Rapid detection of food- and waterborne bacteria using surface-enhanced Raman spectroscopy coupled with silver nanosubstrates

Evaluation of the SERS performances of Tabun and VX label-free detection in complex and multicomponent fluids

Nerve agents represent a serious threat to security worldwide. Chemical terrorism has become an alarming danger since the technological progresses have simplified the production of nerve agents. Therefore, there is an immediate demand for a fast and precise detection of these compounds on-site and real-time. In this perspective, Surface-Enhanced Raman Scattering (SERS) has emerged as a well-suited alternative for on-field detection. SERS performances of unfunctionalized SERS substrates were evaluated in realistic samples. Two nerve agents, Tabun and VX, were diluted in two matrix models: a contact lens solution, and a caffeine-based eye serum. The performance two research-grade instruments and two portable devices were compared. Despite the use of a small sampling volume of complex matrices without any sample pre-treatment, we achieved Tabun detection in both media, with a practical limit of detection (LOD) in the range of 7-9 ppm in contact lens liquid, and of 10.2 ppm in eye serum. VX detection turned out to be more challenging and was achieved only in contact lens solution, with a practical LOD in the range of 0.6-5 ppm. These results demonstrate the feasibility of on-field detection of nerve agents with SERS, that could be implemented when there is suspicion of chemical threat.

In situ monitoring of the shikimate pathway: a combinatorial approach of Raman reverse stable isotope probing and hyperspectral imaging

Sensing and visualization of metabolites and metabolic pathways in situ are significant requirements for tracking their spatiotemporal dynamics in a non-destructive manner. The shikimate pathway is an important cellular mechanism that leads to the de novo synthesis of many compounds containing aromatic rings of high importance such as phenylalanine, tyrosine, and tryptophan. In this work, we present a cost-effective and extraction-free method based on the principles of stable isotope-coupled Raman spectroscopy and hyperspectral Raman imaging to monitor and visualize the activity of the shikimate pathway. We also demonstrated the applicability of this approach for nascent aromatic amino acid localization and tracking turnover dynamics in both prokaryotic and eukaryotic model systems. This method can emerge as a promising tool for both qualitative and semi-quantitative in situ metabolomics, contributing to a better understanding of aromatic ring-containing metabolite dynamics across various organisms.

Surface-enhanced Raman spectroscopy for comparison of biochemical profile of bacteriophage sensitive and resistant methicillin-resistant Staphylococcus aureus (MRSA) strains

Methicillin-resistant Staphylococcus aureus (MRSA) is gram positive bacteria and leading cause of a wide variety of diseases. It is a common cause of hospitalized and community-acquired infections. Development of increasing antibiotic-resistance by methicillin-resistant S. aureus (MRSA) strains demand to develop alternate novel therapies. Bacteriophages are now widely used as antibacterial therapies against antibiotic-resistant gram-positive pathogens. So, there is an urgent need to find fast detection techniques to point out phage susceptible and resistant strains of methicillin-resistant S. aureus (MRSA) bacteria. Samples of two separate strains of bacteria, S. aureus, in form of pellets and supernatant, were used for this purpose. Strain-I was resistant to phage, while the other (strain-II) was sensitive. Surface Enhanced Raman Spectroscopy (SERS) has detected significant biochemical changes in these bacterial strains of pellets and supernatants in the form of SERS spectral features. The protein portion of these two types of strains of methicillin-resistant S. aureus (MRSA) in their relevant pellets and supernatants is major distinguishing biomolecule as shown by their representative SERS spectral features. In addition, multivariate data analysis techniques such as principal component analysis (PCA) and a partial least squares-discriminant analysis (PLS-DA) were found to be helpful in identifying and characterizing various strains of S. aureus which are sensitive and resistant to bacteriophage with 100% specificity, 100% accuracy, and 99.8% sensitivity in case of SERS spectral data sets of bacterial cell pellets. Moreover, in case of supernatant samples, the results of PLS-DA model including 95.5% specificity, 96% sensitivity, and 96.5% accuracy are obtained.

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Surface-Enhanced Raman Scattering (SERS) in Combination with PCA and PLS-DA for the Evaluation of Antibacterial Activity of 1-Isopentyl-3-pentyl-1H-imidazole-3-ium Bromide against Bacillus subtilis

In the current study, surface-enhanced Raman scattering (SERS) was performed to evaluate the antibacterial activity of lab-synthesized drug (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt) and commercial drug tinidazole againstBacillus subtilis. The changes in SERS spectral features were studied for unexposed bacillus and exposed one with various dosages of drug synthesized in the lab (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt), and SERS bands were assigned associated with the drug-induced biochemical alterations in bacteria. Multivariate data analysis tools including principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) have been utilized to analyze the antibacterial activity of the imidazole derivative (lab drug). PCA was employed in differentiating all the SERS spectral data sets associated with the various doses of the lab-synthesized drug. There is clear discrimination among the spectral data sets of a bacterial strain treated with different concentrations of the drug, which are analyzed by PLS-DA with 86% area under the curve in receiver operating curve (ROC), 99% sensitivity, 100% accuracy, and 98% specificity. Various dominant spectral features are observed with a gradual increase in the different concentrations of the applied drug including 715, 850, 1002, 1132, 1237, 1396, 1416, and 1453 cm-1, which indicate the possible biochemical changes caused in bacteria during the antibacterial activity of the lab-synthesized drug. Overall, the findings show that imidazole and imidazolium compounds generated from tinidazole with various alkyl lengths in the amide substitution can be effective antibacterial agents with low cytotoxicity in humans, and these results indicate the efficiency of SERS in pharmaceuticals and biomedical applications.

