Principal Component Discriminant Function Analysis Research Articles

Optical photothermal infrared spectroscopy: A novel solution for rapid identification of antimicrobial resistance at the single-cell level via deuterium isotope labeling.

The rise and extensive spread of antimicrobial resistance (AMR) has become a growing concern, and a threat to the environment and human health globally. The majority of current AMR identification methods used in clinical setting are based on traditional microbiology culture-dependent techniques which are time-consuming or expensive to be implemented, thus appropriate antibiotic stewardship is provided retrospectively which means the first line of treatment is to hope that a broad-spectrum antibiotic works. Hence, culture-independent and single-cell technologies are needed to allow for rapid detection and identification of antimicrobial-resistant bacteria and to support a more targeted and effective antibiotic therapy preventing further development and spread of AMR. In this study, for the first time, a non-destructive phenotyping method of optical photothermal infrared (O-PTIR) spectroscopy, coupled with deuterium isotope probing (DIP) and multivariate statistical analysis was employed as a metabolic fingerprinting approach to detect AMR in Uropathogenic Escherichia coli (UPEC) at both single-cell and population levels. Principal component-discriminant function analysis (PC-DFA) of FT-IR and O-PTIR spectral data showed clear clustering patterns as a result of distinctive spectral shifts (C-D signature peaks) originating from deuterium incorporation into bacterial cells, allowing for rapid detection and classification of sensitive and resistant isolates at the single-cell level. Furthermore, the single-frequency images obtained using the C-D signature peak at 2,163 cm-1 clearly displayed the reduced ability of the trimethoprim-sensitive strain for incorporating deuterium when exposed to this antibiotic, compared to the untreated condition. Hence, the results of this study indicated that O-PTIR can be employed as an efficient tool for the rapid detection of AMR at the single-cell level.

Toxicometabolomics of the new psychoactive substances \u03b1-PBP and \u03b1-PEP studied in HepaRG cell incubates by means of untargeted metabolomics revealed unexpected amino acid adducts

Toxicometabolomics, essentially applying metabolomics to toxicology of endogenous compounds such as drugs of abuse or new psychoactive substances (NPS), can be investigated by using different in vitro models and dedicated metabolomics techniques to enhance the number of relevant findings. The present study aimed to study the toxicometabolomics of the two NPS α-pyrrolidinobutiophenone (1-phenyl-2-(pyrrolidin-1-yl)butan-1-one, α-PBP) and α-pyrrolidinoheptaphenone (1-phenyl-2-(pyrrolidin-1-yl)heptan-1-one, α-PEP, PV8) in HepaRG cell line incubates. Evaluation was performed using reversed-phase and normal-phase liquid chromatography coupled with high-resolution mass spectrometry in positive and negative ionization mode, respectively, to analyze cells and cell media. Statistical evaluation was performed using one-way ANOVA, principal component discriminant function analysis, as well as hierarchical clustering. In general, the analysis of cells did not mainly reveal any features, but the parent compounds of the drugs of abuse. For α-PBP an increase in N-methylnicotinamide was found, which may indicate hepatotoxic potential of the substance. After analysis of cell media, significant features led to the identification of several metabolites of both compounds. Amino acid adducts with glycine and alanine were found, and these have not been described in any study before and are likely to appear in vivo. Additionally, significant changes in the metabolism of cholesterol were revealed after incubation with α-PEP. In summary, the application of metabolomics techniques after HepaRG cells exposure to NPS did not lead to an increased number of identified drug metabolites compared to previously published studies, but gave a wider perspective on the physiological effect of the investigated compounds on human liver cells.

