Convective assembly of bacteria for surface-enhanced Raman scattering

Fabrication of Aluminum Foil Integrated Pegylated Gold Nanoparticle Surface-Enhanced Raman Scattering Substrate for the Detection and Classification of Uropathogenic Bacteria

Rapid detection and classification of pathogenic microbes for food hygiene, healthcare, environmental contamination, and chemical and biological exposures remain a major challenge due to nonavailability of fast and accurate detection methods. The delay in clinical diagnosis of the most frequent bacterial infections, particularly urinary tract infections (UTIs), which affect about half of the population at least once in their lifetime, can be fatal if not detected and treated appropriately. In this work, we have fabricated aluminum (Al) foil integrated pegylated gold nanoparticles (AuNPs) as a potential surface-enhanced Raman scattering (SERS) substrate, which is used for the detection and classification of uropathogens, namely, E. coli, S. aureus, and P. aeruginosa directly from the culture without any pretreatment. The substrate is first drop cast with bacterial pellets and then pegylated AuNPs, and the interaction of two on Al foil base gives identifiable characteristic Raman peaks with good reproducibility. With the use of chemometric methods such as principal component analysis (PCA), the Al foil-based SERS substrate offers a quick, effective detection and classification of three strains of UTI bacteria with the least bacterial concentration (105 cells mL-1) necessary for clinical diagnosis. In addition, this substrate was able to detect E. coli positive clinical samples by giving SERS fingerprint information directly from centrifuged urine samples within minutes. The stability of pegylated AuNPs provides for its application at the point of care with rapid and easy detection of uropathogens as well as the possibility of advancement in healthcare applications.

Recent Advances in Bacterial Detection Using Surface-Enhanced Raman Scattering

Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

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

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

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.

"Magnet" Based on Activated Silver Nanoparticles Adsorbed Bacteria to Predict Refractory Apical Periodontitis Via Surface-Enhanced Raman Scattering

Refractory apical periodontitis (RAP) is an endodontic apical inflammatory disease caused by Enterococcus faecalis (E. faecalis). Bacterial detection using surface-enhanced Raman scattering (SERS) technology is a hot research topic, but the specific and direct detection of oral bacteria is a challenge, especially in real clinical samples. In this paper, we develop a novel SERS-based green platform for label-free detection of oral bacteria. The platform was built on silver nanoparticles with a two-step enhancement way using NaBH4 and sodium (Na+) to form "hot spots," which resulted in an enhanced SERS fingerprint of E. faecalis with fast, quantitative, lower-limit, reproducibility, and stability. In combination with machine learning, four different oral bacteria (E. faecalis, Porphyromonas gingivalis, Streptococcus mutans, and Escherichia coli) could be intelligently distinguished. The unlabeled detection method emphasized the specificity of E. faecalis in simulated saliva, serum, and even real samples from patients with clinical root periapical disease. In addition, the assay has been shown to be environmentally friendly and without secondary contamination through antimicrobial assays. The proposed label-free, rapid, safe, and green SERS detection strategy for oral bacteria provided an innovative solution for the early diagnosis and prevention of RAP and other perioral diseases.

Surface-enhanced Raman spectroscopy of centrifuged blood serum samples of diabetic type II patients by using 50KDa filter devices

Blood serum contains essential biochemical information which are used for early disease diagnosis. Blood serum consisted of higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF). The disease biomarkers are lower molecular weight fraction proteins, and their contribution to disease diagnosis is suppressed due to higher molecular weight fraction proteins. To diagnose diabetes in early stages are difficult because of the presence of huge amount of these HMWF. In the current study, surface-enhanced Raman spectroscopy (SERS) are employed to diagnose diabetes after centrifugation of serum samples using Amicon ultra filter devices of 50 kDa which produced two fractions of whole blood serum of filtrate, low molecular weight fraction, and residue, high molecular weight fraction. Furthermore SERS is employed to study the LMW fractions of healthy and diseased samples. Some prominent SERS bands are observed at 725 cm^-1, 842 cm^-1, 1025 cm^-1, 959 cm^-1, and 1447 cm^-1 due to small molecular weight proteins, and these biomarkers helped to diagnose the disease early stage. Moreover, chemometric techniques such as principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are employed to check the potential of surface-enhanced Raman spectroscopy for the differentiation and classifications of the blood serum samples. SERS can be employed for the early diagnosis and screening of biochemical changes during type II diabetes.

