Surface Enhanced Raman Spectroscopy Spectra Research Articles

AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the model's predictions and the protein's known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.

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.

Investigation of Melphalan interaction as an alkylating agent with nucleotides by using surface enhanced Raman spectroscopy (SERS)

SERS (Surface Enhanced Raman Spectroscopy) is a new Raman spectroscopy which relies on Surface Plasmon Resonance (SPR) of metal nanoparticles. We have applied colloidal silver and gold nanoparticles as amplifier agents to enhance nucleotide Raman signals. It is observed that without these enhancing agents, it is impossible to investigate nucleotide spectrum due to weak Raman signals. Interaction mechanism of Melphalan, an anticancer drug with four nucleotides (Adenine, Cytosine, Guanine, Thymine) was investigated using SERS to detect and identify changes due to alkylating process in Raman spectra. After incubating Melphalan drug with nucleotides for 24 h at 37 °C, some changes occurred in SERS spectrum and interpretation of SERS spectra revealed the influence of the alkyl substitution on peaks and Raman shifts. After incubation of Melphalan with each nucleotide, intensity of relevant SERS signals assigned to Amid III group of Cytosine and Amid I of Thymine decreased significantly, confirming alkylating taking place. In this study, we also investigated the effect of nanoparticles type on nucleotide spectrum. We could not obtain useful information in the cases of guanine nucleotide. The SERS spectrum of Cytosine as an example of nucleotides in aqueous solution compared to solid state and results demonstrated that in solid state better signals were obtained than in liquid state.

Single AgNF@ZIF-8 nanoparticle for deep learning assisted SERS detection of gaseous molecule

Random nanogaps between plasmonic nanoparticles formed by the surface tension of solution bring a great challenge in the application of Surface Enhanced Raman Spectroscopy (SERS) technique. Meanwhile, due to its natural nanogaps on the surface, single plasmonic nanoflower exhibits a unique charm for avoiding random gaps in the quantitative analysis of SERS. Herein, a single core-shell plasmonic nanoparticle consisting of an Ag nanoflower (AgNF) core and zeolite imidazolate frame-8 (ZIF-8) shell is synthesized for SERS detection of the gaseous molecule. Benefiting the porous shell of ZIF-8 to capture gaseous molecules and natural nanogaps on the surface of AgNF to enhance Raman intensity, SERS spectra of the gaseous molecule are successfully collected with high sensitivity at 10−6 M, which also exhibit high SERS stability with RSD at 2.29 % for single nanoparticle and 9.86 % for multiple single nanoparticles. In addition, two levels of automatic identification based on deep learning (DL) are also implemented in this work, whose data demonstrate artificial neural network (ANN) algorithm could identify gaseous glutaraldehyde (GA) as typical volatile organic compounds (VOCs) with high accuracy at 97.5 %. This single core-shell plasmonic MOF nanoparticle overcomes the limitations of traditional SERS detection in nanoparticle aggregating and realizes the detection of gaseous molecules, which enlarges the application of SERS technique.

Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films.

Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhanced Raman spectroscopy (SERS) emerges as a spectral analysis technology with high sensitivity. However, accurate SERS identification of a gaseous molecule with low density and high velocity is still a challenge due to its difficulty in capture. In this work, a flexible paper-based plasmonic metal-organic framework (MOF) film consisting of Ag nanowires@ZIF-8 (AgNWs@ZIF-8) is fabricated for SERS detection of gaseous molecules. Benefiting from its micronanopores generated by the nanowire network and ZIF-8 shell, the effective capture of the gaseous molecule is achieved, and its SERS spectrum is obtained in this paper-based flexible plasmonic MOF nanowire film. With optimal structure parameters, spectra of gaseous 4-aminothiophenol, 4-mercaptophenol, and dithiohydroquinone demonstrate that this film has good SERS performance, which could maintain obvious Raman signals within 30 days during reproducible detection. To realize SERS identification of gaseous molecules, deep learning is performed based on the SERS spectra of the mixed gaseous analyte obtained in this flexible porous film. The results point out that an artificial neural network algorithm could identify gaseous aldehydes (gaseous biomarker of colorectal cancer) in simulated exhaled breath with high accuracy at 93.7%. The integration of the flexible paper-based film sensors with deep learning offers a promising new approach for noninvasive colorectal cancer screening. Our work explores SERS applications in gaseous analyte detection and has broad potential in clinical medicine, food safety, environmental monitoring, etc.

Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis.

The Gram-negative non-motile Klebsiella pneuomoniae is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of K. pneumoniae outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the K. pneumoniae strains, which were then comparatively classified and predicted via six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the K. pneumoniae strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.

Activated carbon@silver nanoparticles conjugates as SERS substrate for capturing malathion analyte molecules for SERS detection

AbstractMalathion is one of the commonly used organophosphate pesticides known to attack the central nervous system, posing a risk to humans and other animals upon exposure. The surface enhanced Raman spectroscopy (SERS) has been identified as an indispensable tool for chemical and biomolecular sensing. In this work, the facile fabrication of activated carbon (AC)‐based colloidal SERS active substrate dubbed AC@AgNPs was designed by trapping AgNPs on the surface of AC for detection of varying concentrations of malathion. Apart from the higher concentrations of malathion, the rest of the normal Raman spectra of malathion standard solutions exhibited weak Raman signals. The intensity of peaks for 0.47 mg L−1 were nearly non‐existent which is an indication that the malathion pesticide could only be detected up to 0.95 mg L−1 when using silica wafer. On the contrary, all the SERS spectra of malathion in wheat extracts adsorbed on AC@AgNPs substrate exhibited strong Raman signals. Quantitative analysis of malathion was performed by regression models developed using PLSR built with the raw spectra (no pretreatment), SNV‐PLSR, and SNV‐CARS‐PLSR. The model with the most remarkable performance was established by using SNV‐PLSR with r = 0.9869 and RPD = 4.61. This research shows that the proposed method can rapidly detect malathion residues in wheat, suggesting that it could be adopted for production process monitoring of other related food products to guarantee their safety for human and animal consumption.

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.

Label free detection of multiple trace antibiotics with SERS substrates and independent components analysis

Surface enhanced Raman spectroscopy (SERS) has been widely studied and recognized as a powerful label-free technique for trace chemical analysis. However, its drawback in simultaneously identifying several molecular species has greatly limited its real-world applications. In this work, we reported a combination between SERS and independent component analysis (ICA) to detect several trace antibiotics which are commonly used in aquacultures, including malachite green, furazolidone, furaltadone hydrochloride, nitrofurantoin, and nitrofurazone. The analysis results indicate that the ICA method is highly effective in decomposing the measured SERS spectra. The target antibiotics could be precisely identified when the number of components and the sign of each independent component loading were properly optimized. With SERS substrates, the optimized ICA can identify trace molecules in a mixture at a concentration of 10−6 M achieving the correlation values to the reference molecular spectra of 71–98%. Furthermore, measurement results obtained from a real-world sample demonstration could also be recognized as an important basis to suggest this method is promising for monitoring antibiotics in a real aquatic environment.

Surface-enhanced Raman spectroscopy for studying the interaction of N-propyl substituted imidazole compound with salmon sperm DNA.

Surface Enhanced Raman Spectroscopy (SERS) is a very promising and fast technique for studying drugs and for detecting chemical nature of a molecule and DNA interaction. In the current study, SERS is employed to check the interaction of different concentrations of n-propyl imidazole derivative ligand with salmon sperm DNA using silver nanoparticles as SERS substrates. Multivariate data analysis technique like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) are employed for the detailed analysis of the SERS spectral features associated with the mode of action of the imidazole derivative ligand with DNA. Silver nanoparticles were used as a SERS substrate in DNA-drug interaction. Five different concentrations of ligands were interacted with DNA and mix with Ag-NPs as substrate. The SERS spectra of were acquired for all seven samples and processed using MATLAB. Additionally, PCA and PLS-DA were used to assessed the ability SERS to differentiate interaction of DNA-drug. Differentiating SERS features having changes in their peak position and intensities are observed including 629, 655, 791, 807, 859, 1337, 1377 and 1456cm-1. These SERS features reveal that binding of ligand with DNA is electrostatic in nature, and have specificity to major groove where it forms GC-CG interstrand cross-linking with the DNA double helix. SERS give significant information regarding to Drug-DNA interaction mechanism, SERS spectra inferred the mode of action of anticancer compound that are imidazole in nature.

