Fourier transform infrared (FT-IR) spectroscopy in bacteriology: towards a reference method for bacteria discrimination

Differentiation of different dibenzothiophene (DBT) desulfurizing bacteria via surface-enhanced Raman spectroscopy (SERS)

Fossil fuels are considered vital natural energy resources on the Earth, and sulfur is a natural component present in them. The combustion of fossil fuels releases a large amount of sulfur in the form of SO x in the atmosphere. SO x is the major cause of environmental problems, mainly air pollution. The demand for fuels with ultra-low sulfur is growing rapidly. In this aspect, microorganisms are proven extremely effective in removing sulfur through a process known as biodesulfurization. A major part of sulfur in fossil fuels (coal and oil) is present in thiophenic structures such as dibenzothiophene (DBT) and substituted DBTs. In this study, the identification and characterization of DBT desulfurizing bacteria (Chryseobacterium sp. IS, Gordonia sp. 4N, Mycolicibacterium sp. J2, and Rhodococcus sp. J16) based on their specific biochemical constituents were conducted using surface-enhanced Raman spectroscopy (SERS). By differentiating DBT desulfurizing bacteria, researchers can gain insights into their unique characteristics, thus leading to improved biodesulfurization strategies. SERS was used to differentiate all these species based on their biochemical differences and different SERS vibrational bands, thus emerging as a potential technique. Moreover, multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to differentiate these DBT desulfurizing bacteria on the basis of their characteristic SERS spectral signals. For all these isolates, the accuracy, sensitivity, and specificity are above 90%, and an AUC (area under the curve) value of close to 1 was achieved for all PLS-DA models.

Bacterial discrimination by Fourier transform infrared spectroscopy, MALDI-mass spectrometry and whole-genome sequencing

Aim: Proof-of-concept study, highlighting the clinical diagnostic ability of FT-IR compared with MALDI-TOF MS, combined with WGS. Materials & methods: 104 pathogenic isolates of Neisseria meningitidis, Streptococcus pneumoniae, Streptococcus pyogenes and Staphylococcus aureus were analyzed. Results: Overall prediction accuracy was 99.6% in FT-IR and 95.8% in MALDI-TOF-MS. Analysis of N. meningitidis serogroups was superior in FT-IR compared with MALDI-TOF-MS. Phylogenetic relationship of S. pyogenes was similar by FT-IR and WGS, but not S. aureus or S. pneumoniae. Clinical severity was associated with the zinc ABC transporter and DNA repair genes in S. pneumoniae and cell wall proteins (biofilm formation, antibiotic and complement permeability) in S. aureus via WGS. Conclusion: FT-IR warrants further clinical evaluation as a promising diagnostic tool.

Antimicrobial Ionic Liquids: Ante-Mortem Mechanisms of Pathogenic EPEC and MRSA Examined by FTIR Spectroscopy

Ionic liquids (ILs) have gained considerable attention due to their versatile and designable properties. ILs show great potential as antibacterial agents, but understanding the mechanism of attack on bacterial cells is essential to ensure the optimal design of IL-based biocides. The final aim is to achieve maximum efficacy while minimising toxicity and preventing resistance development in target organisms. In this study, we examined a dose-response analysis of ILs' antimicrobial activity against two pathogenic bacteria with different Gram types in terms of molecular responses on a cellular level using Fourier-transform infrared (FTIR) spectroscopy. In total, 18 ILs with different antimicrobial active motifs were evaluated on the Gram-negative enteropathogenic Escherichia coli (EPEC) and Gram-positive methicillin-resistant Staphylococcus aureus (MRSA). The results showed that most ILs impact bacterial proteins with increasing concentration but have a minimal effect on cellular membranes. Dose-response spectral analysis revealed a distinct ante-mortem response against certain ILs for MRSA but not for EPEC. We found that at sub-lethal concentrations, MRSA actively changed their membrane composition to counteract the damaging effect induced by the ILs. This suggests a new adaptive mechanism of Gram-positive bacteria against ILs and demonstrates the need for a better understanding before using such substances as novel antimicrobials.

