Standoff photoacoustic detection of explosives using quantum cascade laser and an ultrasensitive microphone.

Standoff detections of explosives using quantum cascade lasers (QCLs) and the photoacoustic (PA) technique were studied. In our experiment, a mid-infrared QCL with emission wavelength near 7.35 µm was used as a laser source. Direct standoff PA detection of trinitrotoluene (TNT) was achieved using an ultrasensitive microphone. The QCL output light was focused on explosive samples in powder form. PA signals were generated and detected directly by an ultrasensitive low-noise microphone with 1 in. diameter. A detection distance up to 8 in. was obtained using the microphone alone. With increasing detection distance, the measured PA signal not only decayed in amplitude but also presented phase delays, which clearly verified the source location. To further increase the detection distance, a parabolic sound reflector was used for effective sound collection. With the help of the sound reflector, standoff PA detection of TNT with distance of 8 ft was demonstrated.

A protein processing filter method for bacterial identification by mass spectrometry-based proteomics.

A "one-pot" alternative method for processing proteins and isolating peptide mixtures from bacterial samples is presented for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and data reduction. The conventional in-solution digestion of the protein contents of bacteria is compared to a small disposable filter unit placed inside a centrifuge vial for processing and digestion of bacterial proteins. Each processing stage allows filtration of excess reactants and unwanted byproduct while retaining the proteins. Upon addition of trypsin, the peptide mixture solution is passed through the filter while retaining the trypsin enzyme. The peptide mixture is then analyzed by LC-MS/MS with an in-house BACid algorithm for a comparison of the experimental unique peptides to a constructed proteome database of bacterial genus, specie, and strain entries. The concentration of bacteria was varied from 10 × 10(7) to 3.3 × 10(3) cfu/mL for analysis of the effect of concentration on the ability of the sample processing, LC-MS/MS, and data analysis methods to identify bacteria. The protein processing method and dilution procedure result in reliable identification of pure suspensions and mixtures at high and low bacterial concentrations.

Identification of Yersinia pestis and Escherichia coli strains by whole cell and outer membrane protein extracts with mass spectrometry-based proteomics.

Whole cell protein and outer membrane protein (OMP) extracts were compared for their ability to differentiate and delineate the correct database organism to an experimental sample and for the degree of dissimilarity to the nearest neighbor database organism strains. These extracts were isolated from pathogenic and nonpathogenic strains of Yersinia pestis and Escherichia coli using ultracentrifugation and a sarkosyl extraction method followed by protein digestion and analysis using liquid chromatography tandem mass spectrometry (MS). Whole cell protein extracts contain many different types of proteins resident in an organism at a given phase in its growth cycle. OMPs, however, are often associated with virulence in Gram-negative pathogens and could prove to be model biomarkers for strain differentiation among bacteria. The mass spectra of bacterial peptides were searched, using the SEQUEST algorithm, against a constructed proteome database of microorganisms in order to determine the identity and number of unique peptides for each bacterial sample. Data analysis was performed with the in-house BACid software. It calculated the probabilities that a peptide sequence assignment to a product ion mass spectrum was correct and used accepted spectrum-to-sequence matches to generate a sequence-to-bacterium (STB) binary matrix of assignments. Validated peptide sequences, either present or absent in various strains (STB matrices), were visualized as assignment bitmaps and analyzed by the BACid module that used phylogenetic relationships among bacterial species as part of a decision tree process. The bacterial classification and identification algorithm used assignments of organisms to taxonomic groups (phylogenetic classification) based on an organized scheme that begins at the phylum level and follows through the class, order, family, genus, and species to the strain level. For both Gram-negative organisms, the number of unique distinguishing proteins arrived at by the whole cell method was less than that of the OMP method. However, the degree of differentiation measured in linkage distance units on a dendrogram with the OMP extract showed similar or significantly better separation than the whole cell protein extract method between the sample and correct database match compared to the next nearest neighbor. The nonpathogenic Y. pestis A1122 strain used does not have its genome available, and thus, data analysis resulted in an equal similarity index to the nonpathogenic 91001 and pathogenic Antiqua and Nepal 516 strains for both extraction methods. Pathogenic and nonpathogenic strains of E. coli were correctly identified with both protein extraction methods, and the pathogenic Y. pestis CO92 strain was correctly identified with the OMP procedure. Overall, proteomic MS proved useful in the analysis of unique protein assignments for strain differentiation of E. coli and Y. pestis. The power of bacterial protein capture by the whole cell protein and OMP extraction methods was highlighted by the data analysis techniques and revealed differentiation and similarities between the two protein extraction approaches for bacterial delineation capability.

Discrimination and phylogenomic classification of Bacillus anthracis-cereus-thuringiensis strains based on LC-MS/MS analysis of whole cell protein digests.

Modern taxonomy, diagnostics, and forensics of bacteria benefit from technologies that provide data for genome-based classification and identification of strains; however, full genome sequencing is still costly, lengthy, and labor intensive. Therefore, other methods are needed to estimate genomic relatedness among strains in an economical and timely manner. Although DNA-DNA hybridization and techniques based on genome fingerprinting or sequencing selected genes like 16S rDNA, gyrB, or rpoB are frequently used as phylogenetic markers, analyses of complete genome sequences showed that global measures of genome relatedness, such as the average genome conservation of shared genes, can provide better strain resolution and give phylogenies congruent with relatedness revealed by traditional phylogenetic markers. Bacterial genomes are characterized by a high gene density; therefore, we investigated the integration of mass spectrometry-based proteomic techniques with statistical methods for phylogenomic classification of bacterial strains. For this purpose, we used a set of well characterized Bacillus cereus group strains isolated from poisoned food to describe a method that relies on liquid chromatography-electrospray ionization-tandem mass spectrometry of tryptic peptides derived from whole cell digests. Peptides were identified and matched to a prototype database (DB) of reference bacteria with fully sequenced genomes to obtain their phylogenetic profiles. These profiles were processed for predicting genomic similarities with DB bacteria estimated by fractions of shared peptides (FSPs). FSPs served as descriptors for each food isolate and were jointly analyzed using hierarchical cluster analysis methods for revealing relatedness among investigated strains. The results showed that phylogenomic classification of tested food isolates was in consonance with results from established genomic methods, thus validating our findings. In conclusion, the proposed approach could be used as an alternative method for predicting relatedness among microbial genomes of B. cereus group members and potentially may circumvent the need for whole genome sequencing for phylogenomic typing of strains.

Double-blind characterization of non-genome-sequenced bacteria by mass spectrometry-based proteomics.

Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized double-blind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.

Bioaerosol analysis with Raman chemical imaging microspectroscopy.

Raman chemical imaging microspectroscopy is evaluated as a technology for waterborne pathogen and bioaerosol detection. Raman imaging produces a three-dimensional data cube consisting of a Raman spectrum at every pixel in a microscope field of view. Binary and ternary mixtures including combinations of polystyrene beads, gram-positive Bacillus anthracis, B. thuringiensis, and B. atrophaeus spores, and B. cereus vegetative cells were investigated by Raman imaging for differentiation and characterization purposes. Bacillus spore aerosol sizes were varied to provide visual proof for corroboration of spectral assignments. Conventional applications of Raman imaging consist of differentiating relatively broad areas of a sample in a microscope field of view. The spectral angle mapping data analysis algorithm was used to compare a library spectrum with experimental spectra from pixels in the microscope field of view. This direct one-to-one matching is straightforward, does not require a training set, is independent of absolute spectral intensity, and only requires univariate statistics. Raman imaging is expanded in its capabilities to differentiate and distinguish between discrete 1-6 microm size bacterial species in single particles, clusters of mixed species, and bioaerosols with interference background particles.

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.

Waterborne pathogen detection using Raman spectroscopy.

Raman spectroscopy is being evaluated as a candidate technology for waterborne pathogen detection. We have investigated the impact of key experimental and background interference parameters on the bacterial species level identification performance of Raman detection. These parameters include laser-induced photodamage threshold, composition of water matrix, and organism aging in water. The laser-induced photodamage may be minimized by operating a 532 nm continuous wave laser excitation at laser power densities below 2300 W/cm(2) for Grampositive Bacillus atrophaeus (formerly Bacillus globigii, BG) vegetative cells, 2800 W/cm(2) for BG spores, and 3500 W/cm(2) for Gram-negative E. coli (EC) organisms. In general, Bacillus spore microorganism preparations may be irradiated with higher laser power densities than the equivalent Bacillus vegetative preparations. In order to evaluate the impact of background interference and organism aging, we selected a biomaterials set comprising Gram-positive (anthrax simulants) organisms, Gram-negative (plague simulant) organisms, and proteins (toxin simulants) and constructed a Raman signature classifier that identifies at the species level. Subsequently, we evaluated the impact of tap water and storage time in water (aging) on the classifier performance when characterizing B. thuringiensis spores, BG spores, and EC cell preparations. In general, the measured Raman signatures of biological organisms exhibited minimal spectral variability with respect to the age of a resting suspension and water matrix composition. The observed signature variability did not substantially degrade discrimination performance at the genus and species levels. In addition, Raman chemical imaging spectroscopy was used to distinguish a mixture of BG spores and EC cells at the single cell level.

Mid-infrared emission from laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is a powerful analytical technique for detecting and identifying trace elemental contaminants by monitoring the visible atomic emission from small plasmas. However, mid-infrared (MIR), generally referring to the wavelength range between 2.5 to 25 microm, molecular vibrational and rotational emissions generated by a sample during a LIBS event has not been reported. The LIBS investigations reported in the literature largely involve spectral analysis in the ultraviolet-visible-near-infrared (UV-VIS-NIR) region (less than 1 microm) to probe elemental composition and profiles. Measurements were made to probe the MIR emission from a LIBS event between 3 and 5.75 microm. Oxidation of the sputtered carbon atoms and/or carbon-containing fragments from the sample and atmospheric oxygen produced CO(2) and CO vibrational emission features from 4.2 to 4.8 microm. The LIBS MIR emission has the potential to augment the conventional UV-VIS electronic emission information with that in the MIR region.

Classification and identification of bacteria using mass spectrometry-based proteomics.

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.

Variable ranking based on the estimated degree of separation for two distributions of data by the length of the receiver operating characteristic curve.

Variable responses are fundamental for all experiments, and they can consist of information-rich, redundant, and low signal intensities. A dataset can consist of a collection of variable responses over multiple classes or groups. Usually some of the variables are removed in a dataset that contain very little information. Sometimes all the variables are used in the data analysis phase. It is common practice to discriminate between two distributions of data; however, there is no formal algorithm to arrive at a degree of separation (DS) between two distributions of data. The DS is defined herein as the average of the sum of the areas from the probability density functions (PDFs) of A and B that contain a≥percentage of A and/or B. Thus, DS90 is the average of the sum of the PDF areas of A and B that contain ≥90% of A and/or B. To arrive at a DS value, two synthesized PDFs or very large experimental datasets are required. Experimentally it is common practice to generate relatively small datasets. Therefore, the challenge was to find a statistical parameter that can be used on small datasets to estimate and highly correlate with the DS90 parameter. Established statistical methods include the overlap area of the two data distribution profiles, Welch's t-test, Kolmogorov-Smirnov (K-S) test, Mann-Whitney-Wilcoxon test, and the area under the receiver operating characteristics (ROC) curve (AUC). The area between the ROC curve and diagonal (ACD) and the length of the ROC curve (LROC) are introduced. The established, ACD, and LROC methods were correlated to the DS90 when applied on many pairs of synthesized PDFs. The LROC method provided the best linear correlation with, and estimation of, the DS90. The estimated DS90 from the LROC (DS90-LROC) is applied to a database, as an example, of three Italian wines consisting of thirteen variable responses for variable ranking consideration. An important highlight of the DS90-LROC method is utilizing the LROC curve methodology to test all variables one-at-a-time with all pairs of classes in a dataset.

Extracellular protein biomarkers for the characterization of enterohemorrhagic and enteroaggregative Escherichia coli strains.

The extracellular proteins (ECPs) of enterohemorrhagic Escherichia coli (EHEC) can cause hemorrhagic colitis which may cause life threatening hemolytic-uremic syndrome, while that of enteroaggregative E. coli (EAEC) can clump to intestinal membranes. Liquid chromatography-electrospray ionization-tandem mass spectrometry based proteomics is used to evaluate a preliminary study on the extracellular and whole cell protein extracts associated with E. coli strain pathogenicity. Proteomics analysis, which is independent of genomic sequencing, of EAEC O104:H4 (unsequenced genome) identified a number of proteins. Proteomics of EHEC O104:H4, causative agent of the Germany outbreak, showed a closest match with E. coli E55989, in agreement with genomic studies. Dendrogram analysis separated EHEC O157:H7 and EHEC/EAEC O104:H4. ECP analysis compared to that of whole cell processing entails few steps and convenient experimental extraction procedures. Bacterial characterization results are promising in exploring the impact of environmental conditions on E. coli ECP biomarkers with a few relatively straightforward protein extraction steps.

Mid-infrared, long wave infrared (4-12 µm) molecular emission signatures from pharmaceuticals using laser-induced breakdown spectroscopy (LIBS).

In an effort to augment the atomic emission spectra of conventional laser-induced breakdown spectroscopy (LIBS) and to provide an increase in selectivity, mid-wave to long-wave infrared (IR), LIBS studies were performed on several organic pharmaceuticals. Laser-induced breakdown spectroscopy signature molecular emissions of target organic compounds are observed for the first time in the IR fingerprint spectral region between 4-12 µm. The IR emission spectra of select organic pharmaceuticals closely correlate with their respective standard Fourier transform infrared spectra. Intact and/or fragment sample molecular species evidently survive the LIBS event. The combination of atomic emission signatures derived from conventional ultraviolet-visible-near-infrared LIBS with fingerprints of intact molecular entities determined from IR LIBS promises to be a powerful tool for chemical detection.

Long-wave, infrared laser-induced breakdown (LIBS) spectroscopy emissions from energetic materials.

Laser-induced breakdown spectroscopy (LIBS) has shown great promise for applications in chemical, biological, and explosives sensing and has significant potential for real-time standoff detection and analysis. In this study, LIBS emissions were obtained in the mid-infrared (MIR) and long-wave infrared (LWIR) spectral regions for potential applications in explosive material sensing. The IR spectroscopy region revealed vibrational and rotational signatures of functional groups in molecules and fragments thereof. The silicon-based detector for conventional ultraviolet-visible LIBS operations was replaced with a mercury-cadmium-telluride detector for MIR-LWIR spectral detection. The IR spectral signature region between 4 and 12 µm was mined for the appearance of MIR and LWIR-LIBS emissions directly indicative of oxygenated breakdown products as well as dissociated, and/or recombined sample molecular fragments. Distinct LWIR-LIBS emission signatures from dissociated-recombination sample molecular fragments between 4 and 12 µm are observed for the first time.

Mass spectrometry-based proteomics combined with bioinformatic tools for bacterial classification.

Timely classification and identification of bacteria is of vital importance in many areas of public health. We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this method, a bacterial proteome database is derived from all potential protein coding open reading frames (ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides obtained by LC-ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the microorganism database. It is shown that, if a sufficient amount of sequence information is obtained from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this proteomics method, leading to its positive identification.

Qualitative, Quantitative and Technological Aspects of the Trichothecene Mycotoxins.

Trichothecene mycotoxins pose a natural threat to plants, foodstuffs, animals and humans. Recently, strong implications regarding artificially induced trichothecene threats to humans in various parts of the world have come to the attention of the general public. This has spawned renewed interest and scientific research into the various properties of the toxins. The trichothecenes display orders of magnitude differences in toxicity levels depending upon the test subject and mode of administration. Potentially more sensitive and specific analytical characterization techniques and convenient, milder and faster organic decontamination reaction schemes exist in comparison to established methods. This review attempts to supply a concise information source as an aid to investigators faced with problems of trichothecene detection, analysis, and decontamination.

Bacterial identification by protein mass mapping combined with an experimentally derived protein mass database.