Determination of Shigella spp. via label-free SERS spectra coupled with deep learning

Accurate discrimination of Shigella spp. sits in the core of shigellosis prevention and control. As a label-free method, surface enhanced Raman spectroscopy (SERS) is being intensively investigated for bacterial diagnostics. In this study, we developed a novel method for rapid and accurate discrimination of Shigella spp. via label-free SERS coupling with multiscale deep-learning method. In particular, SERS spectral deconvolution was used to generate unique barcodes, revealing subtle differences in molecular composition between Shigella spp. Four supervised learning models based on Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and One-Dimensional Multi-Scale CNN (1DMSCNN) were constructed and assessed for their predictive capacities of Shigella spp. The results showed that 1DMSCNN achieved the best performance, which could quickly distinguish four Shigella spp. accurately. Finally, we built a software embedded with 1DMSCNN model to predict raw SERS spectra of Shigella spp., which is freely available at https://github.com/4forfull/1DMSCNN_RAMAN_SHIGELLA.

A SERS based clinical study on HIV-1 viral load quantification and determination of disease prognosis.

In resource limited settings, a cost-effective point-of-care diagnostic testing possessing the characteristics of detecting the minimum viral load of a malady like human immunodeficiency virus (HIV) acquired immune deficiency syndrome (AIDS) is a pressing priority. The present work describes a novel, rapid and field-deployable method using surface enhanced Raman spectroscopy (SERS) for detection and prognosis of HIV positive clinical samples, in seven different viral load ranges varying between 200 and 1 million copies/ml. A relationship between the increasing and decreasing intensity peaks of HIV-1 was also established for quantitation efficacy of the handheld tool. Three different types of SERS substrates: single arm Ag nanorods, double arm Ag nanorods and Au sputtered single arm Ag nanorods were used and the obtained data was compared for the three substrates. It was demonstrated that maximum enhancement was obtained for Au sputtered Ag nanorods. Rigorous coupled wave analysis (RCWA) simulations were performed to study the 'hotspots' in three different SERS substrates. Further, to explore the utility of our platform and to differentiate between the clade specific X4 and R5 tropism, their corresponding SERS spectra were studied using HIV-1 strains belonging to four different HIV-1 subtypes (A, B, C and D) which showed a clear distinction, implying the usefulness of the platform in understanding the disease prognosis. Statistical analysis of the obtained SERS spectra using principal component analysis (PCA) showed good agreement with the experimental results, confirming the ability of SERS platform to quantitate HIV-1 viral load and distinguish HIV-1 strains on the basis of their SERS spectra.

SERS for Detection of Proteinuria: A Comparison of Gold, Silver, Al Tape, and Silicon Substrates for Identification of Elevated Protein Concentration in Urine.

