Compound identification using partial and semipartial correlations for gas chromatography-mass spectrometry data

Compound identification is a key component of data analysis in the applications of gas chromatography-mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and m/z value, respectively.     

A practical toolkit for computational steering

Computational steering refers to the real-time interaction of a scientist with their running simulation code. Despite the many benefits associated with computational steering, its uptake to date has been limited. In this paper we discuss the reasons for this and how the computational steering library and associated tools developed as part of the RealityGrid project aim to tackle them. We describe the functionality of the steering library and the use of Grid services in constructing a generic, dynamic architecture for discovering, steering and connecting visualization software to running simulations. The use of on-line visualization for providing feedback to the scientist is described, including the ways in which it may be enhanced through tools such as Chromium and Access Grid. Finally, we illustrate the flexibility of our approach by describing the functionality that has been added to various simulation codes as part of the RealityGrid project.     

Bioinformatics and mass spectrometry for microorganism identification: proteome-wide post-translational modifications and database search algorithms for characterization of intact H. pylori

MALDI-TOF mass spectrometry has been coupled with Internet-based proteome database search algorithms in an approach for direct microorganism identification. This approach is applied here to characterize intact H. pylori (strain 26695) Gram-negative bacteria, the most ubiquitous human pathogen. A procedure for including a specific and common posttranslational modification, N-terminal Met cleavage, in the search algorithm is described. Accounting for posttranslational modifications in putative protein biomarkers improves the identification reliability by at least an order of magnitude. The influence of other factors, such as number of detected biomarker peaks, proteome size, spectral calibration, and mass accuracy, on the microorganism identification success rate is illustrated as well.     

A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts

Manufacturing of aircraft structural parts has the characteristics of multiple varieties, complex structures and small batches, which make the manufacturing resource allocation highly difficult. This paper proposes a manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation, considering multiple manufacturing resources including process planners, machine tools and cutting tools, as well as manufacturing process schemes of aircraft structural parts. Knowledge in terms of experts’ experience and historical data is used for fuzzy comprehensive evaluation. A manufacturing resource allocation model is proposed based on the analysis of manufacturing processes of aircraft structural parts. The capability of planners, the complexity of structural parts, the reliability of machine tools, the reliability of cutting tools and the correlations between manufacturing resources and structural parts are evaluated using the fuzzy comprehensive evaluation method. Multiple manufacturing resources are allocated based on the fuzzy comprehensive evaluation results. A prototype system has been implemented and a case study is used to validate the proposed approach.     

Effects of binary encoding on pattern recognition and library matching of spectral data

Scott, D.R., 1988. Effects of binary encoding on pattern recognition and library matching of spectral data. Chemometrics and Intelligent Laboratory Systems, 4: 47–63.Binary encoding is frequently employed in pattern recognition studies and matching of unknown against library spectral data. In this study the effect of binary encoding on pattern recognition and library searches is determined using the Hamming and Euclidean distance metrics. The effect on a full intensity spectrum is to compress the total information into a qualitative spectral data vector, the most basic information in the full spectrum. Geometrically, binary encoding of unit normalized spectral data can be visualized as shifting spectral points on the faces of the measurement space hypercube to the corners of a Hamming hypercube. A new classification scheme for comparison of analytical spectra based on their binary encoded spectra is introduced. Quantitative expressions for the effect of binary encoding on general Euclidean distances between spectral points are derived and shown to depend upon their spectral classification. Generally binary encoding increases the interclass distances in pattern recognition and may decrease the intraclass distances. This effect is illustrated with a mass spectral pattern recognition example. The effect of binary encoding on library searches is to produce possible false compound identification in identity searches and to flag spectrally similar compounds in structure searches. A scheme which uses both the Hamming and the Euclidean metrics is proposed for improved library searches. This scheme is illustrated with searches of a small mass spectral library for benzene and p-dioxane spectra.     