Molecular Assessment and Validation of the Selected Enterococcal Strains as Probiotics

Probiotics are live microorganisms which confer health benefits to the host. Lactic acid bacteria (LAB) are used as probiotics since decades. Enterococci being the member of LAB have proven probiotic strains; therefore, this study was aimed at finding out the potential probiotic candidates from the pool of locally isolated strains. For initial screening, one hundred and twenty-two strains were selected and subjected to different confirmatory and phenotypic tests to choose the best strains that have potential probiotic criteria, i.e., no potential virulence traits, antibiotic resistance, and having tolerance properties. Keeping this criterion, only eleven strains (n = 11) were selected for further assessment. All virulence traits such as production of hemolysin, gelatinase, biofilm, and DNase were performed and not found in the tested strains. The molecular assessment indicates the presence of few virulence-associated genes in Enterococcus faecalis strains with variable frequency. The phenotypic and genotypic assessments of antibiotic resistance profile indicate that the selected strain was susceptible to ten commonly used antibiotics, and there were no transferrable antibiotic resistance genes. The presence of CRISPR-Cas genes also confirmed the absence of antibiotic resistance genes. Various enterocin-producing genes like EntP, EntB, EntA, and EntQ were also identified in the selected strains which make them promising probiotic lead strains. Different tolerance assays like acid, NaCl, and gastric juice tolerance that mimic host conditions was also evaluated by providing artificial conditions. Cellular adhesion and aggregation properties like auto- and co-aggregation were also checked and their results reflect all in the favor of lead probiotic strains.

Towards the Understanding of the Function of Lanthipeptide and TOMM-Related Genes in Haloferax mediterranei

Research on secondary metabolites produced by Archaea such as ribosomally synthesized and post-translationally modified peptides (RiPPs) is limited. The genome of Haloferax mediterranei ATCC 33500 encodes lanthipeptide synthetases (medM1, medM2, and medM3) and a thiazole-forming cyclodehydratase (ycaO), possibly involved in the biosynthesis of lanthipeptides and the TOMMs haloazolisins, respectively. Lanthipeptides and TOMMs often have antimicrobial activity, and H. mediterranei has antagonistic activity towards haloarchaea shown to be independent of medM genes. This study investigated (i) the transcription of ycaO and medM genes, (ii) the involvement of YcaO in bioactivity, and (iii) the impact of YcaO and MedM-encoding genes' absence in the biomolecular profile of H. mediterranei. The assays were performed with biomass grown in agar and included RT-qPCR, the generation of knockout mutants, bioassays, and FTIR analysis. Results suggest that ycaO and medM genes are transcriptionally active, with the highest number of transcripts observed for medM2. The deletion of ycaO gene had no effect on H. mediterranei antihaloarchaea activity. FTIR analysis of medM and ycaO knockout mutants suggest that MedMs and YcaO activity might be directly or indirectly related t lipids, a novel perspective that deserves further investigation.

Efficient classification of Escherichia coli and Shigella using FT-IR spectroscopy and multivariate analysis

Accurate and effective discrimination of E. coli and Shigella is an important clinical issue, and there are many limitations in traditional methods of analysis. FT-IR shows great potential in the classification of bacteria with high specificity and low cost. In this study, we evaluated the efficiency of this technique when combined with multivariate analysis for rapid classification of E. coli and Shigella, which is difficult using traditional analytical methods. Machine learning and statistical tools were employed in combination with FT-IR to classify 14 E. coli and 9 Shigella strains. The classification accuracies for select E. coli and Shigella strains from blood agar were 0.7826, 0.8696, and 0.9565 at the genus, species, and strain levels, respectively. In addition, we used the FT-IR data of select strains from three different culture media for cross-validation, yielding an accuracy of 0.3681 at the strain level. These results indicate that the bacterial culture conditions have a significant impact on the FT-IR patterns. Based on this, an improved strategy for training an ensemble classifier model considering bacterial culture factors was constructed, resulting in almost perfect separation with an accuracy of 0.9394 for strain-level classification. These results show the potential of FT-IR combined with multivariate analysis for more reliable bacterial classification.

