Identification of meat-associated pathogens via Raman microspectroscopy

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Oxygen Sensor-Based Respirometry and the Landscape of Microbial Testing Methods as Applicable to Food and Beverage Matrices

The current status of microbiological testing methods for the determination of viable bacteria in complex sample matrices, such as food samples, is the focus of this review. Established methods for the enumeration of microorganisms, particularly, the 'gold standard' agar plating method for the determination of total aerobic viable counts (TVC), bioluminescent detection of total ATP, selective molecular methods (immunoassays, DNA/RNA amplification, sequencing) and instrumental methods (flow cytometry, Raman spectroscopy, mass spectrometry, calorimetry), are analyzed and compared with emerging oxygen sensor-based respirometry techniques. The basic principles of optical O₂ sensing and respirometry and the primary materials, detection modes and assay formats employed are described. The existing platforms for bacterial cell respirometry are then described, and examples of particular assays are provided, including the use of rapid TVC tests of food samples and swabs, the toxicological screening and profiling of cells and antimicrobial sterility testing. Overall, O₂ sensor-based respirometry and TVC assays have high application potential in the food industry and related areas. They detect viable bacteria via their growth and respiration; the assay is fast (time to result is 2-8 h and dependent on TVC load), operates with complex samples (crude homogenates of food samples) in a simple mix-and-measure format, has low set-up and instrumentation costs and is inexpensive and portable.

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.

Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems

Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.

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.

Label-free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV-Resonance Raman spectroscopy (UVRR) and single-cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine-learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.

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

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

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.

Prediction of Trained Panel Sensory Scores for Beef with Non-Invasive Raman Spectroscopy

The objective of this study was to investigate Raman spectroscopy as a tool for the prediction of sensory quality in beef. Raman spectra were collected from M. longissimus thoracis et lumborum (LTL) muscle on a thawed steak frozen 48 h post-mortem. Another steak was removed from the muscle and aged for 14 days before being assessed for 12 sensory traits by a trained panel. The most accurate coefficients of determination of cross validation (R2CV) calibrated within the current study were for the trained sensory panel textural scores; particularly tenderness (0.46), chewiness (0.43), stringiness (0.35) and difficulty to swallow (0.33), with practical predictions also achieved for metallic flavour (0.52), fatty after-effect (0.44) and juiciness (0.36). In general, the application of mathematical spectral pre-treatments to Raman spectra improved the predictive accuracy of chemometric models developed. This study provides calibrations for valuable quality traits derived from a trained sensory panel in a non-destructive manner, using Raman spectra collected at a time-point compatible with meat management systems.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

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

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

Rapid detection and discrimination of food-related bacteria using IR-microspectroscopy in combination with multivariate statistical analysis

Spoilage microorganisms are of great concern for the food industry. While traditional culturing methods for spoilage microorganism detection are laborious and time-consuming, the development of early detection methods has gained a lot of interest in the last decades. In this work a rapid and non-destructive detection and discrimination method of eight important food-related microorganisms (Bacillus subtilis DSM 10, Bacillus coagulans DSM 1, Escherichia coli K12 DSM 498, Escherichia coli TOP10, Micrococcus luteus DSM 20030, Pseudomonas fluorescens DSM 4358, Pseudomonas fluorescens DSM 50090 and Bacillus thuringiensis israelensis DSM 5724) based on IR-microspectroscopy and chemometric evaluation was developed. Sampling was carried out directly from the surface to be tested, without the need for sample preparation such as purification, singulation, centrifugation and washing steps, as an efficient and inexpensive blotting technique using the sample carrier. IR spectra were recorded directly after the blotting from the surface of the sample carrier without any further pretreatments. A combination of data preprocessing, principal component analysis and canonical discriminant analysis was found to be suitable. The spectral range from 400 to 1750 cm^-1 of the IR-microspectrosopic data was determined to be highly sensitive to the time after incubation and sample thickness, resulting in a high standard deviation. Therefore, this area was excluded from the evaluation in favor of the meaningfulness of the chemometric model and, thus, only the spectral range of specific -CH/-NH/-OH excitations (2815-3680 cm^-1) was used for model development. This study showed that the differentiation of food-related microorganisms on genera, species and strain level is feasible. A leave-one-out cross-validation of the training data set showed 100% accuracy. The classification of the ungrouped test data showed with an accuracy of 94.5% that, despite the large biological variance of the analytes such as different times after incubation and the presented sampling (including its variance), a robust and meaningful model for the differentiation of food-related bacteria could be developed by data preprocessing and subsequent chemometric evaluation.

