Assessment of two isolation techniques for bacteria in milk towards their compatibility with Raman spectroscopy

The identification of single microorganism in food samples by conventional plating techniques or molecular genetic methods requires a time consuming enrichment step. Raman spectroscopy in combination with a suitable extraction method however offers the possibility to rapidly identify bacteria on a single cell level. Here we evaluate the two well-known bacteria extraction methods from milk: "buoyant density centrifugation" and "enzymatic milk clearing" towards their recovery efficiency and their compatibility with Raman spectroscopy for a rapid identification of microorganisms in milk. The achieved recovery yields are slightly better compared to those which are already applied for food investigations, where a loss of one order of magnitude is usually reached. For example, buoyant density centrifugation allows collecting up to 35% of the milk-spiked microorganisms. To prove the suitability of the isolation techniques for use in combination with the spectroscopic approach, a small Raman database has been created by recording Raman spectra of well-known contaminants in dairy products. Two subspecies of Escherichia coli and three different Pseudomonas species, which were inoculated to UHT (ultra-high-temperature processed) milk and afterwards extracted by the two techniques mentioned above, were analysed. At a first glance, grave spectral artefacts caused by the matrix itself or especially by the extraction techniques were not obvious. But via chemometric analysis, it could be shown that these factors noticeably influence the identification rates: while the samples prepared via milk clearing did not provide sufficient identification results, buoyant density centrifugation allows an identification of the investigated species with an overall accuracy of 91% in combination with linear discriminant analysis.     

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.     

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.     

基于非接触式拉曼光谱分析人血与犬血的PCA-LDA鉴别方法

将拉曼光谱分析法与数理统计方法有机结合,构建人血与犬血种属判别模型,实现了不同种属血液样本的高效无损鉴别.采用拉曼光谱的无损测试模式对血液样本进行测试,考察了抗凝管管材、聚焦位置及曝光时间等对血液样本拉曼光谱的影响,在激发波长为632.8 nm,光谱扫描范围为200~1800 cm-1,功率衰减率50%,曝光时间5 s及累加次数为2次的优化条件下,获得了无损检测条件下的血液样本拉曼光谱图.针对血液样本组分复杂、拉曼光谱信号基底背景高等问题,提出了基于小波变换去噪,进行分段多项式基线校正的预处理方法,有效解决了血液样本拉曼光谱谱图的高噪音和基线漂移问题.实验选择30例正常人血和33例比格犬血为样本训练集,5例正常人血和5例比格犬血为测试集,基于主成分分析法(PCA)联合线性判别法(LDA)模型,训练集分类正确率达到95.23%,盲测集分类正确率达90.00%.这种基于非接触式血液样本拉曼光谱和PCA-LDA判断模型的测试方法在进出口检验检疫等涉及血液无损鉴别的领域具有广泛的应用价值和前景.     

Raman spectroscopic detection of physiology changes in plasmid-bearing <Emphasis Type="Italic">Escherichia coli</Emphasis> with and without antibiotic treatment

Bacterial resistances against antibiotics are increasingly problematic for medical treatment of pathogenic bacteria, e.g., in hospitals. Resistances are, among other genes, often encoded on plasmids which can be transmitted between bacteria not only within one species, but also between different species, genera, and families. The plasmid pDrive is transformed into bacteria of the model strain Escherichia coli DH5α. Within this investigation, we applied micro-Raman spectroscopy with two different excitation wavelengths in combination with support vector machine (SVM) and linear discriminant analysis (LDA) to differentiate between bacterial cultures according to their cultural plasmid content. Recognition rates of about 92% and 90% are achieved by Raman excitation at 532 and 244 nm, respectively. The SVM loadings reveal that the pDrive transformed bacterial cultures exhibit a higher DNA content compared to the untransformed cultures. To elucidate the influence of the antibiotic, ampicillin-treated cultures are also comprised within this study and are classified with rates of about 97% and 100% for 532 and 244 nm Raman excitation, respectively. The Raman spectra recorded with 532 nm excitation wavelength show differences of the secondary protein structure and enhanced stress-related respiration rates for the ampicillin-treated cultures. Independent cultural replicates of either ampicillin-challenged or non-challenged cultures are successfully identified with identification rates of over 90%.     