Biopolymer encapsulated silver nitrate nanoparticle substrates with surface-enhanced Raman spectroscopy (SERS) for Salmonella detection from chicken rinse

Salmonella is commonly found on broiler chickens during processing. This study investigates the Salmonella detection method that reduces the necessary time for confirmation, by collecting surface-enhanced Raman spectroscopy (SERS) spectra from bacteria colonies, applied to a substrate of biopolymer encapsulated AgNO₃ nanoparticles. Chicken rinses containing Salmonella Typhimurium (ST) were analyzed by SERS and compared to traditional plating and PCR analyses. SERS spectra from confirmed ST and non-Salmonella colonies appear similar in spectra composition, but with different peak intensities. t-Test on the peak intensities showed that ST and non-Salmonella colonies were significantly different (α = 0.0045) at 5 peaks, 692 cm^-1, 718 cm^-1, 791 cm^-1, 859 cm^-1, and 1018 cm^-1. A support vector machine (SVM) classification algorithm was able to separate ST and non-Salmonella samples with an overall classification accuracy of 96.7 %.

Future of Nanotechnology in Food Industry: Challenges in Processing, Packaging, and Food Safety

Over the course of the last several decades, nanotechnology has garnered a growing amount of attention as a potentially valuable technology that has significantly impacted the food industry. Nanotechnology helps in enhancing the properties of materials and structures that are used in various fields such as agriculture, food, pharmacy, and so on. Applications of nanotechnology in the food market have included the encapsulation and distribution of materials to specific locations, the improvement of flavor, the introduction of antibacterial nanoparticles into food, the betterment of prolonged storage, the detection of pollutants, enhanced storage facilities, locating, identifying, as well as consumer awareness. Labeling food goods with nano barcodes helps ensure their security and may also be used to track their distribution. This review article presents a discussion about current advances in nanotechnology along with its applications in the field of food-tech, food packaging, food security, enhancing life of food products, etc. A detailed description is provided about various synthesis routes of nanomaterials, that is, chemical, physical, and biological methods. Nanotechnology is a rapidly improving the field of food packaging and the future holds great opportunities for more enhancement via the development of new nanomaterials and nanosensors.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Characterization of Bacteria Using Surface-Enhanced Raman Spectroscopy (SERS): Influence of Microbiological Factors on the SERS Spectra

SERS is currently being explored as a rapid method for identification of bacteria but variation in the experimental procedures has resulted in considerable variation in the spectra reported for a range of bacterial species. Here, we show that mixing bacteria with a conventional citrate-reduced silver colloid (CRSC) and drying the resulting suspension yield highly reproducible spectra. These signals were due to intracellular components released when the structure of the bacteria was disrupted during sample preparation. This reproducibility allowed us to examine the effects of variables that do not arise in SERS of simple solutions but are relevant in studies of bacteria. These included growth phase and biological variation, which occurred when the same bacterial isolates were cultured under nominally identical conditions on different days. It was found that even under optimal standardized conditions the effect of differences in experimental parameters such as growth phase was very large in some bacterial species but insignificant in others. This suggests that it is important to avoid drawing general conclusions about bacterial SERS based on studies using small numbers of samples. Similarly, discrimination between bacterial species was straightforward when a small number of isolates with distinct spectral features were investigated; however, this became more challenging when more bacterial species were included, as this increased the possibility of finding different species of bacteria with similar spectra. These observations are important because they clearly delineate the challenges that will need to be addressed if SERS is to be used for clinical applications.