Bacterial phenotype dependency from CO2 measured by Raman spectroscopy

In recent years, Raman spectroscopy has become an established method to study medical, biological or environmental samples. Since Raman spectroscopy is a phenotypic method, many parameters can influence the spectra. One of these parameters is the concentration of CO₂, as this never remains stable in nature, but always adjusts itself in a dynamic equilibrium. So, it is obvious that the concentration of CO₂ cannot be controlled but it might have a big impact on the bacteria and bacterial composition in medical samples. When using a phenotypic method like Raman spectroscopy it is also important to know the influence of CO₂ to the dataset. To investigate the influence of CO₂ towards Raman spectra we cultivated E. coli at different concentration of CO₂ since this bacterium is able to switch metabolism from aerobic to microaerophilic conditions. After applying statistic methods small changes in the spectra became visible and it was even possible to observe the change of metabolism in this species according to the concentration of CO₂.

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.

Microbial detection and identification methods: Bench top assays to omics approaches

Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time-to-detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix-assisted laser desorption ionization-time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Optical Identification of Middle Ear Infection

Ear infection is one of the most commonly occurring inflammation diseases in the world, especially for children. Almost every child encounters at least one episode of ear infection before he/she reaches the age of seven. The typical treatment currently followed by physicians is visual inspection and antibiotic prescription. In most cases, a lack of improper treatment results in severe bacterial infection. Therefore, it is necessary to design and explore advanced practices for effective diagnosis. In this review paper, we present the various types of ear infection and the related pathogens responsible for middle ear infection. We outline the conventional techniques along with clinical trials using those techniques to detect ear infections. Further, we highlight the need for emerging techniques to reduce ear infection complications. Finally, we emphasize the utility of Raman spectroscopy as a prospective non-invasive technique for the identification of middle ear infection.

Composition and Antibacterial Activity of Aronia melanocarpa (Michx.) Elliot, Cornus mas L. and Chaenomeles superba Lindl. Leaf Extracts

The purpose of this study was to investigate the composition of leaf extracts from Aronia melanocarpa, Chaenomeles superba, and Cornus mas, and their antimicrobial activity against typical spoilage-causing and pathogenic bacteria found in meat and meat products. The highest total phenolic content (TPC) was detected in C. superba extract, followed by C. mas and A. melanocarpa extracts. The antioxidant capacity of the extracts was measured by DPPH and ABTS assays. The lowest IC₅₀ values were found for C. superba extract, followed by C. mas and A. melanocarpa extracts. LC-MS and HPLC analysis revealed that A. melanocarpa and C. superba extracts contained hydroxycinnamic acid derivatives and flavonoids (mainly flavonols). Hydroxycinnamic acid derivatives were detected in the C. mas extract, as well as flavonols, ellagitannins, and iridoids. The antibacterial activity of the plant extracts was tested against Gram-negative bacteria (Moraxella osloensis, Pseudomonas fragi, Acinetobacter baumanii, Escherichia coli, Enterobacter aerogenes, Salmonella enterica) and Gram-positive bacteria (Enterococcus faecium, Staphylococcus aureus, Brochothrix thermosphacta, Lactobacillus sakei, Listeria monocytogenes) using the microculture method. The extracts acted as bacteriostatic agents, decreasing the growth rate (µ_max) and extending the lag phase (t_lag). C. mas showed most potent antibacterial activity, as confirmed by principal component analysis (PCA).

Evaluation of the impact of buffered peptone water composition on the discrimination between Salmonella enterica and Escherichia coli by Raman spectroscopy

The detection of Salmonella spp. in food samples is regulated by the ISO 6579:2002 standard, which requires that precise procedures are followed to ensure the reliability of the detection process. This standard requires buffered peptone water as a rich medium for the enrichment of bacteria. However, the effects of different brands of buffered peptone water on the identification of microorganisms by Raman spectroscopy are unknown. In this regard, our study evaluated the discrimination between two bacterial species, Salmonella enterica and Escherichia coli, inoculated and analyzed with six of the most commonly used buffered peptone water brands. The results showed that bacterial cells behaved differently according to the brand used in terms of biomass production and the spectral fingerprint. The identification accuracy of the analyzed strains was between 85% and 100% depending on the given brand. Several batches of two brands were studied to evaluate the classification rates between the analyzed bacterial species. The chemical analysis performed on these brands showed that the nutrient content was slightly different and probably explained the observed effects. On the basis of these results, Raman spectroscopy operators are encouraged to select an adequate culture medium and continue its use throughout the identification process to guarantee optimal recognition of the microorganism of interest.