Qualitative study of ethanol content in tequilas by Raman spectroscopy and principal component analysis

Using Raman spectroscopy, with an excitation radiation source of 514.5 nm, and principal component analysis (PCA) was elaborated a method to study qualitatively the ethanol content in tequila samples. This method is based in the OH region profile (water) of the Raman spectra. Also, this method, using the fluorescence background of the Raman spectra, can be used to distinguish silver tequila from aged tequilas. The first three PCs of the Raman spectra, that provide the 99% of the total variance of the data set, were used for the samples classification. The PCA1 and PCA2 are related with the water (or ethanol) content of the sample, whereas the PCA3 is related with the fluorescence background of the Raman spectra.     

Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition

Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.     

Methods for extracting biochemical information from bacterial Raman spectra: focus on a group of structurally similar biomolecules--fatty acids

In this paper we explore the possibilities of Raman spectroscopy in order to deduce information on the fatty acid composition of bacterial cells. Therefore, representative strains of two bacterial taxa were each cultured in different conditions and in parallel analyzed by Raman spectroscopy and gaschromatographic FAME analysis. Raman spectra of pure fatty acids were recorded and used as reference spectra. The culturing conditions for each strain could be easily distinguished by the fatty acid information retrieved from bacterial Raman spectra. Chemometric techniques such as EMSC and PCA allowed to extract information about groups of fatty acids, that was consistent with the results from FAME analysis. Although the information retrieved from Raman spectroscopy is not as refined as that from FAME analysis, the presented methods could be useful to obtain basic information on the fatty acid present in bacteria when performing Raman spectroscopic analysis for fast whole cell profiling, which provides information for different types of cell components (fatty acids, amino acids, primary metabolites, etc.).     

Discrimination of Human and Nonhuman Blood by Raman Spectroscopy and Partial Least Squares Discriminant Analysis

A novel method is reported to discriminate human and animal blood by Raman spectroscopy without complex sample preparation. A partial least squares discriminant analysis model was constructed from a calibration set of Raman spectra from three species of animal blood using 785-nm laser excitation. The discrimination between human and nonhuman blood was calculated by three sigma. Various performance measures, including a blind test and external validation, confirmed the discriminatory performance of the chemometric model. The model provided 100% accuracy in its differentiation between human and nonhuman blood. These results demonstrate that Raman spectroscopy is a promising tool for the discrimination of animal and human blood.     

A novel method for human gender classification using Raman spectroscopy of fingernail clippings

This paper illustrates a novel method for human gender classification by measuring the Raman spectrum of fingernail clippings. As Raman spectroscopy reveals the characteristics of vibrational frequencies of the fingernails, it provides unique chemical fingerprints that can be used to describe the molecular structure differences of fingernail between males and females. As the differences of Raman spectra of human fingernails are very subtle, they are enhanced by using a pattern recognition method. In the present study, a combination algorithm of principal component analysis (PCA) and support vector machines (SVM) was implemented to perform the data classification. This combined algorithm provides a classification accuracy of up to 90%. The success of this present method may be used as an alternative rapid tool to identify human gender in forensic applications.     

Raman spectroscopic identification of single bacterial cells at different stages of their lifecycle

Early, rapid, and reliable bacterial identification is of great importance in natural environments and in medical situations. Numerous studies have shown that Raman spectroscopy can be used to differentiate between different bacteria under controlled laboratory conditions. However, individual bacteria within a population exhibit macromolecular and metabolic heterogeneity over their lifetime. Therefore it is important to be able to identify and classify specific bacteria at different time points of the growth cycle. In this study, four species of bacteria were used to explore the capability of confocal Raman spectroscopy as a tool for the identification of (and discrimination between) diverse bacterial species at various growth time points. The results show that bacterial cells from different growth time points (as well as from a random growth phase) can be discriminated among the four species using principal component analysis (PCA). The results also show that bacteria selected from different growth phases can be classified with the help of a prediction model based on principal component and linear discriminant analysis (PC-LDA). These findings demonstrate that Raman spectroscopy with the application of a PC-LDA model rooted in chemotaxonomic analysis has potential for rapid sensing of microbial cells in environmental and clinical studies.     

