Principal component analysis of TOF-SIMS images of organic monolayers

Principal component analysis (PCA) is a statistical method used to find combinations of variables or factors that describe the most important trends in the data. PCA has been combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) data to extract new information and find relations between species contained in complex systems. Monolayers of dipalmitoylphosphatidylcholine alone and mixed with palmitoyloleoylphosphatidylglycerol prepared using the Langmuir-Blodgett technique are discussed. PCA software provides image scores and corresponding loadings for each significant principal component. Image plots of the scores show the spatial distribution and intensity of the species defined by the loading plots (mass spectral features). The intensity and resolution of the image scores can result in substantial improvement over that of the regular TOF-SIMS images especially when static conditions are used for small analysis areas. Also, some of the effects of topography and matrix in the images can be removed, allowing for a better presentation of chemical variations.     

NMR metabolomics of planktonic and biofilm modes of growth in Pseudomonas aeruginosa

Bacteria often reside in communities where the cells have secreted sticky, polymeric compounds that allow them to attach to surfaces. This sessile lifestyle, referred to as a biofilm, affords the cells within these communities a tolerance of antibiotics and antimicrobial treatments. Biofilms of the bacterium Pseudomonas aeruginosa have been implicated in cystic fibrosis and are capable of colonizing medical implant devices, such as heart valves and catheters, where treatment of the infection often requires the removal of the infected device. This mode of growth is in stark contrast to planktonic, free floating cells, which are more easily eradicated with antibiotics. The mechanisms contributing to a biofilm's tenacity and a planktonic cell's susceptibility are just beginning to be explored. In this study, we have used a metabolomic approach employing nuclear magnetic resonance (NMR) techniques to study the metabolic distinctions between these two modes of growth in P. aeruginosa. One-dimensional 1H NMR spectra of fresh growth medium were compared with spent medium supernatants from batch and chemostat planktonic and biofilms generated in continual flow system culture. In addition, 1H high-resolution magic angle spinning NMR techniques were employed to collect 1H NMR spectra of the corresponding cells. Principal component analysis and spectral comparisons revealed that the overall metabolism of planktonic and biofilm modes of growth appeared similar for the spent media, while the planktonic and biofilm cells displayed marked differences. To determine the robustness of this technique, we prepared cell samples under slightly different preparation methods. Both techniques showed similar results. These feasibility studies show that there exist chemical differences between planktonic and biofilm cells; however, in order to identify these metabolomic differences, more extensive studies would have to be performed, including 1H-1H total correlated spectroscopy.     

Principal component analysis of dynamical features in the peroxidase-oxidase reaction

Inherent variance due to oscillations in the peroxidase-oxidase (PO) reaction was studied using principal component analysis (PCA). The substrates were oxygen and reduced nicotinamide adenine dinucleotide (NADH). Horseradish peroxidase (HRP) catalyzed the reaction. The concentration of a cofactor, methylene blue (MB), was varied, and 2,4-dichlorophenol was kept constant. Increase in the NADH influx was used to change the reaction dynamics from periodic to chaotic. The reaction space was abstracted to the most significant, mutually independent, pairs of absorption and kinetic basis vectors (principal components). Typically, two significant principal components were extracted from the periodic time series and three from the chaotic data. The PCA models accounted for 70-97% of experimental variance. The greatest fraction of the total variance was accounted for in experiments exhibiting periodic dynamics and less than 25 nM MB. More MB induced an increased contribution of NADH to the PO oscillator variance, as did increased NADH influx. A simulated absorption time series, computed from a mass-action model of the chemistry, was analyzed by PCA as well. The comparison of simulation with experiment indicates that the chemical model renders the time series for HRP oxidation forms with fidelity, but incompletely represents NADH chemistry and other salient processes underlying the observed dynamics.     

Principal component analysis applied to filtered signals for maintenance management

This paper presents an approach for detecting and identifying faults in railway infrastructure components. The method is based on pattern recognition and data analysis algorithms. Principal component analysis (PCA) is employed to reduce the complexity of the data to two and three dimensions. PCA involves a mathematical procedure that transforms a number of variables, which may be correlated, into a smaller set of uncorrelated variables called ‘principal components’. In order to improve the results obtained, the signal was filtered. The filtering was carried out employing a state–space system model, estimated by maximum likelihood with the help of the well‐known recursive algorithms such as Kalman filter and fixed interval smoothing. The models explored in this paper to analyse system data lie within the so‐called unobserved components class of models. Copyright © 2009 John Wiley & Sons, Ltd.     

Principal component analysis applied to multiwavelength lidar aerosol backscatter and extinction measurements

The use of powerful Raman backscatter lidars enables one to measure the stratospheric aerosol extinction profile independently of the backscatter, thereby obtaining additional information to aid in retrieving the physical characteristics of the sampled aerosol. We used principal component analysis to construct a self-consistent method for the retrieval of aerosol bulk physical and optical properties from multiwavelength elastic and/or inelastic Raman backscatter lidar signals. The procedure is applied to synthetic and actual lidar signals. We found that aerosol surface area and volume can be usefully estimated and that the use of Raman-derived extinction data leads to a notable improvement in the accuracy of the estimations.     

Principal alarms in multivariate statistical process control using independent component analysis

This article proposes a methodology that helps to predict the main mean shifts, denoted as principal alarms, in a non-normal multivariate process using the available in-control data. The analysis is based on the transformation of the observed correlated variables into independent factors using independent component analysis. These independent components allow us to simulate shifts preserving the covariance structure. The graphical representations of those simulated shifts are helpful in improving the design and control of the process. Two real manufacturing processes are presented showing the advantage of the proposed methodology.     

Environmental conditions affecting exopolysaccharide production by Pseudomonas aeruginosa, Micrococcus sp., and Ochrobactrum sp

Three different chromium-resistant microorganisms (Pseudomonas aeruginosa, Micrococcus sp., and Ochrobactrum sp.) were tested with regard to their EPS production at different pH levels, temperatures, Cr(VI) concentrations, and incubation periods. The optimum pH level was 7 for P. aeruginosa and Micrococcus sp., while it was 8 for Ochrobactrum sp. according to the highest EPS amount at 100 mg/L Cr(VI) concentration. The highest production of EPSs by the three bacteria was obtained under different environmental conditions. P. aeruginosa produced the highest EPS (863.3 mg/L) after incubation for 96 h on media with 50 mg/L Cr(VI) at 20 degrees C, Micrococcus sp. gave the highest yield (444.6 mg/L) after incubation for 72 h on media with 100 mg/L Cr(VI) at the same temperature, and Ochrobactrum sp. had the highest production (430.5 mg/L) on media with 150 mg/L Cr(VI) at 30 degrees C at the end of 48 h of incubation.     

Effect of washing on identification of Bacillus spores by principal-component analysis of fluorescence data

The fluorescence spectra of Bacillus spores are measured at excitation wavelengths of 280, 310, 340, 370, and 400 nm. When cluster analysis is used with the principal-component analysis, the Bacillus globigii spores can be distinguished from the other species of Bacillus spores (B. cereus, B. popilliae, and B. thuringiensis). To test how robust the identification process is with the fluorescence spectra, the B. globigii is obtained from three separate preparations in different laboratories. Furthermore the fluorescence is measured before and after washing and redrying the B. globigii spores. Using the cluster analysis of the first two or three principal components of the fluorescence spectra, one is able to distinguish B. globigii spores from the other species, independent of preparing or washing the spores.     

Principal component analysis: a versatile method for processing and investigation of XPS spectra

Given the relevance of principal component analysis (PCA) to the treatment of spectrometric data, we have evaluated potentialities and limitations of such useful statistical approach for the harvesting of information in large sets of X-ray photoelectron spectroscopy (XPS) spectra. Examples allowed highlighting the contribution of PCA to data treatment by comparing the results of this data analysis with those obtained by the usual XPS quantification methods. PCA was shown to improve the identification of chemical shifts of interest and to reveal correlations between peak components. First attempts to use the method led to poor results, which showed mainly the distance between series of samples analyzed at different moments. To weaken the effect of variations of minor interest, a data normalization strategy was developed and tested. A second issue was encountered with spectra suffering of an even slightly inaccurate binding energy scale correction. Indeed, minor shifts of energy channels lead to the PCA being performed on incorrect variables and consequently to misleading information. In order to improve the energy scale correction and to speed up this step of data pretreatment, a data processing method based on PCA was used. Finally, the overlap of different sources of variation was studied. Since the intensity of a given energy channel consists of electrons from several origins, having suffered inelastic collisions (background) or not (peaks), the PCA approach cannot compare them separately, which may lead to confusion or loss of information. By extracting the peaks from the background and considering them as new variables, the effect of the elemental composition could be taken into account in the case of spectra with very different backgrounds. In conclusion, PCA is a very useful diagnostic tool for the interpretation of XPS spectra, but it requires a careful and appropriate data pretreatment.     

Mechanical robustness of Pseudomonas aeruginosa biofilms

Biofilms grow on various surfaces and in many different environments, a phenomenon that constitutes major problems in industry and medicine. Despite their importance little is known about the viscoelastic properties of biofilms and how these depend on the chemical microenvironment. Here, we find that the mechanical properties of Pseudomonas aeruginosa (P.a.) biofilms are highly robust towards chemical perturbations. Specifically, we observe that P.a. biofilms are able to fully regain their initial stiffness after yielding is enforced, even in the presence of chemicals. Moreover, only trivalent ions and citric acid significantly affect the biofilm elasticity, the first of which also alter the texture of the material. Finally, our results indicate that biofilm mechanics and bacteria viability inside the biofilm are not necessarily linked which suggests that targeting bacteria alone might not be sufficient for biofilm removal strategies.     

Fungus covered insulator materials studied with laser-induced fluorescence and principal component analysis

A method combining laser-induced fluorescence and principal component analysis to detect and discriminate between algal and fungal growth on insulator materials has been studied. Eight fungal cultures and four insulator materials have been analyzed. Multivariate classifications were utilized to characterize the insulator material, and fungal growth could readily be distinguished from a clean surface. The results of the principal component analyses make it possible to distinguish between algae infected, fungi infected, and clean silicone rubber materials. The experiments were performed in the laboratory using a fiber-optic fluorosensor that consisted of a nitrogen laser and an optical multi-channel analyzer system.     

Principal component analysis of X-ray diffraction patterns to yield morphological classification of brucite particles

Principal component analysis was applied to XRD data from a series of Mg(OH)2 samples prepared under different hydrothermal conditions from bischofite (MgCl2.6H2O) and carnallite (KCl.MgCl2.6H2O), owing to differences in full width at half-maximum (fwhm) as well as in the intensity ratio I001/I101 of the respective diffraction peaks. According to the PCA results, the four principal components are able to explain 93% of the total variance and the samples can be classified into four main groups. For instance, the principal component PC1 can be interpreted as the crystallite size along the 101 direction since it is related to the fwhm of this peak. On the other hand, PC3 is related to orientation effects along 001 and 101 directions as it is dominated by the relative intensities of the two peaks. Finally, a comparison of the scanning electron microscopy images of the samples classified in each group revealed that in most of the cases a distinct morphology predominates within each group, which can be explained on the basis of the brucite growth mechanism.     

Principal component transform — Outer product analysis in the PCA context

Outer product analysis is a method that permits the combination of two spectral domains with the aim of emphasizing co-evolutions of spectral regions. This data fusion technique consists in the product of all combinations of the variables that define each spectral domain. The main issue concerning the application of this technique is the very wide data matrix obtained which can be very hard to handle with multivariate techniques such as PCA or PLS, due to computer resources constraints. The present work presents an alternative way to perform outer product analysis in the PCA context without incurring into high demands on computational resources. This works shows that by decomposing each spectral domain with PCA and performing the outer product on the recovered scores, one can obtain the same results as if one calculated the outer product in the original variable space, but using much less computational resources. The results show that this approach will make possible to apply outer product analysis to very wide domains.     

Initial adhesion and surface growth of Pseudomonas aeruginosa on negatively and positively charged poly(methacrylates)

The infection risk of biomaterial implants is determined by an interplay of bacterial adhesion and surface growth of the adhering organisms. In this study, we compared initial adhesion and surface growth of Pseudomonas aeruginosa AK1 (zeta potential –7 mV) on negatively charged (PMMA/MAA, zeta potential –18 mV) and positively charged (PMMA/TMAEMA-Cl, zeta-potential +12 mV) methacrylate copolymers in situ in a parallel plate flow chamber. Initial adhesion was measured using phosphate-buffered saline and subsequent surface growth of the adhering bacteria using nutrient broth as growth medium. Initial adhesion was twice as fast on the positively charged methacrylate than on the negatively charged copolymer. Surface growth, however, was absent on the positively charged copolymer, while on the negatively charged methacrylate the number of bacteria increased exponentially during surface growth with a generation time of 32 min. From the results of this study it can be concluded that positively charged biomaterial surfaces might show reduced risks of biomaterials-centred infections, despite being more adhesive.     

