Curie-point Pyrolysis Mass Spectrometry Research Articles

A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data

Many analytical approaches such as mass spectrometry generate large amounts of data (input variables) per sample analysed, and not all of these variables are important or related to the target output of interest. The selection of a smaller number of variables prior to sample classification is a widespread task in many research studies, where attempts are made to seek the lowest possible set of variables that are still able to achieve a high level of prediction accuracy; in other words, there is a need to generate the most parsimonious solution when the number of input variables is huge but the number of samples/objects are smaller. Here, we compare several different variable selection approaches in order to ascertain which of these are ideally suited to achieve this goal. All variable selection approaches were applied to the analysis of a common set of metabolomics data generated by Curie-point pyrolysis mass spectrometry (Py-MS), where the goal of the study was to classify the Gram-positive bacteria Bacillus. These approaches include stepwise forward variable selection, used for linear discriminant analysis (LDA); variable importance for projection (VIP) coefficient, employed in partial least squares-discriminant analysis (PLS-DA); support vector machines-recursive feature elimination (SVM-RFE); as well as the mean decrease in accuracy and mean decrease in Gini, provided by random forests (RF). Finally, a double cross-validation procedure was applied to minimize the consequence of overfitting. The results revealed that RF with its variable selection techniques and SVM combined with SVM-RFE as a variable selection method, displayed the best results in comparison to other approaches.

A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification of Bacillus species.

BackgroundThe rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS.ResultsWe develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features.ConclusionsThis final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA.

Evaluation of collagen and lipid contents and texture of meat by Curie point pyrolysis–mass spectrometry

The aim of this study was to evaluate the potential of Curie point pyrolysis–mass spectrometry (CpPy–MS) to estimate the collagen and lipids contents and the texture of lamb meat. The work was carried out using the longissimus muscle from 120 commercial lambs originating from different European countries and from contrasting production systems. Meat characteristics varied largely between animals, e.g. 582 (S.D. ± 124) μg hydroxyproline g −1 for collagen content and 22.5 (S.D. ± 9.6) mg g −1 for lipid content, and 2.9 (S.D. ± 1.2) kg cm −2 for shear force value. Non-linear models of prediction were calculated by Artificial Neural Networks (ANNs). The results showed that it is possible to evaluate lipid and collagen contents and meat texture from CpPy–MS fingerprints. The best predictions obtained with test samples were for lipids ( r = 0.89; mean prediction error = 10%), for collagen ( r = 0.85; mean prediction error = 11%) and for texture ( r = 0.78; mean prediction error = 12%).

Curie point pyrolysis–mass spectrometry applied to rapid characterisation of cheeses

The use of Curie-point pyrolysis coupled with mass spectrometry (CpPy–MS) for the rapid characterisation of ‘Camembert’ type cheeses is described. After testing three methods of sample preparation, 5 g of cheese was homogenised in a solution of sodium deoxycholate (3 g l −1). Two microlitres of the resulting cheese suspension was placed on an iron–nickel alloy foil and pyrolysed at 530 °C. The volatile pyrolysis products were then ionised by electron ionisation at 34 eV. The mass spectra thus obtained were considered as ‘fingerprints’ of the analysed cheeses. After several data pre-processing steps a forward stepwise factorial discriminant analysis was used to select a reduced number of mass fragments that allowed a satisfactory classification of the five batches of cheese studied.

Pyrolysis-mass spectrometry for rapid classification of oysters according to rearing area

Current concern for the safety and traceability of food, as well as the desire of oyster farmers, for marketing reason, to emphasise the geographical origin of their production, requires new methods to make possible a real product identification. I n this study, 181 oyster samples were analysed to determine their origin area. These samples were collected in nine French rear- ing areas at four different times of the year (spring, summer, and the beginning and end of autumn) and from four to eight sites in each area to provide a variability parameter. Analysis of fingerprints after Curie point pyrolysis-mass spectrometry, by an a rti- ficial neural network gave a mean classification rate of 89 %. Although the technique requires further improvements, it appears to be a useful discriminative tool for rapid identification of an oyster production area.

Rapid characterisation of deep-sea actinomycetes for biotechnology screening programmes.