Surface-enhanced infrared absorption spectroscopy for microorganisms discrimination on silver nanoparticle substrates

Extracting molecular level label-free information from complex biological processes for a range of purposes including disease diagnosis and microbial identification and discrimination is always a challenging task. This is mostly due to lack of a technique providing rich molecular information with a high spatial and temporal resolution properties. Two surface-enhanced vibrational spectroscopic (SEVS) techniques, surface-enhanced Raman scattering (SERS) and surface-enhanced infrared absorption spectroscopy (SEIRAS), are recently attracting considerable attention to study biosystems at an interface since they can satisfy these requirements to a certain level by providing rich intrinsic molecular information from molecules and molecular systems in a close proximity of nanostructured noble metal surfaces. In this study, these two surface-enhanced vibrational spectroscopic techniques are comparatively evaluated for the discrimination and identification of Candida albicans (C. albicans), Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by paying attention to the source of the observed spectral pattern. The citrate-reduced colloidal silver nanoparticles (AgNPs) were used as substrates. The results show that the SEIRAS provides very rich molecular information about the biomolecular species adsorbed onto AgNPs similar to the case of SERS. The discrimination power of SEIRAS is much improved compared to FTIR demonstrated by PCA analysis. This study suggests that SEIRAS can be a potential technique for microbial analysis.

Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering

Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.

Surface-enhanced Raman spectroscopy for identification of food processing bacteria

Food processing bacteria play important role in providing flavors, ingredients and other beneficial characteristics to the food but at the same time some bacteria are responsible for food spoilage. Therefore, quick and reliable identification of these food processing bacteria is very necessary for the differentiation between different species which may help in the development of more useful food processing methodologies. In this study, analysis of different bacterial species (Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus and Halalkalicoccus jeotgali) was performed with our in-house developed Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method. The SERS spectral data was analyzed by multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Bacterial species were differentiated on the basis of SERS spectral features and potential of SERS was compared with the Raman spectroscopy (RS). SERS along with PCA and PLS-DA was found to be an efficient technique for identification and differentiation of food processing bacterial species. Differentiation with accuracy of 99.5% and sensitivity of 99.7% was depicted by PLS-DA model using leave one out cross validation.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

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

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

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Rapid and sensitive discrimination among carbapenem resistant and susceptible E. coli strains using Surface Enhanced Raman Spectroscopy combined with chemometric tools

BACKGROUND

Raman spectroscopy is a powerful technique for the robust, reliable and rapid detection and discrimination of bacteria.

OBJECTIVES

To develop a rapid and sensitive technique based on surface-enhanced Raman spectroscopy (SERS) with multivariate data analysis tools for discrimination among carbapenem resistant and susceptible E. coli strains.

METHODS

SERS was employed to differentiate different strains of carbapenem resistant and susceptible E. coli by using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, four strains of carbapenem resistant and three strains of carbapenem susceptible E. coli were analyzed by comparing their SERS spectral signatures. Furthermore, multivariate data analysis techniques including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were performed over the spectral range of 400-1800 cm^-1 (fingerprint region) for the identification and differentiation of different E. coli strains.

RESULTS

The SERS spectral features associated with resistant development against carbapenem antibiotics were separated by comparing each spectrum of susceptible strains with each resistant strain. PCA and HCA were found effective for the qualitative differentiation of all the strains analysed. PLS-DA successfully discriminated the carbapenem resistant and susceptible E. coli pellets on the strain level with 99.8 % sensitivity, 100 % specificity, 100 % accuracy and 86 % area under receiver operating characteristic (AUROC) curve.

CONCLUSION

SERS can be employed for the rapid discrimination among carbapenem resistant and susceptible strains of E. coil.