Data analysis tool for comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry

Data processing and identification of unknown compounds in comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC/TOFMS) analysis is a major challenge, particularly when large sample sets are analyzed. Herein, we present a method for efficient treatment of large data sets produced by GC×GC/TOFMS implemented as a freely available open source software package, Guineu. To handle large data sets and to efficiently utilize all the features available in the vendor software (baseline correction, mass spectral deconvolution, peak picking, integration, library search, and signal-to-noise filtering), data preprocessed by instrument software are used as a starting point for further processing. Our software affords alignment of the data, normalization, data filtering, and utilization of retention indexes in the verification of identification as well as a novel tool for automated group-type identification of the compounds. Herein, different features of the software are studied in detail and the performance of the system is verified by the analysis of a large set of standard samples as well as of a large set of authentic biological samples, including the control samples. The quantitative features of our GC×GC/TOFMS methodology are also studied to further demonstrate the method performance and the experimental results confirm the reliability of the developed procedure. The methodology has already been successfully used for the analysis of several thousand samples in the field of metabolomics.     

Identification of bacterial spores using statistical analysis of Fourier transform infrared photoacoustic spectroscopy data

Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) has been applied for the first time to the identification and speciation of bacterial spores. A total of forty specimens representing five strains of Bacillus spores (Bacillus subtilis ATCC 49760, Bacillus atrophaeus ATCC 49337, Bacillus subtilis 6051, Bacillus thuringiensis subsp. kurstaki, and Bacillus globigii Dugway) were analyzed. Spores were deposited, with minimal preparation, into the photoacoustic sample cup and their spectra recorded. Principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations were used on this spectral library to develop algorithms for step-wise classification at three levels: (1) bacterial/nonbacterial, (2) membership within the spore library, and (3) bacterial strain. Internal cross-validation studies on library spectra yielded classification success rates of 87% or better at each of these three levels. Analysis of fifteen blind samples, which included five samples of spores already in the spectral library, two samples of closely related Bacillus globigii 01 spores not in the library, and eight samples of nonbacterial materials, yielded 100% accuracy in distinguishing among bacterial/nonbacterial samples, membership in the library, and bacterial strains within the library.     

Novel search algorithms for a mid-infrared spectral library of cotton contaminants

During harvest, a variety of plant based contaminants are collected along with cotton lint. The USDA previously created a mid-infrared, attenuated total reflection (ATR), Fourier transform infrared (FT-IR) spectral library of cotton contaminants for contaminant identification as the contaminants have negative impacts on yarn quality. This library has shown impressive identification rates for extremely similar cellulose based contaminants in cases where the library was representative of the samples searched. When spectra of contaminant samples from crops grown in different geographic locations, seasons, and conditions and measured with a different spectrometer and accessories were searched, identification rates for standard search algorithms decreased significantly. Six standard algorithms were examined: dot product, correlation, sum of absolute values of differences, sum of the square root of the absolute values of differences, sum of absolute values of differences of derivatives, and sum of squared differences of derivatives. Four categories of contaminants derived from cotton plants were considered: leaf, stem, seed coat, and hull. Experiments revealed that the performance of the standard search algorithms depended upon the category of sample being searched and that different algorithms provided complementary information about sample identity. These results indicated that choosing a single standard algorithm to search the library was not possible. Three voting scheme algorithms based on result frequency, result rank, category frequency, or a combination of these factors for the results returned by the standard algorithms were developed and tested for their capability to overcome the unpredictability of the standard algorithms' performances. The group voting scheme search was based on the number of spectra from each category of samples represented in the library returned in the top ten results of the standard algorithms. This group algorithm was able to identify correctly as many test spectra as the best standard algorithm without relying on human choice to select a standard algorithm to perform the searches.     

Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles

Metabolomics may have the capacity to revolutionize disease diagnosis through the identification of scores of metabolites that vary during environmental, pathogenic, or toxicological insult. NMR spectroscopy has become one of the main tools for measuring these changes since an NMR spectrum can accurately identify metabolites and their concentrations. The predominant approach in analyzing NMR data has been through the technique of spectral binning. However, identification of spectral areas in an NMR spectrum is insufficient for diagnostic evaluation, since it is unknown whether areas of interest are strictly caused by metabolic changes or are simply artifacts. In this paper, we explore differences in gender, diurnal variation, and age in a human population. We use the example of gender differences to compare traditional spectral binning techniques (NMR spectral areas) to novel targeted profiling techniques (metabolites and their concentrations). We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences in a healthy population. Metabolites relating to mitochondrial energy metabolism were found to differentiate gender and age. Dietary components and some metabolites related to circadian rhythms were found to differentiate time of day urine collection. The mechanisms by which these differences arise will be key to the discovery of new diagnostic tests and new understandings of the mechanism of disease.     

XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization

Mass spectrometry based metabolomics represents a new area for bioinformatics technology development. While the computational tools currently available such as XCMS statistically assess and rank LC-MS features, they do not provide information about their structural identity. XCMS(2) is an open source software package which has been developed to automatically search tandem mass spectrometry (MS/MS) data against high quality experimental MS/MS data from known metabolites contained in a reference library (METLIN). Scoring of hits is based on a "shared peak count" method that identifies masses of fragment ions shared between the analytical and reference MS/MS spectra. Another functional component of XCMS(2) is the capability of providing structural information for unknown metabolites, which are not in the METLIN database. This "similarity search" algorithm has been developed to detect possible structural motifs in the unknown metabolite which may produce characteristic fragment ions and neutral losses to related reference compounds contained in METLIN, even if the precursor masses are not the same.     

An algorithm for automated bacterial identification using matrix-assisted laser desorption/ionization mass spectrometry

An algorithm for bacterial identification using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry is being developed. This mass spectral fingerprint comparison algorithm is fully automated and statistically based, providing objective analysis of samples to be identified. Based on extraction of reference fingerprint ions from test spectra, this approach should lend itself well to real-world applications where samples are likely to be impure. This algorithm is illustrated using a blind study. In the study, MALDI-MS fingerprints for Bacillus atrophaeus ATCC 49337, Bacillus cereus ATCC 14579T, Escherichia coli ATCC 33694, Pantoea agglomerans ATCC 33243, and Pseudomonas putida F1 are collected and form a reference library. The identification of test samples containing one or more reference bacteria, potentially mixed with one species not in the library (Shewanella alga BrY), is performed by comparison to the reference library with a calculated degree of association. Out of 60 samples, no false positives are present, and the correct identification rate is 75%. Missed identifications are largely due to a weak B. cereus signal in the bacterial mixtures. Potential modifications to the algorithm are presented and result in a higher than 90% correct identification rate for the blind study data, suggesting that this approach has the potential for reliable and accurate automated data analysis of MALDI-MS.     

Microorganism identification by mass spectrometry and protein database searches.

A method for rapid identification of microorganisms is presented, which exploits the wealth of information contained in prokaryotic genome and protein sequence databases. The method is based on determining the masses of a set of ions by MALDI TOF mass spectrometry of intact or treated cells. Subsequent correlation of each ion in the set to a protein, along with the organismic source of the protein, is performed by searching an Internet-accessible protein database. Convoluting the lists for all ions and ranking the organisms corresponding to matched ions results in the identification of the microorganism. The method has been successfully demonstrated on B. subtilis and E. coli, two organisms with completely sequenced genomes. The method has been also tested for identification from mass spectra of mixtures of microorganisms, from spectra of an organism at different growth stages, and from spectra originating at other laboratories. Experimental factors such as MALDI matrix preparation, spectral reproducibility, contaminants, mass range, and measurement accuracy on the database search procedure are addressed too. The proposed method has several advantages over other MS methods for microorganism identification.     

Advanced spectral imaging for noninvasive microanalysis of cultural heritage materials: review of application to documents in the U.S. Library of Congress

Hyperspectral imaging was originally developed for remote sensing and astronomical applications, but adaptations of this technology have been of great benefit to the preservation of cultural heritage. Developments in noninvasive analytical techniques have advanced the preservation of cultural heritage materials by enabling the identification and analysis of a range of materials, utilizing their unique spectral response to nondestructively determine chemical composition, and determining states of deterioration and change due to environmental conditions. When used as a tool for noninvasive characterization of cultural heritage, these spectral imaging systems allow the collection of chemical identification information about materials without sampling, which is a critical factor for cultural heritage materials. The United States Library of Congress has been developing the application of hyperspectral imaging to the preservation and analysis of cultural heritage materials as a powerful noncontact technique. It allows noninvasive characterization of materials, by identifying and characterizing colorants, inks, and substrates with narrow-band illumination to protect the object while also monitoring deterioration or changes due to exhibit and other environmental conditions. Contiguous illumination from the ultraviolet, visible, and infrared spectral regions allows the capture of lost, obscured, and deteriorated information. The resulting image cube allows greater capabilities for mapping and coordinating a range of complementary chemical and spectral analyses. The capabilities of this technique are illustrated by a review of results from analysis of the Waldseemüller World Map, the L'Enfant plan for Washington, D.C., and the first draft of the U.S. Declaration of Independence.     

Metabolite identification using automated comparison of high-resolution multistage mass spectral trees

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.     

基于模型参考自适应算法的非线性结构损伤识别

许多工程结构在遭受飓风、地震之类的动荷载时,存在迟滞非线性现象,因此,对其结构参数进行实时辨识显得尤为必要.针对迟滞非线性结构的力学性质,采用一种基于模型参考自适应算法的参数辨识方法.该方法能够追踪时变的参数,检测损伤,包括损伤的大小和位置,以及损伤发生的时刻.数值仿真的结果表明,该方法对参数变化具有敏感性,非线性结构的仿真结果证实了该方法能够有效检测结构的损伤.     

Gas-phase separations of electrosprayed peptide libraries.

High-resolution ion mobility spectrometry has been combined with time-of-flight mass spectrometry for analysis of a combinatorial peptide library that is expected to contain 676 components. In this approach, the components of a mixture of three residue peptides, having the general form (D)Phe-Xxx-Xxx-CONH2 (where Xxx is randomized over 26 residues including 10 naturally occurring amino acids and 16 synthetic forms) were ionized by electrospray ionization. Ion mobility/time-of-flight distributions have been recorded for all ions using a nested drift(flight) time technique. The improvement in resolving power [(t/delta t) = 100-150 for singly charged ions] was illustrated by analysis of a mixture of tryptic digest peptides using high- and low-resolution instruments. The approach allows many components of the library (e.g., structural, sequence, and stereo isomers) that cannot be distinguished by mass spectrometry alone to be resolved. Impurities due to side reactions appear to be minimal, comprising < 10% of the total ion signal. Direct evidence for approximately 60-70% of the expected peptides is found. Variation in ion abundance for different components indicates that there are differences in solution concentrations or ionization efficiencies for the components.     

Updating robust reliability using structural test data

The concept of robust reliability is defined to take into account uncertainties from structural modeling in addition to the uncertain excitation that a structure will experience during its lifetime. A Bayesian probabilistic methodology for system identification is integrated with probabilistic structural analysis tools for the purpose of updating the assessment of the robust reliability based on dynamic test data. Methods for updating the structural reliability for both identifiable and unidentifiable models are presented. Application of the methodology to a simple beam model of a single-span bridge with soil-structure interaction at the abutments, including a case with a tuned-mass damper attached to the deck, shows that the robust reliabilities computed before and after updating with “measured” dynamic data can differ significantly.     

Laser-induced breakdown spectroscopy analysis of energetic materials

A number of energetic materials and explosives have been studied by laser-induced breakdown spectroscopy (LIBS). They include black powder, neat explosives such as TNT, PETN, HMX, and RDX (in various forms), propellants such as M43 and JA2, and military explosives such as C4 and LX-14. Each of these materials gives a unique spectrum, and generally the spectra are reproducible shot to shot. We observed that the laser-produced microplasma did not initiate any of the energetic materials studied. Extensive studies of black powder and its ingredients by use of a reference spectral library have demonstrated excellent accuracy for unknown identification. Finally, we observed that these nitrogen- and oxygen-rich materials yield LIBS spectra in air that have correspondingly different O:N peak ratios compared with air. This difference can help in the detection and identification of such energetic materials.     

Identification of polymer materials using laser-induced breakdown spectroscopy combined with artificial neural networks

A combination of laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) has been used for the identification of polymer materials, including polypropylene (PP), polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyoxymethylene (POM), polyethylene (PE), polyamide or nylon (PA), polycarbonate (PC) and poly(methyl methacrylate) (PMMA). After optimization of the experimental setup and the spectrum acquisition protocol, successful identification rates between 81 and 100% were achieved using spectral features gathered from single spectra without averaging (1 second acquisition time) over a wide spectral range (240-820 nm). Furthermore, ten different materials based on PVC were tested using the identification procedure. Correct identifications were obtained as well. Sorting of the materials into sub-categories of PVC materials according to their charges (concentration in trace elements such as Ca) was performed. The demonstrated capacities fit, in practice, the needs of plastic-waste sorting and of producing high-grade recycled plastic materials.