Spatio-temporal based deep learning for rapid detection and identification of bacterial colonies through lens-free microscopy time-lapses

Detection and identification of pathogenic bacteria isolated from biological samples (blood, urine, sputum, etc.) are crucial steps in accelerated clinical diagnosis. However, accurate and rapid identification remain difficult to achieve due to the challenge of having to analyse complex and large samples. Current solutions (mass spectrometry, automated biochemical testing, etc.) propose a trade-off between time and accuracy, achieving satisfactory results at the expense of time-consuming processes, which can also be intrusive, destructive and costly. Moreover, those techniques tend to require an overnight subculture on solid agar medium delaying bacteria identification by 12-48 hours, thus preventing rapid prescription of appropriate treatment as it hinders antibiotic susceptibility testing. In this study, lens-free imaging is presented as a possible solution to achieve a quick and accurate wide range, non-destructive, label-free pathogenic bacteria detection and identification in real-time using micro colonies (10-500 μm) kinetic growth pattern combined with a two-stage deep learning architecture. Bacterial colonies growth time-lapses were acquired thanks to a live-cell lens-free imaging system and a thin-layer agar media made of 20 μl BHI (Brain Heart Infusion) to train our deep learning networks. Our architecture proposal achieved interesting results on a dataset constituted of seven different pathogenic bacteria-Staphylococcus aureus (S. aureus), Enterococcus faecium (E. faecium), Enterococcus faecalis (E. faecalis), Staphylococcus epidermidis (S. epidermidis), Streptococcus pneumoniae R6 (S. pneumoniae), Streptococcus pyogenes (S. pyogenes), Lactococcus Lactis (L. Lactis). At T = 8h, our detection network reached an average 96.0% detection rate while our classification network precision and sensitivity averaged around 93.1% and 94.0% respectively, both were tested on 1908 colonies. Our classification network even obtained a perfect score for E. faecalis (60 colonies) and very high score for S. epidermidis at 99.7% (647 colonies). Our method achieved those results thanks to a novel technique coupling convolutional and recurrent neural networks together to extract spatio-temporal patterns from unreconstructed lens-free microscopy time-lapses.

Machine learning-based typing of Salmonella enterica O-serogroups by the Fourier-Transform Infrared (FTIR) Spectroscopy-based IR Biotyper system

BACKGROUND

Salmonella enterica is among the major burdens for public health at global level. Typing of salmonellae below the species level is fundamental for different purposes, but traditional methods are expensive, technically demanding, and time-consuming, and therefore limited to reference centers. Fourier transform infrared (FTIR) spectroscopy is an alternative method for bacterial typing, successfully applied for classification at different infra-species levels.

AIM

This study aimed to address the challenge of subtyping Salmonella enterica at O-serogroup level by using FTIR spectroscopy. We applied machine learning to develop a novel approach for S. enterica typing, using the FTIR-based IR Biotyper® system (IRBT; Bruker Daltonics GmbH & Co. KG, Germany). We investigated a multicentric collection of isolates, and we compared the novel approach with classical serotyping-based and molecular methods.

METHODS

A total of 958 well characterized Salmonella isolates (25 serogroups, 138 serovars), collected in 11 different centers (in Europe and Japan), from clinical, environmental and food samples were included in this study and analyzed by IRBT. Infrared absorption spectra were acquired from water-ethanol bacterial suspensions, from culture isolates grown on seven different agar media. In the first part of the study, the discriminatory potential of the IRBT system was evaluated by comparison with reference typing method/s. In the second part of the study, the artificial intelligence capabilities of the IRBT software were applied to develop a classifier for Salmonella isolates at serogroup level. Different machine learning algorithms were investigated (artificial neural networks and support vector machine). A subset of 88 pre-characterized isolates (corresponding to 25 serogroups and 53 serovars) were included in the training set. The remaining 870 samples were used as validation set. The classifiers were evaluated in terms of accuracy, error rate and failed classification rate.

RESULTS

The classifier that provided the highest accuracy in the cross-validation was selected to be tested with four external testing sets. Considering all the testing sites, accuracy ranged from 97.0% to 99.2% for non-selective media, and from 94.7% to 96.4% for selective media.

CONCLUSIONS

The IRBT system proved to be a very promising, user-friendly, and cost-effective tool for Salmonella typing at serogroup level. The application of machine learning algorithms proved to enable a novel approach for typing, which relies on automated analysis and result interpretation, and it is therefore free of potential human biases. The system demonstrated a high robustness and adaptability to routine workflows, without the need of highly trained personnel, and proving to be suitable to be applied with isolates grown on different agar media, both selective and unselective. Further tests with currently circulating clinical, food and environmental isolates would be necessary before implementing it as a potentially stand-alone standard method for routine use.

