Rapid detection of Listeria monocytogenes in milk using confocal micro-Raman spectroscopy and chemometric analysis

Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization

Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.

Raman spectroscopy: Principles and recent applications in food safety

Food contaminant is a significant issue because of the adverse effects on human health and economy. Traditional detection methods such as liquid chromatography-mass spectroscopy for detecting food contaminants are expensive and time-consuming, and require highly-trained personnel and complicated sample pretreatment. Raman spectroscopy is an advanced analytical technique in a manner of non-destructive, rapid, cost-effective, and ultrasensitive sensing various hazards in agri-foods. In this chapter, we summarized the principle of Raman spectroscopy and surface enhanced Raman spectroscopy, the methods to process Raman spectra, the recent applications of Raman/SERS (surface-enhanced Raman spectroscopy) in detecting chemical contaminants (e.g., pesticides, antibiotics, mycotoxins, heavy metals, and food adulterants) and microbiological hazards (e.g., Salmonella, Campylobacter, Shiga toxigenic E. coli, Listeria, and Staphylococcus aureus) in foods.

Novel Approaches to Environmental Monitoring and Control of Listeria monocytogenes in Food Production Facilities

Listeria monocytogenes is a serious public health hazard responsible for the foodborne illness listeriosis. L. monocytogenes is ubiquitous in nature and can become established in food production facilities, resulting in the contamination of a variety of food products, especially ready-to-eat foods. Effective and risk-based environmental monitoring programs and control strategies are essential to eliminate L. monocytogenes in food production environments. Key elements of the environmental monitoring program include (i) identifying the sources and prevalence of L. monocytogenes in the production environment, (ii) verifying the effectiveness of control measures to eliminate L. monocytogenes, and (iii) identifying the areas and activities to improve control. The design and implementation of the environmental monitoring program are complex, and several different approaches have emerged for sampling and detecting Listeria monocytogenes in food facilities. Traditional detection methods involve culture methods, followed by confirmation methods based on phenotypic, biochemical, and immunological characterization. These methods are laborious and time-consuming as they require at least 2 to 3 days to obtain results. Consequently, several novel detection approaches are gaining importance due to their rapidness, sensitivity, specificity, and high throughput. This paper comprehensively reviews environmental monitoring programs and novel approaches for detection based on molecular methods, immunological methods, biosensors, spectroscopic methods, microfluidic systems, and phage-based methods. Consumers have now become more interested in buying food products that are minimally processed, free of additives, shelf-stable, and have a better nutritional and sensory value. As a result, several novel control strategies have received much attention for their less adverse impact on the organoleptic properties of food and improved consumer acceptability. This paper reviews recent developments in control strategies by categorizing them into thermal, non-thermal, biocontrol, natural, and chemical methods, emphasizing the hurdle concept that involves a combination of different strategies to show synergistic impact to control L. monocytogenes in food production environments.

DNAzyme-controlled plasmonic coupling for SERS-based determination of Salmonella typhimurium using hybridization chain reaction self-assembled G-quadruplex

A facile and rapid SERS strategy for S. typhimurium detection based on hybridization chain reaction (HCR) self-assembled G-quadruplex DNAzyme (GQH DNAzyme)-controlled plasmonic coupling was developed. GQH DNAzyme is introduced as a biocatalyst to catalyze the oxidation of L-cysteines to cysteines (thiols to disulfides) to assist SERS signal transduction. This is the first time that the self-assembled split GQH DNAzyme-controlled plasmonic coupling is integrated with SERS sensing. The results reveal the proposed SERS strategy can quantify S. typhimurium with a wide linear range (5 to 10⁵ cfu mL^-1) and a low detection limit (4 cfu mL^-1; n = 5, mean ± standard deviation) and RSD of 7%. The method exhibited preeminent detection performance in spiked samples with recoveries of 93.1-117%. The proposed strategy has great potential for being a versatile SERS platform for detecting a wide spectrum of analytes by replacing them with the corresponding recognition elements. Therefore, this study not only creates a practical platform for pathogenic bacteria identification and related food safety testing and environmental monitoring, but also provides a new paradigm for building SERS sensor. A facile and rapid SERS strategy for S. Typhimurium detection based on hybridization chain reaction (HCR) self-assembled G-quadruplex DNAzyme-controlled plasmonic coupling.

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.

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.

