Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions

Microbial contamination is not only a medical problem, but also plays a large role in pharmaceutical clean room production and food processing technology. Therefore many techniques were developed to achieve differentiation and identification of microorganisms. Among these methods vibrational spectroscopic techniques (IR, Raman and SERS) are useful tools because of their rapidity and sensitivity. Recently we have shown that micro-Raman spectroscopy in combination with a support vector machine is an extremely capable approach for a fast and reliable, non-destructive online identification of single bacteria belonging to different genera. In order to simulate different environmental conditions we analyzed in this contribution different Staphylococcus strains with varying cultivation conditions in order to evaluate our method with a reliable dataset. First, micro-Raman spectra of the bulk material and single bacterial cells that were grown under the same conditions were recorded and used separately for a distinct chemotaxonomic classification of the strains. Furthermore Raman spectra were recorded from single bacterial cells that were cultured under various conditions to study the influence of cultivation on the discrimination ability. This dataset was analyzed both with a hierarchical cluster analysis (HCA) and a support vector machine (SVM).     

The influence of intracellular storage material on bacterial identification by means of Raman spectroscopy

Previous studies dealing with bacterial identification by means of Raman spectroscopy have demonstrated that micro-Raman is a suitable technique for single-cell microbial identification. Raman spectra yield fingerprint-like information about all chemical components within one cell, and combined with multivariate methods, differentiation down to species or even strain level is possible. Many microorganisms may accumulate high amounts of polyhydroxyalkanoates (PHA) as carbon and energy storage materials within the cell and the Raman bands of PHA might impede the identification and differentiation of cells. To date, the identification by means of Raman spectroscopy have never been tested on bacteria which had accumulated PHA. Therefore, the aim of this study is to investigate the effect of intracellular polymer accumulation on the bacterial identification rate. Combining fluorescence imaging and Raman spectroscopy, we identified polyhydroxybutyrate (PHB) as a storage polymer accumulating in the investigated cells. The amount of energy storage material present within the cells was dependent on the physiological status of the microorganisms and strongly influenced the identification results. Bacteria in the stationary phase formed granules of crystalline PHB, which obstructed the Raman spectroscopic identification of bacterial species. The Raman spectra of bacteria in the exponential phase were dominated by signals from the storage material. However, the bands from proteins, lipids, and nucleic acids were not completely obscured by signals from PHB. Cells growing under either oxic or anoxic conditions could also be differentiated, suggesting that changes in Raman spectra can be interpreted as an indicator of different metabolic pathways. Although the presence of PHB induced severe changes in the Raman spectra, our results suggest that Raman spectroscopy can be successfully used for identification as long as the bacteria are not in the stationary phase.     

Determination of alkaloids in capsules, milk and ethanolic extracts of poppy (Papaver somniferum L.) by ATR-FT-IR and FT-Raman spectroscopy

Fourier transform (FT) infrared spectroscopy using a diamond composite ATR crystal and NIR-FT-Raman spectroscopy techniques were applied for the simultaneous identification and quantification of the most important alkaloids in poppy capsules. Most of the characteristic Raman signals of the alkaloids can be identified in poppy milk isolated from unripe capsules. But also poppy extracts present specific bands relating clearly to the alkaloid fraction. Raman spectra obtained by excitation with a Nd:YAG laser at 1064 nm show no disturbing fluorescence effects; therefore the plant tissue can be recorded without any special preparation. The used diamond ATR technique allows to measure very small sample amounts (5-10 microL or 2-5 mg) without the necessity to perform time-consuming pre-treatments. When applying cluster analysis a reliable discrimination of "low-alkaloid" and "high-alkaloid" poppy single-plants can be easily achieved. The examples presented in this study provide clear evidence of the benefits of Raman and ATR-IR spectroscopy in efficient quality control, forensic analysis and high-throughput evaluation of poppy breeding material.     

