Rapid detection of Escherichia coli and Salmonella typhimurium by surface-enhanced Raman scattering

Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods

The detection and classification of foodborne pathogenic bacteria is crucial for food safety monitoring, consequently requiring rapid, accurate and sensitive methods. In this study, the surface-enhanced Raman spectroscopy (SERS) technique coupled with chemometrics methods was used to detect and classify six kinds of foodborne pathogenic bacteria, including Salmonella typhimurium (S. typhimurium), Escherichia coli (E. coli) O157:H7, Staphylococcus aureus (S. aureus), Listeria monocytogenes (L. monocytogenes), Listeria innocua (L. innocua), and Listeria welshimeri (L. welshimeri). First, silver nanoparticles (AgNPs) with different particle sizes were prepared as SERS-enhanced substrates by changing the concentration of sodium citrate, and the volume ratio of silver nanosol to bacterial solution was optimised to obtain the optimal SERS signal. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to classify the SERS spectra of six bacteria at three classification levels (Gram type level, genus level and species level), and appropriate classification models were established. Finally, these models were validated on 540 spectra using linear discriminant analysis (LDA), achieving an average accuracy of 95.65%. Overall, it was concluded that the SERS technique combined with chemometrics methods could achieve the rapid detection and classification identification of foodborne pathogenic bacteria, providing an effective means for food safety monitoring.

Label-Free Surface-Enhanced Raman Scattering Detection of Fire Blight Pathogen Using a Pathogen-Specific Bacteriophage

Fire blight is one of the most devastating plant diseases, causing severe social and economic problems. Herein, we report a novel method based on label-free surface-enhanced Raman scattering (SERS) combined with an Erwinia amylovora-specific bacteriophage that allows detecting efficiently fire blight bacteria E. amylovora for the first time. To achieve the highest SERS signals for E. amylovora, we synthesized and compared plasmonic nanoparticles (PNPs) with different sizes, i.e., bimetallic gold core-silver shell nanoparticles (Au@AgNPs) and monometallic gold nanoparticles (AuNPs) and utilized the coffee-ring effect for the self-assembly of PNPs and enrichment of fire blight bacteria. Furthermore, we investigated the changes in the SERS spectra of E. amylovora after incubation with an E. amylovora-specific bacteriophage, and we found considerable differences in the SERS signals as a function of the bacteriophage incubation time. The results indicate that our bacteriophage-based label-free SERS analysis can specifically detect E. amylovora without the need for peak assignment on the SERS spectra but simply by monitoring the changes in the SERS signals over time. Therefore, our facile method holds great potential for the label-free detection of pathogenic bacteria and the investigation of viral-bacterial interactions.

Surface Functionalization and Escherichia coli Detection Using Surface-Enhanced Raman Spectroscopy Driven by Functional Organic Polymer/Gold Nanofilm-Based Microfluidic Chip

In this work, a microfluidic prototype based on polymeric materials was developed to monitor surface processes using surface-enhanced Raman spectroscopy (SERS), keeping the reagents free of environmental contamination. The prototype was fabricated on poly(methyl methacrylic acid) (PMMA). A micrometric membrane of a functional organic polymer (FOP) based on p-terphenyl and bromopyruvic acid monomers was formed on the PMMA surface to promote the formation of metal nanoclusters. Au nanosized film was deposited on the FOP membrane to give rise to the SERS effect. A microchannel was formed on another piece of PMMA using micromachining. A representative 3D model of the prototype layer arrangement was built and simulated in COMSOL Multiphysics® to approximate the electric field distribution and calculate the power enhancement factor as the Au film changes over time. The fabrication process was characterized using UV-visible and Raman spectroscopies and XPS. The prototype was tested using a Raman microscope and liquid solutions of cysteamine and Escherichia coli (E. coli). The simulation results demonstrated that the morphological characteristics of the Au layer give rise to the SERS effect, and the power enhancement factor reaches values as high as 8.8 × 105 on the FOP surface. The characterization results showed the formation of the FOP and the Au film on PMMA and the surface functionalization with amine groups. The Raman spectra of the prototype showed temporal evolution as different compounds were deposited on the upper wall of the microchannel. Characteristic peaks associated with these compounds were detected with continuous monitoring over time. This prototype offers many benefits for applications like monitoring biological processes. Some advantages include timely surface evaluation while avoiding environmental harm, decreased use of reagents and samples, minimal interference with the process by measuring, and detecting microorganisms in just 1 h, as demonstrated with the E. coli sample.

Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.

Endophytes from blueberry (Vaccinium sp.) fruit: Characterization of yeast and bacteria via label-free surface-enhanced Raman spectroscopy (SERS)

Blueberries (Vaccinium sp.) are consumed all around the globe, however, their endophytic community has not been thoroughly researched, specifically their fruit endophytes. We aimed to isolate and analyze easily cultivable blueberry fruit endophytes to help in future research, concerning probiotic microorganisms. Twelve strains were isolated in this pilot study, genetically homologous with Staphylococcus hominis, Staphylococcus cohnii, Salmonella enterica, Leuconostoc mesenteroides, and [Candida] santamariae. To determine the molecular composition of these isolates we used label-free surface-enhanced Raman spectroscopy (SERS). To our knowledge, this is the first time that SERS spectra for L. mesenteroides and C. santamariae are presented, as well as the first report of Candida yeast, isolated specifically from blueberry fruits. Our findings suggest that the differences in tested yeast and bacteria SERS spectra and subsequent differentiation are facilitated by minor shifts in spectral peak positions as well as their intensities. Moreover, we used principal component and discriminant function analyses to differentiate chemotypes within our isolate group, proving the sensitivity of the technique and its usefulness to recognize different strains in plant-associated microbe samples, which will aid to streamline future studies in biofertilizers and biocontrol agents.

Dynamic insights into increasing antibiotic resistance in Staphylococcus aureus by label-free SERS using a portable Raman spectrometer

Rapid and quantitative detection of bacterial antibiotic resistance is of great significance for the prevention and treatment of infections and understanding drug-resistant mechanism. In this study, label-free surface-enhanced Raman spectroscopy (SERS) technology was applied to dynamically explore oxacillin/cefazolin-derived resistance in Staphylococcus aureus using a portable Raman spectrometer. The results showed that S. aureus rapidly responded to oxacillin/cefazolin stimulation and gradually developed different degrees of drug resistance during the 21 days of exposure. The molecular changes that accumulated in the drug-resistant strains were sensitively recorded by SERS in a whole-cell manner. Principal components-linear discriminant analysis correctly distinguished various degrees of drug-resistant strains. The typical Raman peak intensities of I₇₃₄/I₈₆₇ showed a negative and non-linear correlation with the minimum inhibitory concentration (MIC). The correlation coefficient reached above 0.9. The target sites of oxacillin/cefazolin on S. aureus clearly reflected on SERS profiles. The results collected by SERS were further verified by other biological methods including the antibiotic susceptibility test, MIC determination, and PCR results. This study indicates that SERS technology provides a rapid and flexible alternative to current drug susceptibility testing, laying a foundation for qualitative and quantitative evaluation of drug resistance in clinical detection.