English

Changing climate means that there will be more episodes of drought, especially in arid regions. Drought stress is an important environmental factor that reduces crop productivity. Despite the vast amount of literature on drought stress in plants, there are still many uncertainties concerning the molecular responses under water deficit. In particular, if we wish to breed plants that are able to tolerate stress, identifying novel traits that contribute to this is desirable. In this study, Fourier transform infrared (FTIR) spectroscopy was used to investigate the response of maize to progressive drought. The objective was to determine whether FTIR spectroscopy is a technique that might be used in monitoring the molecular processes involved in drought responses in plants. Maize seeds were grown for three weeks in a controlled environment before drought was imposed by withdrawing water for up to 12 days. Principal component-discriminant function analysis (PC-DFA) indicated that water deficit elicited changes in the FTIR spectra within the wavenumber ranges of 3050 to 2800, 1750 to 1250 and 1250 to 900 cm-1. With water deficit, the intensities of the absorption bands were reduced. PC-DFA loadings confirmed that the signals to the changes were lipids, amides and carbohydrates, respectively. This work shows that FTIR spectroscopy can provide valuable insights into drought responses of plants and maybe useful for identifying novel drought tolerance traits. Key words: Metabolic fingerprint, molecular changes, multivariate analysis, principal component-discriminant function analysis (PC-DFA), principal component analysis (PCA).

Rapid, Accurate, and Quantitative Detection of Propranolol in Multiple Human Biofluids via Surface-Enhanced Raman Scattering.

There has been an increasing demand for rapid and sensitive techniques for the identification and quantification of pharmaceutical compounds in human biofluids during the past few decades, and surface-enhanced Raman scattering (SERS) is one of a number of physicochemical techniques with the potential to meet these demands. In this study we have developed a SERS-based analytical approach for the assessment of human biofluids in combination with chemometrics. This novel approach has enabled the detection and quantification of the β-blocker propranolol spiked into human serum, plasma, and urine at physiologically relevant concentrations. A range of multivariate statistical analysis techniques, including principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least-squares regression (PLSR) were employed to investigate the relationship between the full SERS spectral data and the level of propranolol. The SERS spectra when combined with PCA and PC-DFA demonstrated clear differentiation of neat biofluids and biofluids spiked with varying concentrations of propranolol ranging from 0 to 120 μM, and clear trends in ordination scores space could be correlated with the level of propranolol. Since PCA and PC-DFA are categorical classifiers, PLSR modeling was subsequently used to provide accurate propranolol quantification within all biofluids with high prediction accuracy (expressed as root-mean-square error of predictions) of 0.58, 9.68, and 1.69 for serum, plasma, and urine respectively, and these models also had excellent linearity for the training and test sets between 0 and 120 μM. The limit of detection as calculated from the area under the naphthalene ring vibration from propranolol was 133.1 ng/mL (0.45 μM), 156.8 ng/mL (0.53 μM), and 168.6 ng/mL (0.57 μM) for serum, plasma, and urine, respectively. This result shows a consistent signal irrespective of biofluid, and all are well within the expected physiological level of this drug during therapy. The results of this study demonstrate the potential of SERS application as a diagnostic screening method, following further validation and optimization to improve detection of pharmaceutical compounds and quantification in human biofluids, which may open up new exciting opportunities for future use in various biomedical and forensic applications.

Metabolic analysis of the response of Pseudomonas putida DOT-T1E strains to toluene using Fourier transform infrared spectroscopy and gas chromatography mass spectrometry.

IntroductionAn exceptionally interesting stress response of Pseudomonas putida strains to toxic substances is the induction of efflux pumps that remove toxic chemical substances from the bacterial cell out to the external environment. To exploit these microorganisms to their full potential a deeper understanding of the interactions between the bacteria and organic solvents is required. Thus, this study focuses on investigation of metabolic changes in P. putida upon exposure to toluene.ObjectiveInvestigate observable metabolic alterations during interactions of three strains of P. putida (DOT-T1E, and its mutants DOT-T1E-PS28 and DOT-T1E-18) with the aromatic hydrocarbon toluene.MethodsThe growth profiles were measured by taking optical density (OD) measurement at 660 nm (OD660) at various time points during incubation. For fingerprinting analysis, Fourier-transform infrared (FT-IR) spectroscopy was used to investigate any phenotypic changes resulting from exposure to toluene. Metabolic profiling analysis was performed using gas chromatography-mass spectrometry (GC–MS). Principal component—discriminant function analysis (PC-DFA) was applied to the FT-IR data while multiblock principal component analysis (MB-PCA) and N-way analysis of variance (N-way ANOVA) were applied to the GC–MS data.ResultsThe growth profiles demonstrated the effect of toluene on bacterial cultures and the results suggest that the mutant P. putida DOT-T1E−18 was more sensitive (significantly affected) to toluene compared to the other two strains. PC-DFA on FT-IR data demonstrated the differentiation between different conditions of toluene on bacterial cells, which indicated phenotypic changes associated with the presence of the solvent within the cell. Fifteen metabolites associated with this phenotypic change, in P. putida due to exposure to solvent, were from central metabolic pathways. Investigation of MB-PCA loading plots and N-way ANOVA for condition | strain × time blocking (dosage of toluene) suggested ornithine as the most significant compound that increased upon solvent exposure.ConclusionThe combination of metabolic fingerprinting and profiling with suitable multivariate analysis revealed some interesting leads for understanding the mechanism of Pseudomonas strains response to organic solvent exposure.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-016-1054-1) contains supplementary material, which is available to authorized users.

