Process analytical technologies (PAT) are used within industry to give real-time measurements of critical quality parameters, ultimately improving the quality by design (QbD) of the final product and reducing manufacturing costs. Spectroscopic and spectrophotometric methods are readily employed within PAT due to their ease of use, compatibility toward a range of sample types, robustness, and multiplexing capabilities. We have developed a UV resonance Raman (UVRR) spectroscopy approach to quantify industrially relevant biotransformations accurately, focusing on nitrile metabolizing enzymes: nitrile hydratase (NHase) and amidase versus nitrilase activity. Sensitive detection of the amide intermediate by UVRR spectroscopy enabled discrimination between the two nitrile-hydrolyzing pathways. Development of a flow-cell apparatus further exemplifies its suitability toward PAT measurements, incorporating in situ analysis within a closed system. Multivariate curve resolution–alternating least-squares (MCR-ALS) was applied to the UVRR spectra, as well as off-line HPLC measurements, to enable absolute quantification of substrate, intermediate, and product. Further application of hard modeling to MCR-ALS deconvolved concentration profiles enabled accurate kinetic determinations, thus removing the requirement for comparative off-line HPLC. Finally, successful quantitative measurements of in vivo activity using whole-cell biotransformations, where two Escherichia coli strains expressing either NHase (transforming benzonitrile to benzamide) or amidase (further conversion of benzamide to benzoic acid), illustrate the power, practicality, and sensitivity of this novel approach of multistep and, with further refinement, we believe, multiple micro-organism biotransformations.

In this study surface enhanced Raman scattering (SERS) combined with the isotopic labelling (IL) principle has been used for the quantification of codeine spiked into both water and human plasma. Multivariate statistical approaches were employed for the analysis of these SERS spectral data, particularly partial least squares regression (PLSR) which was used to generate models using the full SERS spectral data for quantification of codeine with, and without, an internal isotopic labelled standard. The PLSR models provided accurate codeine quantification in water and human plasma with high prediction accuracy (Q2). In addition, the employment of codeine-d6 as the internal standard further improved the accuracy of the model, by increasing the Q2 from 0.89 to 0.94 and decreasing the low root-mean-square error of predictions (RMSEP) from 11.36 to 8.44. Using the peak area at 1281 cm−1 assigned to C–N stretching, C–H wagging and ring breathing, the limit of detection was calculated in both water and human plasma to be 0.7 μM (209.55 ng mL−1) and 1.39 μM (416.12 ng mL−1), respectively. Due to a lack of definitive codeine vibrational assignments, density functional theory (DFT) calculations have also been used to assign the spectral bands with their corresponding vibrational modes, which were in excellent agreement with our experimental Raman and SERS findings. Thus, we have successfully demonstrated the application of SERS with isotope labelling for the absolute quantification of codeine in human plasma for the first time with a high degree of accuracy and reproducibility. The use of the IL principle which employs an isotopolog (that is to say, a molecule which is only different by the substitution of atoms by isotopes) improves quantification and reproducibility because the competition of the codeine and codeine-d6 for the metal surface used for SERS is equal and this will offset any difference in the number of particles under analysis or any fluctuations in laser fluence. It is our belief that this may open up new exciting opportunities for testing SERS in real-world samples and applications which would be an area of potential future studies.

Many studies report the development of new thin films for surface enhanced Raman scattering (SERS). However, the assessment of these surfaces in terms of their reproducibility for SERS is often subjective and whilst many spectra could and indeed should be reported, very few repeat measurements are typically used. Here, the performance of three SERS thin film substrates is assessed objectively using both univariate and novel multivariate methods. The silver on copper substrate (SoC) was synthesised in-house via galvanic displacement, whilst the other two substrates Klarite and QSERS are commercially available. The reproducibility of these substrates was assessed using rhodamine 6G (R6G) as a probe analyte and seven common vibrational bands that were observed in all R6G spectra were evaluated. In order to be as objective as possible a total of seven different data analysis methods were used to evaluate the surfaces revealing that overall the SoC substrate demonstrates much greater reproducibility when compared to the commercial substrates. Finally, through the collection of large datasets containing 6400 spectra per single substrate we also provide guidelines as to the typical number of spectra that should be collected in order to assess a substrate's performance objectively, and we conclude that this must be a minimum of 180 spectra collected randomly from across the region of interest.

