoparticle (AgNP) electrode-position onto tin-doped indium oxide (ITO) using cyclic voltammetry. The size and surface coverage of the deposited AgNPs were controlled by changing the scan rate and the number of scans. The SERS performance of these substrates was analyzed by studying its reproducibility, repeatability and signal enhancement measured from p-aminothiophenol (p-ATP) covalently bonded to the substrate. We compared the SERS performance for samples with different Ag particle coverage and particle sizes. The performance was also compared with a commercial substrate. Our substrates exhibited a SERS enhancement factor of around 10⁷ for p-ATP which is three orders of magnitude larger than for the commercial substrate. Apart from this high enhancement effect the substrate also shows extremely good reproducibility. The average spectral correlation coefficient (Gamma) is 0.96. This is larger than for the commercial substrate (0.85) exhibiting a much lower SERS signal intensity. Finally, the application of our substrates as SERS bio-sensors was demonstrated with the detection of the neurotransmitters acetylcholine, dopamine, epinephrine and choline, the precursor for acetylcholine. The intensive SERS spectra observed for low concentrations of choline (2 x 10^-6 M), acetylcholine (4 x 10^-6 M), dopamine (1 x 10^-7 M) and epinephrine (7 x 10^-4 M) demonstrated the high sensitivity of our substrate. The high sensitivity and fast data acquisition make our substrates suitable for testing physiological samples.

Surface-enhanced Raman spectroscopy (SERS) is a potentially important tool in the rapid and accurate detection of pathogenic bacteria in biological fluids. However, for diagnostic application of this technique, it is necessary to develop a highly sensitive, stable, biocompatible and reproducible SERS-active substrate. In this work, we have developed a silver-gold bimetallic SERS surface by a simple potentiostatic electrodeposition of a thin gold layer on an electrochemically roughened nanoscopic silver substrate. The resultant substrate was very stable under atmospheric conditions and exhibited the strong Raman enhancement with the high reproducibility of the recorded SERS spectra of bacteria (E. coli, S. enterica, S. epidermidis, and B. megaterium). The coating of the antibiotic over the SERS substrate selectively captured bacteria from blood samples and also increased the Raman signal in contrast to the bare surface. Finally, we have utilized the antibiotic-coated hybrid surface to selectively identify different pathogenic bacteria, namely E. coli, S. enterica and S. epidermidis from blood samples.

This paper demonstrates a renewed procedure for the quantification of surface-enhanced Raman scattering (SERS) enhancement factors with improved precision. The principle of this method relies on deducting the resonance Raman scattering (RRS) contribution from surface-enhanced resonance Raman scattering (SERRS) to end up with the surface enhancement (SERS) effect alone. We employed 1,8,15,22-tetraaminophthalocyanato-cobalt(II) (4 alpha-Co(II)TAPc), a resonance Raman-and electrochemically redoxactive chromophore, as a probe molecule for RRS and SERRS experiments. The number of 4 alpha-Co(II)TAPc molecules contributing to RRS and SERRS phenomena on plasmon inactive glassy carbon (GC) and plasmon active GC/Au surfaces, respectively, has been precisely estimated by cyclic voltammetry experiments. Furthermore, the SERS substrate enhancement factor (SSEF) quantified by our approach is compared with the traditionally employed methods. We also demonstrate that the present approach of SSEF quantification can be applied for any kind of different SERS substrates by choosing an appropriate laser line and probe molecule.

Efficient and low-cost surface-enhanced Raman scattering (SERS) substrates based on Au coated zinc oxide layers for the detection of neopterin were prepared. These substrates showed high sensitivity to p-mercaptobenzoic acid (p-MBA) at a low concentration of 10^-9 M and an enhancement factor of over 10⁷ was achieved. The uniform density of SERS-active 'hot-spots' on a Si/ZnO/Au surface results in high reproducibility towards detecting p-MBA at 50 different, randomly selected positions on a single substrate (RSD = 9%) and on six different SERS substrates prepared under identical conditions (RSD = 11 %). These SERS substrates show good performance in the detection of neopterin, a biologically important molecule whose concentration levels reflect the stage of activation of the cellular immune system, which is of value in the studies of pathogenesis and progression of various diseases. The detection limit is found to be as low as 1.4 nmol L-1 in blood plasma, which is comparable to that of classic ELISA methods. The average relative standard deviation (RSD) of the proposed method is less than 10 %. Moreover, this label-free strategy of detection gives exact results over a large range, reflecting clinically relevant neopterin concentrations in body fluids. The detection and quantification of neopterin levels in blood or urine might be useful in clinical practice for monitoring the disease activity during treatment and for early detection of many infections and autoimmune, inflammatory, and malignant diseases.