Raman‐Spektroskopie. Biomedizinische Diagnostik

Raman microspectroscopy has established itself in the last years as an extremely capable analytical method for biomedical diagnosis because it is labelfree and provides high molecular selectivity. That way Raman microspectroscopy allows for a fast identification and characterization of microorganisms like e.g. pathogens on a single cell level. Linear Raman spectroscopy and non‐linear Raman techniques like CARS microscopy have great potential for an objective evaluation of cells or tissue for an early diagnosis of diseases like e.g. cancer.     

Raman spectroscopy applied to the horizontal methods ISO 6579:2002 to identify Salmonella spp. in the food industry

Food safety is a major concern for suppliers in the food chain to ensure the safety of their products. The identification procedure requested by norms is tedious, and it often requires systematic controls and qualified staff to perform the necessary analyses. Raman spectroscopy offers new opportunities to rapidly and efficiently ascertain the presence of pathogens in samples. Nevertheless, this technique requires a standardized procedure to be applied in the industrial context. Our study shows that the variability between spectral fingerprints is related to the physiological state of the microbial species and the growth phase of the bacteria plays a crucial role in its identification by Raman spectroscopy. To improve the discrimination between closely related bacterial species, a procedure based on the selection of bacterial spectra in the exponential growth phase was proposed. Different ways to introduce Raman spectroscopy in the ISO 6579:2002 standards are also proposed from the entire process to a shorter protocol. In the latter case, the identification of bacterial colonies after the selective enrichment step was proposed with the advantages of this path in terms of simplicity and rapidity (analysis time is reduced up to 50 h from the 100 h required by the standard). The protocol validated using six food categories from industrial partners have presented a good correlation by confirmation with other laboratory classical methods. In the future, this procedure could be introduced to the control system of the food production chain with a reliable database for various microorganisms encountered in this field.     

表面增强拉曼光谱技术在食品安全检测中的应用

表面增强拉曼光谱是一种强有力的食品检验技术,当待测样品吸附于具有纳米量级粗糙度的金属结构表面时,样品分子的拉曼信号将得到极大的增强。该检测技术具有灵敏度高、响应迅速以及"指纹"识别等特点,在快速检测食品污染物等方面具有巨大的应用前景。该文介绍了表面增强拉曼光谱技术的发展历史、基本原理、基底分类以及联用技术,综述了该技术在重金属离子、兽药残留、农药残留、非法添加物、食源性致病微生物等方面的最新应用,最后提出了亟需解决的问题与未来的发展趋势(引用文献74篇)。     

Recent studies on advance spectroscopic techniques for the identification of microorganisms: A review

The continuous development of resistance to antibiotic drugs by microorganisms causes high mortality and morbidity. Pathogens with distinct features and biochemical abilities make them destructive to human health. Therefore, early identification of the pathogen is of substantial importance for quick ailments and healthcare outcomes. Several phenotype methods are used for the identification and resistance determination but most of the conventional procedures are time-consuming, costly, and give qualitative results. Recently, great focus has been made on the utilization of advanced techniques for microbial identification. This review is focused on the research studies performed in the last five years for the identification of microorganisms particularly, bacteria using advanced spectroscopic techniques including mass spectrometry (MS), infrared (IR) spectroscopy, Raman spectroscopy (RS), and nuclear magnetic resonance (NMR) spectroscopy. Among all the techniques, MS techniques, particularly MALDI-TOF/MS have been widely utilized for microbial identification. A total of 44 bacteria i.e., 6 Staphylococcus spp., 3 Enterococcus spp., 6 Bacillus spp., 4 Streptococcus spp., 6 Salmonella spp., and one from each genus including Escherichia, Acinetobacter, Pseudomonas, Proteus, Clostridioides, Candida, Brucella, Burkholderia, Francisella, Yersinia, Moraxella, Vibrio, Shigella, Serratia, Citrobacter, and Haemophilus (spp.) were discussed in the review for their identification using the above-mentioned techniques. Among all the identified microorganisms, 21% of studies have been conducted for the identification of E. coli, 14% for S. aureus followed by 37% for other microorganisms.     

Raman spectroscopy based analysis of milk using random forest classification

The development of a classification system based on the Raman spectra of milk samples is proposed in present study. Such development could be useful for nutritionists in suggesting healthy food to infants for their proper growth. Previously, molecular structures in milk samples have been exploited by Raman spectroscopy. In the current study, Raman spectral data of milk samples of different species is utilized for multi-class classification using a dimensionality reduction technique in combination with random forest (RF) classifier. Quantitative and experimental analysis is based on locally collected milk samples of different species including cow, buffalo, goat and human. This classification is based on the variations (different concentrations of the components present in milk such as proteins, milk fats, lactose etc.) in the intensities of Raman peaks of milk samples. Principal component analysis (PCA) is used as a dimensionality reduction technique in combination with RF to highlight the variations which can differentiate the Raman spectra of milk samples from different species. The proposed technique has demonstrated sufficient potential to be used for differentiation between milk samples of different species as the average accuracy of about 93.7%, precision of about 94%, specificity of about 97% and sensitivity of about 93% has been achieved.     

Use of Raman spectroscopy to determine the kinetics of chemical transformation in yogurt production

The kinetics of chemical transformation during the yogurt production was obtained using micro-Raman spectroscopy: Raman spectra were obtained as a function of the incubation time in the fermentation process from milk to yogurt. The milk fermentation by the lactic acid bacteria produces morphological, chemical and textural changes. The chemical transformations were followed using micro-Raman spectroscopy, while the aggregation process and some textural properties through the use of dynamic light scattering, viscosity and pH. Samples with three different starter culture concentrations and different incubation temperatures were prepared. The results indicate the presence of two regimes: the first one (primary metabolism) is characterized by an increment in the initial number of bacteria to reach a high concentration according to the conditions of food, temperature and space, together with some initial chemical transformations. The second regime corresponds properly to the fermentation process accelerated by the continuous reduction in pH due to the lactic acid production; this is accompanied by physical and chemical changes where new structures are created. Knowledge of the kinetics of chemical and physical transformations allows having a better control of the final product with an increase in the quality and shelf life of the final product; problems like phase separation, homogeneity, particle size, acidity, etc., can be controlled.     

Application of vibrational spectroscopy techniques for material identification from fire debris

The primary goal of this research is to demonstrate the use of vibrational spectroscopy techniques as a tool for the identification of materials post fire. This paper discusses the use micro-Raman spectroscopy and ATR-FTIR to identify materials found in fire debris. The polymeric materials under study were high density and low density polyethylene (HDPE and LDPE), polyvinyl chloride (PVC), polymethyl methacrylate (PMMA) and cotton. These are commonly materials found in households around the world, their identification from the debris provides useful forensic information on the spatial distribution of fuels in a fire compartment, thus allowing for accurate analysis and modelling. Earlier work has established Raman spectroscopy to be a very good tool for material identification post fire. The addition of ATR-FTIR spectroscopy as a technique in developing this novel tool for identification of materials post fire has established vibrational spectroscopy in the area of fire investigation. This study indicated that the limitations associated with Raman spectroscopy in post fire identification, could be made insignificant by the strengths of ATR-FTIR spectroscopy and vice versa. To further establish the validity of this identification process principal component analysis was used to discriminate between the spectrum of the burnt materials and an error analysis computed. Both techniques demonstrated that identification could be done with very minimal confusion between the materials studied.     

Raman spectroscopic identification of single bacterial cells under antibiotic influence

The identification of pathogenic bacteria is a frequently required task. Current identification procedures are usually either time-consuming due to necessary cultivation steps or expensive and demanding in their application. Furthermore, previous treatment of a patient with antibiotics often renders routine analysis by culturing difficult. Since Raman microspectroscopy allows for the identification of single bacterial cells, it can be used to identify such difficult to culture bacteria. Yet until now, there have been no investigations whether antibiotic treatment of the bacteria influences the Raman spectroscopic identification. This study aims to rapidly identify bacteria that have been subjected to antibiotic treatment on single cell level with Raman microspectroscopy. Two strains of Escherichia coli and two species of Pseudomonas have been treated with four antibiotics, all targeting different sites of the bacteria. With Raman spectra from untreated bacteria, a linear discriminant analysis (LDA) model is built, which successfully identifies the species of independent untreated bacteria. Upon treatment of the bacteria with subinhibitory concentrations of ampicillin, ciprofloxacin, gentamicin, and sulfamethoxazole, the LDA model achieves species identification accuracies of 85.4, 95.3, 89.9, and 97.3 %, respectively. Increasing the antibiotic concentrations has no effect on the identification performance. An ampicillin-resistant strain of E. coli and a sample of P. aeruginosa are successfully identified as well. General representation of antibiotic stress in the training data improves species identification performance, while representation of a specific antibiotic improves strain distinction capability. In conclusion, the identification of antibiotically treated bacteria is possible with Raman microspectroscopy for diverse antibiotics on single cell level.

Raman spectroscopic quantification of milk powder constituents

Raman spectroscopy has significant potential for the quantification of food products. Milk powder is an important foodstuff and ingredient that is produced on large scale (over 20 million tonnes per annum). Raman spectroscopy, unlike near- and mid-infrared spectroscopies, has not been used extensively to quantify milk powder constituents. The effect of sample presentation on spectroscopic calibrations of protein and fat for 136 New Zealand milk powders was assessed using Raman spectroscopy. Prediction models were produced to quantify a protein concentration range of 32.19-37.65% w/w for skim milk powder, and a protein concentration range of 23.34-25.02% w/w and a fat concentration range of 26.26-29.68% w/w for whole milk powder (where ratios of prediction to deviation exceeded 2.6 with one exception). The resultant calibrations were not influenced by sample orientation; the sample temperature during data collection did affect the calibrations. Calcium fortification in the form of calcium carbonate was identified within a sub-set of samples, reinforcing the efficacy of Raman spectroscopy for identifying both crystalline and non-crystalline constituents within milk powder.     

Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

Raman spectroscopy: Recent advancements,techniques and applications

Vibrational spectroscopy has proven itself to be a valuable contributor in the study of various fields of science, primarily due to the extraordinary versatility of sampling methods. Raman measurement gives the vibrational spectrum of the analyte, which can be treated as its “fingerprint,” allows easy interpretation and identification. Over the last years, there has been tremendous technical improvement in Raman spectroscopy, as overcome by the problems like fluorescence, poor sensitivity or reproducibility. This article reviews the recent advances in Raman spectroscopy and its new trend of applications ranging from ancient archaeology to advanced nanotechnology. It includes the aspects of Raman spectroscopic measurements to the analysis of various substances categorized into distinct application areas such as biotechnology, mineralogy, environmental monitoring, food and beverages, forensic science, medical and clinical chemistry, diagnostics, pharmaceutical, material science, surface analysis, etc. Advances in the instrumental design of Raman spectrometers coupled with newly developed sampling methodologies have also been described which enable trace level detection and satisfactory analysis.     

Rapid detection of single cell bacteria as a novel approach in food microbiology

Solid-phase cytometry (SPC) is a novel technique that allows rapid detection of bacteria at the single cell level, without the need for a growth phase. After filtration of the sample, the retained microorganisms are fluorescently labeled on the membrane filter and automatically counted by a laser scanning device. Each fluorescent spot can be visually inspected with an epifluorescence microscope connected to the ChemScan by a computer-driven moving stage. Depending on the fluorogenic labels used, information on the identity and the physiological status of the microorganisms can be obtained within a few hours. Although SPC was originally recommended for the determination of the total viable microbial count in water and other liquid samples, it may also be a promising technique for the detection and enumeration of bacteria in food samples, provided they can be isolated from the unfilterable matrix. The short detection time inherent in this approach is a considerable advantage over conventional plate counting, especially for slow-growing microorganisms. The basic principles of SPC are discussed as well as its potential for the detection of Mycobacterium paratuberculosis, a model example of a slow-growing bacterium in milk.     

Characterization of Paint by Fourier-Transform Infrared Spectroscopy,Raman Microscopy,and Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy

The identification of automotive coatings has become important for forensic scientists to trace suspects. Popular automotive coatings include acrylic, amino, alkyd, nitro, and polyurethane paints. Various pigments and additives are added to the coatings, which may create difficulty in characterization of paint evidence. In this study, Fourier-transform infrared spectroscopy, Raman spectroscopy and scanning electron microscopy-energy dispersive X-ray spectroscopy were used to characterize an eleven-layer paint sample. Most layers were only a few micrometers thick. Infrared spectroscopy has several advantages in identifying resins and additives, while Raman is more effective in characterizing additives and inorganic pigments. Scanning electron microscopy-energy dispersive X-ray spectroscopy provides elemental analysis information. The results reveal that the combination of these techniques provides more accurate paint identification than using them separately.     

Pigment identification in paintings employing laser induced breakdown spectroscopy and Raman microscopy

Laser-induced breakdown spectroscopy (LIBS) was used in combination with Raman microscopy, for the identification of pigments in different types of painted works of art. More specifically, a 19th century post-Byzantine icon from Greece and two miniature paintings from France were examined and detailed spectral data are presented which lead to the identification of the pigments used. LIBS measurements yielded information on the presence of pigments or mixtures of pigments based on the characteristic emission from specific elements. Identification of most pigments was performed by Raman microscopy. As demonstrated in this work, the combined use of LIBS and Raman microscopy, two complementary techniques, leads to a detailed characterization of the paintings examined with respect to the pigments used.     

Resonance Raman and surface- and tip-enhanced Raman spectroscopy methods to study solid catalysts and heterogeneous catalytic reactions

Resonance Raman (RR) spectroscopy has several advantages over the normal Raman spectroscopy (RS) widely used for in situ characterization of solid catalysts and catalytic reactions. Compared with RS, RR can provide much higher sensitivity and selectivity in detecting catalytically-significant surface metal oxides. RR can potentially give useful information on the nature of excited states relevant to photocatalysis and on the anharmonic potential of the ground state. In this critical review a detailed discussion is presented on several types of RR experimental systems, three distinct sources of so-called Raman (fluorescence) background, detection limits for RR compared to other techniques (EXAFS, PM-IRAS, SFG), and three well-known methods to assign UV-vis absorption bands and a band-specific unified method that is derived mainly from RR results. In addition, the virtues and challenges of surface-enhanced Raman spectroscopy (SERS) are discussed for detecting molecular adsorbates at catalytically relevant interfaces. Tip-enhanced Raman spectroscopy (TERS), which is a combination of SERS and near-field scanning probe microscopy and has the capability of probing molecular adsorbates at specific catalytic sites with an enormous surface sensitivity and nanometre spatial resolution, is also reviewed (300 references).     

Fast determination of milk fat content using Raman spectroscopy

In our work, we have demonstrated the capability of VIS Raman spectroscopy in combination with partial least square regression (PLS) as a rapid technique for direct milk fat determination. Raman spectra of milk samples revealed contributions from proteins, but mainly from their fat content with different spectral characteristics. Three different methods of sample preparations were applied: (i) liquid milk contained in an open dish, (ii) dried milk droplets on glass plates covered with Al foil, and (iii) liquid milk contained in quartz cuvettes. Methods (i) and (ii) showed a good PLS model for milk fat prediction with low root mean square errors and high correlation coefficients. The main advantage of milk sample contained in the dish lies in its simplicity as well as the fact that the open container maximizes the signal of interest avoiding background contributions. Our results show that Raman spectroscopy is suited for in-line monitoring purposes.