Fuzzy characterization and classification of bacteria species detected at single-cell level by surface-enhanced Raman scattering

Advanced chemometric methods, such as fuzzy c-means, a semi-supervised clustering method, and fuzzy linear discriminant analysis (FLDA), a new robust supervised classification method in combination with principal component analysis (PCA), namely PCA-FLDA, have been successfully applied for characterization and classification of bacterial species detected at single-cell level by surface-enhanced Raman scattering (SERS) spectroscopy. SERS spectra of three species (S. aureus, E. faecalis and P. aeruginosa) were recorded in an original fashion, using in situ laser induced silver spot as metallic substrate. The detection process of bacteria was isolated inside a hermetically sealed in-house built microfluidic device, connected to a syringe pump for injecting the analytes and a portable Raman spectrometer as detection tool. The obtained results (fuzzy partitions) and spectra of the prototypes (robust fuzzy spectra mean corresponding to each fuzzy partition) clearly demonstrated the efficiency and information power of the advanced fuzzy methods in bacteria characterization and classification based on SERS spectra, and allowed a rationale assigning to a specific group. Also, this powerful detection and classification methodology generates the premises for future investigations of Raman and other spectroscopic data obtained for various samples.

Improvement of Surface-Enhanced Raman Scattering Method for Single Bacterial Cell Analysis

Surface-enhanced Raman scattering (SERS) is a useful tool for label-free analysis of bacteria at the single cell level. However, low reproducibility limits the use of SERS. In this study, for the sake of sensitive and reproducible Raman spectra, we optimized the methods for preparing silver nanoparticles (AgNPs) and depositing AgNPs onto a cell surface. We found that fast dropwise addition of AgNO₃ into the reductant produced smaller and more stable AgNPs, with an average diameter of 45 ± 4 nm. Compared with that observed after simply mixing the bacterial cells with AgNPs, the SERS signal was significantly improved after centrifugation. To optimize the SERS enhancement method, the centrifugal force, method for preparing AgNPs, concentration of AgNPs, ionic strength of the solution used to suspend the cells, and density of the cells were chosen as impact factors and optimized through orthogonal experiments. Finally, the improved method could generate sensitive and reproducible SERS spectra from single Escherichia coli cells, and the SERS signals primarily arose from the cell envelope. We further verified that this optimal method was feasible for the detection of low to 25% incorporation of ¹³C isotopes by the cells and the discrimination of different bacterial species. Our work provides an improved method for generating sensitive and reproducible SERS spectra.

An Overview of the Applications of Nanomaterials and Nanodevices in the Food Industry

The efficient progress in nanotechnology has transformed many aspects of food science and the food industry with enhanced investment and market share. Recent advances in nanomaterials and nanodevices such as nanosensors, nano-emulsions, nanopesticides or nanocapsules are intended to bring about innovative applications in the food industry. In this review, the current applications of nanotechnology for packaging, processing, and the enhancement of the nutritional value and shelf life of foods are targeted. In addition, the functionality and applicability of food-related nanotechnologies are also highlighted and critically discussed in order to provide an insight into the development and evaluation of the safety of nanotechnology in the food industry.

Development of uncoated near-spherical gold nanoparticles for the label-free quantification of Lactobacillus rhamnosus GG by surface-enhanced Raman spectroscopy

The Surface-enhanced Raman spectroscopy (SERS) method based on gold nanoparticles as SERS substrate was investigated for the label-free detection and quantification of probiotic bacteria that are widely used in various pharmaceutical formulations. Indeed, the development of a simple and fast SERS method dedicated to the quantification of bacteria should be very useful for the characterization of such formulations in a more convenient way than the usually performed tedious and time-consuming conventional counting method. For this purpose, uncoated near-spherical gold nanoparticles were developed at room temperature by acidic treatment of star-like gold nanoparticle precursors. In this study, we first investigated the influence of acidic treatment conditions on both the nanoparticle physicochemical properties and SERS efficiency using Rhodamine 6G (R6G) as "model" analyte. Results highlighted that an effective R6G Raman signal enhancement was obtained by promoting chemical effect through R6G-anion interactions and by obtaining a suitable aggregation state of the nanoparticles. Depending on the nanoparticle synthesis conditions, R6G SERS signals were up to 10²-10³-fold greater than those obtained with star-like gold nanoparticles. The synthesized spherical gold nanoparticles were then successfully applied for the detection and quantification of Lactobacillus rhamnosus GG (LGG). In that case, the signal enhancement was especially due to the combination of anion-induced chemical enhancement and nanoparticle aggregation on LGG cell wall consecutive to non-specific interactions. Both the simplicity and speed of the procedure, achieved under 30 min, including nanoparticle synthesis, sample preparation, and acquisition of SERS spectra, appeared as very relevant for the characterization of pharmaceutical formulations incorporating probiotics. Graphical abstract.