Metabolic Fingerprinting of Pseudomonas putida DOT-T1E Strains: Understanding the Influence of Divalent Cations in Adaptation Mechanisms Following Exposure to Toluene.

Pseudomonas putida strains can adapt and overcome the activity of toxic organic solvents by the employment of several resistant mechanisms including efflux pumps and modification to lipopolysaccharides (LPS) in their membranes. Divalent cations such as magnesium and calcium play a crucial role in the development of solvent tolerance in bacterial cells. Here, we have used Fourier transform infrared (FT-IR) spectroscopy directly on cells (metabolic fingerprinting) to monitor bacterial response to the absence and presence of toluene, along with the influence of divalent cations present in the growth media. Multivariate analysis of the data using principal component-discriminant function analysis (PC-DFA) showed trends in scores plots, illustrating phenotypic alterations related to the effect of Mg2+, Ca2+ and toluene on cultures. Inspection of PC-DFA loadings plots revealed that several IR spectral regions including lipids, proteins and polysaccharides contribute to the separation in PC-DFA space, thereby indicating large phenotypic response to toluene and these cations. Finally, the saturated fatty acid ratio from the FT-IR spectra showed that upon toluene exposure, the saturated fatty acid ratio was reduced, while it increased in the presence of divalent cations. This study clearly demonstrates that the combination of metabolic fingerprinting with appropriate chemometric analysis can result in practicable knowledge on the responses of important environmental bacteria to external stress from pollutants such as highly toxic organic solvents, and indicates that these changes are manifest in the bacterial cell membrane. Finally, we demonstrate that divalent cations improve solvent tolerance in P. putida DOT‑T1E strains.

Metabolomics investigation of recombinant mTNFα production in Streptomyces lividans.

BackgroundWhilst undergoing differentiation, Streptomyces produce a large quantity of hydrolytic enzymes and secondary metabolites, and it is this very ability that has focussed increasing interest on the use of these bacteria as hosts for the production of various heterologous proteins. However, within this genus, the exploration and understanding of the metabolic burden associated with such bio-products has only just begun. In this study our overall aim was to apply metabolomics approaches as tools to get a glimpse of the metabolic alterations within S.lividans TK24 when this industrially relevant microbe is producing recombinant murine tumour necrosis factor alpha (mTNFα), in comparison to wild type and empty (non-recombinant protein containing) plasmid-carrying strains as controls.ResultsWhilst growth profiles of all strains demonstrated comparable trends, principal component-discriminant function analysis of Fourier transform infrared (FT-IR) spectral data, showed clear separation of wild type from empty plasmid and mTNFα-producing strains, throughout the time course of incubation. Analysis of intra- and extra-cellular metabolic profiles using gas chromatography–mass spectrometry (GC–MS) displayed similar trends to the FT-IR data. Although the strain carrying the empty plasmid demonstrated metabolic changes due to the maintenance of the plasmid, the metabolic behaviour of the recombinant mTNFα-producing strain appeared to be the most significantly affected. GC–MS results also demonstrated a significant overflow of several organic acids (pyruvate, 2-ketoglutarate and propanoate) and sugars (xylitol, mannose and fructose) in the mTNFα-producing strain.ConclusionThe results obtained in this study have clearly demonstrated the metabolic impacts of producing mTNFα in S. lividans TK24, while displaying profound metabolic effects of harbouring the empty PIJ486 plasmid. In addition, the level of mTNFα produced in this study, further highlights the key role of media composition towards the efficiency of a bioprocess and metabolic behaviour of the host cells, which directly influences the yield of the recombinant product.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-015-0350-1) contains supplementary material, which is available to authorized users.