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

Infectious diseases caused by bacteria still pose major diagnostic challenges in spite of the availability of various molecular approaches. Irrespective of the type of infection, rapid identification of the causative pathogen with a high degree of sensitivity and specificity is essential for initiating appropriate treatment. While existing methods like PCR possess high sensitivity, they are incapable of identifying the viability status of the pathogen and those which can, like culturing, are inherently slow. To overcome these limitations, we developed a diagnostic platform based on Raman microspectroscopy, capable of detecting biochemical signatures from a single bacterium for identification as well as viability assessment. The study also establishes a decontamination protocol for handling live pathogenic bacteria which does not affect identification and viability testing, showing applicability in the analysis of sputum samples containing pathogenic mycobacterial strains. The minimal sample processing along with multivariate analysis of spectroscopic signatures provides an interface for automatic classification, allowing the prediction of unknown samples by mapping signatures onto available datasets. Also, the novelty of the current work is the demonstration of simultaneous identification and viability assessment at a single bacterial level for pathogenic bacteria. Graphical abstract.

Sample preparation for Raman microspectroscopy

Raman spectroscopy and its variants allow for the investigation of a wide range of biological and biomedical samples, i. e. tissue sections, single cells and small molecules. The obtained information is on a molecular level. By making use of databases and chemometrical approaches, the chemical composition of complex samples can also be defined. The measurement procedure is straight forward, however most often sample preparation protocols must be implemented. While pure samples, such as high purity powders or highly concentrated chemicals in aqueous solutions, can be directly measured without any prior sample purification step, samples of biological origin, such as tissue sections, pathogens in suspension or biofluids, food and beverages often require pre-processing steps prior to Raman measurements. In this book chapter, different strategies for handling and processing various sample matrices for a subsequent Raman microspectroscopic analysis were introduced illustrating the high potential of this promising technique for life science and medical applications. The presented methods range from standalone techniques, such as filtration, centrifugation or immunocapture to innovative platform approaches which will be exemplary addressed. Therefore, the reader will be introduced to methods that will simplify the complexity of the matrix in which the targeted molecular species are present allowing direct Raman measurements with bench top or portable setups.

Isolation matters-processing blood for Raman microspectroscopic identification of bacteria

Bacteremia with its high mortality is a frequent case in clinical health care. Further, bacteremia includes the considerable risk of progressing to a sepsis. Even in case of survival, sepsis still entails diminished quality of life for the survivors and high indirect cost for the society. The crucial factor in sepsis is time. Therefore, timely description of adequate antibiotics is vital to reduce mortality and improve quality of life after survival. Despite that, the current gold standard of clinical bacteria diagnostic is based on cultivation of bacteria, which requires an average of 13-h cultivation. Consequently, there is a necessity for culture free identification methods without sacrificing the range of bacteria strains which can be identified. Raman microspectroscopy in general requires only single bacteria cells and has proven to offer high identification accuracies. However, the prerequisite for Raman microspectroscopy is a suitable isolation strategy to obtain single unharmed bacteria cells free from matrix. Moreover, in blood, bacteria are outnumbered by billions of blood cells. In this study, we present an isolation strategy to recover single bacteria cells from blood and evaluate their suitability for Raman microspectroscopic identification. Graphical abstract.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.

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

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

Sample-Size Planning for Multivariate Data: A Raman-Spectroscopy-Based Example

The goal of sample-size planning (SSP) is to determine the number of measurements needed for statistical analysis. This SSP is necessary to achieve robust and significant results with a minimal number of measurements that need to be collected. SSP is a common procedure for univariate measurements, whereas for multivariate measurements, like spectra or time traces, no general sample-size-planning method exists. Sample-size planning becomes more important for biospectroscopic data because the data generation is time-consuming and costly. Additionally, ethical reasons do not allow the use of unnecessary samples and the measurement of unnecessary spectra. In this paper, a general sample-size-planning algorithm is presented that is based on learning curves. The learning curve quantifies the improvement of a classifier for an increasing training-set size. These curves are fitted by the inverse-power law, and the parameters of this fit can be utilized to predict the necessary training-set size. Sample-size planning is demonstrated for a biospectroscopic task of differentiating six different bacterial species, including Escherichia coli, Klebsiella terrigena, Pseudomonas stutzeri, Listeria innocua, Staphylococcus warneri, and Staphylococcus cohnii, on the basis of their Raman spectra. Thereby, we estimate the required number of Raman spectra and biological replicates to train a classification model, which consists of principal-component analysis (PCA) combined with linear-discriminant analysis (LDA). The presented algorithm revealed that 142 Raman spectra per species and seven biological replicates are needed for the above-mentioned biospectroscopic-classification task. Even though it was not demonstrated, the learning-curve-based sample-size-planning algorithm can be applied to any multivariate data and in particular to biospectroscopic-classification tasks.