Excessive protein excretion in human urine is an early and sensitive marker of diabetic nephropathy and primary and secondary renal disease. Kidney problems, particularly chronic kidney disease, remain among the few growing causes of mortality in the world. Therefore, it is important to develop an efficient, expressive, and low-cost method for protein determination. Surface enhanced Raman spectroscopy (SERS) methods are potential candidates to achieve these criteria. In this paper, a SERS method was developed to distinguish patients with proteinuria from the healthy group. Commercial gold nanoparticles (AuNPs) with diameters of 60 nm and 100 nm, and silver nanoparticles (AgNPs) with a diameter of 100 nm were tested on the surface of four different substrates including silver and gold films, silicon, and aluminum tape. SERS spectra were acquired from 111 unique human urine samples prepared and measured for each of the seven different nanoparticle plus substrate combinations. Data analysis by the PCA-LDA algorithm and the ROC curves gave results for the diagnostic figures of merits. The best sensitivity, specificity, accuracy, and AUC were 0.91, 0.84, 0.88, and 0.94 for the set with 100 nm Au NPs on the silver substrate, respectively. Among the three metal substrates, the substrate with AuNPs and Al tape performed slightly worse than the other three substrates, and 100 nm gold nanoparticles on average produced better results than 60 nm gold nanoparticles. The 60 nm diameter AuNPs and silicon, which is about one order of magnitude more cost-effective than AuNPs and gold film, showed a relative performance close to the performance of 60 nm AuNPs and Au film (average AUC 0.88 (Si) vs. 0.89 (Au)). This is likely the first reported application of unmodified silicon in SERS substrates applied for direct detection of proteins in any biofluid, particularly in urine. These results position silicon and AuNPs@Si in particular as a perspective SERS substrate for direct urine analysis, including clinical diagnostics of proteinuria.

Analysis of riboflavin/ultraviolet a corneal cross-linking by molecular spectroscopy

Corneal cross-linking (CXL) with riboflavin and ultraviolet A light is a therapeutic procedure to restore the mechanical stability of corneal tissue. The treatment method is applied to pathological tissue, such as keratoconus and induces the formation of new cross-links. At present, the molecular mechanisms of induced cross-linking are still not known exactly. In this study, we investigated molecular alterations within porcine cornea tissue after treatment with riboflavin and ultraviolet A light by surface enhanced Raman spectroscopy (SERS). For that purpose, after CXL treatment a thin silver layer was vapor-deposited onto cornea flaps. To explore molecular alterations induced by the photochemical process hierarchical cluster analysis (HCA) was used. The detailed analysis of SERS spectra reveals that there is no general change in collagen secondary structure while modifications on amino acid side chains are the most dominant outcome. The formation of secondary and aromatic amine groups as well as methylene and carbonyl groups were observed. Even though successful cross-linking could not be registered in all treated samples, Raman signals of newly formed chemical groups are already present in riboflavin only treated corneas.

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Determination of blood species using echelle Raman spectrometer and surface enhanced Raman spectroscopy

Blood species identification of human and animals has attracted much attention in the areas of customs inspection and forensic science. The combination of vibrational spectroscopy and machine learning has been proven to be feasible and effective for this purpose. However, the popularization of this technology needs instrument which is compact, robust and more suitable for field application. Besides the quantity of the blood sample should be as little as possible. In this study, we proposed a system using echelle Raman spectrometer combined with surface enhanced Raman spectroscopy (SERS), which protocol combines the advantages of broadband and high resolution of echelle Raman spectrometer with the advantages of high SERS spectral sensitivity. The SERS spectra of 26 species including human were collected with echelle Raman spectrometer, and the convolutional neural network was used for species identification, with an accuracy rate of over 94%. The feasibility, validity and reliability of the combination of echelle Raman spectrometer and SERS for blood species identification were realized.

Silver Nanoparticle Films Obtained by Convective Self-Assembly for Surface-Enhanced Raman Spectroscopy Analyses of the Pesticides Thiabendazole and Endosulfan.