Principal component analysis for evaluating the feasibility of cellular manufacturing without initial machine-part matrix clustering

We present a set of diagnostic tools for machine-part matrix clustering. Machine-part matrix clustering is traditionally viewed as the first step in designing a Cellular Manufacturing System. Clustering is achieved by permuting the rows and columns of the matrix to get a Block Diagonal Form (BDF). Without actual matrix clustering being done, most methods for clustering fail to predict objectively the adaptability of the given matrix for Cellular Manufacturing. This paper presents an approach which predicts clusters accurately without initial machine-part matrix clustering. In addition, it also provides quantitative information on whether the matrix encourages Cellular Manufacturing without obtaining or partitioning the BDF. Lastly, the method also identifies the part families corresponding to each machine group in the same sequence for both dimensions of the matrix. This yields a BDF rather than a Block Checkerboard Form for the clustered matrix which happens when the machine groups and part families are not correctly sequenced in both dimensions of the matrix.     

Characteristics of phenol biodegradation in saline solutions by monocultures of Pseudomonas aeruginosa and Pseudomonas pseudomallei

Phenol is a highly toxic and carcinogenic compound and its biodegradation is very important to meet the environmental regulations. Two bacterial strains capable of utilizing phenol as a sole source of carbon were isolated from the wastewater of a pharmaceutical industry. On the basis of morphological and biochemical characteristics these strains were identified as Pseudomonas aeruginosa and Pseudomonas pseudomallei. Both of these strains were very efficient for phenol degradation. P. pseudomallei degraded phenol at a maximum concentration of 1500 mg L(-1) within seven days with a specific growth rate of 0.013 h(-1) and phenol degradation rate of 13.85 mg L(-1)h(-1). Maximum initial concentration of phenol utilized by P. aeruginosa was 2600 mg L(-1) with 0.016 h(-1) specific growth rate and 26.16 mg L(-1)h(-1) phenol degradation rate. Moreover, the effect of various salts i.e., NaCl, KCl, Na(2)SO(4) and K(2)SO(4) on the growth of these strains and phenol degradation rate (at 1000 mg L(-1)) was studied. In the presence of these salts, P. aeruginosa showed up to 1.53 and 1.34 times faster phenol degradation rate and specific growth rate, respectively as compared to P. pseudomallei. In addition, P. aeruginosa exhibited higher chemical oxygen demand (COD) and biochemical oxygen demand (BOD) reduction rates as compared to the strain P. pseudomallei.     

Principal component analysis for multiple quality characteristics optimization of metal inert gas welding aluminum foam plate

Principal component analysis (PCA) coupled with Taguchi methods are employed in the study for multiple quality characteristics optimization of metal inert gas (MIG) arc welding aluminum foam plates. The quality characteristics investigated are the micro-hardness and the bending strength of the weldments. Eight control factors selected are the type of filler material, MIG current, welding speed, MIG gas flow rate, workpiece gap, MIG arcing angle, groove angle, and electrode extension length.     

In situ degradation of phenol and promotion of plant growth in contaminated environments by a single Pseudomonas aeruginosa strain

For bioremediation of contaminated environments, a bacterial strain, SZH16, was isolated and found to reduce phenol concentration in a selective medium. Using the reaction vessel containing the soil mixed with phenol and bacteria, we found that the single strain degraded efficiently the phenol level in soil samples. The strain was identified as Pseudomonas aeruginosa on the basis of biochemical tests and by comparison of 16S rDNA sequences, and phosphate solubilization and IAA production were not observed in the strain. Simultaneous examination of the role of strain SZH16 in the plant growth and phenol biodegradation was performed. Results showed that inoculation of the single strain in the phenol-spiked soil resulted in corn growth promotion and in situ phenol degradation and the increase in plant biomass correlated with the decrease in phenol content. Colonization experiments showed that the population of the SZH16 strain remained relatively constant. All these findings indicated that the corn growth promotion might be due to reduction in phytotoxicity, a result of phenol biodegradation by the single strain SZH16. Furthermore, the strain was found to stimulate corn growth and reduce phenol concentration simultaneously in phenol-containing water, and even historically contaminated field soils. It is attractive for environment remediation and agronomic applications.