Pharmaceutical tablet compression: measuring temporal and radial concentration profiles to better assess segregation

Concentration monitoring inside a tablet press feed frame is important not only to assess the composition of the powder blend being compressed into tablets but also to detect quality affecting phenomena such as powder segregation. Near infrared spectroscopy has been successfully used to monitor powder concentration inside feed frame; however, so far, this methodology does not provide information on local spatial variability, since it probes a very small area of powder sample. Near infrared chemical imaging (NIR CI) has the potential to improve process monitoring because it can simultaneously acquire a plurality of spectra covering nearly the entire width of feed frame, thereby making it possible to detect local variations in powder concentration.The present work uses both NIRS and NIR CI to monitor the concentration of Ibuprofen and Ascorbic acid in multi-component mock pharmaceutical blends flowing through the feed frame of an industrial tablet press. The concentrations of Ibuprofen and Ascorbic acid were successfully monitored in multi-component powder blends. NIR spectral wavelength ranges and pre-treatments were simultaneously optimized via a genetic algorithm. N-way PLS approach for concentration monitoring was found to be more suitable than regular PLS when analyzing spectral images and provided the ability to visualize spatial segregation.

Decision tree-based identification of Staphylococcus aureus via infrared spectral analysis of ambient gas

In this study, eight types of bacteria were cultivated, including Staphylococcus aureus. The infrared absorption spectra of the gas surrounding cultured bacteria were recorded at a resolution of 0.5 cm⁻¹ over the wavenumber range of 7500–500 cm⁻¹. From these spectra, we searched for the infrared wavenumbers at which characteristic absorptions of the gas surrounding Staphylococcus aureus could be measured. This paper reports two wavenumber regions, 6516–6506 cm⁻¹ and 2166–2158 cm⁻¹. A decision tree–based machine learning algorithm was used to search for these wavenumber regions. The peak intensity or the absorbance difference was calculated for each region, and the ratio between them was obtained. When these ratios were used as training data, decision trees were created to classify the gas surrounding Staphylococcus aureus and the gas surrounding other bacteria into different groups. These decision trees show the potential effectiveness of using absorbance measurement at two wavenumber regions in finding Staphylococcus aureus.

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.

Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards

Calibration transfer is an important field for near-infrared (NIR) spectroscopy in practical applications. However, most transfer methods are constructed with standard samples, which are expensive and difficult to obtain. Taking this problem into account, this paper proposes a calibration transfer method based on affine invariance without transfer standards (CTAI). Our method can be utilized to adjust the difference between two instruments by affine transformation. CTAI firstly establishes a partial least squares (PLS) model of the master instrument to obtain score matrices and predicted values of the two instruments, and then the regression coefficients between each of the score vectors and predicted values are computed for the master instrument and the slave instrument, respectively. Next, angles and biases are calculated between the regression coefficients of the master instrument and the corresponding regression coefficients of the slave instrument, respectively. Finally, by introducing affine transformation, new samples are predicted based on the obtained angles and biases. A comparative study between CTAI and the other five methods was conducted, and the performances of these algorithms were tested with two NIR spectral datasets. The obtained experimental results show clearly that, in general CTAI is more robust and can also achieve the best Root Mean Square Error of test sets (RMSEPs). In addition, the results of statistical difference with the Wilcoxon signed rank test show that CTAI is generally better than the others, and at least statistically the same.

FTIR Spectroscopy Suggests a Revised Mode of Action for the Cationic Side-Chain Effect of Ionic Liquids

Over the past decades, ionic liquids (ILs) have gained considerable attention from the scientific community because of their versatile and designable properties. As a result, there are numerous IL applications, not only in organic synthesis, catalysis, or extraction but also as active pharmaceutical ingredients or novel antimicrobials. While considerable effort has been put into developing quantitative structure-activity relationship (QSAR) models for IL toxicity prediction, little is known about their actual mode of action. In this study, Fourier transform infrared (FTIR) spectroscopy is used to monitor IL induced molecular responses directly at the cellular level. Investigation of the well-known cationic alkyl side-chain effect (increasing side-chain length leads to increasing toxicity) of imidazolium- and ammonium-based ILs on two bacterial pathogens, enteropathogenic  Escherichia coli (EPEC) and methicillin-resistant Staphylococcus aureus (MRSA), surprisingly revealed two distinct modes of action. Contrary to prior models, it was only for [TMC₁₆A][Cl], where a molecular response in the membrane was found, while ILs with shorter side-chain lengths predominantly affected bacterial proteins. The results of this study highlight the importance of further direct investigations of the impact of ILs at the cellular level to improve toxicity prediction and assess the usefulness of spectroscopic methods, such as FTIR spectroscopy at achieving this goal.

