Wide-Range, Rapid, and Specific Identification of Pathogenic Bacteria by Surface-Enhanced Raman Spectroscopy

Rapid and accurate identification of foodborne bacteria: a combined approach using confocal Raman micro-spectroscopy and explainable machine learning

This study proposes a rapid identification method for foodborne pathogens by combining Raman spectroscopy with explainable machine learning. Spectral data of nine common foodborne pathogens are collected using a laser confocal Raman spectrometer, and their characteristic Raman peaks are identified and analyzed. Key spectral features are extracted using competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA), while t-distributed stochastic neighbor embedding (t-SNE) is employed for visualization. Subsequently, classification models, including support vector machine (SVM) and random forest (RF), are developed, and the optimal model is selected based on classification accuracy (ACC), with the RF model achieving a test accuracy of 98.91%. To enhance the interpretability of the model, Shapley Additive exPlanations (SHAP) analysis is applied to evaluate the contribution of each spectral feature to the classification results, identifying critical Raman shifts significantly influencing pathogen classification. The results demonstrate that CARS-SPA feature selection not only improves the accuracy and efficiency of the classification model but also enhances its transparency and reliability. This study optimizes the workflow for food safety testing, reduces the risk of foodborne diseases, and provides robust technical support for public health and safety.

Surface-Enhanced Raman Spectroscopy and Multivariate Analysis for Elucidating Mechanisms of Action in Antibacterial Agents

Antibiotic and antibacterial-resistant bacteria continue to pose a global-health threat. Understanding the mechanism of action (MoA) of antibacterial agents is crucial for developing precise and novel treatment methods. Traditionally, the MoA of a novel treatment is studied with genome sequencing and mass spectrometry, which are both labor-intensive and costly. In contrast, surface-enhanced Raman spectroscopy (SERS) provides a rapid, sensitive, and noninvasive alternative for analyzing bacterial molecular responses to antibacterial agents. In this study, we employed SERS to analyze the effects of various antibacterial agents on Escherichia coli. We treated E. coli cultures with agents that have different known MoAs, including oxidative stress, metabolic disruption, and membrane lysis. Through partial least-squares (PLS) analysis, we correlated changes in the SERS spectra with bacterial viability, achieving high predictive accuracy (R2 > 0.98). From the PLS models, we were able to extract variable importance projection scores, which were used to identify the MoA in subsets of the data. Our results revealed distinct spectral signatures associated with each MoA, demonstrating the potential of SERS to differentiate between different antibacterial treatments. This study highlights the feasibility of using SERS combined with multivariate analysis to rapidly characterize the molecular effects of antibacterial agents even with smaller data sets. By providing a real-time method for monitoring bacterial responses, this SERS approach could accelerate the discovery of novel antibacterial therapies while reducing dependency on more time-consuming and expensive analytical techniques.

Diagnosis Accuracy of Raman Spectroscopy in the Identification of Pathogenic Bacteria

As an emerging technology, Raman spectroscopy (RS) has been used to identify pathogenic bacteria with excellent performance. The aim of this study was to verify the diagnosis accuracy of RS in identification of pathogenic bacteria. This meta-analysis systematically evaluated the accuracy of RS for identification of pathogenic bacteria. We searched the electronic databases of PubMed and Web of Science to obtain relevant articles; STATA 15.1 was used to analyze all sensitivities, specificies, and their 95% confidence interval (CI). The summary receiver operating characteristic curves (SROC) and area under the curve (AUC) were used to display more performance of RS. Nineteen articles were included according to the inclusion and exclusion criteria. The pooled sensitivity and specificity of RS for the identification of pathogenic bacteria were 0.94 (95% CI, 0.89-0.96) and 0.99 (95% CI, 0.97-0.99). The diagnostic odds ratio (DOR) was 1209 (95% CI, 367-3980), and AUC of SROC was 0.99 (95% CI, 0.98-1.00). For gram-positive bacteria, the sensitivity and specificity of different species ranged from 0.00 to 1.00 and 0.96 to 1.00, with a pooled sensitivity and specificity of 0.96 (95% CI, 0.90-0.98) and 0.99 (95% CI, 0.98-1.00). For gram-negative bacteria, the sensitivity and specificity of different species ranged from 0.30 to 1.00 and 0.92 to 1.00, with a pooled sensitivity and specificity of 0.92 (95% CI, 0.76-0.98) and 0.99 (95% CI, 0.98-1.00). For acid-fast bacteria, the sensitivity and specificity of different species ranged from 0.83 to 1.00 and 0.96 to 1.00, with a pooled sensitivity and specificity of 0.96 (95% CI, 0.84-0.99) and 1.00 (95% CI, 0.96-1.00). RS provides a new method for pathogenic bacteria identification and demonstrates high sensitivity and specificity for most included species.

Innovative Applications and Perspectives of Surface-Enhanced Raman Spectroscopy Technology in Biomedicine

Surface-enhanced Raman spectroscopy (SERS) has become a revolutionary technique in the biomedical field, providing unparalleled sensitivity for the detection and characterization of biological samples. In this review, recent SERS innovations are comprehensively discussed, including advanced substrate materials, different SERS detection strategies, and multimodal approaches that combine SERS with other biotechnologies. Among them, the role of SERS in the accurate diagnosis of tumors is highlighted, which has promoted accurate molecular analysis and real-time monitoring of treatment effects. In addition, the growing potential of SERS in the treatment of chronic diseases such as cardiovascular disease, diabetes, and neurodegenerative diseases is discussed. Moreover, the integration with microfluidic chip systems for precise single-cell analysis is presented. To give a forward-looking view, the key challenges faced by SERS technology are also proposed, and possible solutions to overcome these obstacles are provided.

Application of Raman Spectroscopy in Non-Invasive Analysis of the Gut Microbiota and Its Impact on Gastrointestinal Health

The gut microbiota, a complex community of microorganisms, plays a crucial role in gastrointestinal (GI) health, influencing digestion, metabolism, immune function, and the gut-brain axis. Dysbiosis, or an imbalance in microbiota composition, is associated with GI disorders, including irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and colorectal cancer (CRC). Conventional microbiota analysis methods, such as next-generation sequencing (NGS) and nuclear magnetic resonance (NMR), provide valuable insights but are often expensive, time-consuming, and destructive. Raman spectroscopy (RS) is a non-invasive, cost-effective, and highly sensitive alternative. This analytical technique relies on inelastic light scattering to generate molecular "fingerprints", enabling real-time, marker-free analysis of microbiota composition and metabolic activity. This review explores the principles, sample preparation techniques, and advancements in RS, including surface-enhanced Raman spectroscopy (SERS), for microbiota research. RS facilitates identifying microbial species, analysing key metabolites like short-chain fatty acids (SCFA), and monitoring microbiota responses to dietary and therapeutic interventions. The comparative analysis highlights RS's advantages over conventional techniques, such as the minimal sample preparation, real-time capabilities, and non-destructive nature. The integration of RS with machine learning enhances its diagnostic potential, enabling biomarker discovery and personalised treatment strategies for GI disorders. Challenges, including weak Raman signals and spectral complexity, are discussed alongside emerging solutions. As RS technology advances, mainly through portable spectrometers and AI integration, its clinical application in microbiota diagnostics and personalised medicine is poised to transform GI healthcare, bridging microbiota research with practical therapeutic strategies.

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

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.

Three-dimensional composite substrate based on pyramidal pitted silicon array adhered Au@Ag nanospheres for high-performance surface-enhanced Raman scattering

As a noninvasive and label-free optical technique, Raman spectroscopy offers significant advantages in studying the structure and properties of biomacromolecules, as well as real-time changes in cellular molecular structure. However, its practical applications are hindered by weak scattering responses, low signal intensity, and poor spectral uniformity, which affect the subsequent accuracy of spectral analysis. To address these issues, we report a novel surface-enhanced Raman scattering (SERS) substrate based on a pyramidal pitted silicon (PPSi) array structure adhered with Au-shell Ag-core nanospheres (Au@Ag NSs). By preparing a highly uniform PPSi array substrate with controllable size and arrangement, and constructing SERS-active Au@Ag NSs on this substrate, a three-dimensional (3D) composite SERS substrate is realized. The enhancement performance and spectral uniformity of 3D composite SERS substrate were examined using crystal violet (CV) and Rhodamine 6G (R6G) molecules, achieving a minimum detectable concentration of R6G at 10-9 M and the analytical enhancement factor (AEF) of 4.2 × 108. Moreover, SERS detection of biological samples with varying concentrations of Staphylococcus aureus demonstrated excellent biocompatibility of the SERS substrate and enabled quantitative analysis of bacterial concentration (R 2 = 99.7 %). Theoretical simulations using finite-difference time-domain (FDTD) analysis were conducted to examine the electromagnetic field distribution of the three-dimensional SERS composite substrate, confirming its local electric field enhancement effect. These experimental and theoretical results indicate that the Au@Ag NSs/PPSi substrate with a regulable pyramidal pitted array is a promising candidate for sensitive, label-free SERS detection in medical and biotechnological applications.

Biomedical SERS - the current state and future trends

Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues. This review summarizes the current state in sample collection and pretreatment in SERS detection protocols, SERS detection schemes, i.e. direct and indirect SERS as well as targeted and non-targeted SERS, and SERS-active sensing surfaces. Moreover, the recent developments and advances of SERS in biomedical application scenarios, such as infectious diseases, cancer diagnostics and therapeutic drug monitoring is given, which enables the readers to identify the sample collection and preparation protocols, SERS substrates and detection strategies that are best-suited for their specific applications in biomedicine.

Characterization of vaginal Lactobacillus in biologically relevant fluid using surface-enhanced Raman spectroscopy

The native vaginal microbiome plays a crucial role in maintaining vaginal health and disruption can have significant consequences for women during their lifetime. While the composition of the vaginal microbiome is important, current methods for monitoring this community are lacking. Clinically used techniques routinely rely on subjective analysis of vaginal fluid characteristics or time-consuming microorganism culturing. Surface-enhanced Raman spectroscopy (SERS) can aid in filling this gap in timely detection of alterations in the vaginal microbiome as it can discriminate between bacterial species in complex solutions including bacterial mixtures and biofluids. SERS has not previously been applied to study variations in vaginal Lactobacillus, the most common species found in the vaginal microbiome, in complex solutions. Herein, the SERS spectra of Lactobacillus crispatus (L. crispatus) and Lactobacillus iners (L. iners), two of the most common vaginal bacteria, was characterized at physiologically relevant concentrations. Subsequently, the ability of SERS to detect L. crispatus and L. iners in both pure mixtures and when mixed with a synthetic vaginal fluid mimicking solution was determined. In both pure and complex solutions, SERS coupled with partial least squares regression predicted the ratiometric bacterial content with less than 10% error and strong goodness of prediction (Q2 > 0.9). This developed method highlights the applicability of SERS to predict the dominant Lactobacillus in the vaginal micro-environment toward the monitoring of this community.

RamanCluster: A deep clustering-based framework for unsupervised Raman spectral identification of pathogenic bacteria

Raman spectroscopy serves as a powerful and reliable tool for the characterization of pathogenic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenic bacteria has become paramount for expediting disease diagnosis. However, the development of prevailing supervised artificial intelligence algorithms is still constrained by costly and limited well-annotated Raman spectroscopy datasets. Furthermore, tackling various high-dimensional and intricate Raman spectra of pathogenic bacteria in the absence of annotations remains a formidable challenge. In this paper, we propose a concise and efficient deep clustering-based framework (RamanCluster) to achieve accurate and robust unsupervised Raman spectral identification of pathogenic bacteria without the need for any annotated data. RamanCluster is composed of a novel representation learning module and a machine learning-based clustering module, systematically enabling the extraction of robust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria. The extensive experimental results show that RamanCluster has achieved high accuracy on both Bacteria-4 and Bacteria-6, with ACC values of 77 % and 74.1 %, NMI values of 75 % and 73 %, as well as AMI values of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of-the-art methods, RamanCluster exhibits the superior accuracy on handling various complicated pathogenic bacterial Raman spectroscopy datasets, including situations with strong noise and a wide variety of pathogenic bacterial species. Additionally, RamanCluster also demonstrates commendable robustness in these challenging scenarios. In short, RamanCluster has a promising prospect in accelerating the development of low-cost and widely applicable disease diagnosis in clinical medicine.

Surface-Enhanced Raman Spectroscopy Sensor Integrated with Ag@ZIF-8@Au Core-Shell-Shell Nanowire Membrane for Enrichment, Ultrasensitive Detection, and Inactivation of Bacteria in the Environment

A core-shell-shell sandwich material is developed with silver nanowires as the core, ZIF-8 as an inner shell, and gold nanoparticles as the outer shell, namely, Ag@ZIF-8@Au nanowires (AZA-NW). Then, the synthesized AZA-NW is transformed into a surface-enhanced Raman spectroscopy (SERS) sensor (named M-AZA) by the vacuum filtration method and used to enrich, detect, and inactivate traces of bacteria in the environment. The M-AZA sensor has three main functions: (1) trace bacteria are effectively enriched, with an enrichment efficiency of 91.4%; (2) ultrasensitive detection of trace bacteria is realized, with a minimum detectable concentration of 1 × 101 CFU/mL; (3) bacteria are effectively killed up to 92.4%. The shell thickness of ZIF-8 (5-75 nm) is controlled by adjusting the synthesis conditions. At an optimum shell thickness of 15 nm, the effect of gold nanoparticles and ZIF-8 shell on the sensor's stability, SERS activity, and antibacterial performance is investigated. The simulation of the SERS sensor using the finite difference time domain (FDTD) method is consistent with the experimental results, theoretically demonstrating the role of the gold nanoparticles and the ZIF-8 shell. The sensor also shows excellent stability, safety, and generalizability. The campus water sample is then tested on-site by the M-AZA SERS sensor, indicating its potential for practical applications.