Application of Raman Spectroscopic Methods in Food Safety: A Review

Food detection technologies play a vital role in ensuring food safety in the supply chains. Conventional food detection methods for biological, chemical, and physical contaminants are labor-intensive, expensive, time-consuming, and often alter the food samples. These limitations drive the need of the food industry for developing more practical food detection tools that can detect contaminants of all three classes. Raman spectroscopy can offer widespread food safety assessment in a non-destructive, ease-to-operate, sensitive, and rapid manner. Recent advances of Raman spectroscopic methods further improve the detection capabilities of food contaminants, which largely boosts its applications in food safety. In this review, we introduce the basic principles of Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), and micro-Raman spectroscopy and imaging; summarize the recent progress to detect biological, chemical, and physical hazards in foods; and discuss the limitations and future perspectives of Raman spectroscopic methods for food safety surveillance. This review is aimed to emphasize potential opportunities for applying Raman spectroscopic methods as a promising technique for food safety detection.

Recent progress of personal glucose meters integrated methods in food safety hazards detection

Development of personal glucose meters (PGMs) for blood glucose monitoring and management by the diabetic patients has been a long history since its first invention in 1968 and commercial application in 1975. The main reasons for its wide acceptance and popularity can be attributed mainly to the easy operation, test-to-result model, low cost, and small volume of sample required for blood glucose concentration test. During past decades, advances in analytical techniques have repurposed the use of PGMs into a general point-of-care testing platform for a variety of non-glucose targets, especially the food hazards. In this review, we summarized the recent published research using PGMs to detect the food safety hazards of mycotoxins, illegal additives, pathogen bacteria, and pesticide and veterinary drug residues detection with PGMs. The progress on PGM-based detection achieved in food safety have been carefully compared and analyzed. Furthermore, the current bottlenecks and challenges for practical applications of PGM for hazards detection in food safety have also been proposed.

Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level

Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and more attention in the field of diagnosing food-borne pathogens in recent years. In the research, 15,890 single-cell Raman spectra of 23 common strains from 7 genera were obtained at the single cell level. Then, the nonlinear features of raw data were extracted by kernel principal component analysis, and the individual bacterial cell was evaluated and discriminated at the serotype level through the decision tree algorithm. The results demonstrated that the average correct rate of prediction on independent test set was 86.23 ± 0.92% when all strains were recognized by only one model, but there were high misjudgment rates for certain strains. Therefore, the four-level classification models were introduced, and the different hierarchies of the identification models achieved accuracies in the range of 87.1%-95.8%, which realized the efficient prediction of strains at the serotype level. In summary, Raman spectroscopy combined with machine learning based on fingerprint difference was a prospective strategy for the rapid diagnosis of pathogenic bacteria.

Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks

Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

Investigation of the Applicability of Raman Spectroscopy as Online Process Control during Consumer Milk Production

Online detection of product defects using fast spectroscopic measurements is beneficial for producers in the dairy industry since it allows readjustment of product characteristics or redirection of product streams during production. Raman spectroscopy has great potential for such application due to the fast and simple measurement. Its suitability as online sensor for process control was investigated at typical control points in consumer milk production being raw milk storage, standardization, and heat treatment. Additionally, the appropriateness of Raman spectroscopy to act as indicator for product application parameters was investigated using the example of barista foam. To assess the suitability of a pure online system, the merit of Raman spectra was evaluated by a principal component analysis (PCA). Thereby, proteolytic spoilage due to the presence of extracellular enzymes of Pseudomonas sp. was detected and samples based on the applied heat treatment (extended shelf life (ESL) and ultra-high temperature (UHT)) could be separated. A correlation of the content of free fatty acids and foam stability with spectra of the respective milk samples was found, allowing a prediction of the technofunctional quality criterion “Barista” suitability of a UHT milk. The results underlined the suitability of Raman spectroscopy for the detection of deviations from a defined product standard of consumer milk.

Applications of Raman spectroscopic techniques for quality and safety evaluation of milk: A review of recent developments

Milk is a complete nutrient source for humans. The quality and safety of milk are critical for both producers and consumers, thereby the dairy industry requires rapid and nondestructive methods to ensure milk quality and safety. However, conventional methods are time-consuming and laborious, and require complicated preparation procedures. Therefore, the exploration of new milk analytical methods is essential. This current review introduces the principles of Raman spectroscopy and presents recent advances since 2012 of Raman spectroscopic techniques mainly involving surface-enhanced Raman spectroscopy (SERS), fourier-transform (FT) Raman spectroscopy, near-infrared (NIR) Raman spectroscopy, and micro-Raman spectroscopy for milk analysis including milk compositions, microorganisms and antibiotic residues in milk, as well as milk adulterants. Additionally, some challenges and future outlooks are proposed. The current review shows that Raman spectroscopic techniques have the promising potential for providing rapid and nondestructive detection of milk parameters. However, the application of Raman spectroscopy on milk analysis is not common yet since some limitations of Raman spectroscopy need to be overcome before making it a routine tool for the dairy industry.