Novel surface enhanced Raman spectroscopy (SERS) platforms have been prepared and used for the bacteria detection. Unlike typical, expensive SERS platforms prepared from gold or silver, the presented platforms are prepared using copper. A new, simple, cost-efficient and fast high pressure method is used for platform fabrication, through the decomposition of copper hydride. The platform enhancement factors are verified using the malachite green isothiocyanate as a standard. The platforms exhibit extremely high SERS enhancement factors depending on pressure used for their preparation. The calculated enhancement factors have been found in the range between 1.5 x 10⁶ and 4.6 x 10⁷. The SERS spectra reproducibility is established both across a single platform and among different platforms. The average spectral correlation coefficient has been calculated to be 0.82. Fully characterized SERS platforms have then been used for detecting Staphylococcus aureus bacteria. These novel platforms have great potential to become excellent tools for biological or medical diagnostics as an alternative to more common silver or gold SERS platforms.

This article describes the detection of DNA mutations using novel Au-Ag coated GaN substrate as SERS (surface-enhanced Raman spectroscopy) diagnostic platform. Oligonucleotide sequences corresponding to the BCR-ABL (breakpoint cluster region-Abelson) gene responsible for development of chronic myelogenous leukemia were used as a model system to demonstrate the discrimination between the wild type and Met244Val mutations. The thiolated ssDNA (single-strand DNA) was  immobilized on the SERS-active surface and then hybridized to a labeled target sequence from solution. An intense SERS signal of the reporter molecule MGITC was detected from the complementary target due to formation of double helix. The SERS signal was
either not observed, or decreased dramatically for a negative control sample consisting of labeled DNA that was not complementary  to the DNA probe. The results indicate that our SERS substrate offers an opportunity for the development of novel diagnostic assays.

A highly sensitive immunoassay utilizing surface-enhanced Raman scattering (SERS) has been developed with a new Raman reporter and a unique SERS-active substrate incorporated into a microfluidic device. An appropriately designed Raman reporter, basic fuchsin (FC), gives strong SERS enhancement and has the ability to bind both the antibody and gold nanostructures. The fuchsin-labeled immuno-Au nano-flowers can form a sandwich structure with the antigen and the antibody immobilized on the SERS-active substrate based on Au–Ag coated GaN. Our experimental results indicate that this SERS-active substrate with its strong surface-enhancement factor, high stability and reproducibility plays a crucial role in improving the efficiency of SERS immunoassay. This SERS assay was applied to the detection of Hepatitis B virus antigen (HBsAg) in human blood plasma. A calibration curve was obtained by plotting the intensity of SERS signal of FC band at 1178 cm
-1 versus the concentration of antigen. The low detection limit for Hepatitis B virus antigen was estimated to be 0.01IU/mL. The average relative standard deviation (RSD) of this method is less than 10%. This SERS immunoassay gives exact results over a broad linear range, reflecting clinically relevant HBsAg concentrations. It also exhibits high biological specificity for the detection of Hepatitis B virus antigen

This work demonstrates the development of a new class of SERS substrates that allows for the simultaneous:

(i) filtration of bacteria from any solution (blood, urine, water, or milk),

(ii) immobilization ofbacteria on the SERS platform, and

(iii) enhancing the Raman signal of bacteria.

The proposed platform is based on an electrospun polymer mat covered with a 90 nm layer of gold.

The procedure for identifying components in a mixture was developed and tested on Raman spectra of mixtures of solid amino acids, using the spectra of single amino acids as templates. The method is based on finding the optimum scaling coefficients of the linear combination of template spectra that minimize the Canberra distance between measured and reconstructed spectra. The Canberra distance, used here as a measure of dissimilarity between spectra, defines the non-convex objective function in the related optimization process. In view of the possibility of the presence of local minima, differential evolution, which is a non-gradient stochastic method for finding the global minimum, was chosen for optimization.
The method was tested on twenty measured spectra of mixtures of solid powders containing one to eight amino acids taken from the collection of twenty that are coded in living organisms. The results show that the procedure can successfully identify several amino acids, and, in general, several components in a mixture. The method was shown to compare favorably against the least squares and partial least squares methods, the procedures used in commercially available chemometrics packages.

The article presents surface enhanced Raman scattering (SERS) technique associated with the principal component analysis (PCA) as a fast and reliable method for the study of interactions between the A, B, AB and O (abr. ABO) blood groups antigen and complementary monoclonal A and B antibodies. The possibility of simultaneous detection and differentiation within the ABO group was evaluated. Using 785 nm excitation wavelength, distinctive spectral changes among all types of the studied blood groups were found for mixtures of red blood cells (RBCs) with the A or B antibody. For PCA analysis, all the spectral data were divided into two main groups based on the type of antibody. The obtained PC scores in the area of antigen-antibody interactions (1311-1345 cm^-1) allow differentiation within blood groups with accuracy from 96 % to 98 %. Additionally, for this region the characteristic marker bands of specific antigen-antibody interactions in relation to both ABO system and antibody were established. The results show excellent segregation of the obtained data and the possibility to use SERS for determination of ABO blood group. Our study proves that SERS is one of the most sensitive techniques for investigations of biological samples and may be used as a new tool that provides one-step comprehensive and reliable medical diagnosis.