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.

Python-assisted detection and photothermal inactivation of Salmonella typhimurium and Staphylococcus aureus on a background-free SERS chip

In this study, a background-free surface-enhanced Raman scattering (SERS) chip with a sandwich configuration was fabricated to enable reliable detection and photothermal inactivation of multiple bacteria. The SERS chip consists of a graphene-coated, phenylboronic-modified plasmonic gold substrate (pAu/G/PBA), and two aptamer-functionalized core (gold)-shell (Prussian blue/Poly-L-lysine and 4-mercaptobenzonitrile/polydopamine) SERS tags (Au@PB@PLL@Apt and Au@MB@PDA@Apt). The detection signals rely on the characteristic and nonoverlapping Raman bands of the SERS tags within the Raman-silent region (1800-2800 cm-1), where no background signals from the sample matrix are observed, leading to improved detection sensitivity and accuracy. Considering the relatively large size of bacteria (e.g., micron level), a rapid Raman mapping technique was chosen over conventional point-scan methods to achieve more reliable quantitative analysis of bacteria. This technique involves collecting and analyzing intensity signals of SERS tags from all the scattering points with an average ensemble effect, which is facilitated by the use of Python. As a proof-of-concept, model bacterium of Salmonella typhimurium and Staphylococcus aureus were successfully detected using the SERS chip with a dynamic range of 10-107 CFU/mL. Additionally, the SERS chip demonstrated successful detection of these bacteria in whole blood samples. Moreover, the photothermal effect of pAu/G led to efficient bacteria elimination, achieving approximately 100% eradication. This study integrated a background-free SERS chip with a Python-assisted rapid Raman mapping technique, resulting in a reliable, rapid and accurate method for detecting and eliminating multiple bacteria, which may provide a promising alternative for multiple screening of bacteria in real samples.

Surface-Enhanced Raman Scattering-Based Surface Chemotaxonomy: Combining Bacteria Extracellular Matrices and Machine Learning for Rapid and Universal Species Identification

Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research.

Reducing Mass Confusion over the Microbiome

As genetic tools continue to emerge and mature, more information is revealed about the identity and diversity of microbial community members. Genetic tools can also be used to make predictions about the chemistry that bacteria and fungi produce to function and communicate with one another and the host. Ongoing efforts to identify these products and link genetic information to microbiome chemistry rely on analytical tools. This tutorial highlights recent advancements in microbiome studies driven by techniques in mass spectrometry.

Optical biosensors for environmental monitoring: Recent advances and future perspectives in bacterial detection

The environmental contamination due to bacterial proliferation vs their identification is the major deciding factor in the spread of diseases leading to pandemics. The advent of drug-resistant pathogenic contaminants in our environment has further added to the load of complications associated with their diagnosis and treatment. Obstructing the spread of such infections, prioritizes the expansion of sensor-based diagnostics, effectuating, a sturdy detection of disease-causing microbes, contaminating our surroundings in shortest possible time, with minimal expenditure. Among many sensors known, optical biosensors promote the recognition of pathogens befouling the environment through a comparatively intuitive, brisk, portable, multitudinous, and thrifty approach. This article reviews the recent progresses in optical biosensor-based systems for effective environmental monitoring. The technical and methodological perspectives of fundamental optical-sensing platforms are reviewed, combined with the pros and cons of every procedure. Eventually, the obstacles lying in the path of development of an effective optical biosensor device for bio-monitoring and its future perspectives are highlighted in the present work.

Nanorod reassembling on a sprayed SERS substrate under confined evaporation inducing ultrasensitive TPhT detection

Triphenyltin is an estrogen like pollutant that poses significant environmental threats due to its highly accumulative toxicity. To improve regulation, a fast and sensitive detection method is urgently needed. SERS can capture fingerprint information and is capable of trace detection, making it an ideal solution. Here, we present a sprayed substrate comprised of lightconfining structures and gold nanorod assemblies that are easy to prepare, low-cost, and can form dense hotspots under confined evaporation. The substrates are three-layered: initially, a gold nanorod layer is sprayed as a support, then sputter Ag film on the surface to form a lightconfining structure, followed by another gold nanorod layer sprayed on the Ag film. The coupling of nanorod assembly with lightconfining Ag films leads to 10-fold sensitivity. In addition, sample droplet evaporation in a limited area called confined evaporation contributes to nanorod migration and reassembly on the corner of the substrate, enhancing analytes absorption, and substantially lowered the detection limits. By systematically evaluating the substrate performance, we were able to obtain an average enhancement factor of 3.31 × 106. After confined evaporation, the detection limit reached 10-18 M for R6G and for triphenyltin, it achieved 10-9 M. This novel method represents a significant advancement toward SERS application in detecting trace pollutants.