SERS detection of foodborne pathogens in beverage with Au nanostars

The aim of this study is to develop a simple but rapid method for the determination of foodborne pathogens in complex matrices (beverages) by surface enhanced Raman spectroscopy (SERS) combined with Au nanostar solid-phase substrates. The star-shaped singlet Au nanostructure was formed on the surface of a stainless steel sheet by chemical replacement reaction. Rhodamine 6G verified the sensitivity and reproducibility of this substrate, and the relative standard deviations of the SERS intensity at 1312 cm-1, 1364 cm-1, and 1510 cm-1 displacements were 3.40%, 5.64%, and 3.48%, respectively. By detecting four pathogens in beverage samples on Au nanostar substrates, the utility of the SERS assay was demonstrated, while the combination of principal component analysis (PCA) and hierarchical cluster analysis (HCA) further enabled the isolation and identification of pathogens. The results of spiked beverages were validated in conventional culture identification and Vitek 2 Compact biochemical identification system experiments. Thus, this research demonstrated that Au nanostar substrates can be effectively utilized for the recognition of pathogenic bacteria and have immense promise to advance the progress of quick detection of foodborne pathogens and food safety.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications

BACKGROUND

The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.

AIM OF REVIEW

Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

Monitoring of microbial proteome dynamics using Raman stable isotope probing

Abnormal protein kinetics could be a cause of several diseases associated with essential life processes. An accurate understanding of protein dynamics and turnover is essential for developing diagnostic or therapeutic tools to monitor these changes. Raman spectroscopy in combination with stable isotope probes (SIP) such as carbon-13, and deuterium has been a breakthrough in the qualitative and quantitative study of various metabolites. In this work, we are reporting the utility of Raman-SIP for monitoring dynamic changes in the proteome at the community level. We have used ¹³ C-labeled glucose as the only carbon source in the medium and verified its incorporation in the microbial biomass in a time-dependent manner. A visible redshift in the Raman spectral vibrations of major biomolecules such as nucleic acids, phenylalanine, tyrosine, amide I, and amide III were observed. Temporal changes in the intensity of these bands demonstrating the feasibility of protein turnover monitoring were also verified. Kanamycin, a protein synthesis inhibitor was used to assess the feasibility of identifying effects on protein turnover in the cells. Successful application of this work can provide an alternate/adjunct tool for monitoring proteome-level changes in an objective and nondestructive manner.

Surface-enhanced Raman spectroscopy (SERS) for the characterization of supernatants of bacterial cultures of bacterial strains causing sinusitis

BACKGROUND

Sinusitis is defined as inflammation of the paranasal sinus mucous membrane lining caused by bacteria which usually invade the sinus by upper respiratory tract viral infections (UTI).

OBJECTIVES

In the present study, Surface-enhanced Raman spectroscopy (SERS) has been applied to differentiate and characterize supernatant samples, in triplicate, of three different types of bacteria which are considered leading cause of sinusitis disease.

METHODS

For this purpose, supernatant samples of three different strains of bacteria namely Staphylococcus aureus, Klebsiella pneumoniae and Enterococcus faecalis. The SERS has identified significant changes as a result of secretions of biomolecules by these bacteria in their supernatants which can be helpful to explore the potential of this technique for the identification and characterization of different strains of bacteria causing same disease.

RESULTS

These differentiating characteristic SERS spectral features including 552 cm^-1 (C-S-S-C bonds), 951 cm^-1 (CN stretching), 1008 cm^-1 (Phenylalanine), 1032 cm^-1 (In plane CH bending mode Phenylalanine), 1280 cm^-1, 1320 cm^-1, 1329 cm^-1 (Amide III band), 1368 cm^-1, 1400 cm^-1, 1420 cm^-1 (COO^-sym. stretching and CH bending), 1583 cm^-1 (Tyrosine) correspond to Proteins and 1051 cm^-1 (C-C, C-O, -C-OH def.) correspond to carbohydrates contents of these three different types of bacterial secretions in their respective supernatants. Furthermore, multivariate data analysis techniques like principal component analysis (PCA) and a supervised method partial least squares-discriminant analysis (PLS-DA) were found to be useful for the identification and characterization of different bacterial supernatants.

CONCLUSIONS

Surface-enhanced Raman spectroscopy is proven to be a helpful approach for the characterization and discrimination of three bacterial supernatants including S. aureus, K. pneumonia and E. faecalis.

Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is explored to design a rapid screening method for the characterization and diagnosis of typhoid fever by employing filtrate fractions of blood serum samples obtained by centrifugal filtration with 50 KDa filters.

OBJECTIVES

The purpose of this study, to separate the filtrate portions of blood serum samples in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire the SERS spectral features of smaller proteins more effectively which are probably associated with typhoid disease. Disease caused by Salmonella typhi diagnose more effectively by using surface-enhanced Raman spectroscopy (SERS) and multivariate data analysis tools.