Raman chemical feature extraction for quality control of dairy products

As a fast information acquisition technique, Raman spectroscopy can be used to control the quality of dairy products. Feature extraction is a necessary processing step to improve the efficiency of Raman spectral data. Principal component analysis is a traditional method that can effectively extract the features and reduce the dimension of spectral data. However, it is difficult to analyze the chemical information of the extracted feature, thus limiting its practical application. In this work, Raman spectral chemical feature extraction was carried out. The quality control of Dingxin dairy products (a famous dairy brand in China; purchased from Heilongjiang Zhaodong Tianlong Dairy Co. Ltd., Heilongjiang, China) was used as an example. Raman peak intensity, peak area, and peak ratio were extracted as chemical features and further investigated using Euclidean distance and the quality fluctuation control chart. The potential quality discrimination ability of the Raman feature extraction methods was demonstrated. The results showed that the Puzhen dairy products (purchased from Inner Mongolia Yinuo Halal Food Co. Ltd., Inner Mongolia, China) and Xueyuan dairy products (used as a control; purchased from Inner Mongolia Wulanchabu City Jining Xueyuan Dairy Co. Ltd., Inner Mongolia, China) could be distinguished from Dingxin dairy products when the Raman chemical features special peak intensity, peak area, and peak ratio were used, and their discriminatory ability increased in sequence. Hence, it was shown that Raman chemical feature extraction can achieve quality control and discriminant analysis of dairy products and that the spectral information is clear.

Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens

A novel disposable all-printed electronic biosensor is proposed for a fast detection and classification of bacteria. This biosensor is applied to classify three types of popular pathogens: Salmonella typhimurium, and the Escherichia coli strains JM109 and DH5-α. The proposed sensor consists of inter-digital silver electrodes fabricated through an inkjet material printer and silver nanowires uniformly decorated on the electrodes through the electrohydrodynamic technique on a polyamide based polyethylene terephthalate substrate. The best sensitivity of the proposed sensor is achieved at 200 µm teeth spaces of the inter-digital electrodes along the density of the silver nanowires at 30 × 10³/mm². The biosensor operates on ±2.5 V and gives the impedance value against each bacteria type in 8 min after sample injection. The sample data are measured through an impedance analyzer and analyzed through pattern recognition methods such as linear discriminate analysis, maximum likelihood, and back propagation artificial neural network to classify each type of bacteria. A perfect classification and cross-validation is achieved by using the unique fingerprints extracted from the proposed biosensor through all the applied classifiers. The overall experimental results demonstrate that the proposed disposable all-printed biosensor is applicable for the rapid detection and classification of pathogens.

A Negative Regulator of Cellulose Biosynthesis, bcsR, Affects Biofilm Formation, and Adhesion/Invasion Ability of Cronobacter sakazakii

Cronobacter sakazakii is an important foodborne pathogen that causes neonatal meningitis and sepsis, with high mortality in neonates. However, very little information is available regarding the pathogenesis of C. sakazakii at the genetic level. In our previous study, a cellulose biosynthesis-related gene (bcsR) was shown to be involved in C. sakazakii adhesion/invasion into epithelial cells. In this study, the detailed functions of this gene were investigated using a gene knockout technique. A bcsR knockout mutant (ΔbcsR) of C. sakazakii ATCC BAA-894 showed decreased adhesion/invasion (3.9-fold) in human epithelial cell line HCT-8. Biofilm formation by the mutant was reduced to 50% of that exhibited by the wild-type (WT) strain. Raman spectrometry was used to detect variations in biofilm components caused by bcsR knockout, and certain components, including carotenoids, fatty acids, and amides, were significantly reduced. However, another biofilm component, cellulose, was increased in ΔbcsR, suggesting that bcsR negatively affects cellulose biosynthesis. This result was also verified via RT-PCR, which demonstrated up-regulation of five crucial cellulose synthesis genes (bcsA, B, C, E, Q) in ΔbcsR. Furthermore, the expression of other virulence or biofilm-related genes, including flagellar assembly genes (fliA, C, D) and toxicity-related genes (ompA, ompX, hfq), was studied. The expression of fliC and ompA in the ΔbcsR mutant was found to be remarkably reduced compared with that in the wild-type and the others were also affected excepted ompX. In summary, bcsR is a negative regulator of cellulose biosynthesis but positively regulates biofilm formation and the adhesion/invasion ability of C. sakazakii.

Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis

Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei) and Listeria monocytogenes (L. monocytogenes) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data.