Optimised spectral pre-processing for discrimination of biofluids via ATR-FTIR spectroscopy

Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients, the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.

Rapid discrimination of bacteria using a miniature mass spectrometer

Bacteria colonies were analyzed using paper spray ionization coupled with a portable mass spectrometer. The spectra were averaged and processed using multivariate analysis to discriminate between different species of bacteria based on their unique phospholipid profiles. Full scan mass spectra and product ion MS/MS data were compared to those recorded using a benchtop linear ion trap mass spectrometer.

New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.

Approved and novel strategies in diagnostics of rat bite fever and other Streptobacillus infections in humans and animals

Rat bite fever (RBF), a worldwide occurring and most likely under-diagnosed zoonosis caused by Streptobacillus moniliformis, represents the most prominent disease of Streptobacillus infections. Recently, novel members have been described, from which a reservoir in rats and other animal species and a zoonotic potential can be assumed. Despite regularly published case reports, diagnostics of RBF continues to represent a 'diagnostic dilemma', because the mostly applied 16S rRNA sequence analysis may be uncertain for proper pathogen identification. Virtually nothing is known regarding prevalence in humans and animal reservoirs. For a realistic assessment of the pathogen's spread, epidemiology and virulence traits, future studies should focus on the genomic background of Streptobacillus. Full genome sequence analyses of a representative collection of strains might facilitate to unequivocally identify and type isolates. Prevalence studies using selective enrichment mechanisms may also enable the isolation of novel strains and candidate species of this neglected group of microorganisms.

FTIR metabolomic fingerprint reveals different modes of action exerted by active pharmaceutical ingredient based ionic liquids (API-ILs) on Salmonella typhimurium

FTIR spectroscopic analysis of two IL species with similar high antimicrobial activity ([TC₈MA]/[TMC₁₆A]) revealed independently of their anion different modes of action against S. typhimurium through alterations in the bacterial membrane fluidity.

Identification and classification of silks using infrared spectroscopy

Lepidopteran silks number in the thousands and display a vast diversity of structures, properties and industrial potential. To map this remarkable biochemical diversity, we present an identification and screening method based on the infrared spectra of native silk feedstock and cocoons. Multivariate analysis of over 1214 infrared spectra obtained from 35 species allowed us to group silks into distinct hierarchies and a classification that agrees well with current phylogenetic data and taxonomies. This approach also provides information on the relative content of sericin, calcium oxalate, phenolic compounds, poly-alanine and poly(alanine-glycine) β-sheets. It emerged that the domesticated mulberry silkmoth Bombyx mori represents an outlier compared with other silkmoth taxa in terms of spectral properties. Interestingly, Epiphora bauhiniae was found to contain the highest amount of β-sheets reported to date for any wild silkmoth. We conclude that our approach provides a new route to determine cocoon chemical composition and in turn a novel, biological as well as material, classification of silks.

Fourier transform infrared with attenuated total reflectance applied to the discrimination of freshwater planktonic coccoid green microalgae

Despite the recent advances on fine taxonomic discrimination in microorganisms, namely using molecular biology tools, some groups remain particularly problematic. Fine taxonomy of green algae, a widely distributed group in freshwater ecosystems, remains a challenge, especially for coccoid forms. In this paper, we propose the use of the Fourier Transform Infrared (FTIR) spectroscopy as part of a polyphasic approach to identify and classify coccoid green microalgae (mainly order Sphaeropleales), using triplicated axenic cultures. The attenuated total reflectance (ATR) technique was tested to reproducibility of IR spectra of the biological material, a primary requirement to achieve good discrimination of microalgal strains. Spectral window selection was also tested, in conjunction with the first derivative treatment of spectra, to determine which regions of the spectrum provided better separation and clustering of strains. The non-metric multidimensional scaling (NMDS), analysis of similarities (ANOSIM) and hierarchical clusters (HCA), demonstrated a correct discrimination and classification of closely related strains of chlorophycean coccoid microalgae, with respect to currently accepted classifications. FTIR-ATR was highly reproducible, and provided an excellent discrimination at the strain level. The best separation was achieved by analyzing the spectral windows of 1500–1200 cm⁻¹ and 900–675 cm⁻¹, which differs from those used in previously studies for the discrimination of broad algal groups, and excluding spectral regions related to storage compounds, which were found to give poor discrimination. Furthermore, hierarchical cluster analyses have positioned the strains tested into clades correctly, reproducing their taxonomic orders and families. This study demonstrates that FTIR-ATR has great potential to complement classical approaches for fine taxonomy of coccoid green microalgae, though a careful spectrum region selection is needed.