Rapid, accurate, and comparative differentiation of clinically and industrially relevant microorganisms via multiple vibrational spectroscopic fingerprinting

Despite the fact that various microorganisms (e.g., bacteria, fungi, viruses, etc.) have been linked with infectious diseases, their crucial role towards sustaining life on Earth is undeniable. The huge biodiversity, combined with the wide range of biochemical capabilities of these organisms, have always been the driving force behind their large number of current, and, as of yet, undiscovered future applications. The presence of such diversity could be said to expedite the need for the development of rapid, accurate and sensitive techniques which allow for the detection, differentiation, identification and classification of such organisms. In this study, we employed Fourier transform infrared (FT-IR), Raman, and surface enhanced Raman scattering (SERS) spectroscopies, as molecular whole-organism fingerprinting techniques, combined with multivariate statistical analysis approaches for the classification of a range of industrial, environmental or clinically relevant bacteria (P. aeruginosa, P. putida, E. coli, E. faecium, S. lividans, B. subtilis, B. cereus) and yeast (S. cerevisiae). Principal components-discriminant function analysis (PC-DFA) scores plots of the spectral data collected from all three techniques allowed for the clear differentiation of all the samples down to sub-species level. The partial least squares-discriminant analysis (PLS-DA) models generated using the SERS spectral data displayed lower accuracy (74.9%) when compared to those obtained from conventional Raman (97.8%) and FT-IR (96.2%) analyses. In addition, whilst background fluorescence was detected in Raman spectra for S. cerevisiae, this fluorescence was quenched when applying SERS to the same species, and conversely SERS appeared to introduce strong fluorescence when analysing P. putida. It is also worth noting that FT-IR analysis provided spectral data of high quality and reproducibility for the whole sample set, suggesting its applicability to a wider range of samples, and perhaps the most suitable for the analysis of mixed cultures in future studies. Furthermore, our results suggest that while each of these spectroscopic approaches may favour different organisms (sample types), when combined, they would provide complementary and more in-depth knowledge (structural and/or metabolic state) of biological systems. To the best of our knowledge, this is the first time that such a comparative and combined spectroscopic study (using FT-IR, Raman and SERS) has been carried out on microbial samples.

Toward Better Understanding of EBPR Systems via Linking Raman-Based Phenotypic Profiling with Phylogenetic Diversity

This study reports a proof-of concept study to demonstrate the novel approach of phenotyping microbial communities in enhanced biological phosphorus removal (EBPR) systems using single cell Raman microspectroscopy and link it with phylogentic structures. We use hierarchical clustering analysis (HCA) of single-cell Raman spectral fingerprints and intracellular polymer signatures to separate and classify the functionally relevant populations in EBPR systems, namely polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), as well as other microbial populations. We then investigated the link between Raman-based community phenotyping and 16S rRNA gene-based phylogenetic characterization of four lab-scale EBPR systems with varying solid retention time (SRT) to gain insights into possible genotype-function relationships. Combined and simultaneous phylogenetic and phenotypic evaluation of EBPR ecosystems revealed SRT-dependent phylogenetic and phenotypic characteristics of the PAOs and GAOs, and their association with EBPR performance. The phenotypic diversity and plasticity of PAO populations, which otherwise could not be obtained with phylogenetic analysis alone, showed complex but potentially crucial association with EBPR process stability.