Detection and Characterization of Antibiotic-Resistant Bacteria Using Surface-Enhanced Raman Spectroscopy

This mini-review summarizes the most recent progress concerning the use of surface-enhanced Raman spectroscopy (SERS) for the detection and characterization of antibiotic-resistant bacteria. We first discussed the design and synthesis of various types of nanomaterials that can be used as the SERS-active substrates for biosensing trace levels of antibiotic-resistant bacteria. We then reviewed the tandem-SERS strategy of integrating a separation element/platform with SERS sensing to achieve the detection of antibiotic-resistant bacteria in the environmental, agri-food, and clinical samples. Finally, we demonstrated the application of using SERS to investigate bacterial antibiotic resistance and susceptibility as well as the working mechanism of antibiotics based on spectral fingerprinting of the whole cells.

Plasmonic Nanotrough Networks for Scalable Bacterial Raman Biosensing

We demonstrate a novel approach for fabricating surface enhanced Raman scattering (SERS) substrates for single bacterial biosensing based on Ag cylindrical nanotrough networks (CNNs). This approach is developed with large scalability by leveraging a cellulose nanofiber template fabrication via facile electrospinning. Specifically, a concave nanotrough structure consisting of interconnected concave Ag nanoshells is demonstrated by depositing a thin layer of Ag atop a sacrificial electrospun nanofiber template and then completely removing the cellulose core in water. Our investigations of the scattering properties and SERS performances of single isolated Ag nanotroughs of different diameters reveal that nanotrough-based substrates provide tunable optical responses and enhanced SERS intensities. Further, Ag CNNs are fabricated in highly interconnected networks that yield reproducible SERS signals for molecular monolayers and whole bacterial cells, enabling rapid spectral discrimination between different bacterial strains. Finally, by performing principal component analysis on a large number of measured SERS spectra (40 spectra per bacterium), we demonstrate successful spectral discrimination between two types of Escherichia coli ( E. coli) bacteria, that is, E. coli K12 with its derivative E. coli DH 5α and E. coli BL21(DE3). The demonstrated cost-effective substrates feature several advantages over conventional SERS substrates including environmentally friendly and scalable fabrication compatible with versatile devices and provide an alternative approach to rapid SERS detection and screening of biochemicals.

Prospects of using nanotechnology for food preservation, safety, and security

The rapid development of nanotechnology has transformed many domains of food science, especially those that involve the processing, packaging, storage, transportation, functionality, and other safety aspects of food. A wide range of nanostructured materials (NSMs), from inorganic metal, metal oxides, and their nanocomposites to nano-organic materials with bioactive agents, has been applied to the food industry. Despite the huge benefits nanotechnology has to offer, there are emerging concerns regarding the use of nanotechnology, as the accumulation of NSMs in human bodies and in the environment can cause several health and safety hazards. Therefore, safety and health concerns as well as regulatory policies must be considered while manufacturing, processing, intelligently and actively packaging, and consuming nano-processed food products. This review aims to provide a basic understanding regarding the applications of nanotechnology in the food packaging and processing industries and to identify the future prospects and potential risks associated with the use of NSMs.

Surface-Enhanced Raman Scattering for Rapid Detection and Characterization of Antibiotic-Resistant Bacteria

As the prevalence of antibiotic-resistant bacteria continues to rise, biosensing technologies are needed to enable rapid diagnosis of bacterial infections. Furthermore, understanding the unique biochemistry of resistance mechanisms can facilitate the development of next generation therapeutics. Surface-enhanced Raman scattering (SERS) offers a potential solution to real-time diagnostic technologies, as well as a route to fundamental, mechanistic studies. In the current review, SERS-based approaches to the detection and characterization of antibiotic-resistant bacteria are covered. The commonly used nanomaterials (nanoparticles and nanostructured surfaces) and surface modifications (antibodies, aptamers, reporters, etc.) for SERS bacterial detection and differentiation are discussed first, and followed by a review of SERS-based detection of antibiotic-resistant bacteria from environmental/food processing and clinical sources. Antibiotic susceptibility testing and minimum inhibitory concentration testing with SERS are then summarized. Finally, recent developments of SERS-based chemical imaging/mapping of bacteria are reviewed.