Bacterial Detection Using Peptide-Based Platform and Impedance Spectroscopy

Antimicrobial peptides have the ability to function as bio-recognition elements in the detection of bacteria. For instance, we showed that Leucocin A, an antimicrobial peptide from class IIa bacteriocins, binds gram-positive Listeria monocytogenes with higher affinity than other gram-positive bacteria like S. aureus, L. innocua, and E. faecalis. The binding was detected using impedance spectroscopy when Leucocin A immobilized on impedance electrodes binds bacteria from a sample. Here we highlight the strength of utilizing Leucocin A as a bio-recognition probe in biosensor platforms and provide details on its application in real-time bacterial detection using electrochemical impedance spectroscopy. A simple new generation impedance array analyzer is utilized that works at very low frequencies to identify interactions between peptide and the target bacteria.

Screening of genes involved in interactions with intestinal epithelial cells in Cronobacter sakazakii

Cronobacter sakazakii possesses a significant ability to adhere to and invade epithelial cells in its host. However, the molecular mechanisms underlying this process are poorly understood. In the current study, the adhesive and invasive capabilities of 56 C. sakazakii strains against human epithelial cells were evaluated, and one of them was selected for construction of a mutant library using the Tn5 transposon. In a systematic analysis of the adhesive and invasive capabilities of 1084 mutants, 10 mutants that showed more than a 50 % reduction in adhesion or invasion were obtained. Tail-PCR was used to sequence the flanking regions of the inserted transposon and 8 different genes (in 10 different mutants) were identified that encoded an exonuclease subunit, a sugar transporter, a transcriptional regulator, two flagellar biosynthesis proteins, and three hypothetical proteins. Raman spectroscopy was used to analyze variations in the biochemical components of the mutants, and the results showed that there were fewer amide III proteins, protein -CH deformations, nucleic acids and tyrosines and more phenylalanine, carotenes, and fatty acids in the mutants than in the wild type strain. Real-time PCR was used to further confirm the involvement of the genes in the adhesive and invasive abilities of C. sakazakii, and the results indicated that the expression levels of the 8 identified genes were upregulated 1.2- to 11.2-fold. The results of this study provide us with insight into the mechanism by which C. sakazakii infects host cells at molecular level.

Differentiation of aged fibers by Raman spectroscopy and multivariate data analysis

Raman spectroscopy followed by multivariate data analysis was used to analyze cotton fibers dyed using similar formulations and submitted to different aging conditions. Spectra were collected on a commercial instrument using a near-infrared laser with a 780nm light source. Discriminant analysis allowed to correctly classify the aged fibers 100% of the time. The prediction ability of the calculated model was estimated to be 100% by the "leave-one-out" cross-validation for 3 out of the 4 series under investigation. Finally, reliability of the developed approach for the discrimination of aged vs new fibers was confirmed by the analysis of commercial polyamide and polyester textiles submitted to the same aging process.

Detection of ureolytic activity of bacterial strains isolated from entomopathogenic nematodes using infrared spectroscopy

The pathogenicity of entomopathogenic nematodes (EPNs) depends directly on the presence of bacteria in the nematode digestive tracts. Based on 16S rRNA and MALDI-TOF analyses 20 isolated bacteria were assigned to 10 species with 10 isolates classified as Pseudomonas ssp. Six strains (30%) show ureolytic activity on Christensen medium. Spectroscopic analysis of the strains showed that the ureolytic activity is strongly correlated with the following wavenumbers: 935 cm(-1) in window W4, which carries information about the bacterial cell wall construction and 1158 cm(-1) in window W3 which corresponds to proteins in bacterial cell. A logistic regression model designed on the basis of the selected wavenumbers differentiates ureolytic from non-ureolytic bacterial strains with an accuracy of 100%. Spectroscopic studies and mathematical analyses made it possible to differentiate EPN-associated Pseudomonas sp. strains from clinical Pseudomonas aeruginosa PAO1. These results suggest, that infrared spectra of EPN-associated Pseudomonas sp. strains may reflect its adaptation to the host.

Detection of Listeria innocua on roll-to-roll produced SERS substrates with gold nanoparticles

The rapid and accurate detection of food pathogens plays a critical role in the early prevention of foodborne epidemics. Combination of low cost sensing platforms and SERS detection can offer a solution for the pathogen detection.

Prospects for point-of-care pathogen diagnostics using surface-enhanced Raman scattering (SERS)

This review highlights recent advances in the application of surface-enhanced Raman scattering (SERS) in pathogen detection and discusses many of the challenges in moving this technology to the point-of-care (POC) arena.