Emerging Applications of Nanobiosensors in Pathogen Detection in Water and Food

Food and waterborne illnesses are still a major concern in health and food safety areas. Every year, almost 0.42 million and 2.2 million deaths related to food and waterborne illness are reported worldwide, respectively. In foodborne pathogens, bacteria such as Salmonella, Shiga-toxin producer Escherichia coli, Campylobacter, and Listeria monocytogenes are considered to be high-concern pathogens. High-concern waterborne pathogens are Vibrio cholerae, leptospirosis, Schistosoma mansoni, and Schistosima japonicum, among others. Despite the major efforts of food and water quality control to monitor the presence of these pathogens of concern in these kinds of sources, foodborne and waterborne illness occurrence is still high globally. For these reasons, the development of novel and faster pathogen-detection methods applicable to real-time surveillance strategies are required. Methods based on biosensor devices have emerged as novel tools for faster detection of food and water pathogens, in contrast to traditional methods that are usually time-consuming and are unsuitable for large-scale monitoring. Biosensor devices can be summarized as devices that use biochemical reactions with a biorecognition section (isolated enzymes, antibodies, tissues, genetic materials, or aptamers) to detect pathogens. In most cases, biosensors are based on the correlation of electrical, thermal, or optical signals in the presence of pathogen biomarkers. The application of nano and molecular technologies allows the identification of pathogens in a faster and high-sensibility manner, at extremely low-pathogen concentrations. In fact, the integration of gold, silver, iron, and magnetic nanoparticles (NP) in biosensors has demonstrated an improvement in their detection functionality. The present review summarizes the principal application of nanomaterials and biosensor-based devices for the detection of pathogens in food and water samples. Additionally, it highlights the improvement of biosensor devices through nanomaterials. Nanomaterials offer unique advantages for pathogen detection. The nanoscale and high specific surface area allows for more effective interaction with pathogenic agents, enhancing the sensitivity and selectivity of the biosensors. Finally, biosensors' capability to functionalize with specific molecules such as antibodies or nucleic acids facilitates the specific detection of the target pathogens.

Recent Advances in Optical Sensing for the Detection of Microbial Contaminants

Microbial contaminants are responsible for several infectious diseases, and they have been introduced as important potential food- and water-borne risk factors. They become a global burden due to their health and safety threats. In addition, their tendency to undergo mutations that result in antimicrobial resistance makes them difficult to treat. In this respect, rapid and reliable detection of microbial contaminants carries great significance, and this research area is explored as a rich subject within a dynamic state. Optical sensing serving as analytical devices enables simple usage, low-cost, rapid, and sensitive detection with the advantage of their miniaturization. From the point of view of microbial contaminants, on-site detection plays a crucial role, and portable, easy-applicable, and effective point-of-care (POC) devices offer high specificity and sensitivity. They serve as advanced on-site detection tools and are pioneers in next-generation sensing platforms. In this review, recent trends and advances in optical sensing to detect microbial contaminants were mainly discussed. The most innovative and popular optical sensing approaches were highlighted, and different optical sensing methodologies were explained by emphasizing their advantages and limitations. Consequently, the challenges and future perspectives were considered.

Detection and analysis of rotavirus in clinical stool samples using silver nanoparticle functionalized paper as SERS substrate

Among the different analytical techniques, surface-enhanced Raman scattering (SERS) approach is a widely used technique for the detection and analysis of various chemicals and biological samples. Present study reports a low-cost, sensitive SERS substrate that has an ability to detect rotavirus in clinical stool samples. The proposed SERS substrate has been fabricated through drop-casting of silver nanoparticles (AgNPs) on a printing-grade paper. Rotavirus particles were extracted from clinical stool samples. The presence of rotavirus antigen in stool samples was confirmed using enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and sequencing. The characteristic Raman peaks of rotavirus (RV) particles in solution were found to be significantly enhanced when Raman signals were recorded from the paper-based SERS substrates. Using the proposed SERS substrate, rotavirus samples with concentration as low as 1% could be reliably recorded by the Raman spectrometer. The paper SERS substrate reported herein is an extremely cost-efficient platform and may find applications in other research and clinical laboratories as well.

Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag-CuxO Nanostructures

Bacteria cause many common infections and are the culprit of many outbreaks throughout history that have led to the loss of millions of lives. Contamination of inanimate surfaces in clinics, the food chain, and the environment poses a significant threat to humanity, with the increase in antimicrobial resistance exacerbating the issue. Two key strategies to address this issue are antibacterial coatings and effective detection of bacterial contamination. In this study, we present the formation of antimicrobial and plasmonic surfaces based on Ag-CuxO nanostructures using green synthesis methods and low-cost paper substrates. The fabricated nanostructured surfaces exhibit excellent bactericidal efficiency and high surface-enhanced Raman scattering (SERS) activity. The CuxO ensures outstanding and rapid antibacterial activity within 30 min, with a rate of >99.99% against typical Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus bacteria. The plasmonic Ag nanoparticles facilitate the electromagnetic enhancement of Raman scattering and enables rapid, label-free, and sensitive identification of bacteria at a concentration as low as 103 cfu/mL. The detection of different strains at this low concentration is attributed to the leaching of the intracellular components of the bacteria caused by the nanostructures. Additionally, SERS is coupled with machine learning algorithms for the automated identification of bacteria with an accuracy that exceeds 96%. The proposed strategy achieves effective prevention of bacterial contamination and accurate identification of the bacteria on the same material platform by using sustainable and low-cost materials.

Advances in surface-enhanced Raman spectroscopy technology for detection of foodborne pathogens

Rapid control and prevention of diseases caused by foodborne pathogens is one of the existing food safety regulatory issues faced by various countries and has received wide attention from all sectors of society. The development of rapid and reliable detection methods for foodborne pathogens remains a hot research area for food safety and public health because of the limitations of complex steps, time-consuming, low sensitivity, or poor selectivity of commonly used methods. Surface-enhanced Raman spectroscopy (SERS), as a novel spectroscopic technique, has the advantages of high sensitivity, selectivity, rapid and nondestructive detection and has exhibited broad application prospects in the determination of pathogenic bacteria. In this study, the enhancement mechanisms of SERS are briefly introduced, then the characteristics and properties of liquid-phase, rigid solid-phase, and flexible solid-phase are categorized. Furthermore, a comprehensive review of the advances in label-free or label-based SERS strategies and SERS-compatible techniques for the detection of foodborne pathogens is provided, and the advantages and disadvantages of these methods are reviewed. Finally, the current challenges of SERS technology applied in practical applications are listed, and the possible development trends of SERS in the field of foodborne pathogens detection in the future are discussed.

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.

Fast Track Diagnostic Tools for Clinical Management of Sepsis: Paradigm Shift from Conventional to Advanced Methods

Sepsis is one of the deadliest disorders in the new century due to specific limitations in early and differential diagnosis. Moreover, antimicrobial resistance (AMR) is becoming the dominant threat to human health globally. The only way to encounter the spread and emergence of AMR is through the active detection and identification of the pathogen along with the quantification of resistance. For better management of such disease, there is an essential requirement to approach many suitable diagnostic techniques for the proper administration of antibiotics and elimination of these infectious diseases. The current method employed for the diagnosis of sepsis relies on the conventional culture of blood suspected infection. However, this method is more time consuming and generates results that are false negative in the case of antibiotic pretreated samples as well as slow-growing microbes. In comparison to the conventional method, modern methods are capable of analyzing blood samples, obtaining accurate results from the suspicious patient of sepsis, and giving all the necessary information to identify the pathogens as well as AMR in a short period. The present review is intended to highlight the culture shift from conventional to modern and advanced technologies including their limitations for the proper and prompt diagnosing of bloodstream infections and AMR detection.

Petri-plate, bacteria, and laser optical scattering sensor

Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.

SERS-PLSR Analysis of Vaginal Microflora: Towards the Spectral Library of Microorganisms