It is clear that investigating how bacterial cells work by analysing their functional roles in microbial communities is very important in environmental, clinical and industrial microbiology. The benefits of linking genes to their respective functions include the reliable identification of the causative agents of various diseases, which would permit appropriate and timely treatment in healthcare systems. In industrial and municipal wastewater treatment and management, such knowledge may allow for the manipulation of microbial communities, such as through bioaugmentation, in order to improve the efficiency and effectiveness of bioremediation processes. Stable isotope probing coupled with identification techniques has emerged to be a potentially reliable tool for the discrimination, identification and characterization of bacteria at community and single cell levels, knowledge which can be utilized to link microbially mediated bioprocesses to phylogeny. Development of the surface-enhanced Raman scattering (SERS) technique offers an exciting alternative to the Raman and Fourier-transform infrared spectroscopic techniques in understanding the metabolic processes of microorganisms in situ. SERS employing Ag and Au nanoparticles can significantly enhance the Raman signal, making it an exciting candidate for the analysis of the cellular components of microorganisms. In this study, Escherichia coli cells were cultivated in minimal medium containing different ratios of 12C/13C glucose and/or 14N/15N ammonium chloride as the only carbon and nitrogen sources respectively, with the overall final concentrations of these substrates being constant. After growth, the E. coli cells were analyzed with SERS employing an in situ synthesis of Ag nanoparticles. This novel investigation of the SERS spectral data with multivariate chemometrics demonstrated clear clusters which could be correlated to the SERS spectral shifts of biomolecules from cells grown and hence labelled with 13C and 15N atoms. These shifts reflect the isotopic content of the bacteria and quantification of the isotope levels could be established using chemometrics based on partial least squares regression.

Raman spectroscopy has attracted considerable interest during the past two decades as a vibrational technique used for the molecular characterisation of different molecules. Whilst the Raman effect is known to be generally weak, it is also known that this can be greatly improved using surface-enhanced Raman scattering (SERS). Indeed, in recent years, the power of SERS for rapid identification and quantification of target analytes in a wide range of applications has been repeatedly demonstrated in multiple studies. Moreover, the application of SERS in combination with an isotopically labelled compound (ILC), as an internal standard, has also very recently shown promising results for quantitative SERS measurements, by improving both its accuracy and precision. This is due to the 12C and 13C or 1H and 2H (D) having similar physicochemical properties. The use of these internal standards results in the reduction of any influences due to the number of nanoparticles within the analysis zone and fluctuations in laser fluence. Thus, in this study we have employed SERS for quantitative detection of tryptophan (Trp) and caffeine. These have been chosen because Trp is readily available as the deuterated form and caffeine is available in both 12C and 13C. Quantum chemical calculations based on density functional theory (DFT) have been utilized to determine the vibrational characteristics of the target analytes. For SERS analysis incorporating isotopologues of tryptophan three independent experiments were conducted with three different batches of nanoparticles over a 12 month period; our results show that the use of this internal standard improves quantification of this target molecule. In particular for the independent test sets (i.e., samples not used in quantitative partial least squares regression (PLSR) model construction) we observed improvements in the linearity for test set predictions, as well as lower errors in test set predictions, when isotope internal standards were used during SERS for both deuterated tryptophan as well as 13C caffeine. This work is an extension of and a natural progression from our earlier studies. By exploring additional analytes of interest, allowing for the assessment of the different types of stable isotopes as internal standards, and demonstrating the transfer/robustness of isotopologues for use with SERS, we believe this approach could be readily extended to other biologically-relevant compounds.

High levels of uric acid in urine and serum can be indicative of hypertension and the pregnancy related condition, preeclampsia. We have developed a simple, cost-effective, portable surface enhanced Raman scattering (SERS) approach for the routine analysis of uric acid at clinically relevant levels in urine patient samples. This approach, combined with the standard addition method (SAM), allows for the absolute quantification of uric acid directly in a complex matrix such as that from human urine. Results are highly comparable and in very good agreement with HPLC results, with an average <9% difference in predictions between the two analytical approaches across all samples analyzed, with SERS demonstrating a 60-fold reduction in acquisition time compared with HPLC. For the first time, clinical prepreeclampsia patient samples have been used for quantitative uric acid detection using a simple, rapid colloidal SERS approach without the need for complex data analysis.

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics ‘(’omic)’ approaches have been used for therapeutic interventions previously. Metabolomics now a well-established’omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

The correct glycosylation of biopharmaceutical glycoproteins and their formulations is essential for them to have the desired therapeutic effect on the patient. It has recently been shown that Raman spectroscopy can be used to quantify the proportion of glycosylated protein from mixtures of native and glycosylated forms of bovine pancreatic ribonuclease (RNase). Here we show the first steps toward not only the detection of glycosylation status but the characterization of glycans themselves from just a few protein molecules at a time using tip-enhanced Raman scattering (TERS). While this technique generates complex data that are very dependent on the protein orientation, with the careful development of combined data preprocessing, univariate and multivariate analysis techniques, we have shown that we can distinguish between the native and glycosylated forms of RNase. Many glycoproteins contain populations of subtly different glycoforms; therefore, with stricter orientation control, we believe this has the potential to lead to further glycan characterization using TERS, which would have use in biopharmaceutical synthesis and formulation research.

Biocatalyst discovery and directed evolution are central to many pharmaceutical research programs, yet the lack of robust high-throughput screening methods for large libraries of enzyme variants generated (typically 106–108) has hampered progress and slowed enzyme optimization. We have developed a label-free generally applicable approach based on Raman spectroscopy which results in significant reductions in acquisition times (>30-fold). Surface enhanced Raman scattering (SERS) is employed to monitor the enzyme-catalyzed conversion by xanthine oxidase of hypoxanthine to xanthine to uric acid. This approach measures the substrates and products directly and does not require chromogenic substrates or lengthy chromatography, was successfully benchmarked against HPLC, and shows high levels of accuracy and reproducibility. Furthermore, we demonstrate that this SERS approach has utility in monitoring enzyme inhibition illustrating additional medical significance to this high-throughput screening method.

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has successfully been used for the analysis of high molecular weight compounds, such as proteins and nucleic acids. By contrast, analysis of low molecular weight compounds with this technique has been less successful due to interference from matrix peaks which have a similar mass to the target analyte(s). Recently, a variety of modified matrices and matrix additives have been used to overcome these limitations. An increased interest in lipid analysis arose from the feasibility of correlating these components with many diseases, e.g. atherosclerosis and metabolic dysfunctions. Lipids have a wide range of chemical properties making their analysis difficult with traditional methods. MALDI-TOF-MS shows excellent potential for sensitive and rapid analysis of lipids, and therefore this study focuses on computational-analytical optimization of the analysis of five lipids (4 phospholipids and 1 acylglycerol) in complex mixtures using MALDI-TOF-MS with fractional factorial design (FFD) and Pareto optimality. Five different experimental factors were investigated using FFD which reduced the number of experiments performed by identifying 720 key experiments from a total of 8064 possible analyses. Factors investigated included the following: matrices, matrix preparations, matrix additives, additive concentrations, and deposition methods. This led to a significant reduction in time and cost of sample analysis with near optimal conditions. We discovered that the key factors used to produce high quality spectra were the matrix and use of appropriate matrix additives.

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.