Preparation of an AgNPs@Polydimethylsiloxane (PDMS) multi-hole filter membrane chip for the rapid identification of food-borne pathogens by surface-enhanced Raman spectroscopy

The consumption of food infected with food-borne pathogens has become a global public health problem. Therefore, it is monitor food-borne infections to avoid health and financial consequences. The rapid detection and differentiation of bacteria for biomedical and food safety applications continues to be a significant challenge. Herein, we present a label-free surface-enhanced Raman scattering approach for separating harmful bacteria from food. The method relies on the ascorbic acid reduction method to synthesize silver nanoparticles (AgNPs) and a polydimethylsiloxane (PDMS) multi-hole filter membrane chip (AgNPs@PDMS multi-hole filter membrane chip). Surface-enhanced Raman spectroscopy (SERS) was used, followed by multivariate statistical analysis to differentiate five important food-borne pathogens, including Staphylococcus aureus, Salmonella typhimurium, Listeria monocytogenes, Clostridium difficiles and Clostridium perfringens. The results demonstrated that compared to normal Raman signals, the intensity of the SERS signal was greatly enhanced with an analytical enhancement factor of 5.2 × 10³. The spectral ranges of 400-1800 cm^-1 were analyzed using principal component analysis (PCA) and stepwise linear discriminant analysis (SWLDA) were used to determine the optimal parameters for the discrimination of food-borne pathogens. The first three principal components (PC1, PC2, and PC3) accounted for 87.3% of the total variance in the spectra. The established SWLDA model had 100% accuracy and cross-validation accuracy, which accurately distinguished the SERS spectra of the five species. In conclusion, the SERS technology based on the AgNPs@PDMS multi-hole filter membrane chip was useful for the rapid identification of food-borne pathogens and can be employed for food quality management.

Bacterial identification and adhesive strength evaluation based on a mannose biosensor with dual-mode detection

A biosensor integrated with mannose nano-surface was developed for the identification and adhesive strength evaluation of bacteria. Different bacteria were studied on the designed surface by both electrochemical impedance spectroscopy (EIS) and surface enhanced Raman spectroscopy (SERS). S. typhimurium and E. coli JM109 (type 1 pili) were found to be captured by the mannose nano-surface. SERS spectra were used to identify the species of captured bacteria by combing with partial least squares discriminant analysis (PLS-DA). Meanwhile, binding affinities of the two captured bacteria to mannose nano-surface were obtained by EIS measurements and Frumkin isotherm model analysis, which were 6.859 × 10²³ M^-1 and 2.054 × 10¹⁷ M^-1 respectively. A higher binding affinity indicates a stronger adhesive strength. Hence the results show the S. typhimurium has a stronger adhesive strength to mannose. Normalized impedance change (NIC) was proved to have a positive relevant relationship with binding affinities, which could be used as an indicator for the adhesive strength of bacteria. It was demonstrated that 100% accuracy of bacteria species discrimination and good consistency of NIC and adhesive strength for blind samples. The developed biosensor can provide both qualitative and quantitative information of selective recognition between bacteria and mannose.

Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria

Raman spectroscopy combined with artificial intelligence algorithms have been widely explored and focused on in recent years for food safety testing. It is still a challenge to overcome the cumbersome culture process of bacteria and the need for a large number of samples, which hinder qualitative analysis, to obtain a high classification accuracy. In this paper, we propose a method based on Raman spectroscopy combined with generative adversarial network and multiclass support vector machine to classify foodborne pathogenic bacteria. 30,000 iterations of generative adversarial network are trained for three strains of bacteria, generative model G generates data similar to the actual samples, discriminant model D verifies the accuracy of the generated data, and 19 feature variables are obtained by selecting the feature bands according to the Raman spectroscopy pattern. Better classification results are obtained by optimising the parameters of the multi-class support vector machine, etc. Our detection and classification method not only solves the problem of needing a large number of samples as training set, but also improves the accuracy of the classification model. Therefore, this GAN-SVM classification model provides a new idea for the detection of bacteria based on Raman spectroscopy technology combined with artificial intelligence algorithms.

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.

Study of biochip integrated with microelectrodes modified by poly-dopamine-co-chitosan composite gel for separation, enrichment and detection of microbes in the aerosol

As the urgent need for rapid detection of airborne microbes in a specific environment, a biochip which was integrated with the functions of enrichment and detection was designed and developed. It was composed of cover plate, copper microelectrodes modified with poly-dopamine-co-chitosan (PDA-co-CS) composite gel, sealing washer and substrate containing copper sheet electrode. The microbes were enriched due to the good ventilation efficiency and adhesion of the PDA-co-CS composite gel. The enrichment efficiency of microbes was 99.9%. The electrical impedance spectrum (EIS) test system which was composed of the copper electrodes and the copper sheet electrode were used to detect the concentrated microbes and establish the quantitative detection method of single microbe (S. aureus ATCC 6538) and mixed microbes (S. aureus ATCC 6538, E. coli JM109, and Candida albicans). It was shown that the biochip could respond to the aerosol with 1.26 × 10³ cfu/m³S. aureus ATCC 6538, which was 25 times as high as the detection limit of natural deposition method. Meanwhile, the Surface-enhanced Raman Spectrum (SERS) of different microbes were detected in-situ with the help of the silver sol. The SERS data of S. aureus, E. coli and Candida albicans had been analyzed to establish recognition model by the principal component analysis (PCA) method and the three microbes were successfully identified. It was demonstrated that the designed biochip could be applied for separation, enrichment and detection of microbes in the aerosol.

Raman Spectroscopy Study of Phosphorites Combined with PCA-HCA and OPLS-DA Models

Phosphorite is a nonrenewable strategic resource, a convenient and rapid method of phosphorite grade identification and classification is important to improve phosphate utilization. In this study, Raman spectroscopy has been combined with principal component analysis and hierarchical clustering analysis (PCA-HCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models for an investigation of different grade phosphorite samples. Both the PCA-HCA and OPLS-DA models showed that different grade phosphorite samples can be clearly distinguished by statistically analyzing the Raman spectra after smoothing, baseline correction, and first derivation. In addition, the S-line study on the OPLS-DA model clearly demonstrated that the symmetrical stretching vibrational mode of phosphate near 960 cm−1 had a much more significant contribution than other vibrational modes for the differentiation of different grade phosphorite samples.

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.

Molecular origin of surface-enhanced Raman spectra of E. coli suspensions excited at 532 and 785 nm using silver nanoparticle sols as sers substrates

Research into the molecular origin of surface-enhanced Raman spectra (SERS) of bacteria is a crucial step in assessing the future of SERS-based discrimination and identification of bacteria in clinical analysis, food quality control, etc. Previous studies have revealed that at 785 nm excitation wavelength SERS of bacterial cells placed on a solid surface functionalized with in-situ grown aggregated gold nanoparticles covered with SiO2 originate from a mixture of 6 purine derivatives (adenine, guanine, AMP, hypoxanthine, xanthine, and uric acid) that are released by the cells into the medium. The aim of the present work was to investigate whether such interpretation is possible with a different class of SERS substrates: silver nanoparticle sols at excitation wavelengths of 785 and 532 nm. The suspension of the Escherichia coli DH5α strain was used as a model bacterium. Sols of silver nanoparticles were obtained by reducing silver nitrate in the presence of alkaline hydroxylamine hydrochloride. Number-weighted mean hydrodynamic diameter of the particles was 43±2 nm. We confirm that at both excitation wavelengths the spectra can be best described as a superposition of 4 purine derivatives: adenine, guanine, hypoxanthine, and xanthine. Importantly, we have discovered that 1) the spectra of the purine mixture are characteristic of viable cells only; 2) due to the variations in the concentrations of purine metabolites released by the cells into the surrounding medium the spectra of a bacterial strain can vary significantly when a silver nanoparticle sol is used as a SERS substrate.