Characterization of the surface enhanced raman scattering (SERS) of bacteria

The surface enhanced Raman scattering (SERS) of a number of species and strains of bacteria obtained on novel gold nanoparticle (approximately 80 nm) covered SiO(2) substrates excited at 785 nm is reported. Raman cross-section enhancements of >10(4) per bacterium are found for both Gram-positive and Gram-negative bacteria on these SERS active substrates. The SERS spectra of bacteria are spectrally less congested and exhibit greater species differentiation than their corresponding non-SERS (bulk) Raman spectra at this excitation wavelength. Fluorescence observed in the bulk Raman emission of Bacillus species is not apparent in the corresponding SERS spectra. Despite the field enhancement effects arising from the nanostructured metal surface, this fluorescence component appears "quenched" due to an energy transfer process which does not diminish the Raman emission. The surface enhancement effect allows the observation of Raman spectra of single bacterial cells excited at low incident powers and short data acquisition times. SERS spectra of B. anthracis Sterne illustrate this single cell level capability. Comparison with previous SERS studies reveals how the SERS vibrational signatures are strongly dependent on the morphology and nature of the SERS active substrates. The potential of SERS for detection and identification of bacterial pathogens with species and strain specificity on these gold particle covered glassy substrates is demonstrated by these results.     

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.     

Tumour cell identification by means of Raman spectroscopy in combination with optical traps and microfluidic environments

Raman spectroscopy has been recognized to be a powerful tool for label-free discrimination of cells. Sampling methods are under development to utilize the unique capabilities to identify cells in body fluids such as saliva, urine or blood. The current study applied optical traps in combination with Raman spectroscopy to acquire spectra of single cells in microfluidic glass channels. Optical traps were realized by two 1070 nm single mode fibre lasers. Microflows were controlled by a syringe pump system. A novel microfluidic glass chip was designed to inject single cells, modify the flow speed, accommodate the laser fibres and sort cells after Raman based identification. Whereas the integrated microchip setup used 514 nm for excitation of Raman spectra, a quartz capillary setup excited spectra with 785 nm laser wavelength. Classification models were trained using linear discriminant analysis to differentiate erythrocytes, leukocytes, acute myeloid leukaemia cells (OCI-AML3), and breast tumour cells BT-20 and MCF-7 with accuracies that are comparable with previous Raman experiments of dried cells and fixed cells in a Petri dish. Implementation into microfluidic environments enables a high degree of automation that is required to improve the throughput of the approach for Raman activated cell sorting.     

High-wavenumber FT-Raman spectroscopy for in vivo and ex vivo measurements of breast cancer

The identification of normal and cancer breast tissue of rats was investigated using high-frequency (HF) FT-Raman spectroscopy with a near-infrared excitation source on in vivo and ex vivo measurements. Significant differences in the Raman intensities of prominent Raman bands of lipids and proteins structures (2,800?C3,100?cm^?1) as well as in the broad band of water (3,100?C3,550?cm^?1) were observed in mean normal and cancer tissue spectra. The multivariate statistical analysis methods of principal components analysis (PCA) and linear discriminant analysis (LDA) were performed on all high-frequency Raman spectra of normal and cancer tissues. LDA results with the leave-one-out cross-validation option yielded a discrimination accuracy of 77.2, 83.3, and 100% for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy HF Raman spectra. Despite the lower discrimination value for the in vivo transcutaneous measurements, which could be explained by the breathing movement and skin influences, our results showed good accuracy in discriminating between normal and cancer breast tissue samples. To support this, the calculated integration areas from the receiver-operating characteristic (ROC) curve yielded 0.86, 0.94, and 1.0 for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy measurements, respectively. The feasibility of using HF Raman spectroscopy as a clinical diagnostic tool for breast cancer detection and monitoring is due to no interfering contribution from the optical fiber in the HF Raman region, the shorter acquisition time due to a more intense signal in the HF Raman region, and the ability to distinguish between normal and cancerous tissues.     

A combined theoretical and experimental study of the structure and vibrational absorption, vibrational circular dichroism, Raman and Raman optical activity spectra of the L-histidine zwitterion

A combined theoretical and experimental study of the vibrational absorption (VA)/IR, vibrational circular dichroism (VCD), Raman and Raman optical activity (ROA) spectra of l-histidine in aqueous solution has been undertaken to answer the questions (i) what are the species present and (ii) which conformers of the species are present under various experimental conditions. The VA spectra of l-histidine have been measured in aqueous solution and the spectral bands which can be used to identify both species (cation, zwitterion, anion) and conformer of the species have been identified and subsequently used to identify the species (zwitterion) and conformer (gauche minus minus, gauche minus plus for the side chain dihedral angles) present in solution at pH 7.6. The VCD spectral intensities have been used subsequently in combination with further theoretical studies to confirm the conclusions that have been arrived at by only analyzing the VA/IR spectra. Finally a comparison of measured Raman and ROA spectra of l-histidine with Raman and ROA spectral simulations for the conformers and species derived from the combined VA/IR and VCD experimental and theoretical work is presented as a validation of the conclusions arrived at from VA/IR and VCD spectroscopy. The combination of VA/IR and VCD with Raman and ROA is clearly superior and both sets of experiments should be performed.     