METHODS

SERS was used as a diagnostic tool for typhoid fever by comparison between healthy and diseased samples. For this purpose, all the samples were analyzed by comparing their SERS spectral features. Over the spectral range of 400-1800cm^-1, multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) are applied to diagnose and differentiate different filtrate fractions of blood serum samples of patients of typhoid fever and healthy ones.

RESULTS

By comparing SERS spectra of healthy filtrate with that of filtrate of typhoid sample, the SERS spectral features associated with disease development are identified including PCA is found to be efficient for the qualitative differentiation of all of the samples analyzed. Moreover, PLS-DA successfully identified and classified healthy and typhoid positive blood serum samples with 97 % accuracy, 99 % specificity, 91 % sensitivity and 0.78 area under the receiver operating characteristic (AUROC) curve.

CONCLUSIONS

Surface enhanced Raman spectroscopy using silver nanoparticles SERS substrate, is found to be useful technique for the quick identification and evaluation of filtrate fractions of the blood serum samples of healthy and typhoid samples for disease diagnosis.

Identification of foodborne pathogenic bacteria using confocal Raman microspectroscopy and chemometrics

Rapid and accurate identification of foodborne pathogenic bacteria is of great importance because they are often responsible for the majority of serious foodborne illnesses. The confocal Raman microspectroscopy (CRM) is a fast and easy-to-use method known for its effectiveness in detecting and identifying microorganisms. This study demonstrates that CRM combined with chemometrics can serve as a rapid, reliable, and efficient method for the detection and identification of foodborne pathogenic bacteria without any laborious pre-treatments. Six important foodborne pathogenic bacteria including S. flexneri, L. monocytogenes, V. cholerae, S. aureus, S. typhimurium, and C. botulinum were investigated with CRM. These pathogenic bacteria can be differentiated based on several characteristic peaks and peak intensity ratio. Principal component analysis (PCA) was used for investigating the difference of various samples and reducing the dimensionality of the dataset. Performances of some classical classifiers were compared for bacterial detection and identification including decision tree (DT), artificial neural network (ANN), and Fisher's discriminant analysis (FDA). Correct recognition ratio (CRR), area under the receiver operating characteristic curve (ROC), cumulative gains, and lift charts were used to evaluate the performance of models. The impact of different pretreatment methods on the models was explored, and pretreatment methods include Savitzky-Golay algorithm smoothing (SG), standard normal variate (SNV), multivariate scatter correction (MSC), and Savitzky-Golay algorithm 1st Derivative (SG 1st Der). In the DT, ANN, and FDA model, FDA is more robust for overfitting problem and offers the highest accuracy. Most pretreatment methods raised the performance of the models except SNV. The results revealed that CRM coupled with chemometrics offers a powerful tool for the discrimination of foodborne pathogenic bacteria.

Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing

Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.

A New Biorecognition-Element-Free IDμE Sensor for the Identification and Quantification of E. coli

The label-free biosensor has emerged as an effective tool for the purpose of early detection of causative pathogens such as Escherichia coli as a preventive measure. In this study, a biorecognition-element-free interdigitated microelectrode (IDμE) sensor is designed and developed with this in mind, with good reliability and affordability. Results show that the designed sensor can identify E. coli with good selectivity using an impedance and capacitance of 7.69 MHz. At its optimum impedance of 1.3 kHz, the IDμE sensor can reliably quantify E. coli in a range of measurement (10^3.2~10⁶ cfu/mL), linearity (R² = 0.97), sensitivity (18.15 kΩ/log (cfu/mL)), and limit of detection (10^3.2 cfu/mL). In summary, the IDμE sensor developed possesses high potential for industrial and clinical applications.

Surface-enhanced Raman spectroscopy enabled evaluation of bacterial inactivation

An improved understanding of bacterial inactivation mechanisms will provide useful insights for infectious disease control and prevention. We evaluated bacterial response to several inactivation methods using surface-enhanced Raman spectroscopy (SERS). The results indicate that changes in the SERS signal are highly related to cellular disruption and that cellular changes arising after cell inactivation cannot be ignored. The membrane integrity of heat and the combination of UV₂₅₄ and free chlorine (UV₂₅₄/chlorine) treated Pseudomonas syringae (P. syringae) cells were severely disrupted, leading to significantly increased peak intensities. Conversely, ethanol treated bacteria exhibited intact cell morphologies and the SERS spectra remained virtually unchanged. On the basis of time dependent SERS signals, we extracted dominant SERS patterns. Peaks related to nucleic acids accounted for the main changes observed during heat, UV₂₅₄, and UV₂₅₄/chlorine treatment, likely due to their outward diffusion from the cell cytoplasm. For free chlorine treated P. syringae, carbohydrates and proteins on the cell membrane were denatured or lost, resulting in a decrease in related peak intensities. The nucleobases were likely oxidized when treated with UV₂₅₄ and chlorine, thus leading to shifts in the related peaks. The generality of the method was verified using two additional bacterial strains: Escherichia coli and Bacillus subtilis as well as in different water matrices. The results suggest that SERS spectral analysis is a promising means to examine bacterial stress response at the molecular level and has applicability in diverse environmental implementations.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study

Simple Summary

In recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.