Pesticides pose a great threat to human health and their rapid detection has become an urgent public safety issue engaging the scientific community to search for fast and reliable detection techniques. In this context, Surface Enhanced Raman Spectroscopy (SERS) has emerged as a valuable detection and analysis tool due to its high sensitivity and selectivity, proving its suitability for the food industry and environmental monitoring applications. Here, we report on the fabrication of colloidal silver nanoparticle (AgNP) films by convective self-assembly (CSA) on solid planar substrate and their use for the SERS analyses of two types of pesticides, the fungicide thiabendazole (TBZ) and the insecticide α-endosulfan (α-ES). Electron microscopy shows that these nanoparticle films are dense, highly compact, and uniform across several mm2 areas. The SERS efficiency of the fabricated AgNP films is evaluated using a well-known Raman probe, p-aminothiophenol, for multiple excitation laser lines (532 nm, 633 nm, and 785 nm). The films exhibit the largest SERS enhancement factors for 785 nm excitation, reaching values larger than 105. Thiabendazole could be readily adsorbed on the AgNPs without any sample surface functionalization and detected down to 10−6 M, reaching the sub-ppm range. Endosulfan, a challenging analyte with poor affinity to metal surfaces, was captured near the metal surface by using self-assembled alkane thiol monolayers (hexanethiol and octanethiol), as demonstrated by the thorough vibrational band analysis, and supported by density functional theory (DFT) calculations. In addition, principal component analysis (PCA) based on SERS spectra offers significant leverage in discrimination of the molecules anchored onto the metallic nanostructured surface. This present study demonstrates the utility of self-assembled colloidal nanoparticle films as SERS substrates for a broad range of analytes (para-aminothiophenol, thiabendazole, α-endosulfan, and alkanethiols) and contributes to the development of SERS-based sensors for pesticides detection, identification and monitoring.

Green Textile Materials for Surface Enhanced Raman Spectroscopy Identification of Pesticides Using a Raman Handheld Spectrometer for In-Field Detection.

Surface enhanced Raman spectroscopy (SERS) has evolved into a powerful analytical method in food and environmental analytical sciences due to its high sensitivity. Pesticide analysis is a major discipline therein. Using sustainable materials has become increasingly important to adhere to Green Chemistry principles. Hence, the green textiles poly-(L-lactic acid) (PLA) and the mixed fabric polyethylene terephthalate polyamide (PET/PA) were investigated for their applicability as solid supports for gold nanoparticles to yield SERS substrates. Gold nanoparticle solutions and green textile supports were prepared after preparation optimization. Particle size, dispersity, and particle distribution over the textiles were characterized by absorption spectroscopy and transmission electron imaging. The performance of the SERS substrates was tested using the three pesticides imidacloprid, paraquat, and thiram and a handheld Raman spectrometer with a laser wavelength of 785 nm. The resulting SERS spectra possessed an intra-substrate variation of 7-8% in terms of the residual standard deviation. The inter-substrate variations amounted to 15% for PET/PA and to 27% for PLA. Substrate background signals were smaller with PLA but more enhanced through PET/PA. The pesticides could be detected at 1 pg on PET/PA and at 3 ng on PLA. Hence, PET/PA woven textile soaked with gold nanoparticle solution provides green SERS substrates and might prove, in combination with fieldable Raman spectrometers, suitable for in-field analytics for pesticide identification.

Surface-enhanced Raman spectroscopy of polymerase chain reaction (PCR) products of Rifampin resistant and susceptible tuberculosis patients

BackgroundRaman spectroscopy is an effective tool for detecting and discriminating microorganisms that is robust, reliable, and rapid. ObjectivesTo develop a polymerase chain reaction technique (PCR) based on Surface Enhanced Raman Spectroscopy (SERS) technique with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to assess diagnostic capability of SERS for distinguishing between tuberculosis (TB) positive rifampin resistant and tuberculosis (TB) positive rifampin susceptible samples. MethodsSilver nanoparticles (Ag NPs) were used as SERS substrates and technique was used to distinguish TB positive rifampin (RIF) resistant and TB positive rifampin (RIF) susceptible patients on the basis of characteristic SERS spectral features of their respective PCR products. SERS spectra were acquired from 52 samples of PCR products including 22 samples of TB positive rifampin susceptible, 30 samples of TB positive rifampin resistant and negative control samples. All these samples were collected from individuals of same age. Furthermore, multivariate data analyses techniques such as PCA and PLS-DA were used to assess diagnostic capability of SERS for distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples. ResultsPCA is found helpful for successful differentiation among these two groups of spectral data sets. Moreover, PLS-DA provides this classification quantitatively by predicting the class of SERS spectral data set with 73% area under curve, 96% sensitivity, 95.6% specificity and 95% accuracy. ConclusionSERS can be employed for the rapid distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples.