Identification and discrimination of Pseudomonas aeruginosa bacteria grown in blood and bile by laser-induced breakdown spectroscopy

Pseudomonas aeruginosa bacteria colonies have been analyzed by laser-induced breakdown spectroscopy using nanosecond laser pulses. LIBS spectra were obtained after transferring the bacteria from a nutrient-rich culture medium to a nutrient-free agar plate for laser ablation. To study the dependence of the LIBS spectrum on growth and environmental conditions, colonies were cultured on three different nutrient media: a trypticase soy agar (TSA) plate, a blood agar plate, and a medium chosen deliberately to induce bacteria membrane changes, a MacConkey agar plate containing bile salts. Nineteen atomic and ionic emission lines in the LIBS spectrum, which was dominated by inorganic elements such as calcium, magnesium and sodium, were used to identify and classify the bacteria. A discriminant function analysis was used to discriminate between the P. aeruginosa bacteria and two strains of E. coli: a non-pathogenic environmental strain and the pathogenic strain enterohemorrhagic E. coli 0157:H7 (EHEC). Nearly identical spectra were obtained from P. aeruginosa grown on the TSA plate and the blood agar plate, while the bacteria grown on the MacConkey plate exhibited easily distinguishable differences from the other two. All P. aeruginosa samples, independent of initial growth conditions, were readily discriminated from the two E. coli strains.     

The study of the relationship between the new topological index A(m) and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

The newly developed topological indices A_m1-A_m3 and the molecular connectivity indices ^mX were applied to multivariate analysis in structure-property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices ²X and A_m1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.     

Analysis and classification of bacteria by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and a chemometric approach

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a useful technique for the identification of bacteria on the basis of their characteristic protein mass spectrum fingerprint. Highly standardized instrumental analytical performance and bacterial culture conditions are required to achieve useful information. A chemometric approach based on multivariate analysis techniques was developed for the analysis of MALDI data of different bacteria to allow their identification from their fingerprint. Principal component analysis, linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) were applied to the analysis of the MALDI MS mass spectra of two pathogenic bacteria, Escherichia coli O157:H7 and Yersinia enterocolitica, and the non-pathogenic E. coli MC1061. Spectra variability was assessed by growing bacteria in different media and analysing them at different culture growth times. After selection of the relevant variables, which allows the evaluation of an m/z value pattern with high discriminant power, the identification of bacteria by LDA and SIMCA was performed independently of the experimental conditions used. In order to better evaluate the analytical performance of the approach used, the ability to correctly classify different bacteria, six wild-type strains of E. coli O157:H7, was also studied and a combination of different chemometric techniques with a severe validation was developed. The analysis of spiked bovine meat samples and the agreement with an independent chemiluminescent enzyme immunoassay demonstrated the applicability of the method developed for the detection of bacteria in real samples. The easy automation of the MALDI method and the ability of multivariate techniques to reduce interlaboratory variability associated with bacterial growth time and conditions suggest the usefulness of the proposed MALDI MS approach for rapid routine food safety checks. Figure Workflow of the developed MALDI-TOF MS and chemometric approach for the analysis and classification of bacteria Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Use of electrophoretic techniques and MALDI–TOF MS for rapid and reliable characterization of bacteria: analysis of intact cells, cell lysates, and “washed pellets”