Interference-Enhanced Raman Spectroscopy as a Promising Tool for the Detection of Biomolecules on Raman-Compatible Surfaces

Raman spectroscopy in combination with appropriate sample preparation strategies, for example, enrichment of bacteria on metal surfaces, has been proven to be a promising approach for rapidly diagnosing infectious diseases. Unfortunately, the fabrication of the required chip substrates is usually very challenging due to the lack of feasible instruments that can be used for quality control in the surface modification process. The intrinsically weak Raman signal of the biomolecules, employed for the enrichment of the micro-organisms on the chip surface, does not allow for monitoring of the successful immobilization by means of a Raman spectroscopic approach. Within this contribution, we demonstrate how a simple modification of a plain aluminum surface enables enhancement (or a decrease, if desired) of the Raman signal of molecules deposited on that surface. The manipulation of the Raman signal strength is achieved via exploiting interference effects that occur, if the highly reflective aluminum surface is modified with thin layers of transparent dielectrics like aluminum oxide. The thicknesses of these layers were determined by theoretical considerations and calculations. For the first time, it is shown that the interference effects can be used for the detection of biomolecules as well by investigating the siderophore ferrioxamine B. The observed degree of enhancement was approximately 1 order of magnitude. Moreover, the employed aluminum/aluminum oxide layers have been thoroughly characterized using atomic force and scanning electron microscopy as well as X-ray reflectometry and UV-Vis measurements.

Detection of molecular changes induced by antibiotics in Escherichia coli using vibrational spectroscopy

This study aimed to test Raman (400-1800cm^-1) and Infra-red (1900-500cm^-1) spectroscopies followed by statistical analysis (principal component analysis) to detect molecular changes induced by antibiotics (ampicillin, cefotaxime - cell wall synthesis inhibitors, tetracycline - protein synthesis inhibitor, ciprofloxacin - DNA synthesis inhibitor) against Escherichia coli TOP10. In case of ampicillin and cefotaxime, a decrease in protein bands in both Raman (1240, 1660cm^-1), and IR spectra (1230, 1530, 1630cm^-1), and an increase in carbohydrate bands (1150, 1020cm^-1) in IR spectra were observed. Tetracycline addition caused an increase in nucleic acid bands (775, 1478, 1578cm^-1), a sharp decrease in phenylalanine (995cm^-1) in Raman spectra and the amide I and amide II bands (1630, 1530cm^-1) in IR spectra, an increase in DNA in both Raman (1083cm^-1) and IR spectra (1080cm^-1). Regarding ciprofloxacin, an increase in nucleic acids (775, 1478, 1578cm^-1) in Raman spectra and in protein bands (1230, 1520, 1630cm^-1), in DNA (1080cm^-1) in IR spectra were detected. Clear discrimination of antibiotic-treated samples compared to the control was recorded, showing that Raman and IR spectroscopies, coupled to principal component analysis for data, could be used to detect molecular modifications in bacteria exposed to different classes of antibiotics. These findings contribute to the understanding of the mechanisms of action of antibiotics in bacteria.

Differentiation between Staphylococcus aureus and Staphylococcus epidermidis strains using Raman spectroscopy

AIM

Raman spectroscopy is an analytical method with a broad range of applications across multiple scientific fields. We report on a possibility to differentiate between two important Gram-positive species commonly found in clinical material - Staphylococcus aureus and Staphylococcus epidermidis - using this rapid noninvasive technique.

MATERIALS & METHODS

For this, we tested 87 strains, 41 of S. aureus and 46 of S. epidermidis, directly from colonies grown on a Mueller-Hinton agar plate using Raman spectroscopy.

DISCUSSION & CONCLUSION

The method paves a way for separation of these two species even on high number of samples and therefore, it can be potentially used in clinical diagnostics.

Pioneering particle-based strategy for isolating viable bacteria from multipart soil samples compatible with Raman spectroscopy

The study of edaphic bacteria is of great interest, particularly for evaluating soil remediation and recultivation methods. Therefore, a fast and simple strategy to isolate various bacteria from complex soil samples using poly(ethyleneimine) (PEI)-modified polyethylene particles is introduced. The research focuses on the binding behavior under different conditions, such as the composition, pH value, and ionic strength, of the binding buffer, and is supported by the characterization of the surface properties of particles and bacteria. The results demonstrate that electrostatic forces and hydrophobicity are responsible for the adhesion of target bacteria to the particles. Distinct advantages of the particle-based isolation strategy include simple handling, enrichment efficiency, and the preservation of viable bacteria. The presented isolation method allows a subsequent identification of the bacteria using Raman microspectroscopy in combination with chemometrical methods. This is demonstrated with a dataset of five different bacteria (Escherichia coli, Bacillus subtilis, Pseudomonas fluorescens, Streptomyces tendae, and Streptomyces acidiscabies) which were isolated from spiked soil samples. In total 92% of the Raman spectra could be identified correctly.