Towards a detailed understanding of bacterial metabolism--spectroscopic characterization of Staphylococcus epidermidis.

Bacteria are a major cause of infection. To fight disease and growing resistance, research interest is focused on understanding bacterial metabolism. For a detailed evaluation of the involved mechanisms, a precise knowledge of the molecular composition of the bacteria is required. In this article, various vibrational spectroscopic techniques are applied to comprehensively characterize, on a molecular level, bacteria of the strain Staphylococcus epidermidis, an opportunistic pathogen which has evolved to become a major cause of nosocomial infections. IR absorption spectroscopy reflects the overall chemical composition of the cells, with major focus on the protein vibrations. Smaller sample volumes-down to a single cell-are sufficient to probe the overall chemical composition by means of micro-Raman spectroscopy. The nucleic-acid and aromatic amino-acid moieties are almost exclusively explored by UV resonance Raman spectroscopy. In combination with statistical evaluation methods [hierarchical cluster analysis (HCA), principal component analysis (PCA), linear discriminant analysis (LDA)], the protein and nucleic-acid components that change during the different bacterial growth phases can be identified from the in vivo vibrational spectra. Furthermore, tip-enhanced Raman spectroscopy (TERS) provides insight into the surface structures and follows the dynamics of the polysaccharide and peptide components on the bacterial cells with a spatial resolution below the diffraction limit. This might open new ways for the elucidation of host-bacteria and drug-bacteria interactions.     

基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

Raman spectroscopic signature of blood and its potential application to forensic body fluid identification

Near-infrared (NIR) Raman spectroscopy was used to measure spectra of dried human blood samples from multiple donors. Two major questions addressed in this paper involve the influence of sample heterogeneity and potential Raman spectral variations that could arise between different donors of blood. Advanced statistical analysis of spectra obtained from multiple spots on dry samples showed that dry blood is chemically heterogeneous, and its Raman spectra could be presented very well as a linear combination of a fluorescent background and two Raman spectroscopic components that are dominated by hemoglobin and fibrin, respectively. Each sample Raman spectrum contains the same major peaks, but the relative contribution of the hemoglobin and fibrin components varies with the donor. Therefore, no single spectrum could adequately represent an experimental Raman spectrum of dry blood in a quantitative way, but rather the combination of hemoglobin and fibrin spectral components could be considered to be a spectroscopic signature for blood. This proof-of-concept approach shows the potential for Raman spectroscopy to be used in forensic analysis to identify an unknown substance such as blood.     

Fluorescence rejection in Raman spectra of Syncrude Sweet Blend distillation fractions

Four techniques for the reduction or elimination of fluorescence from Raman spectra of Syncrude process samples were examined in this study. These methods are based on the retrieval of Raman bands from differential, or derivative spectra. Differential data were generated by subtracting similar spectra of a given sample obtained in three ways: (a) shifted detection utilizing an array detector and two successive spectrometer settings; (b) shifted excitation (dispersive Raman) where the two spectra are recorded using neighbouring laser lines and ordinary photon counting; (c) shifted excitation (FT-Raman) in which the laser frequency is changed in software before acquisition of the second spectrum. In addition to these differential techniques, derivative spectra were acquired directly with a dispersive Raman system by modulating the wavelength during scanning. These fluorescence rejection methods were applied to two groups of Syncrude Sweet Blend distillation fractions. For light gas oils (boiling range, 195-343 degrees C) the ratio of monocyclic and bicyclic aromatic species was determined and bands due to aliphatic CH(n) groups were characterized. Heavy gas oils (343-524 degrees C) yielded bands that allowed quantitation of monocyclic, bicyclic and total aromatic groups. Bands due to aliphatics were also identified for the heavy gas oils. These results constitute a significant advance compared to the information obtainable using conventional dispersive and FT-Raman spectroscopy for the analysis of hydrocarbon distillation fractions.