A novel surface-enhanced Raman scattering (SERS) detection for natural gas exploration using methane-oxidizing bacteria

Methane-oxidizing bacteria (MOB), a unique group of Gram-negative bacteria utilizing methane as a sole source of carbon and energy, have been proved to be a biological indicator for gas prospecting. Field and cultivation-free detection of MOB is important but still challenging in current microbial prospecting of oil and gas (MPOG) system. Herein, SERS was used for the first time to our knowledge to investigate two species of methanotrophs and four closely relevant bacteria that universally coexisted in the upper soil of natural gas. A special but very simple approach was utilized to make silver nanoparticles (Ag NPs) sufficiently contact with every single bacterial cell, and highly strong and distinct Raman signals free from any native fluorescence have been obtained, and successfully utilized for distinguishing MOB from other species. A more convincing multi-Raman criterion based on single Raman bands, and further the entire Raman spectrum in combination with statistical analysis (e.g., principal component analysis (PCA)), which were found capable of classifying MOB related bacterial cells in soil with an accuracy of 100%. This study therefore demonstrated sensitive and rapid SERS measurement technique accompanied by complete Raman database of various gas reservoirs related bacteria could aid field exploration of natural gas reservoir.

Raman and Surface-Enhanced Raman Scattering for Biofilm Characterization

Biofilms are a communal way of living for microorganisms in which microorganism cells are surrounded by extracellular polymeric substances (EPS). Most microorganisms can live in biofilm form. Since microorganisms are everywhere, understanding biofilm structure and composition is crucial for making the world a better place to live, not only for humans but also for other living creatures. Raman spectroscopy is a nondestructive technique and provides fingerprint information about an analyte of interest. Surface-enhanced Raman spectroscopy is a form of this technique and provides enhanced scattering of the analyte that is in close vicinity of a nanostructured noble metal surface such as silver or gold. In this review, the applications of both techniques and their combination with other biofilm analysis techniques for characterization of composition and structure of biofilms are discussed.

Review on SERS of Bacteria

Surface enhanced Raman spectroscopy (SERS) has been widely used for chemical detection. Moreover, the inherent richness of the spectral data has made SERS attractive for use in detecting biological materials, including bacteria. This review discusses methods that have been used to obtain SERS spectra of bacteria. The kinds of SERS substrates employed to obtain SERS spectra are discussed as well as how bacteria interact with silver and gold nanoparticles. The roll of capping agents on Ag/Au NPs in obtaining SERS spectra is examined as well as the interpretation of the spectral data.

Large-Area, Highly Sensitive SERS Substrates with Silver Nanowire Thin Films Coated by Microliter-Scale Solution Process

A microliter-scale solution process was used to fabricate large-area, uniform films of silver nanowires (AgNWs). These thin films with cross-AgNWs were deposited onto Au substrates by dragging the meniscus of a microliter drop of a coating solution trapped between two plates. The hot spot density was tuned by controlling simple experimental parameters, which changed the optical properties of the resulting films. The cross-AgNW films on the Au surface served as excellent substrates for surface-enhanced Raman spectroscopy, with substantial electromagnetic field enhancement and good reproducibility.