Metabolite profiling and fingerprinting of Suillus species (Basidiomycetes) by electrospray mass spectrometry

The genus Suillus is known for the occurrence of a series of prenylated phenols and boviquinones. The extracts of four different Suillus species [S. bovinus, S. granulatus, S. tridentinus and S.variegatus) were investigated by using rapid ultra-performance Liquid chromatography/electrospray ionization mass spectrometry (UPLC/ESI-MS) and direct infusion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS). While direct infusion ESI-FT-ICR mass spectra give a fast overview concerning the elemental compositions of the compounds and, therefore, hints to the main metabolites, UPLC/ESI-tandem mass spectrometry is shown to be a useful tool for their identification. A principal component analysis (PCA) and hierarchical cluster analysis (HCA) based on the UPLC/ESI-MS clearly showed that the metabolite profiles can be used not only for the identification and classification of such fungi but also as a sophisticated and powerful tool for the chemotaxonomy of fungi. Furthermore, a clear discrimination of various types of biological samples (fruiting bodies versus mycelial cultures) is also possible. The orthogonal partial least squares (OPLS) two-class models of both UPLC/ESI-MS and ESI-FT-ICR-MS possess a clear differentiation of two compared Suillus species representing the between class variation and the within class variation. Based on generated S-plots and Loading plots, statistically significant metabolites could be identified as potential biomarker for one species.

Identification and discrimination of bacteria using Fourier transform infrared spectroscopy

Bacterial spectra were obtained in the wavenumber range of 4000-600 cm(-1) using FTIR spectroscopy. FTIR spectral patterns were analyzed and matched with 16S-rRNA signatures of bacterial strains OS1 and OS2, isolated from oil sludge. Specific spectral bands obtained from OS1 (FJ226761), reference strain Bacillus flexus (ATCC 49095), OS2 (FJ215874) and reference strain Stenotrophomonas maltophilia (ATCC 19861) respectively, suggested that OS1 and ATCC 49095 were closely related whereas OS2 was different. The bands probably represent groups of proteins and lipids of specific bacteria. Separate peaks found in B. flexus were similar to those of OS1. The S. maltophilia (ATCC 19861) and OS2 exhibited a similar peak at 3272 cm(-1). Amide bands (I, II and III) exhibited that OS1 and B. flexus were closely related, but were different from OS2. In the fingerprint region, peak at 1096 cm(-1) and 1360 cm(-1) exhibited the specific fingerprints of OS2 and reference strain S. maltophilia (ATCC 19861), respectively. The specific fingerprint signature was found at 1339 cm(-1) for OS1 and at 1382 cm(-1) for B. flexus ATCC 49095, allowing these two strains of B. flexus to be differentiated. This spectral signature originated from phospholipid and RNA components of the cell. Principle components analysis (PCA) of spectral regions exhibited with distinct sample clusters between Bacillus flexus (ATCC 49095), S. maltophilia (ATCC 19861), OS1 and OS2 in amide and fingerprint region.

Identification of microorganisms by FTIR spectroscopy: perspectives and limitations of the method

Fourier transform infrared (FTIR) spectroscopy was introduced in 1991 as a technique to identify and classify microbes. Since then, it has gained growing interest and has undergone a remarkable evolution. Highly sophisticated spectrometers have been developed, enabling a high sample throughput. Today, the generation of high-quality data in a short time and the application of the technique for rapid and reliable identification of microbes to the species level are well documented. What makes FTIR spectroscopy even more attractive is the fact that spectral information can also be exploited for strain typing purposes, which is particularly important for epidemiological analyses and some technological applications. Accordingly, in recent years, FTIR spectroscopy has been increasingly used for typing and classifying microorganisms below the species level. The intention of this review is to give an overview over current knowledge and strategies of using FTIR spectroscopy for species identification and to describe different approaches for strain typing.