The accurate identification of microorganisms belonging to vaginal microflora is crucial for establishing which microorganisms are responsible for microbial shifting from beneficial symbiotic to pathogenic bacteria and understanding pathogenesis leading to vaginosis and vaginal infections. In this study, we involved the surface-enhanced Raman spectroscopy (SERS) technique to compile the spectral signatures of the most significant microorganisms being part of the natural vaginal microbiota and some vaginal pathogens. Obtained data will supply our still developing spectral SERS database of microorganisms. The SERS results were assisted by Partial Least Squares Regression (PLSR), which visually discloses some dependencies between spectral images and hence their biochemical compositions of the outer structure. In our work, we focused on the most common and typical of the reproductive system microorganisms (Lactobacillus spp. and Bifidobacterium spp.) and vaginal pathogens: bacteria (e.g., Gardnerella vaginalis, Prevotella bivia, Atopobium vaginae), fungi (e.g., Candida albicans, Candida glabrata), and protozoa (Trichomonas vaginalis). The obtained results proved that each microorganism has its unique spectral fingerprint that differentiates it from the rest. Moreover, the discrimination was obtained at a high level of explained information by subsequent factors, e.g., in the inter-species distinction of Candida spp. the first three factors explain 98% of the variance in block Y with 95% of data within the X matrix, while in differentiation between Lactobacillus spp. and Bifidobacterium spp. (natural flora) and pathogen (e.g., Candida glabrata) the information is explained at the level of 45% of the Y matrix with 94% of original data. PLSR gave us insight into discriminating variables based on which the marker bands representing specific compounds in the outer structure of microorganisms were found: for Lactobacillus spp. 1400 cm⁻¹, for fungi 905 and 1209 cm⁻¹, and for protozoa 805, 890, 1062, 1185, 1300, 1555, and 1610 cm⁻¹. Then, they can be used as significant marker bands in the analysis of clinical subjects, e.g., vaginal swabs.

Recent Progress in Spectroscopic Methods for the Detection of Foodborne Pathogenic Bacteria

Detection of foodborne pathogens at an early stage is very important to control food quality and improve medical response. Rapid detection of foodborne pathogens with high sensitivity and specificity is becoming an urgent requirement in health safety, medical diagnostics, environmental safety, and controlling food quality. Despite the existing bacterial detection methods being reliable and widely used, these methods are time-consuming, expensive, and cumbersome. Therefore, researchers are trying to find new methods by integrating spectroscopy techniques with artificial intelligence and advanced materials. Within this progress report, advances in the detection of foodborne pathogens using spectroscopy techniques are discussed. This paper presents an overview of the progress and application of spectroscopy techniques for the detection of foodborne pathogens, particularly new trends in the past few years, including surface-enhanced Raman spectroscopy, surface plasmon resonance, fluorescence spectroscopy, multiangle laser light scattering, and imaging analysis. In addition, the applications of artificial intelligence, microfluidics, smartphone-based techniques, and advanced materials related to spectroscopy for the detection of bacterial pathogens are discussed. Finally, we conclude and discuss possible research prospects in aspects of spectroscopy techniques for the identification and classification of pathogens.

Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains

The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Triple-Function Au-Ag-Stuffed Nanopancakes for SERS Detection, Discrimination, and Inactivation of Multiple Bacteria

New strategies combining sensitive pathogenic bacterial detection and high antimicrobial efficacy are urgently desirable. Here, we report smart triple-functional Au-Ag-stuffed nanopancakes (AAS-NPs) exhibiting (1) controllably oxidative Ag-etching thickness for simultaneously obtaining the best surface-enhanced Raman scattering (SERS) enhancement and high Ag-loading antibacterial drug delivery, (2) expressive Ag⁺-accelerated releasing capability under neutral phosphate-buffered saline (PBS) (pH ∼ 7.4) stimulus and robust antibacterial effectiveness involving sustainable Ag⁺ release, and (3) three-in-one features combining specific discrimination, sensitive detection, and inactivation of different pathogenic bacteria. Originally, AAS-NPs were synthesized by particle growth of the selective Ag-etched Au@Ag nanoparticles with K₃[Fe(CN)₆], followed by the formation of an unstable Prussian blue analogue for specifically binding with bacteria through the cyano group. Using specific bacterial "fingerprints" resulting from the introduction of dual-function 4-mercaptophenylboronic acid (4-MPBA, serving as both the SERS tag and internal standard) and a SERS sandwich nanostructure that was made of bacteria/SERS tags/AAS-NPs, three bacteria (E. coli, S. aureus, and P. aeruginosa) were highly sensitively discriminated and detected, with a limit of detection of 7 CFU mL^-1. Meanwhile, AAS-NPs killed 99% of 1 × 10⁵ CFU mL^-1 bacteria within 60 min under PBS (pH ∼ 7.4) pretreatment. Antibacterial activities of PBS-stimulated AAS-NPs against S. aureus, E. coli, and P. aeruginosa were extraordinarily increased by 64-fold, 72-fold, and 72-fold versus PBS-untreated AAS-NPs, respectively. The multiple functions of PBS-stimulated AAS-NPs were validated by bacterial sensing, inactivation in human blood samples, and bacterial biofilm disruption. Our work exhibits an effective strategy for simultaneous bacterial sensing and inactivation.

A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing

Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.