In this study electrophoretic and mass spectrometric analysis of three types of bacterial sample (intact cells, cell lysates, and “washed pellets”) were used to develop an effective procedure for the characterization of bacteria. The samples were prepared from specific bacterial strains. Five strains representing different species of the family Rhizobiaceae were selected as model microorganisms: Rhizobium leguminosarum bv. trifolii, R. leguminosarum bv. viciae, R. galegae, R. loti, and Sinorhizobium meliloti. Samples of bacteria were subjected to analysis by four techniques: capillary zone electrophoresis (CZE), capillary isoelectric focusing (CIEF), gel IEF, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS). These methods are potential alternatives to DNA-based methods for rapid and reliable characterization of bacteria. Capillary electrophoretic (CZE and CIEF) analysis of intact cells was suitable for characterization of different bacterial species. CIEF fingerprints of “washed pellets” and gel IEF of cell lysates helped to distinguish between closely related bacterial species that were not sufficiently differentiated by capillary electrophoretic analysis of intact cells. MALDI–TOF MS of “washed pellets” enabled more reliable characterization of bacteria than analysis of intact cells or cell lysates. Electrophoretic techniques and MALDI–TOF MS can both be successfully used to complement standard methods for rapid characterization of bacteria.     

Applying Fourier-transform infrared spectroscopy and chemometrics to the characterization and identification of lactic acid bacteria

The infrared absorption spectra of 55 lactic acid bacteria belonging to the genera Lactobacillus, Weissella and Carnobacterium were obtained and mathematically analyzed. Sixteen reference strains and 39 food strains isolated from meat and meat products and belonging to the genera Lactobacillus (6 species), Weissella (3 species) and Carnobacterium (2 species) were processed under standardized conditions and their medium infrared spectra obtained using Fourier-transform infrared (FT-IR) spectroscopy. Reproducibility indexes and similarities between FT-IR spectra were calculated using modified correlation coefficients to detect the ranges with the best reproducibility and discrimination properties. Hierarchical cluster analysis and stepwise discriminant analysis (SDA) were subsequently carried out to detect classes and create library groups. Reference strains could be distinguished on the basis of their spectral data and their clustering was in agreement with differences in chemical composition of the cell wall. For the 39 food isolates, the capability of two identification systems was compared. Unknown strains were identified (a) using the linear functions obtained from SDA (canonical variables) of the variables that provide the best discrimination of spectra, and (b) by calculating a differentiation index when a range of the unknown's transformed IR spectrum was compared to all spectra included in a reference library. The system based on the differentiation index obtained a higher rate of identification, allowing for detection of outliers. FT-IR spectroscopy is shown to afford additional information to phenotypic and genotypic data which may help to establish a more robust taxonomic classification.     

Near-infrared diffuse reflectance spectroscopy and neural networks for measuring nutritional parameters in chocolate samples

A rapid and non-destructive method has been developed for the characterization of chocolate samples based on diffuse reflectance near-infrared Fourier transform spectroscopy (DRIFTS) and artificial neural networks (ANNs). This methodology provides a potentially useful alternative to time-consuming chemical methods of analysis. To assess its utility, 36 chocolate samples purchased from the Spanish market were analyzed for the determination of the main nutritional parameters like carbohydrates, fat, proteins, energetic value and cocoa content.Direct triplicate measurements of each sample were carried out by DRIFTS. Cluster hierarchical analysis was used for selecting calibration and validation data sets, resulting in a calibration set comprised of 19 samples and a validation data set of 17 samples. As it is common the presence of non-linear effect in reflectance spectroscopy, ANNs was chosen for data pretreatment. The root-mean-square error of prediction (RMSEP) values obtained for carbohydrates, fat, energetic value and cocoa were 1.0% (w/w), 1.0% (w/w), 50 kJ (100 g)⁻¹ and 1.4%, respectively. The mean difference (d_x-y) and standard deviation of mean differences (s_x-y) of the carbohydrates, fat, proteins content, energetic value and cocoa content were 0.9 and 2.4% (w/w), 0.2 and 1.0% (w/w), 9.1 and 50 kJ (100 g)⁻¹, and −0.5 and 1.4%, respectively. The maximum relative error for the prediction (QC) of any of these parameters for a new sample did not exceed 5.2%.     

Enhanced infrared absorption in a comparative study between multi-sensitive and multiresistant bacteria of the genus Klebsiella sp.

We study the infrared spectral profile of bacteria of the genus Klebsiella sp. with respect to sensitivity and resistance. The differentiation between these organisms can properly guide the therapeutic conduct. We processed the samples obtained from the laboratory of Hospital Santos Dumont in a Mac Conckey culture medium. After that, we processed a micromass to provide a thin film for evaluation by FTIR spectroscopy. The pilot study was performed using glass slides and glass slides coated with a copper sheet of 45 μm thickness as support for the samples. After the bibliographical review, we highlight the relationship between the amino acid serine and bacterial resistance in this type of bacteria, which occurs through plasmids. We collected spectra from 50 bacterial strains divided in two groups: 25 sensitive and 25 resistant strains on glass, and 21 sensitive and 21 resistant strains on the copper sheet. We obtained the composition of the samples by evaluating the spectra with multivariate analysis using the second derivative. We concluded that infrared spectroscopy could also be used to identify multiresistant bacteria of the genus Klebsiella sp. by means of the amino acid serine, which can be considered as a marker of resistance found only in multiresistant forms.     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.     

Repeatability and pattern recognition of bacterial fatty acid profiles generated by direct mass spectrometric analysis of in situ thermal hydrolysis/methylation of whole cells

Direct CI mass spectrometry profiling of fatty acid methyl esters (FAMEs) from in situ thermal hydrolysis/methylation (THM) of whole bacterial cells with tetramethylammonium hydroxide (TMAH) has been demonstrated as a potential method for real time and fieldable detection/identification of microorganisms. Bacillus anthracis (Ames), Yersinia pestis (Nair. Kenya), Vibrio cholerae (E1 Tor), Brucella melitensis (Abortus wild) and Francisella tularensis (LVS vaccine) were profiled by this method during a 10-month period. Repeatability of the in situ FAME data was calculated using one-way analysis of variance (ANOVA) and a t-test. Artificial neural network (ANN) and multivariate statistics of the FAME profiles were also compared for bacterial identification/classification. Equivalent results were obtained with a multivariate rule building expert system (MuRES) and the ANN. However, the ANN analysis required much less computer time and was deemed the best choice for this application. In situ THM FAME profiles of the bacterial samples provided comparable results with those obtained from the Microbial Identification System (MIDI) (Newark, DE) wet chemistry-gas chromatographic based system.     

Forward selection radial basis function networks applied to bacterial classification based on MALDI-TOF-MS

Forward selection improved radial basis function (RBF) network was applied to bacterial classification based on the data obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The classification of each bacterium cultured at different time was discussed and the effect of parameters of the RBF network was investigated. The new method involves forward selection to prevent overfitting and generalized cross-validation (GCV) was used as model selection criterion (MSC). The original data was compressed by using wavelet transformation to speed up the network training and reduce the number of variables of the original MS data. The data was normalized prior training and testing a network to define the area the neural network to be trained in, accelerate the training rate, and reduce the range the parameters to be selected in. The one-out-of-n method was used to split the data set of p samples into a training set of size p−1 and a test set of size 1. With the improved method, the classification correctness for the five bacteria discussed in the present paper are 87.5, 69.2, 80, 92.3, and 92.8%, respectively.     

The Impact of Apple Variety and the Production Methods on the Antibacterial Activity of Vinegar Samples