Abstract

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

Combining multilayered wrinkled polymer SERS substrates and spectral data processing for low concentration analyte detection

A series of thermally shrinkable polymer surface-enhanced Raman scattering (SERS) substrates were prepared with bimetallic Au and Ag (oxidized or not) films and with Au nanoparticles (AuNPs) located at different places in the layered structure to evaluate the synergistic effect of different known SERS amplification methods to enhance the Raman signal for low concentration dopamine detection. A bimetallic Au and Ag layered structure improved the Raman signal by 5 and 2 times compared to the single-layered Au and Ag films. Oxidizing the Ag layer prior to deposition of Au further improved the signal by a factor of 2, while adding AuNP on wrinkled films increased another 10 times the intensity of the Raman signal. It was found that the enhancement was another 10 times stronger when using AuNPs in combination with other means of enhancement such as with a silver underlayer or surface wrinkling. Wrinkling alone only gave a few-fold increase compared to a flat film, but the combination of wrinkling with AuNPs and a silver underlayer improved the SERS intensity by more than 3 orders of magnitude, showing the synergistic effect of these enhancement methods. The optimized sensors were then tested in dynamic SERS with low concentration dopamine solutions, where the signal showed characteristics of a digital SERS response. Raman spectra preprocessing and sorting software was developed to triage the SERS-active spectra from the null spectra, to count the detection events such as the ones observed in single molecule experiments.

Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy

As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%.

Magnetic-Core/Gold-Shell Nanoparticles for the Detection of Hydrophobic Chemical Contaminants

Magnetic-core/gold-shell nanoparticles (MAuNPs) are of interest for enabling rapid and portable detection of trace adulterants in complex media. Gold coating provides biocompatibility and facile functionalization, and a magnetic core affords analyte concentration and controlled deposition onto substrates for surface-enhanced Raman spectroscopy. Iron oxide cores were synthesized and coated with gold by reduction of HAuCl₄ by NH₂OH. MAuNPs were grafted with polyethylene glycol (PEG) and/or functionalized with 4-mercaptobenzoic acid (4-MBA) and examined using a variety of microscopic, spectroscopic, magnetometric, and scattering techniques. For MAuNPs grafted with both PEG and 4-MBA, the order in which they were grafted impacted not only the graft density of the individual ligands, but also the overall graft density. Significant Raman signal enhancement of the model analyte, 4-MBA, was observed. This enhancement demonstrates the functionality of MAuNPs in direct detection of trace contaminants. The magnetic deposition rate of MAuNPs in chloroform and water was explored. The presence of 4-MBA slowed the mass deposition rate, and it was postulated that the rate disparity originated from differing NP-substrate surface interactions. These findings emphasize the importance of ligand choice in reference to the medium, target analyte, and substrate material, as well as functionalization procedure in the design of similar sensing platforms.

Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance

•

Handheld Raman spectrometer is able to generate SERS spectra with sufficient quality for Mycobacterium tuberculosis detection.

•

It is feasible to accurately discriminate Mtb-positive sputum from Mtb-negative sputum through SERS spectrometry.

•

Pulmonary and extra-pulmonary Mtb strains were able to be accurately distinguished via SERS spectral analysis.

•

Profiling of antibiotic resistance of Mtb strains was successfully achieved through machine learning analysis of SERS spectra.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

Surface Enhanced Raman Spectroscopy: Applications in Agriculture and Food Safety

Recent global warming has resulted in shifting of weather patterns and led to intensification of natural disasters and upsurges in pests and diseases. As a result, global food systems are under pressure and need adjustments to meet the change—often by pesticides. Unfortunately, such agrochemicals are harmful for humans and the environment, and consequently need to be monitored. Traditional detection methods currently used are time consuming in terms of sample preparation, are high cost, and devices are typically not portable. Recently, Surface Enhanced Raman Scattering (SERS) has emerged as an attractive candidate for rapid, high sensitivity and high selectivity detection of contaminants relevant to the food industry and environmental monitoring. In this review, the principles of SERS as well as recent SERS substrate fabrication methods are first discussed. Following this, their development and applications for agrifood safety is reviewed, with focus on detection of dye molecules, melamine in food products, and the detection of different classes of pesticides such as organophosphate and neonicotinoids.

Identification of antibiotic resistance and virulence-encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning

Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing with classical K. pneumoniae strains. Rapid differentiation of clinical K. pneumoniae with high resistance/hypervirulence from classical K. pneumoniae would allow us to develop rational and timely treatment plans. In this study, we developed a convolution neural network (CNN) as a prediction method using Raman spectra raw data for rapid identification of ARGs, hypervirulence‐encoding factors and resistance phenotypes from K. pneumoniae strains. A total of 71 K. pneumoniae strains were included in this study. The minimum inhibitory concentrations (MICs) of 15 commonly used antimicrobial agents on K. pneumoniae strains were determined. Seven thousand four hundred fifty‐five spectra were obtained using the InVia Reflex confocal Raman microscope and used for deep learning‐based and machine learning (ML) algorithms analyses. The quality of predictors was estimated in an independent data set. The results of antibiotic resistance and virulence‐encoding factors identification showed that the CNN model not only simplified the classification system for Raman spectroscopy but also provided significantly higher accuracy to identify K. pneumoniae with high resistance and virulence when compared with the support vector machine (SVM) and logistic regression (LR) models. By back‐testing the Raman‐CNN platform on 71 K. pneumoniae strains, we found that Raman spectroscopy allows for highly accurate and rationally designed treatment plans against bacterial infections within hours. More importantly, this method could reduce healthcare costs and antibiotics misuse, limiting the development of antimicrobial resistance and improving patient outcomes.

Differentiation of Closely Related Oak-Associated Gram-Negative Bacteria by Label-Free Surface Enhanced Raman Spectroscopy (SERS)

Due to the harmful effects of chemical fertilizers and pesticides, the need for an eco-friendly solution to improve soil fertility has become a necessity, thus microbial biofertilizer research is on the rise. Plant endophytic bacteria inhabiting internal tissues represent a novel niche for research into new biofertilizer strains. However, the number of species and strains that need to be differentiated and identified to facilitate faster screening in future plant-bacteria interaction studies, is enormous. Surface enhanced Raman spectroscopy (SERS) may provide a platform for bacterial discrimination and identification, which, compared with the traditional methods, is relatively rapid, uncomplicated and ensures high specificity. In this study, we attempted to differentiate 18 bacterial isolates from two oaks via morphological, physiological, biochemical tests and SERS spectra analysis. Previous 16S rRNA gene fragment sequencing showed that three isolates belong to Paenibacillus, 3-to Pantoea and 12-to Pseudomonas genera. Additional tests were not able to further sort these bacteria into strain-specific groups. However, the obtained label-free SERS bacterial spectra along with the high-accuracy principal component (PCA) and discriminant function analyses (DFA) demonstrated the possibility to differentiate these bacteria into variant strains. Furthermore, we collected information about the biochemical characteristics of selected isolates. The results of this study suggest a promising application of SERS in combination with PCA/DFA as a rapid, non-expensive and sensitive method for the detection and identification of plant-associated bacteria.

Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains

Tigecycline (TGC) is recognised as last resort of drugs against several antibiotic-resistant bacteria. Bacterial resistance to tigecycline due to presence of plasmid-mediated mobile TGC resistance genes (tet X3/X4) has broken another defense line. Therefore, rapid and reproducible detection of tigecycline-resistant E. coli (TREC) is required. The current study is designed for the identification and differentiation of TREC from tigecycline-sensitive E. coli (TSEC) by employing SERS by using Ag NPs as a SERS substrate. The SERS spectral fingerprints of E. coli strains associated directly or indirectly with the development of resistance against tigecycline have been distinguished by comparing SERS spectral data of TSEC strains with each TREC strain. Moreover, the statistical analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed to check the diagnostic potential of SERS for the differentiation among TREC and TSEC strains. The qualitative identification and differentiation between resistant and sensitive strains and among individual strains have been efficiently done by performing both PCA and HCA. The successful discrimination among TREC and TSEC at the strain level is performed by PLS-DA with 98% area under ROC curve, 100% sensitivity, 98.7% specificity and 100% accuracy.

An all-in-one magnetic SERS nanosensor for ratiometric detection of Escherichia coli in foods

An all-in-one nanosensor was developed for the magnetic enrichment and ratiometric surface-enhanced Raman scattering (SERS) detection of Escherichia coli (E. coli). The all-in-one nanosensor was constructed through the chemical integration of four components into a single nanoparticle, which include a manganese ferrite nanoparticle serving as the magnetic core, a thin silver shell as the SERS substrate, a self-assembled layer of 4-mercaptobenzoic acid (MBA) molecules as the SERS internal standard, and a MBA-conjugated layer of aptamer sequences as the capture probe of E. coli. In the detection of E. coli in food, the target cells were first captured by the nanosensors and magnetically enriched in a short time of 15 min, and then the ratiometric SERS was performed through the Raman intensity ratio between two specific SERS peaks produced by the captured E. coli and the internal MBA. The pre-concentration and ratiometry enabled the nanosensor to detect E. coli with a detection limit down to 10 CFU/mL. The all-in-one nanosensor was successfully applied for the detection of E. coli in various liquid foods including milk, juice, tea, and coffee, with recoveries ranging from 89 to 110% and relative standard deviation lower than 1.7%. In comparison with the previous sandwich strategy adopted by most SERS sensors, this nanosensor endowed with an easier use and a lower cost is more sensitive and reproducible, leading to a great potential in practical applications.

Surface-enhanced Raman spectroscopy for comparison of serum samples of typhoid and tuberculosis patients of different stages

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is a reliable tool for the identification and differentiation of two different human pathological conditions sharing the same symptomology, typhoid and tuberculosis (TB).

OBJECTIVES

To explore the potential of surface-enhanced Raman spectroscopy for differentiation of two different diseases showing the same symptoms and analysis by principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

METHODS

Serum samples of clinically diagnosed typhoid and tuberculosis infected individuals were analyzed and differentiated by SERS using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, the collected serum samples were analyzed under the SERS instrument and unique SERS spectra of typhoid and tuberculosis were compared showing notable spectral differences in protein, lipid and carbohydrates features. Different stages of the diseased class of typhoid (Early acute and late acute stage) and tuberculosis (Pulmonary and extra-pulmonary stage) were compared with each other and with healthy human serum samples, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

RESULTS

SERS Spectral data of typhoid and tuberculosis showed clear differences and were significantly separated using PCA. SERS spectral data of both stages of typhoid and tuberculosis were separated according to 1st principle component. Moreover, by analyzing data using partial least square discriminate analysis, differentiation of two disease classes were considered more valid with a 100% value of sensitivity, specificity and accuracy.