Gold nanospheres assembly via corona discharge technique for flexible SERS substrate

Noble metal nanoparticles (NMNPs) assembly substrates with strongly enhanced local electromagnetic fields provide new possibilities for surface-enhanced Raman spectroscopy (SERS) sensing. Although the external-electric-field-based self-assembly (EEFSA) strategy for decreasing NMNP gap in liquid phase is relatively developed, it is rarely described in solid phase. Here, by combining corona discharge technique (CDT) as a simple EEFSA approach on flexible substrate surface modification, a flexible SERS substrate medicated with gold nanospheres (AuNSs) is produced. Because of the CDT's peculiar discharge event, makes AuNSs aggregation simply achieved. The modified flexible SERS substrate is sensitive to the detection limit of ∼10^-5 mM for Rhodamine 6G (R6G), with a maximum enhancement factor of 2.79×10⁶. Furthermore, finite-difference time-domain (FDTD) simulation confirms the SERS enhancement impact of AuNSs-based substrate. This study not only provides a low-cost, simple-to-process, high-yield, high sensitivity, and activity flexible SERS substrate, but also suggests a more practical and adaptable NMNPs self-assembly approach.

Opposite Raman Shift of Ring Stretching Dependent on the Coordinated Silver Volume in Surface-Enhanced Raman Spectroscopy of Polypyrrole

Surface-enhanced Raman spectroscopy (SERS) can sense some molecules in a nondestructive manner. Using SERS, we investigate the shifts in the Raman peaks of polypyrrole (PPy) with two different coordinated silver (Ag) structures, Ag nanoparticles (NPs) and Ag dendrite film. The SERS spectrum of PPy with Ag NPs presents a ring-stretching peak that is red-shifted compared to the ring-stretching peak in the Raman spectrum of PPy. In contrast, the spectrum of the PPy with the Ag dendrite film exhibits a blue-shifted ring stretching peak. The various coordinated Ag nanostructures result in opposite Raman shifts of the ring stretching peak; this phenomenon has been investigated and confirmed by density functional theory (DFT) calculations of the Raman shift of the pyrrole (Py) molecule with a Ag layer (SERS of PPy with Ag NPs) and that of a charge-transferred Py molecule (SERS of PPy with Ag dendrite films). This result demonstrates that DFT calculations can be an effective tool to scrutinize Raman shifts in SERS.

Graphene Oxide/Silver Nanoparticles Platforms for the Detection and Discrimination of Native and Fibrillar Lysozyme: A Combined QCM and SERS Approach

We propose a sensing platform based on graphene oxide/silver nanoparticles arrays (GO/AgNPs) for the detection and discrimination of the native and toxic fibrillar forms of an amyloid-prone protein, lysozyme, by means of a combination of Quartz Crystal Microbalance (QCM) and Surface Enhanced Raman Scattering (SERS) measurements. The GO/AgNPs layer system was obtained by Langmuir-Blodgett assembly of the silver nanoparticles followed by controlled adsorption of GO sheets on the AgNPs array. The adsorption of native and fibrillar lysozyme was followed by means of QCM, the measurements provided the kinetics and the mechanism of adsorption as a function of protein concentration as well as the mass and thickness of the adsorbed protein on both nanoplatforms. The morphology of the protein layer was characterized by Confocal Laser Scanning Microscopy experiments on Thioflavine T-stained samples. SERS experiments performed on arrays of bare AgNPs and of GO coated AgNP after native, or fibrillar, lysozyme adsorption allowed for the discrimination of the native form and toxic fibrillar structure of lysozyme. Results from combined QCM/SERS studies indicate a general construction paradigm for an efficient sensing platform with high selectivity and low detection limit for native and amyloid lysozyme.

Culture-independent susceptibility determination of E. coli isolated directly from patients’ urine using FTIR and machine-learning

One of the most common human bacterial infections is the urinary tract infection (UTI).

Recent advances of vibrational spectroscopy and chemometrics for forensic biological analysis

Biological materials found at a crime scene are crucially important evidence for forensic investigation because they provide contextual information about a crime and can be linked to the donor-individuals through combination with DNA analysis. Applications of vibrational spectroscopy to forensic biological analysis have been emerging because of its advantageous characteristics such as the non-destructivity, rapid measurement, and quantitative evaluation, compared to most current methods based on histological observation or biochemical techniques. This review presents an overview of recent developments in vibrational spectroscopy for forensic biological analysis. We also emphasize chemometric techniques, which can elicit reliable and advanced analytical outputs from highly complex spectral data from forensic biological materials. The analytical subjects addressed herein include body fluids, hair, soft tissue, bones, and bioagents. Promising applications for various analytical purposes in forensic biology are presented. Simultaneously, future avenues of study requiring further investigation are discussed.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.