A rapid method for the differentiation of yeast cells grown under carbon and nitrogen-limited conditions by means of partial least squares discriminant analysis employing infrared micro-spectroscopic data of entire yeast cells

This paper shows the ease of application and usefulness of mid-IR measurements for the investigation of orthogonal cell states on the example of the analysis of Pichia pastoris cells. A rapid method for the discrimination of entire yeast cells grown under carbon and nitrogen-limited conditions based on the direct acquisition of mid-IR spectra and partial least squares discriminant analysis (PLS-DA) is described. The obtained PLS-DA model was extensively validated employing two different validation strategies: (i) statistical validation employing a method based on permutation testing and (ii) external validation splitting the available data into two independent sub-sets. The Variable Importance in Projection scores of the PLS-DA model provided deeper insight into the differences between the two investigated states. Hence, we demonstrate the feasibility of a method which uses IR spectra from intact cells that may be employed in a second step as an in-line tool in process development and process control along Quality by Design principles.

Differences among Epilobium and Hypericum species revealed by four IR spectroscopy modes: transmission, KBr tablet, diffuse reflectance and ATR

INTRODUCTION

Quality control of dried plant material in assessments of suitability of herbal medicinal products is of extreme importance. Commonly used procedures for identification of species are time consuming and expensive. The development of multivariate statistical methods has enabled application of vibrational spectroscopy for establishing plant species membership.

OBJECTIVE

To determine which infrared spectroscopy mode gives most informative spectra for plant species identification.

METHODOLOGY

Different modes of infrared spectroscopy were applied for investigation of differences among Epilobium and Hypericum species: diffuse reflectance (DR), attenuated total reflectance (ATR), direct transmission of whole leaves and KBr tablet transmission with comminuted leaves. The same chemometrical methods were applied to all spectra. The informative wave numbers were chosen by one-way analysis of variance. Afterwards the colinearity was reduced with principal component analysis. In the final step the species identification was determined with discriminant analysis.

RESULTS

Transmission and diffuse reflectance mode did not give satisfactory results. Best results for discrimination among Epilobium species were obtained with ATR mode (98%), while best results for Hypericum species were obtained with KBr transmission mode (97%). This might be explained by the morphological properties of the two genera. Epilobium species differ in distribution and morphology of trichomes on the surface of the leaves. Hypericum species differ in structures and secondary metabolites in the interior of the leaves.

CONCLUSION

For best results in using infrared spectroscopy for plant species identification in quality control, the morphological properties of plant material should be taken into consideration.

Influence of cell geometry and number of replicas in the reproducibility of whole cell FTIR analysis

Fourier Transform InfraRed (FTIR) spectroscopy is an increasingly used technique in biology, especially for whole cell metabolomic fingerprint. The reproducibility of this technique is influenced by a large number of factors such as the physiological state of cells, sample manipulation and growth conditions. Evidence exists suggesting that the cell shape and dimension can be further elements to consider in whole cell FTIR analysis. In this study we aimed to address the effect of cell geometry on the FTIR spectra and to define the extent of variability occurring between machine and biological replicas with a standardized protocol. The yeast species Saccharomyces cerevisiae (large oval-shaped cells) and Debaryomyces hansenii (small round shaped cells) were employed for their different morphology. Thirty machine replicas of each were analyzed separately and after averaging in groups of three, showing a three to four-fold reduction of the variability. Similarly, a two-fold reduction of variability was observed when thirty biological replicas of the two yeast species were analyzed. The optimal number of replicas to average was then estimated with a bootstrap-like procedure in which biological and machine replicas were randomly resampled 2000 times and averaged in groups spanning from 2 to 12 replicas. This simulation has shown that little if any advantage can be obtained by increasing the number of replicas over five and that the variability exhibited by the small regular cells of D. hansenii was always roughly half of that displayed by the large S. cerevisiae cells, confirming the results obtained with standard non-bootstrapped averages.