Surface-enhanced Raman spectroscopy of cell lysates mixed with silver nanoparticles for tumor classification

The throughput of spontaneous Raman spectroscopy for cell identification applications is limited to the range of one cell per second because of the relatively low sensitivity. Surface-enhanced Raman scattering (SERS) is a widespread way to amplify the intensity of Raman signals by several orders of magnitude and, consequently, to improve the sensitivity and throughput. SERS protocols using immuno-functionalized nanoparticles turned out to be challenging for cell identification because they require complex preparation procedures. Here, a new SERS strategy is presented for cell classification using non-functionalized silver nanoparticles and potassium chloride to induce aggregation. To demonstrate the principle, cell lysates were prepared by ultrasonication that disrupts the cell membrane and enables interaction of released cellular biomolecules to nanoparticles. This approach was applied to distinguish four cell lines - Capan-1, HepG2, Sk-Hep1 and MCF-7 - using SERS at 785 nm excitation. Six independent batches were prepared per cell line to check the reproducibility. Principal component analysis was applied for data reduction and assessment of spectral variations that were assigned to proteins, nucleotides and carbohydrates. Four principal components were selected as input for classification models based on support vector machines. Leave-three-batches-out cross validation recognized four cell lines with sensitivities, specificities and accuracies above 96%. We conclude that this reproducible and specific SERS approach offers prospects for cell identification using easily preparable silver nanoparticles.

Understanding and Discrimination of Biofilms of Clinically Relevant Microorganisms Using Surface-Enhanced Raman Scattering

Biofilm formation is a defense mechanism for microorganisms to survive under both natural and stress conditions. Clinically relevant microorganisms threaten patient health through biofilm formation on medical devices and implants. It is very important to identify biofilm formation in order to suppress their pathogenic activities in early stages. With the aim for better understanding biofilm formation and possibility of detection, in this study, biofilm formation of clinically important microorganisms, Pseudomonas aeruginosa, Staphylococcus epidermidis, and Candida albicans are monitored with surface-enhanced Raman scattering (SERS). The SERS spectra were collected by mapping a dried droplet area where a volume of colloidal silver nanoparticle (AgNP) suspension is placed on microorganism culture plate. The spectral changes on the SERS spectra with increasing incubation time of the model microorganisms from 4 to 120 h are monitored. The unique spectra originating from the biofilms of three pathogenic microorganisms and the spectral changes as a result of time-dependent concentration fluctuations of biomolecular species in their biofilms including carbohydrates, lipids, proteins, and genetic materials allow not only identification but also discrimination of biofilms using principal component analysis.

Rapid Detection of Bacteria from Blood with Surface-Enhanced Raman Spectroscopy

Traditional methods for identifying pathogens in bacteremic patients are slow (24-48+ h). This can lead to physicians making treatment decisions based on an incomplete diagnosis and potentially increasing the patient's mortality risk. To decrease time to diagnosis, we have developed a novel technology that can recover viable bacteria directly from whole blood and identify them in less than 7 h. Our technology combines a sample preparation process with surface-enhanced Raman spectroscopy (SERS). The sample preparation process enriches viable microorganisms from 10 mL of whole blood into a 200 μL aliquot. After a short incubation period, SERS is used to identify the microorganisms. We further demonstrated that SERS can be used as a broad detection method, as it identified a model set of 17 clinical blood culture isolates and microbial reference strains with 100% identification agreement. By applying the integrated technology of sample preparation and SERS to spiked whole blood samples, we were able to correctly identify both Staphylococcus aureus and Escherichia coli 97% of the time with 97% specificity and 88% sensitivity.

Magnetic-optical nanohybrids for targeted detection, separation, and photothermal ablation of drug-resistant pathogens