Apple vinegar is a natural product widely used in food and traditional medicine as it contains many bioactive compounds. The apple variety and production methods are two factors that play a major role in determining the quality of vinegar. Therefore, this study aims to determine the quality of apple vinegar samples from different varieties (Red Delicious, Gala, Golden Delicious, and Starking Delicious) prepared by three methods using small apple pieces, apple juice, and crushed apple, through determining the physicochemical properties and antibacterial activity of these samples. The antibacterial activity was studied against five pathogenic bacteria: Staphylococcus aureus, Klebsiella pneumonia, Escherichia coli (ATB: 57), Escherichia coli (ATB: 97), and Pseudomonas aeruginosa, using two methods, disk diffusion and microdilution, for determining the minimum inhibitory concentrations and the minimum bactericidal concentrations. The results of this study showed that the lowest pH value was 3.6 for Stark Delicious, obtained by liquid fermentation, and the highest acetic acid values were 4.7 and 4% for the vinegar of Red Delicious and Golden Delicious, prepared by solid fermentation, respectively. The results of the antibacterial activity showed considerable activity of apple vinegar on the tested strains. Generally, the Staphylococcus aureus strain appears less sensitive and Pseudomonas aeruginosa seems to be very sensitive against all samples, while the other strains have distinct sensitivities depending on the variety studied and the method used. A higher antibacterial activity was found in vinegar obtained by the apple pieces method and the Red Delicious variety, with a low MIC and MBC recorded, at 1.95 and 3.90 µL/mL, respectively. This study has shown that the choice of both apple variety and production method is therefore an essential step in determining and aiming for the desired quality of apple vinegar.     

UPLC-UV-MSE analysis for quantification and identification of major carotenoid and chlorophyll species in algae

A fast method for quantification and identification of carotenoid and chlorophyll species utilizing liquid chromatography coupled with UV detection and mass spectrometry has been demonstrated and validated for the analysis of algae samples. This method allows quantification of targeted pigments and identification of unexpected compounds, providing isomers separation, UV detection, accurate mass measurements, and study of fragment ions for structural elucidation in a single run. This is possible using parallel alternating low- and high-energy collision spectral acquisition modes, which provide accurate mass full scan chromatograms and accurate mass high-energy chromatograms. Here, it is shown how this approach can be used to confirm carotenoid and chlorophyll species by identification of key diagnostic fragmentations during high-energy mode. The developed method was successfully applied for the analysis of Dunaliella salina samples during defined red LED lighting growth conditions, identifying 37 pigments including 19 carotenoid species and 18 chlorophyll species, and providing quantification of 7 targeted compounds. Limit of detections for targeted pigments ranged from 0.01?ng/mL for lutein to 0.24?ng/mL for chlorophyll a. Inter-run precision ranged for of 3 to 24 (RSD%) while inter-run inaccuracy ranged from ?17 to 11.

Raw Meat Contaminated with Cephalosporin-Resistant Enterobacterales as a Potential Source of Human Home Exposure to Multidrug-Resistant Bacteria

The prevalence of cephalosporine-resistant (3GC-R) strains among United States community-related research samples ranged from 5.6 to 10.8%, while, in the European countries, it was 1.2% to 10.1%. Several studies suggest that meat of animal origin could be one of the reservoirs of 3GC-R bacteria. Here, 86 raw meat samples (turkey, pork, chicken and beef) were collected randomly and verified for the presence of 3GC-R bacteria. The 3GC-R bacteria were isolated, identified and characterized phenotypically (antibiotic resistance, motility and biofilm) and genotypically (repetitive-sequence-based rep-PCR) to elucidate any correlations with principal component analysis (PCA). From 28 3GC-R positive samples, 41 strains were isolated, from which the majority belonged to Serratia fonticola (39%), followed by Escherichia coli (19.5%), Enterobacter cloacae (17.1%) and Klebsiella pneumoniae (14.6%). The isolates of E. coli and S. fonticola presented diverse profiles in rep-PCR. Generally, 3GC-R strains were more resistant to antibiotics used in veterinary medicine than in human medicine. PCA derived from antibiotic resistance, motility and biofilm formation of S. fonticola and E. coli strains showed that resistance to beta-lactams was separated from the resistance to other antibiotic classes. Moreover, for the S. fonticola, E. coli and En. cloacae, the type of meat can create a specific tendency towards antibiotic resistance and phenotypic characteristics for S. fonticola, while these relationships were not found for other tested species.     

A quick and easy method to identify bacteria by matrix‐assisted laser desorption/ionisation time‐of‐flight mass spectrometry

Concerns with water quality have increased in recent years, in part due to the more frequent contamination of water by pathogens like E. coli and L. pneumophila. Current methods for the typing of bacteria in water samples are based on culture of samples on specific media. These techniques are time‐consuming, subject to the impact of interferents and do not totally meet all the requirements of prevention. There is a need for accurate and rapid identification of these microorganisms. This report deals with the detection of bacteria, more precisely of Legionella spp., and the development of an analytical strategy for a rapid and unambiguous identification of these pathogens in water from diverse origins. Therefore, a protein mass mapping using matrix‐assisted laser desorption/ionisation mass spectrometry (MALDI MS) of whole bacteria combined with a home‐made database of bacteria spectra is applied. A large variety of different bacteria and microorganisms is used to approach the actual composition of samples with numerous interferents. The objective is to propose a universal method for sampling preparation before MALDI MS analysis and optimised spectrometric conditions for reproducible intense peaks. Several experimental factors known to influence signal quality such as time and media of culture have been studied. The proposed method gives promising results for a sure differentiation of Legionella species and subspecies and a rapid identification of bacteria which are the most dangerous or difficult to eradicate. This method is easy to perform with an excellent reproducibility. The analytical protocol and the corresponding database were validated on samples from different origins (cooling tower, plumbing hot water). Copyright © 2010 John Wiley & Sons, Ltd.     

FTIR characterization of Candida species: a study on some reference strains and pathogenic C. albicans isolates from HIV+ patients

ATR–FTIR has been used together with principal components analysis to study some reference strains belonging to five different Candida species and 20 Candida albicans isolates from three HIV⁺ patients under different fluconazole regimens. The five reference strains were easily differentiated by using first derivative spectra of the carbohydrate region comprising the spectral range 900–1200 cm⁻¹. For the C. albicans clinical isolates, classification obtained by Fourier-transform infrared (FTIR) spectroscopy was compared with data from the genotypic method, PFGE. Results show a good correlation between the two methods for one patient who presented a partially conserved immune system with no previous fluconazole treatment, where all C. albicans strains isolated were susceptible to this antifungal agent. Divergences in the two methods appear for the two immuno-compromised patients receiving long-term fluconazole treatment. It is very probable that such iterative antifungal treatment can cause strong alterations in the yeast cell wall membrane and that the mismatch of the two techniques highlights the complex situation of strain-level identification and differentiation of clinical isolates originating from patients under prolonged treatment.     

Rapid identification and classification of Mycobacterium spp. using whole-cell protein barcodes with matrix assisted laser desorption ionization time of flight mass spectrometry in comparison with multigene phylogenetic analysis

The need of quick diagnostics and increasing number of bacterial species isolated necessitate development of a rapid and effective phenotypic identification method. Mass spectrometry (MS) profiling of whole cell proteins has potential to satisfy the requirements. The genus Mycobacterium contains more than 154 species that are taxonomically very close and require use of multiple genes including 16S rDNA for phylogenetic identification and classification. Six strains of five Mycobacterium species were selected as model bacteria in the present study because of their 16S rDNA similarity (98.4–99.8%) and the high similarity of the concatenated 16S rDNA, rpoB and hsp65 gene sequences (95.9–99.9%), requiring high identification resolution. The classification of the six strains by MALDI TOF MS protein barcodes was consistent with, but at much higher resolution than, that of the multi-locus sequence analysis of using 16S rDNA, rpoB and hsp65. The species were well differentiated using MALDI TOF MS and MALDI BioTyper™ software after quick preparation of whole-cell proteins. Several proteins were selected as diagnostic markers for species confirmation. An integration of MALDI TOF MS, MALDI BioTyper™ software and diagnostic protein fragments provides a robust phenotypic approach for bacterial identification and classification.     