A protein mass mapping approach using mass spectrometry (MS) combined with an experimentally derived protein mass database is presented for rapid and effective identification of bacterial species. A prototype mass database from the protein extracts of nine bacterial species has been created by off-line high-performance liquid chromatography (HPLC) matrix-assisted laser desorption/ionization (MALDI) MS, in which the microbiological parameter of bacterial growth time is considered. A numerical method using a statistical weight factor algorithm is devised for matching the protein masses of an unknown bacterial sample against the database. The sum of these weight factors produces a corresponding summed weight factor score for each bacterial species listed in the database, and the database species producing the highest score represents the identity of the respective unknown bacterium. The applicability and reliability of this protein mass mapping approach has been tested with seven bacterial species in a single-blind study by both direct MALDI MS and HPLC electrospray ionization MS methods, and identification results with 100% accuracy are obtained. Our studies have demonstrated that the protein mass database can be rapidly established and readily adopted with relatively less dependency on experimental factors. Furthermore, it is shown that a number of proteins can be detected using a protein sample amount equivalent to an extract of less than 1000 cells, demonstrating that this protein mass mapping approach can potentially be highly sensitive for rapid bacterial identification.

Identification of bacteria using tandem mass spectrometry combined with a proteome database and statistical scoring.

Detection and identification of pathogenic bacteria and their protein toxins play a crucial role in a proper response to natural or terrorist-caused outbreaks of infectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens new diagnostic possibilities for identification of bacteria by retrieving their genomic or proteomic information. We describe a method for identification of bacteria based on tandem mass spectrometric (MS/MS) analysis of peptides derived from bacterial proteins. This method involves bacterial cell protein extraction, trypsin digestion, liquid chromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MS spectral matching results for bacterial identification. To facilitate spectral data searching, a proteome database was constructed by translating genomes of bacteria of interest with fully or partially determined sequences. In this work, a prototype database was constructed by the automated analysis of 87 publicly available, fully sequenced bacterial genomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done by SEQUEST. To gauge the relative significance of the SEQUEST-generated matching parameters for correct peptide assignment, discriminant function (DF) analysis of these parameters was applied and DF scores were used to calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. The peptides with DF scores exceeding a threshold value determined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filtering or removal of degenerate peptides matched with multiple bacteria was then performed to further improve identification. It is demonstrated that using a preset criterion with known distributions of discriminant function scores and probabilities of correct peptide sequence assignments, a test bacterium within the 87 database microorganisms can be unambiguously identified.

Correlation of mass spectrometry identified bacterial biomarkers from a fielded pyrolysis-gas chromatography-ion mobility spectrometry biodetector with the microbiological gram stain classification scheme.

A pyrolysis-gas chromatography-ion mobility spectrometry (Py-GC-IMS) briefcase system has been shown to detect and classify deliberately released bioaerosols in outdoor field scenarios. The bioaerosols included Gram-positive and Gram-negative bacteria, MS-2 coliphage virus, and ovalbumin protein species. However, the origin and structural identities of the pyrolysate peaks in the GC-IMS data space, their microbiological information content, and taxonomic importance with respect to biodetection have not been determined. The present work interrogates the identities of the peaks by inserting a time-of-flight mass spectrometry system in parallel with the IMS detector through a Tee connection in the GC module. Biological substances producing ion mobility peaks from the pyrolysis of microorganisms were identified by their GC retention time, matching of their electron ionization mass spectra with authentic standards, and the National Institutes for Standards and Technology mass spectral database. Strong signals from 2-pyridinecarboxamide were identified in Bacillus samples including Bacillus anthracis, and its origin was traced to the cell wall peptidoglycan macromolecule. 3-Hydroxymyristic acid is a component of lipopolysaccharides in the cell walls of Gram-negative organisms. The Gram-negative Escherichia coli organism showed significant amounts of 3-hydroxymyristic acid derivatives and degradation products in Py-GC-MS analyses. Some of the fatty acid derivatives were observed in very low abundance in the ion mobility spectra, and the higher boiling lipid species were absent. Evidence is presented that the Py-GC-ambient temperature and pressure-IMS system generates and detects bacterial biochemical information that can serve as components of a biological classification scheme directly correlated to the Gram stain reaction in microorganism taxonomy.