A continual need in natural product discovery is dereplication, that is the ability to exclude previously tested microorganisms from screening programmes. Whole-cell fingerprinting techniques offer an ideal solution to this problem because of their rapidity and reproducibility, dependence on small samples, and automation. One such technique, Curie-point pyrolysis mass spectrometry (PyMS), has been deployed for the characterisation of a unique collection of actinomycetes recovered from Pacific Ocean sediments approximately 2000 to 6500 m below sea level. This paper addresses the question: to what extent are pyrogroups, defined on the basis of PyMS fingerprinting, related to classifications derived from more conventional microbial systematics? A collection of 44 randomly chosen deep-sea rhodococci were coded and subjected to a double-blind PyMS and numerical taxonomic (NT) analysis; the latter sorted the strains into clusters (taxospecies) using large sets of equally weighted phenotypic data. At the end of the experiment the codes were disclosed and the NT classification shown to generate 6 homogeneous clusters corresponding to different deep-sea sites. The matching of these clusters with the resulting pyrogroups was very high with an overall congruence of nearly 98%. Thus, PyMS characterisation is directly ascribable to the phenotypic variation being sought for biotechnology screens. Moreover, the exquisite discriminatory power of PyMS readily revealed infraspecific diversity in these industrially important bacteria.

Detection of the dipicolinic acid biomarker in Bacillus spores using Curie-point pyrolysis mass spectrometry and Fourier transform infrared spectroscopy.

Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.

The detection of caffeine in a variety of beverages using Curie-point pyrolysis mass spectrometry and genetic programming

Freeze dried coffee, filter coffee, tea and cola were analysed by Curie-point pyrolysis mass spectrometry (PyMS). Cluster analysis showed, perhaps not surprisingly, that the discrimination between coffee, tea and cola was very easy. However, cluster analysis also indicated that there was a secondary difference between these beverages which could be attributed to whether they were caffeine-containing or decaffeinated. Artificial neural networks (ANNs) could be trained, with the pyrolysis mass spectra from some of the freeze dried coffees, to classify correctly the caffeine status of the unseen spectra of freeze dried coffee, filter coffee, tea and cola in an independent test set. However, the information in terms of which masses in the mass spectrum are important was not available, which is why ANNs are often perceived as a ‘black box’ approach to modelling spectra. By contrast, genetic programs (GPs) could also be used to classify correctly the caffeine status of the beverages, but which evolved function trees (or mathematical rules) enabling the deconvolution of the spectra and which highlighted that m/z 67, 109 and 165 were the most significant masses for this classification. Moreover, the chemical structure of these mass ions could be assigned to the reproducible pyrolytic degradation products from caffeine.

Amino acid and oligopeptide analysis using Curie-point pyrolysis mass spectrometry with in-situ thermal hydrolysis and methylation: mechanistic considerations

In-situ thermal hydrolysis and methylation-mass spectrometry has been used to study amino acids and oligopeptides containing glycine and alanine. Using tetramethylammonium hydroxide (TMAH) during Curie-point pyrolysis, methylation occurred on groups containing acidic hydrogen moeities (O–H and N–H groups). Multiple methylation reactions were found to occur on compounds containing more than one such functionality. Thermal dehydration reactions to form cyclic dipeptides were observed for oligopeptides as well as amino acids during pyrolysis with TMAH. The methylation and thermal dehydration mechanisms are discussed. In addition, the decomposition mechanisms of amino acids and oligopeptides are presented.

Activated sludge foaming: the true extent of actinomycete diversity

Isolates from activated sludge foam were provisionally assigned to the genera Gordona and Tsukamurella on the basis of colony morphology and pigmentation. Representatives of the first group were compared with marker strains of validly described species of Gordona by using Curie-point pyrolysis mass spectrometry. Most of the isolates fell into a number of taxa which were equated with groups of marker strains which corresponded to the known Gordona species. In a corresponding experiment the activated sludge isolates, which were provisionally labelled “Tsukamurella spumae” formed a group which was well separated from the marker strains of the genus. Representatives of the isolates were found to have chemical properties consistent with their classification in the genus Tsukamurella. 16S rDNA sequence data, when taken with previous DNA:DNA relatedness results, suggest that Tsukamurella paurometabola is heterogeneous. Similarly, the sequence data provide some evidence that “Tsukamurella spumae” may merit recognition as a novel species.

Activated sludge foaming: The true extent of actinomycete diversity

Isolates from activated sludge foam were provisionally assigned to the genera Gordona and Tsukamurella on the basis of colony morphology and pigmentation. Representatives of the first group were compared with marker strains of validly described species of Gordona by using Curie-point pyrolysis mass spectrometry. Most of the isolates fell into a number of taxa which were equated with groups of marker strains which corresponded to the known Gordona species. In a corresponding experiment the activated sludge isolates, which were provisionally labelled –Tsukamurella spumae– formed a group which was well separated from the marker strains of the genus. Representatives of the isolates were found to have chemical properties consistent with their classification in the genus Tsukamurella. 16S rDNA sequence data, when taken with previous DNA:DNA relatedness results, suggest that Tsukamurella paurometabola is heterogeneous. Similarly, the sequence data provide some evidence that “Tsukamurella spumae” may merit recognition as a novel species.

Biodiversity of bradyrhizobia nodulating Lupinus spp.

The genetic structure of Bradyrhizobium isolates recovered from three Lupinus species (Lupinus campestris, Lupinus montanus, and Lupinus exaltatus) grown in Mexico was examined. Among 41 Bradyrhizobium isolates, 18 electrophoretic types (ETs) were distinguished by multilocus enzyme electrophoresis of five metabolic enzymes. The mean genetic diversity, 0.64, indicated that there was great genetic diversity in the population sampled. Most isolates (63%) fell into two closely related clusters (clusters I and II) and were the types most frequently isolated from the root nodules of L. montanus and L. campestris. ET cluster III isolates were frequent nodule occupants of L. exaltatus. The isolates also were assigned to three main groups by using Curie point pyrolysis mass spectrometry. In general, the multilocus enzyme electrophoretic data and pyrolysis mass spectrometric data agreed. We determined the 16S rRNA sequences of representative Lupinus isolates and of Bradyrhizobium japonicum USDA 6T and found that the lupine isolates were highly related to the B. japonicum type strain, although not all B. japonicum type strains (subcultures maintained in different bacterial collections) had identical small-subunit rRNA.

97/03369 Sample preparation techniques for filtration testing of fly ash with nonwoven geotextiles

Samples obtained from the high-volatile bituminous Indiana cuticle-rich and paper coals (Indiana U.S.A.) were investigated by white light and blue light microscopy and by Curie point pyrolysis-mass spectrometry in combination with multivariate data analysis, Curie point pyrolysis-gas chromatography and Curie point pyrolysis-gas chromatographymass spectrometry. Samples obtained from several sites and of different degrees of weathering were analyzed. The macerai cutinite from the same samples was studied as well. The cutinite was concentrated by a standard palynological chemical oxidation technique as well as by density gradient centrifugation. The major constituent of the Indiana paper coal samples appeared to be a recently discovered non-saponifiable polymethylenic biopolymer, present in both recent and fossil plant cuticles. The maceral cutinite is almost exclusively made up of this highly resistant biopolymer. It is shown that upon severe natural weathering this biopolymer, because of its resistance against organic alteration and diagenesis, is relatively enriched and manifests itself in humic coals as the maceral cutinite.

Determination of the geographical origin of Italian extra virgin olive oil using pyrolysis mass spectrometry and artificial neural networks

Olives were collected from five important regions of Italy, from as many cultivars and locales as possible. For each region a number of samples were produced, representative of the area as a whole. Once collected the olives were washed and processed using standard methods within the ISE olive mill to produce DOC extra virgin olive oils of known region, province and variety (cultivar). These oils were analysed in triplicate by Curie-point pyrolysis mass spectrometry and the spectra collected. Spectra were normalised and sorted according to region. The data-splitting program, Multiplex (A. Jones, D.B. Kell and J. Rowland, Submitted to Analytica Chimica Acta (1996)) was used to sort the spectra into training and test sets split in the ratio 2:1 for Abruzzo:Sardinia and Apulia:Sardinia predictions and a ratio of 1:1 for Lazio:Sicily. Using artificial neural nets with a single output that represented the network's estimation of the geographical provenance as a numeric code, all unknown samples (as triplicates) from an Abruzzo/Sardinia challenge were successfully identified. Samples form a Lazio/Sicily were successfully predicted/separated when the outputs from the network for each triplicates were averaged, and Apulia/Sardinia were predicted with only a single error for each region. This represents the first report in which the precision and discrimination of pyrolysis mass spectrometry has been shown, when combined with artificial neural networks, to allow the discrimination of olive oils by region.

Quantitative analysis of the adulteration of orange juice with sucrose using pyrolysis mass spectrometry and chemometrics

A 10% (w/v) beet sucrose solution was used to adulterate freshly squeezed orange juice over the range 0–20% (or 0–20 g l −1 of added sucrose). Samples were analysed by the rapid automated screening technique of Curie-point pyrolysis mass spectrometry (PyMS). To deconvolve these spectra neural cognition-based methods of multilayer perceptrons (MLPs) and radial basis functions (RBFs) and the linear regression technique of partial least squares (PLS) were studied. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the level of sucrose adulteration at levels below 1% for samples, with an accuracy of ± 1.3%, on which they had not been trained. The best results were obtained using PLS when 8 latent variables were employed for predictions. Furthermore, the inputs to MLPs could be reduced using principal components analysis (PCA) from 150 masses to 8 PC scores without any deterioration of the predictive ability of the model, highlighting that PCA is an excellent pre-processing step which has the potential to speed up neural network learning as there are fewer weights to update. Since any foodstuff can be pyrolysed in this way, the combination of PyMS with chemometrics constitutes a rapid, powerful and novel approach to the quantitative assessment of food adulteration generally.