Metabolic responses of eukaryotic microalgae to environmental stress limit the ability of FT-IR spectroscopy for species identification

Fourier Transform Infrared (FT-IR) spectroscopy is a robust method for macromolecular analysis and differentiation of microorganisms. However, most studies are performed in controlled conditions and it is unclear whether this tool is appropriate for the identification of eukaryotic microalgae species from variable environments. In order to address this, nine closely-related species of marine and freshwater microalgae were grown under controlled (non-stressed) and variable (non-stressed and stressed) conditions, including nutrient-stressed and wastewater-stressed conditions. Following optimization of data processing methods, FT-IR spectra from all species and conditions were compared. The substantial metabolic changes that were caused by nutrient starvation restricted the ability of FT-IR spectroscopy to differentiate the microalgal species grown under variable conditions efficiently. Comparison of unsupervised and supervised multivariate data analysis methods found that principal component-discriminant function analysis was able best to differentiate between some species under controlled conditions but still gave poor differentiation under variable environmental conditions.

Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions

The control and interaction between nitrogen and carbon assimilatory pathways is essential in both photosynthetic and non-photosynthetic tissue in order to support metabolic processes without compromising growth. Physiological differences between the basal and mature region of wheat (Triticum aestivum) primary leaves confirmed that there was a change from heterotrophic to autotrophic metabolism. Fourier Transform Infrared (FT-IR) spectroscopy confirmed the suitability and phenotypic reproducibility of the leaf growth conditions. Principal Component–Discriminant Function Analysis (PC–DFA) revealed distinct clustering between base, and tip sections of the developing wheat leaf, and from plants grown in the presence or absence of nitrate. Gas Chromatography-Time of Flight/Mass Spectrometry (GC-TOF/MS) combined with multivariate and univariate analyses, and Bayesian network (BN) analysis, distinguished different tissues and confirmed the physiological switch from high rates of respiration to photosynthesis along the leaf. The operation of nitrogen metabolism impacted on the levels and distribution of amino acids, organic acids and carbohydrates within the wheat leaf. In plants grown in the presence of nitrate there was reduced levels of a number of sugar metabolites in the leaf base and an increase in maltose levels, possibly reflecting an increase in starch turnover. The value of using this combined metabolomics analysis for further functional investigations in the future are discussed.

Untargeted metabolomic analysis of urine samples in the diagnosis of some inherited metabolic disorders.

Metabolomics is becoming an important tool in clinical research and the diagnosis of human diseases. It has been used in the diagnosis of inherited metabolic disorders with pronounced biochemical abnormalities. The aim of this study was to determine if it could be applied in the diagnosis of inherited metabolic disorders (IMDs) with less clear biochemical profiles from urine samples using an untargeted metabolomic approach. A total of 14 control urine samples and 21 samples from infants with cystinuria, maple syrup urine disease, adenylosuccinate lyase deficiency and galactosemia were tested. Samples were analyzed by liquid chromatography on aminopropyl column in aqueous normal phase separation system using gradient elution of acetonitrile/ammonium acetate. Detection was performed by time-of-flight mass spectrometer fitted with electrospray ionisation in positive mode. The data were statistically processed using principal component analysis (PCA), principal component discriminant function analysis (PCA-DFA) and partial least squares (PLS) regression. All patient samples were first distinguished from controls using unsupervised PCA. Discrimination of the patient samples was then unambiguously verified using supervised PCA-DFA. Known markers of the diseases in question were successfully confirmed and a potential new marker emerged from the PLS regression. This study showed that untargeted metabolomics can be applied in the diagnosis of mild IMDs with less clear biochemical profiles.

The influence of scaling metabolomics data on model classification accuracy

Correctly measured classification accuracy is an important aspect not only to classify pre-designated classes such as disease versus control properly, but also to ensure that the biological question can be answered competently. We recognised that there has been minimal investigation of pre-treatment methods and its influence on classification accuracy within the metabolomics literature. The standard approach to pre-treatment prior to classification modelling often incorporates the use of methods such as autoscaling, which positions all variables on a comparable scale thus allowing one to achieve separation of two or more groups (target classes). This is often undertaken without any prior investigation into the influence of the pre-treatment method on the data and supervised learning techniques employed. Whilst this is useful for deriving essential information such as predictive ability or visual interpretation in many cases, as shown in this study the standard approach is not always the most suitable option available. Here, a study has been conducted to investigate the influence of six pre-treatment methods—autoscaling, range, level, Pareto and vast scaling, as well as no scaling—on four classification models, including: principal components-discriminant function analysis (PC-DFA), support vector machines (SVM), random forests (RF) and k-nearest neighbours (kNN)—using three publically available metabolomics data sets. We have demonstrated that undertaking different pre-treatment methods can greatly affect the interpretation of the statistical modelling outputs. The results have shown that data pre-treatment is context dependent and that there was no single superior method for all the data sets used. Whilst we did find that vast scaling produced the most robust models in terms of classification rate for PC-DFA of both NMR spectroscopy data sets, in general we conclude that both vast scaling and autoscaling produced similar and superior results in comparison to the other four pre-treatment methods on both NMR and GC–MS data sets. It is therefore our recommendation that vast scaling is the primary pre-treatment method to use as this method appears to be more stable and robust across all the different classifiers that were conducted in this study.

Implementation of Fourier transform infrared spectroscopy for the rapid typing of uropathogenic Escherichia coli

In this paper, we demonstrate that Fourier transform infrared (FT-IR) spectroscopy is able to discriminate rapidly between uropathogenic Escherichia coli (UPEC) of key lineages with only relatively simple sample preparation. A total of 95 bacteria from six different epidemiologically important multilocus sequence types (ST10, ST69, ST95, ST73, ST127 and ST131) were used in this project and principal component-discriminant function analysis (PC-DFA) of these samples produced clear separate clustering of isolates, based on the ST. Analysis of data using partial least squares-discriminant analysis (PLS-DA), incorporating cross-validation, indicated a high prediction accuracy of 91.19% for ST131. These results suggest that FT-IR spectroscopy could be a useful method for the rapid identification of members of important UPEC STs.

Sensitive and specific discrimination of pathogenic and nonpathogenic Escherichia coli using Raman spectroscopy—a comparison of two multivariate analysis techniques

The determination of bacterial identity at the strain level is still a complex and time-consuming endeavor. In this study, visible wavelength spontaneous Raman spectroscopy has been used for the discrimination of four closely related Escherichia coli strains: pathogenic enterohemorrhagic E. coli O157:H7 and non-pathogenic E. coli C, E. coli Hfr K-12, and E. coli HF4714. Raman spectra from 600 to 2000 cm−1 were analyzed with two multivariate chemometric techniques, principal component-discriminant function analysis and partial least squares-discriminant analysis, to determine optimal parameters for the discrimination of pathogenic E. coli from the non-pathogenic strains. Spectral preprocessing techniques such as smoothing with windows of various sizes and differentiation were investigated. The sensitivity and specificity of both techniques was in excess of 95%, determined by external testing of the chemometric models. This study suggests that spontaneous Raman spectroscopy with visible wavelength excitation is potentially useful for the rapid identification and classification of clinically-relevant bacteria at the strain level.

Detection and Quantification of Bacterial Spoilage in Milk and Pork Meat Using MALDI-TOF-MS and Multivariate Analysis