CONCLUSION

SERS can be employed for identification and comparison of two different human pathological conditions sharing same symptomology.

In situ food-borne pathogen sensors in a nanoconfined space by surface enhanced Raman scattering

The incidence of disease arising from food-borne pathogens is increasing continuously and has become a global public health problem. Rapid and accurate identification of food-borne pathogens is essential for adopting disease intervention strategies and controlling the spread of epidemics. Surface-enhanced Raman spectroscopy (SERS) has attracted increasing interest due to the attractive features including simplicity, rapid measurement, and high sensitivity. It can be used for rapid in situ sensing of single and multicomponent samples within the nanostructure-based confined space by providing molecular fingerprint information and has been demonstrated to be an effective detection strategy for pathogens. This article aims to review the application of SERS to the rapid sensing of food-borne pathogens in food matrices. The mechanisms and advantages of SERS, and detection strategies are briefly discussed. The latest progress on the use of SERS for rapid detection of food-borne bacteria and viruses is considered, including both the labeled and label-free detection strategies. In closing, according to the current situation regarding detection of food-borne pathogens, the review highlights the challenges faced by SERS and the prospects for new applications in food safety.

Graphical abstract

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural network

Salmonella is a prevalent pathogen causing serious morbidity and mortality worldwide. There are over 2600 serovars of Salmonella. Among them, Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Paratyphi were reported to be the most common foodborne pathogenic serovars in the EU and China. In order to provide a more efficient approach to detect and distinguish these serovars, a new analytical method was developed by combining surface-enhanced Raman spectroscopy (SERS) with multi-scale convolutional neural network (CNN). We prepared 34-nm gold nanoparticles (AuNPs) as the label-free Raman substrate, measured 1854 SERS spectra of these three Salmonella serovars, and then proposed a multi-scale CNN model with three parallel CNNs to achieve multi-dimensional extraction of SERS spectral features. We observed the impact of the number of iterations and training samples on the recognition accuracy by changing the ratio of the number of the training and testing sets. By comparing the calculated data with experimental one, it was shown that our model could reach recognition accuracy more than 97%. These results indicate that it was not only feasible to combine SERS spectroscopy with multi-scale CNN for Salmonella serotype identification, but also for other pathogen species and serovar identifications.

Specific detection of Staphylococcus aureus infection and marker for Alzheimer disease by surface enhanced Raman spectroscopy using silver and gold nanoparticle-coated magnetic polystyrene beads

Targeted and effective therapy of diseases demands utilization of rapid methods of identification of the given markers. Surface enhanced Raman spectroscopy (SERS) in conjunction with streptavidin-biotin complex is a promising alternative to culture or PCR based methods used for such purposes. Many biotinylated antibodies are available on the market and so this system offers a powerful tool for many analytical applications. Here, we present a very fast and easy-to-use procedure for preparation of streptavidin coated magnetic polystyrene-Au (or Ag) nanocomposite particles as efficient substrate for surface SERS purposes. As a precursor for the preparation of SERS active and magnetically separable composite, commercially available streptavidin coated polystyrene (PS) microparticles with a magnetic core were utilized. These composites of PS particles with silver or gold nanoparticles were prepared by reducing Au(III) or Ag(I) ions using ascorbic acid or dopamine. The choice of the reducing agent influences the morphology and the size of the prepared Ag or Au particles (15-100 nm). The prepare composites were also characterized by HR-TEM images, mapping of elements and also magnetization measurements. The content of Au and Ag was determined by AAS analysis. The synthesized composites have a significantly lower density against magnetic composites based on iron oxides, which considerably decreases the tendency to sedimentation. The polystyrene shell on a magnetic iron oxide core also pronouncedly reduces the inclination to particle aggregation. Moreover, the preparation and purification of this SERS substrate takes only a few minutes. The PS composite with thorny Au particles with the size of approximately 100 nm prepared was utilized for specific and selective detection of Staphylococcus aureus infection in joint knee fluid (PJI) and tau protein (marker for Alzheimer disease).

Surface Enhanced Raman Spectroscopy of the serum samples for the diagnosis of Hepatitis C and prediction of the viral loads

In this study, Surface Enhanced Raman Spectroscopy (SERS) was used for the characterization of Hepatitis C virus (HCV) in blood serum samples. For this purpose silver nanoparticles (Ag NPs) were used as substrates and SERS spectra were acquired from different clinically diagnosed HCV positive serum samples as well as from healthy individuals. Notably, same set of samples were also evaluated with Raman spectroscopy and SERS was found to be more helpful for the identification of the spectral features associated with the development of HCV infection. Different SERS features associated with the RNA bases were observed solely in the HCV positive serum as compared to the healthy samples which can be considered as SERS spectral markers of the HCV infection. Furthermore, principal component analysis (PCA) of the SERS spectral data was found to be very helpful in differentiation of spectral data of serum samples with different viral loads PLSR model was constructed to compare the capability of SERS and Raman analysis in the prediction of viral loads. It is found that SERS shows lower root mean square error of cross validation (RMSECV) and higher goodness of the model (R²) values than Raman data.

Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Bacterial infection is a global burden that results in numerous hospital visits and deaths annually. The rise of multi-drug resistant bacteria has dramatically increased this burden. Therefore, there is a clinical need to detect and identify bacteria rapidly and accurately in their native state or a culture-free environment. Current diagnostic techniques lack speed and effectiveness in detecting bacteria that are culture-negative, as well as options for in vivo detection. The optical detection of bacteria offers the potential to overcome these obstacles by providing various platforms that can detect bacteria rapidly, with minimum sample preparation, and, in some cases, culture-free directly from patient fluids or even in vivo. These modalities include infrared, Raman, and fluorescence spectroscopy, along with optical coherence tomography, interference, polarization, and laser speckle. However, these techniques are not without their own set of limitations. This review summarizes the strengths and weaknesses of utilizing each of these optical tools for rapid bacteria detection and identification.

Rapid and ultrasensitive detection of food contaminants using surface-enhanced Raman spectroscopy-based methods

With the globalization of food and its complicated networking system, a wide range of food contaminants is introduced into the food system which may happen accidentally, intentionally, or naturally. This situation has made food safety a critical global concern nowadays and urged the need for effective technologies capable of dealing with the detection of food contaminants as efficiently as possible. Hence, Surface-enhanced Raman spectroscopy (SERS) has been taken as one of the primary choices for this case, due to its extremely high sensitivity, rapidity, and fingerprinting interpretation capabilities which account for its competency to detect a molecule up to a single level. Here in this paper, we present a comprehensive review of various SERS-based novel approaches applied for direct and indirect detection of single and multiple chemical and microbial contaminants in food, food products as well as water. The aim of this paper is to arouse the interest of researchers by addressing recent SERS-based, novel achievements and developments related to the investigation of hazardous chemical and microbial contaminants in edible foods and water. The target chemical and microbial contaminants are antibiotics, pesticides, food adulterants, Toxins, bacteria, and viruses. In this paper, different aspects of SERS-based reports have been addressed including synthesis and use of various forms of SERS nanostructures for the detection of a specific analyte, the coupling of SERS with other analytical tools such as chromatographic methods, combining analyte capture and recognition strategies such as molecularly imprinted polymers and aptasensor as well as using multivariate statistical analyses such as principal component analysis (PCA)to distinguish between results. In addition, we also report some strengths and limitations of SERS as well as future viewpoints concerning its application in food safety.

Surface-enhanced Raman scattering as a discrimination method of Streptococcus spp. and alternative approach for identifying capsular types of S. pneumoniae isolates

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.

Real-time monitoring of live mycobacteria with a microfluidic acoustic-Raman platform

Tuberculosis (TB) remains a leading cause of death worldwide. Lipid rich, phenotypically antibiotic tolerant, bacteria are more resistant to antibiotics and may be responsible for relapse and the need for long-term TB treatment. We present a microfluidic system that acoustically traps live mycobacteria, M. smegmatis, a model organism for M. tuberculosis. We then perform optical analysis in the form of wavelength modulated Raman spectroscopy (WMRS) on the trapped M. smegmatis for up to eight hours, and also in the presence of isoniazid (INH). The Raman fingerprints of M. smegmatis exposed to INH change substantially in comparison to the unstressed condition. Our work provides a real-time assessment of the impact of INH on the increase of lipids in these mycobacteria, which could render the cells more tolerant to antibiotics. This microfluidic platform may be used to study any microorganism and to dynamically monitor its response to different conditions and stimuli.

Baron et al. describe a microfluidic system that acoustically traps live mycobacteria and acquires label-free optical measurements over time using wavelength modulated Raman spectroscopy. Using acoustically trapped live M. smegmatis, they show that under stressed conditions produced by an antibiotic, bacteria displayed an increase in lipids which could render the cells more tolerant to antibiotics.

High photoluminescence nitrogen, phosphorus co-doped carbon nanodots for assessment of microbial viability

Assessment of microbial viability plays a key role in human health protection. Optical imaging based on fluorescent dyes is a simple and convenient way to assess microbial viability. However, it is still a challenge to obtain stable, nontoxic and low-cost dyes. Herein, we prepared a nitrogen and phosphorus co-doped carbon nanodots (N, P-CDs) via a one-step solvothermal method. The prepared CDs possess plenty of functional groups and exhibit high stability, good biocompatibility, excellent photoluminescent and low toxicity. Especially, the properties of high quantum yield (89.9%) and highly negative surface charge (-41.9 mV) make the prepared N, P-CDs ideal materials for microbial differentiation. Compared with commercial dyes, our CDs are more stable, cost less, which can rapidly distinguish dead microorganisms from living ones with higher specificity.

Challenges in SERS-based pesticide detection and plausible solutions

Surface-enhanced Raman spectroscopy (SERS) can be used for the detection of trace amounts of pesticides in foods to ensure consumer safety. In this perspective, we highlight the trends of SERS-based assays in pesticide detection and the various challenges associated with their selectivity, reproducibility, and nonspecific binding. We also discuss and compare the target analyte capture techniques, such as the use of antibodies, aptamers, and molecularly imprinted polymers (MIPs), coupled with SERS to overcome the drawbacks as mentioned above. In addition, issues related to the nonspecific binding of analytes and its potential solution are discussed.

Sources of variability in SERS spectra of bacteria: comprehensive analysis of interactions between selected bacteria and plasmonic nanostructures

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.