Application of Fourier transform infrared spectroscopy and chemometrics for differentiation of Salmonella enterica serovar Enteritidis phage types

Fourier transform infrared (FT-IR) spectroscopy and chemometric techniques were used to discriminate five closely related Salmonella enterica serotype Enteritidis phage types, phage type 1 (PT1), PT1b, PT4b, PT6, and PT6a. Intact cells and outer membrane protein (OMP) extracts from bacterial cell membranes were subjected to FT-IR analysis in transmittance mode. Spectra were collected over a wavenumber range from 4,000 to 600 cm(-1). Partial least-squares discriminant analysis (PLS-DA) was used to develop calibration models based on preprocessed FT-IR spectra. The analysis based on OMP extracts provided greater separation between the Salmonella Enteritidis PT1-PT1b, PT4b, and PT6-PT6a groups than the intact cell analysis. When these three phage type groups were considered, the method based on OMP extract FT-IR spectra was 100% accurate. Moreover, complementary local models that considered only the PT1-PT1b and PT6-PT6a groups were developed, and the level of discrimination increased. PT1 and PT1b isolates were differentiated successfully with the local model using the entire OMP extract spectrum (98.3% correct predictions), whereas the accuracy of discrimination between PT6 and PT6a isolates was 86.0%. Isolates belonging to different phage types (PT19, PT20, and PT21) were used with the model to test its robustness. For the first time it was demonstrated that FT-IR analysis of OMP extracts can be used for construction of robust models that allow fast and accurate discrimination of different Salmonella Enteritidis phage types.

Classification of select category A and B bacteria by Fourier transform infrared spectroscopy

Fourier transform infrared (FT-IR) spectroscopy historically is a powerful tool for the taxonomic classification of bacteria by genus, species, and strain when they are grown under carefully controlled conditions. Relatively few reports have investigated the determination and classification of pathogens such as the National Institute of Allergy and Infectious Diseases (NIAID) Category A Bacillus anthracis spores and cells (BA), Yersinia species, Francisella tularensis (FT), and Category B Brucella species from FT-IR spectra. We investigated the multivariate statistics classification ability of the FT-IR spectra of viable pathogenic and non-pathogenic NIAID Category A and B bacteria. The impact of different growth media, growth time and temperature, rolling circle filter of the data, and wavelength range were investigated for their microorganism differentiation capability. Viability of the bacteria was confirmed by agar plate growth after the FT-IR experimental procedures were performed. Principal component analysis (PCA) was reduced to maps of two PC vectors in order to distill the FT-IR spectral features into manageable, visual presentations. The PCA results of the strains of BA, FT, Brucella, and Yersinia spectra from conditions of varying growth media and culture time were readily separable in two-dimensional (2D) PC plots. FT spectra were separated from those of the three other genera. The BA pathogenic spore strains 1029, LA1, and Ames were clearly differentiated from the rest of the dataset. Yersinia rhodei, Y. enterocolitica, and Y. pestis species were distinctly separated from the remaining dataset and could also be classified by growth media. Different growth media produced distinct subsets in the FT, BA, and Yersinia spp. regions in the 2D PC plots. Various 2D PC plots provided differential degrees of separation with respect to the four viable bacterial genera including the BA sub-categories of pathogenic spores, vegetative cells, and nonpathogenic vegetative cells. This work provided evidence that FT-IR spectroscopy can indeed separate the four major pathogenic bacterial genera of NIAID Category A and B biological threat agents including details according to the growth conditions and statistical parameters.

Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy

The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular emphasis on the advantageous interaction between multivariate data analysis and MIR spectroscopy.

Antibacterial activity of a bacteriocin-like substance produced by Bacillus sp. P34 that targets the bacterial cell envelope

The objective of this study was to investigate the mode of action of BLS P34, a bacteriocin-like substance (BLS) produced by a novel Bacillus sp. strain P34 isolated from the Amazon basin. The effect of the BLS was tested against Listeria monocytogenes, showing a bactericidal effect at 200 AU (activity units) ml(-1), while no inhibition of spore outgrowth of Bacillus cereus was observed with a dose of 1,600 AU ml(-1). Growth of Escherichia coli and Salmonella Enteritidis was inhibited, but only when the chelating agent EDTA was co-added with the BLS. The effect of BLS P34 on L. monocytogenes was also investigated by Fourier transform infrared spectroscopy. Treated cells showed an important frequency increase in 1,452 and 1,397 cm(-1) and decrease in 1,217 and 1,058 cm(-1), corresponding assignments of fatty acids and phospholipids. Transmission electron microscopy showed damaged cell envelope and loss of protoplasmic material. BLS P34 was bactericidal to Gram-positive, and also showed inhibitory effect against Gram-negative bacteria. There is evidence that its mode of action corresponds to that of a membrane-active substance. The knowledge about the mode of action of this BLS is essential to determine its effective application as an antimicrobial agent.