Evaluation of Streptomyces Species-Groups by Pyrolysis Mass Spectrometry

Curie-point pyrolysis mass spectrometry was used to evaluate the taxonomic integrity of three subclusters, provisionally labelled S. albidoflavus, S. anulatus and S. halstedii, which formed a species-group in an extensive numerical phenetic survey of the genus Streptomyces. Excellent agreement was found between the results of the triplicate analyses of each strain while the duplicated strains clustered adjacent to one another. Sequential principal component-canonical variates analysis of the experimental data collected on the 32 representative organisms showed that the S. albidoflavus strains formed a distinct group. This result taken together with earlier chemical, molecular and numerical taxonomic data indicates that the S. albidoflavus subcluster corresponds to a distinct species. In contrast, the subclusters equated with S. anulatus and S. halstedii were found to be heterogeneous and hence in need of further study. However, it is evident from the present investigation that Curie-point pyrolysis mass spectrometry provides a rapid and reproducible way of evaluating the taxonomic integrity of Streptomyces species-groups.

Differentiation of Mycobacterium senegalense from Related Non-chromogenic Mycobacteria Using Pyrolysis Mass Spectrometry

Twenty-six representative strains of Mycobacterium chelonae, Mycobacterium fortuitum, Mycobacterium peregrinum and Mycobacterium senegalense were compared by Curie point pyrolysis mass spectrometry. The M. chelonae and M. senegalense strains formed distinct groups. A third, relatively diffuse group, contained the M. fortuitum and M. peregrinum strains. These results, together with those from corresponding analyses, suggest that pyrolysis mass spectrometry provides a rapid and reliable way of distinguishing between members of closely related mycobacterial species which are difficult to differentiate using conventional taxonomic procedures.

Typing of the Fish Pathogen Listonella (Vibrio) anguillara by Pyrolysis Mass Spectrometry

Twenty-eight representatives of Listonella (Vibrio) anguillara serovars O1, O2 and O3 were compared by Curie-point pyrolysis mass spectrometry (PyMS). The representatives of serovars O1 and O3 formed discrete, homogeneous groups in ordination plots of the PyMS data. Strains from serovar O2 were recovered in two groups, one of which encompassed six strains including the type strain of the species and the reference strain for serovar O2, and the other included two strains which showed cross-reactions between serovars O2 and O5. The almost complete agreement found between the PyMS and the serological data suggests that pyrolysis mass spectrometry will prove to be an effective method for interstrain comparison within the species Listonella anguillara.

Classification of Photobacteria Associated with Spoilage of Fish Products by Numerical Taxonomy and Pyrolysis Mass Spectrometry

Forty strains of luminous and non-luminous Photobacterium phosphoreum isolates from cod (Gadus morhua) and seven reference strains of psychrotolerant and mesophilic photobacteria were examined for 156 unit characters in a numerical taxonomic study. The fish strains were isolated from the intestines, from spoiled products and by using a specific detection method. The data were analysed using the similarity coefficient and the unweighted pair-group with arithmetic averages algorithm. In addition twenty-six of the fish isolates and five reference strains were analysed by Curie-point pyrolysis mass spectrometry. The mesophilic and psychrotolerant photobacteria were clearly separated in each of the analyses. Both procedures indicated that the spoilage isolates of P. phosphoreum had originated from the live fish as strains from the fish intestines and from the spoiled products were recovered in the same sub-groups. One sub-group of psychrotolerant P. phosphoreum strains, which was selected in modified atmosphere packed fish stored at low temperature, was also highlighted using each of the methods. The importance of classifying food spoilage bacteria has been shown and a simple key generated for the identification of luminous and non-luminous isolates of Photobacterium phosphoreum.

Curie-Point Pyrolysis Mass Spectrometry as a Tool in Clinical Microbiology

Pyrolysis mass spectrometry is a well established analytical tool that has received a considerable boost from the development of low cost, dedicated instruments and sophisticated statistical analyses on personal computers. Further analytical developments, especially in the area of neural networks, are pushing the technology to the forefront of methods for the discrimination and identification of microorganisms and their products. The speed and reproducibility of pyrolysis mass spectrometry and its applicability to a wide range of microorganisms make it an attractive method for epidemiological studies. For inter-strain comparisons, the method is at least as discriminatory as conventional typing systems and usually gives discrimination similar to that of nucleic acid fingerprinting techniques. There has been some success in using neural networks to make identifications across pyrolysis mass spectrometric batches. Further development of methods used to handle data from multiple PyMS analyses can be expected to extend the value of pyrolysis mass spectrometry in clinical microbiology.