Microbiological safety is one of the cornerstones of quality control in the food industry. Identification and quantification of spoilage bacteria in pasteurized milk and meat in the food industry currently relies on accurate and sensitive yet time-consuming techniques which give retrospective values for microbial contamination. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), a proven technique in the field of protein and peptide identification and quantification, may be a valuable alternative approach for the rapid assessment of microbial spoilage. In this work we therefore developed MALDI-TOF-MS as a novel analytical approach for the assessment of food that when combined with chemometrics allows for the detection and quantification of milk and pork meat spoilage bacteria. To develop this approach, natural spoilage of pasteurized milk and raw pork meat samples incubated at 15 °C and at room temperature, respectively, was conducted. Samples were collected for MALDI-TOF-MS analysis (which took 4 min per sample) at regular time intervals throughout the spoilage process, with concurrent calculation and documentation of reference total viable counts using traditional microbiological methods (these took 2 days). Multivariate statistical techniques such as principal component discriminant function analysis, canonical correlation analysis, partial least-squares (PLS) regression, and kernel PLS (KPLS) were used to analyze the data. The results from MALDI-TOF-MS combined with PLS or KPLS gave excellent bacterial quantification results for both milk and meat spoilage, and typical root mean squared errors for prediction in test spectra were between 0.53 and 0.79 log unit. Overall these novel findings strongly indicate that MALDI-TOF-MS when combined with chemometric approaches would be a useful adjunct for routine use in the milk and meat industry as a fast and accurate viable bacterial detection and quantification method.

Fourier Transform Infrared and Raman Spectroscopies for the Rapid Detection, Enumeration, and Growth Interaction of the Bacteria Staphylococcus aureus and Lactococcus lactis ssp. cremoris in Milk

Staphylococcus aureus is one of the main pathogenic microorganisms found in milk and dairy products and has been involved in bacterial foodborne outbreaks in the past. Current enumeration techniques for bacteria are very time-consuming, typically taking 24 h or longer, and bacterial antagonism in the form of lactic acid bacteria (LAB) may inhibit the growth of S. aureus . Therefore, the aim of this investigation was to establish the accuracy and sensitivity of rapid nondestructive metabolic fingerprinting techniques, such as Fourier transform infrared (FT-IR) spectroscopy and Raman spectroscopy (RS), in combination with multivariate analysis techniques, for the detection and enumeration of S. aureus in milk, as well as to study the growth interaction between S. aureus and Lactococcus lactis ssp. cremoris , a common LAB. The two bacterial species were investigated both in a pure monoculture and in a combined inoculated coculture after inoculation into ultraheated milk during the first 24 h of growth at 37 °C. Plating techniques were used to obtain primary reference data for viable bacteria counts. Principal component discriminant function analysis, canonical correlation analysis, partial least-squares (PLS), and kernel PLS (KPLS) multivariate statistical techniques were employed to analyze the data. FT-IR provided very reasonable quantification results both with PLS and KPLS, the latter providing marginally better predictions, with correlation coefficients in the test set (Q(2)) and training set (R(2)) varying from 0.64 to 0.76 and from 0.78 to 0.88 for different bacterial sample combinations. RS results were less encouraging with high degrees of error and poor correlation to viable bacterial counts. S. aureus growth was not inhibited by the presence of the LAB, but metabolic fingerprinting of the coculture indicated that the phenotype of this dual bacterial culture was closer to that of pure LAB cultures. In conclusion, FT-IR spectroscopy in combination with the above multivariate techniques appears to be a promising discrimination and enumeration analytical technique for the two bacterial species. In addition, it has been demonstrated that the L. cremoris metabolic effect in milk dominates that of S. aureus even though there was no growth antagonism observed.

Impact of Silver(I) on the Metabolism of Shewanella oneidensis

Anaerobic cultures of Shewanella oneidensis MR-1 reduced toxic Ag(I), forming nanoparticles of elemental Ag(0), as confirmed by X-ray diffraction analyses. The addition of 1 to 50 microM Ag(I) had a limited impact on growth, while 100 microM Ag(I) reduced both the doubling time and cell yields. At this higher Ag(I) concentration transmission electron microscopy showed the accumulation of elemental silver particles within the cell, while at lower concentrations the metal was exclusively reduced and precipitated outside the cell wall. Whole organism metabolite fingerprinting, using the method of Fourier transform infrared spectroscopy analysis of cells grown in a range of silver concentrations, confirmed that there were significant physiological changes at 100 microM silver. Principal component-discriminant function analysis scores and loading plots highlighted changes in certain functional groups, notably, lipids, amides I and II, and nucleic acids, as being discriminatory. Molecular analyses confirmed a dramatic drop in cellular yields of both the phospholipid fatty acids and their precursor molecules at high concentrations of silver, suggesting that the structural integrity of the cellular membrane was compromised at high silver concentrations, which was a result of intracellular accumulation of the toxic metal.