Detecting Biothreat Agents: From Current Diagnostics to Developing Sensor Technologies

Although a fundamental understanding of the pathogenicity of most biothreat agents has been elucidated and available treatments have increased substantially over the past decades, they still represent a significant public health threat in this age of (bio)terrorism, indiscriminate warfare, pollution, climate change, unchecked population growth, and globalization. The key step to almost all prevention, protection, prophylaxis, post-exposure treatment, and mitigation of any bioagent is early detection. Here, we review available methods for detecting bioagents including pathogenic bacteria and viruses along with their toxins. An introduction placing this subject in the historical context of previous naturally occurring outbreaks and efforts to weaponize selected agents is first provided along with definitions and relevant considerations. An overview of the detection technologies that find use in this endeavor along with how they provide data or transduce signal within a sensing configuration follows. Current "gold" standards for biothreat detection/diagnostics along with a listing of relevant FDA approved in vitro diagnostic devices is then discussed to provide an overview of the current state of the art. Given the 2014 outbreak of Ebola virus in Western Africa and the recent 2016 spread of Zika virus in the Americas, discussion of what constitutes a public health emergency and how new in vitro diagnostic devices are authorized for emergency use in the U.S. are also included. The majority of the Review is then subdivided around the sensing of bacterial, viral, and toxin biothreats with each including an overview of the major agents in that class, a detailed cross-section of different sensing methods in development based on assay format or analytical technique, and some discussion of related microfluidic lab-on-a-chip/point-of-care devices. Finally, an outlook is given on how this field will develop from the perspective of the biosensing technology itself and the new emerging threats they may face.

Chemically imaging bacteria with super-resolution SERS on ultra-thin silver substrates

Plasmonic hotspots generate a blinking Surface Enhanced Raman Spectroscopy (SERS) effect that can be processed using Stochastic Optical Reconstruction Microscopy (STORM) algorithms for super-resolved imaging. Furthermore, by imaging through a diffraction grating, STORM algorithms can be modified to extract a full SERS spectrum, thereby capturing spectral as well as spatial content simultaneously. Here we demonstrate SERS and STORM combined in this way for super-resolved chemical imaging using an ultra-thin silver substrate. Images of gram-positive and gram-negative bacteria taken with this technique show excellent agreement with scanning electron microscope images, high spatial resolution at <50 nm, and spectral SERS content that can be correlated to different regions. This may be used to identify unique chemical signatures of various cells. Finally, because we image through as-deposited, ultra-thin silver films, this technique requires no nanofabrication beyond a single deposition and looks at the cell samples from below. This allows direct imaging of the cell/substrate interface of thick specimens or imaging samples in turbid or opaque liquids since the optical path doesn’t pass through the sample. These results show promise that super-resolution chemical imaging may be used to differentiate chemical signatures from cells and could be applied to other biological structures of interest.

Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor

BACKGROUND

It is challenging to achieve ultrasensitive and selective detection of waterborne pathogens at extremely low levels (i.e., single cell/mL) using conventional methods. Even with molecular methods such as ELISA or PCR, multi-enrichment steps are needed which are labor and cost intensive. In this study, we incorporated nano-dielectrophoretic microfluidic device with Surface enhanced Raman scattering (SERS) technique to build a novel portable biosensor for easy detection and characterization of Escherichia coli O157:H7 at high sensitivity level (single cell/mL).

RESULTS

A multiplexing dual recognition SERS scheme was developed to achieve one-step target detection without the need to separate target-bound probes from unbound ones. With three different SERS-tagged molecular probes targeting different epitopes of the same pathogen being deployed simultaneously, detection of pathogen targets was achieved at single cell level with sub-species specificity that has not been reported before in single-step pathogen detection.

CONCLUSION

The self-referencing protocol implements with a Nano-dielectrophoretic microfluidic device potentially can become an easy-to-use, field-deployable spectroscopic sensor for onsite detection of pathogenic microorganisms.

Label-free mapping of single bacterial cells using surface-enhanced Raman spectroscopy

Here we presented a simple, rapid and label-free surface-enhanced Raman spectroscopy (SERS) based mapping method for the detection and discrimination ofSalmonella entericaandEscherichia coli onsilver dendrites.