A rapid, sensitive and quantitative immunoassay for the targeted detection and decontamination of E. coli based on Fe3O4 magnetic nanoparticles (MNPs) and plasmonic popcorn-shaped gold nanostructure attached single-walled carbon nanotubes (AuNP@SWCNT) is presented. The MNPs were synthesized as the support for a monoclonal antibody (mAb@MNP). E. coli (49979) was captured and rapidly preconcentrated from the sample with the mAb@MNP, followed by binding with Raman-tagged concanavalin A-AuNP@SWCNTs (Con A-AuNP@SWCNTs) as detector nanoprobes. A Raman tag 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB) generated a Raman signal upon 670 nm laser excitation enabling the detection and quantification of E. coli concentration with a limit of detection of 10(2) CFU mL(-1) and a linear logarithmic response range of 1.0 × 10(2) to 1.0 × 10(7) CFU mL(-1). The mAb@MNP could remove more than 98% of E. coli (initial concentration of 1.3 × 10(4) CFU mL(-1)) from water. The potential of the immunoassay to detect E. coli bacteria in real water samples was investigated and the results were compared with the experimental results from the classical count method. There was no statistically significant difference between the two methods (p > 0.05). Furthermore, the MNP/AuNP@SWCNT hybrid system exhibits an enhanced photothermal killing effect. The sandwich-like immunoassay possesses potential for rapid bioanalysis and the simultaneous biosensing of multiple pathogenic agents.

The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS

The dominant molecular species contributing to the surface-enhanced Raman spectroscopy (SERS) spectra of bacteria excited at 785 nm are the metabolites of purine degradation: adenine, hypoxanthine, xanthine, guanine, uric acid, and adenosine monophosphate. These molecules result from the starvation response of the bacterial cells in pure water washes following enrichment from nutrient-rich environments. Vibrational shifts due to isotopic labeling, bacterial SERS spectral fitting, SERS and mass spectrometry analysis of bacterial supernatant, SERS spectra of defined bacterial mutants, and the enzymatic substrate dependence of SERS spectra are used to identify these molecular components. The absence or presence of different degradation/salvage enzymes in the known purine metabolism pathways of these organisms plays a central role in determining the bacterial specificity of these purine-base SERS signatures. These results provide the biochemical basis for the development of SERS as a rapid bacterial diagnostic and illustrate how SERS can be applied more generally for metabolic profiling as a probe of cellular activity. Graphical Abstract Bacterial typing by metabolites released under stress.

SERS substrates fabricated using ceramic filters for the detection of bacteria

SERS substrates were fabricated by filtering either Ag or Au colloidal particles onto rigid, ceramic filters - onto which suspensions of bacteria were then filtered. SERS spectra of the bacteria were obtained using a Raman spectrometer that has an 'orbital raster scan' capability. It was shown that bacteria samples prepared in this manner were uniformly distributed onto the surface of the SERS substrate. The effect of common buffer systems on the SERS spectra was investigated and the utility of using the SERS technique for speciation of bacteria was explored.

The origin of the band at around 730 cm−1 in the SERS spectra of bacteria: a stable isotope approach

A stable isotope approach combined with SERS analysis of bacteria allows clarification of the origin of a pronounced band at 730 cm⁻¹.

Hollow silver alginate microspheres for drug delivery and surface enhanced Raman scattering detection

Multifunctional silver alginate hydrogel microspheres are assembled via a template assisted approach using calcium carbonate cores.

New post-processing method of preparing nanofibrous SERS substrates with a high density of silver nanoparticles

The protocol to control density of AgNP on surfaces of nanofibers, and thus electromagnetic hotspots by variation of Tollens' reagent is established. Nanofiber films enable SERS either of solutes or macromolecular structures such as bacterial cells.

Discrimination of urinary tract infection pathogens by means of their growth profiles using surface enhanced Raman scattering

Urinary tract infection (UTI) is a widespread infection and affects millions of people around the globe. The gold standard for identification of microorganisms causing infection is urine culture. However, current methods require at least 24 h for the results. In clinical settings, identification and discrimination of bacteria with less time-consuming and cheaper methods are highly desired. In recent years, the power of surface-enhanced Raman scattering (SERS) for fast identification of bacteria and biomolecules has been demonstrated. In this study, we show discrimination of urinary tract infection causative pathogens within 1 h of incubation using principal component analysis (PCA) of SERS spectra of seven different UTI causative bacterial species. In addition, we showed differentiation of them at their different growth phases. We also analyzed origins of bacterial SERS spectra and demonstrated the highly dynamic structure of the bacteria cell wall during their growth. Graphical Abstract Collection of bacteria from urine sample, and their discrimination using their SERS spectra and multivariate analysis.