Investigating FTIR based histopathology for the diagnosis of prostate cancer

Prostate cancer is the most common gender specific cancer. The current gold standard for diagnosis, histopathology, is subjective and limited by variation between different pathologists. The diagnostic problems associated with the correct grading and staging of prostate cancer (CaP) has led to an interest in the development of spectroscopic based diagnostic techniques. FTIR microspectroscopy used in combination with a Principal Component Discriminant Function Analysis (PC-DFA) was applied to investigate FTIR based histopathology for the diagnosis of CaP. In this paper we report the results of a large patient study in which FTIR has been proven to grade CaP tissue specimens to a high degree of sensitivity and specificity.

FTIR-based spectroscopic analysis in the identification of clinically aggressive prostate cancer

Fourier transform infrared (FTIR) spectroscopy is a vibrational spectroscopic technique that uses infrared radiation to vibrate molecular bonds within the sample that absorbs it. As different samples contain different molecular bonds or different configurations of molecular bonds, FTIR allows us to obtain chemical information on molecules within the sample. Fourier transform infrared microspectroscopy in conjunction with a principal component-discriminant function analysis (PC-DFA) algorithm was applied to the grading of prostate cancer (CaP) tissue specimens. The PC-DFA algorithm is used alongside the established diagnostic measures of Gleason grading and the tumour/node/metastasis system. Principal component-discriminant function analysis improved the sensitivity and specificity of a three-band Gleason score criterion diagnosis previously reported by attaining an overall sensitivity of 92.3% and specificity of 99.4%. For the first time, we present the use of a two-band criterion showing an association of FTIR-based spectral characteristics with clinically aggressive behaviour in CaP manifest as local and/or distal spread. This paper shows the potential for the use of spectroscopic analysis for the evaluation of the biopotential of CaP in an accurate and reproducible manner.

ToF-SIMS PC-DFA analysis of prostate cancer cell lines

Three closely related cancer cell lines have been analysed with ToF-SIMS using a C 60 + primary ion beam. Principal component-discriminant function analysis (PC-DFA) has been applied for spectral classification. Various spectral pre-processing methods are discussed and assessed for optimum discrimination of this data set. The sum-normalised PC-DFA spectral model produced sensitivities as high as 83.3% and specificities as high as 100% at the 99% confidence limit. At this confidence limit only one errant spectrum was misclassified. The resulting loadings plots suggest that a range of lipid and amino-acid related signals are responsible for the cell line discrimination.

Rapid and quantitative detection of the microbial spoilage in milk using Fourier transform infrared spectroscopy and chemometrics

Microbiological safety plays a very significant part in the quality control of milk and dairy products worldwide. Current methods used in the detection and enumeration of spoilage bacteria in pasteurized milk in the dairy industry, although accurate and sensitive, are time-consuming. FT-IR spectroscopy is a metabolic fingerprinting technique that can potentially be used to deliver results with the same accuracy and sensitivity, within minutes after minimal sample preparation. We tested this hypothesis using attenuated total reflectance (ATR), and high throughput (HT) FT-IR techniques. Three main types of pasteurized milk - whole, semi-skimmed and skimmed - were used and milk was allowed to spoil naturally by incubation at 15 degrees C. Samples for FT-IR were obtained at frequent, fixed time intervals and pH and total viable counts were also recorded. Multivariate statistical methods, including principal components-discriminant function analysis and partial least squares regression (PLSR), were then used to investigate the relationship between metabolic fingerprints and the total viable counts. FT-IR ATR data for all milks showed reasonable results for bacterial loads above 10(5) cfu ml(-1). By contrast, FT-IR HT provided more accurate results for lower viable bacterial counts down to 10(3) cfu ml(-1) for whole milk and, 4 x 10(2) cfu ml(-1) for semi-skimmed and skimmed milk. Using FT-IR with PLSR we were able to acquire a metabolic fingerprint rapidly and quantify the microbial load of milk samples accurately, with very little sample preparation. We believe that metabolic fingerprinting using FT-IR has very good potential for future use in the dairy industry as a rapid method of detection and enumeration.