Label-Free SERS Discrimination and In Situ Analysis of Life Cycle in Escherichia coli and Staphylococcus epidermidis

Surface enhanced Raman spectroscopy (SERS) has been proven suitable for identifying and characterizing different bacterial species, and to fully understand the chemically driven metabolic variations that occur during their evolution. In this study, SERS was exploited to identify the cellular composition of Gram-positive and Gram-negative bacteria by using mesoporous silicon-based substrates decorated with silver nanoparticles. The main differences between the investigated bacterial strains reside in the structure of the cell walls and plasmatic membranes, as well as their biofilm matrix, as clearly noticed in the corresponding SERS spectrum. A complete characterization of the spectra was provided in order to understand the contribution of each vibrational signal collected from the bacterial culture at different times, allowing the analysis of the bacterial populations after 12, 24, and 48 h. The results show clear features in terms of vibrational bands in line with the bacterial growth curve, including an increasing intensity of the signals during the first 24 h and their subsequent decrease in the late stationary phase after 48 h of culture. The evolution of the bacterial culture was also confirmed by fluorescence microscope images.

Surface-Enhanced Raman Scattering (SERS) With Silver Nano Substrates Synthesized by Microwave for Rapid Detection of Foodborne Pathogens

Rapid and sensitive methods have been developed to detect foodborne pathogens, a development that is important for food safety. The aim of this study is to explore Surface-enhanced Raman scattering (SERS) with silver nano substrates to detect and identify the following three foodborne pathogens: Escherichia coli O157: H7, Staphylococcus aureus and Salmonella. All the cells were resuspended with 10 mL silver colloidal nanoparticles, making a concentration of 107 CFU/mL, and were then exposed to 785 nm laser excitation. In this study, the results showed that all the bacteria can be sensitively and reproducibly detected directly by SERS. The distinctive differences can be observed in the SERS spectral data of the three food-borne pathogens, and the silver colloidal nanoparticles can be used as highly sensitive SERS-active substrates. In addition, the assay time required only a few minutes, which indicated that SERS coupled with the silver colloidal nanoparticles is a promising method for the detection and characterization of food-borne pathogens. At the same time, principle component analysis (PCA) and hierarchical cluster analysis (HCA) made the different bacterial strains clearly differentiated based on the barcode spectral data reduction. Therefore, the SERS methods hold great promise for the detection and identification of food-borne pathogens and even for applications in food safety.

Comparison of time-gated surface-enhanced raman spectroscopy (TG-SERS) and classical SERS based monitoring of Escherichia coli cultivation samples

The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIR-process Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1533-1542, 2018.

Bare laser-synthesized Au-based nanoparticles as nondisturbing surface-enhanced Raman scattering probes for bacteria identification

The ability of noble metal-based nanoparticles (NPs) (Au, Ag) to drastically enhance Raman scattering from molecules placed near metal surface, termed as surface-enhanced Raman scattering (SERS), is widely used for identification of trace amounts of biological materials in biomedical, food safety and security applications. However, conventional NPs synthesized by colloidal chemistry are typically contaminated by nonbiocompatible by-products (surfactants, anions), which can have negative impacts on many live objects under examination (cells, bacteria) and thus decrease the precision of bioidentification. In this article, we explore novel ultrapure laser-synthesized Au-based nanomaterials, including Au NPs and AuSi hybrid nanostructures, as mobile SERS probes in tasks of bacteria detection. We show that these Au-based nanomaterials can efficiently enhance Raman signals from model R6G molecules, while the enhancement factor depends on the content of Au in NP composition. Profiting from the observed enhancement and purity of laser-synthesized nanomaterials, we demonstrate successful identification of 2 types of bacteria (Listeria innocua and Escherichia coli). The obtained results promise less disturbing studies of biological systems based on good biocompatibility of contamination-free laser-synthesized nanomaterials.

A Graphene-Silver Nanoparticle-Silicon Sandwich SERS Chip for Quantitative Detection of Molecules and Capture, Discrimination, and Inactivation of Bacteria

There currently exists increasing concerns on the development of a kind of high-performance SERS platform, which is suitable for sensing applications ranging from the molecular to cellular (e.g., bacteria) level. Herein, we develop a novel kind of universal SERS chip, made of graphene (G)-silver nanoparticle (AgNP)-silicon (Si) sandwich nanohybrids (G@AgNPs@Si), in which AgNPs are in situ grown on a silicon wafer through hydrofluoric acid-etching-assisted chemical reduction, followed by coating with single-layer graphene via a polymer-protective etching method. The resultant chip features a strong, stable, reproducible surface-enhanced Raman scattering (SERS) effect and reliable quantitative capability. By virtues of these merits, the G@AgNPs@Si platform is capable for not only molecular detection and quantification but also cellular analysis in real systems. As a proof-of-concept application, the chip allows ultrahigh sensitive and reliable detection of adenosine triphosphate (ATP), with a detection limit of ∼1 pM. In addition, the chip, serving as a novel multifunctional platform, enables simultaneous capture, discrimination, and inactivation of bacteria. Typically, the bacterial capture efficiency is 54% at 10⁸ CFU mL^-1 bacteria, and the antibacterial rate reaches 93% after 24 h of treatment. Of particular note, Escherichia coli and Staphylococcus aureus spiked into blood can be readily distinguished via the chip, suggesting its high potential for clinical applications.