Development and evaluation of a loop-mediated isothermal amplification method in conjunction with an enzyme-linked immunosorbent assay for specific detection of Salmonella serogroup D

Loop-mediated isothermal amplification in conjunction with enzyme-linked immunosorbent assay (LAMP-ELISA) provides a sensitive, specific and cost-effective method for detection of etiological causes of infections. The present study developed a reliable LAMP-ELISA diagnostic kit for identification of Salmonella serogroup D strains and evaluated its potential use in the detection of Salmonella serovars Enteritidis and Typhi. The LAMP-ELISA assay used a serogroup D/A-specific primer set to amplify a region of the prt gene, followed by hybridization of the digoxigenin-labeled products to a highly specific oligonucleotide probe for exact identification of serogroup D serovars. Among the bacteria tested, a positive reaction was only observed for strains belong to Salmonella serogroup D. The detection limit of the LAMP-ELISA assay was 4 CFU per tube, which was lower than PCR-ELISA assay with the same target gene (50 CFU per tube). Finally, the technique was successfully applied to an artificially contaminated meat sample with a detection limit 10³ CFU mL⁻¹, which was 10 times more sensitive than PCR-ELISA. Overall, the LAMP-ELISA assay could be used as a sensitive alternative method to PCR-ELISA for the specific detection of Salmonella serogroup D serovars in routine food microbiology or clinical laboratories worldwide.     

Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality

Rapid methods for the identification of wheat varieties and their end-use quality have been developed. The methods combine the analysis of wheat protein extracts by mass spectrometry with partial least-squares regression in order to predict the variety or end-use quality of unknown wheat samples. The whole process takes approximately 30 min. Extracts of alcohol-soluble storage proteins (gliadins) from wheat were analysed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Partial least-squares regression was subsequently applied using these mass spectra for making models that could predict the wheat variety or end-use quality. Previously, an artificial neural network was used to identify wheat varieties based on their protein mass spectra profiles. The present study showed that partial least-squares regression is at least as useful as neural networks for this identification. Furthermore, it was demonstrated that partial least-squares regression could be used to predict wheat end-use quality, which has not been possible using neural networks.     

Specific identification of Bacillus anthracis strains

Accurate identification of human pathogens is the initial vital step in treating the civilian terrorism victims and military personnel afflicted in biological threat situations. We have applied a powerful multi-dimensional protein identification technology (MudPIT) along with newly generated software termed Profiler to identify the sequences of specific proteins observed for few strains of Bacillus anthracis, a human pathogen. Software termed Profiler was created to initially screen the MudPIT data of B. anthracis strains and establish the observed proteins specific for its strains. A database was also generated using Profiler containing marker proteins of B. anthracis and its strains, which in turn could be used for detecting the organism and its corresponding strains in samples. Analysis of the unknowns by our methodology, combining MudPIT and Profiler, led to the accurate identification of the anthracis strains present in samples. Thus, a new approach for the identification of B. anthracis strains in unknown samples, based on the molecular mass and sequences of marker proteins, has been ascertained.     

Antimicrobial activity of new bicyclic lactones with three or four methyl groups obtained both synthetically and biosynthetically

Ten new derivatives of isophorone were obtained through a five-step synthesis. Among the products were several unsaturated, bicyclic lactones with three or four methyl groups. These lactones were used as the substrates for biotransformation mediated by selected fungal strains (Fusarium species, Syncephalastrum racemosum, Cunninghamella japonica, Penicillium species, Absidia species, and Pleurotus ostreatus). Four new hydroxylactones were obtained as a result of biotransformation. Because the unsaturated lactone with four methyl groups was a diastereoisomeric mixture, a structural analysis was conducted. The hydroxylactones were also included in this analysis. Both the unsaturated lactones and hydroxylactones were examined for their antimicrobial activity. It was found that some of these compounds exhibited growth inhibition against pathogenic strains of bacteria (Staphylococcus aureus, Pseudomonas fluorescens), yeasts (Candida albicans) and filamentous fungi (Alternaria sp., Penicillium sp.). All obtained compounds were also subjected to scent analysis.