Despite the fact that various microorganisms (e.g., bacteria, fungi, viruses, etc.) have been linked with infectious diseases, their crucial role towards sustaining life on Earth is undeniable. The huge biodiversity, combined with the wide range of biochemical capabilities of these organisms, have always been the driving force behind their large number of current, and, as of yet, undiscovered future applications. The presence of such diversity could be said to expedite the need for the development of rapid, accurate and sensitive techniques which allow for the detection, differentiation, identification and classification of such organisms. In this study, we employed Fourier transform infrared (FT-IR), Raman, and surface enhanced Raman scattering (SERS) spectroscopies, as molecular whole-organism fingerprinting techniques, combined with multivariate statistical analysis approaches for the classification of a range of industrial, environmental or clinically relevant bacteria (P. aeruginosa, P. putida, E. coli, E. faecium, S. lividans, B. subtilis, B. cereus) and yeast (S. cerevisiae). Principal components-discriminant function analysis (PC-DFA) scores plots of the spectral data collected from all three techniques allowed for the clear differentiation of all the samples down to sub-species level. The partial least squares-discriminant analysis (PLS-DA) models generated using the SERS spectral data displayed lower accuracy (74.9%) when compared to those obtained from conventional Raman (97.8%) and FT-IR (96.2%) analyses. In addition, whilst background fluorescence was detected in Raman spectra for S. cerevisiae, this fluorescence was quenched when applying SERS to the same species, and conversely SERS appeared to introduce strong fluorescence when analysing P. putida. It is also worth noting that FT-IR analysis provided spectral data of high quality and reproducibility for the whole sample set, suggesting its applicability to a wider range of samples, and perhaps the most suitable for the analysis of mixed cultures in future studies. Furthermore, our results suggest that while each of these spectroscopic approaches may favour different organisms (sample types), when combined, they would provide complementary and more in-depth knowledge (structural and/or metabolic state) of biological systems. To the best of our knowledge, this is the first time that such a comparative and combined spectroscopic study (using FT-IR, Raman and SERS) has been carried out on microbial samples.

Adulteration of high quality food products with sub-standard and cheaper grades is a world-wide problem taxing the global economy. Currently, many traditional tests suffer from poor specificity, highly complex outputs and a lack of high-throughput processing. Metabolomics has been successfully used as an accurate discriminatory technique in a number of applications including microbiology, cancer research and environmental studies and certain types of food fraud. In this study, we have developed metabolomics as a technique to assess the adulteration of meat as an improvement on current methods. Different grades of beef mince and pork mince, purchased from a national retail outlet were combined in a number of percentage ratios and analysed using GC-MS and UHPLC-MS. These techniques were chosen because GC-MS enables investigations of metabolites involved in primary metabolism whilst UHPLC-MS using reversed phase chromatography provides information on lipophilic species. With the application of chemometrics and statistical analyses, a panel of differential metabolites were found for identification of each of the two meat types. Additionally, correlation was observed between metabolite content and percentage of fat declared on meat products’ labelling.

Campylobacter species are one of the main causes of food poisoning worldwide. Despite the availability of established culturing and molecular techniques, due to the fastidious nature of these microorganisms, simultaneous detection and species differentiation still remains challenging. This study focused on the differentiation of eleven Campylobacter strains from six species, using Fourier transform infrared (FT-IR) and Raman spectroscopies, together with matrix-assisted laser desorption ionisation-time of flight-mass spectrometry (MALDI-TOF-MS), as physicochemical approaches for generating biochemical fingerprints. Cluster analysis of data from each of the three analytical approaches provided clear differentiation of each Campylobacter species, which was generally in agreement with a phylogenetic tree based on 16S rRNA gene sequences. Notably, although C. fetus subspecies fetus and venerealis are phylogenetically very closely related, using FT-IR and MALDI-TOF-MS data these subspecies were readily differentiated based on differences in the lipid (2920 and 2851 cm−1) and fingerprint regions (1500–500 cm−1) of the FT-IR spectra, and the 500–2000 m/z region of the MALDI-TOF-MS data. A finding that was further investigated with targeted lipidomics using liquid chromatography-mass spectrometry (LC-MS). Our results demonstrate that such metabolomics approaches combined with molecular biology techniques may provide critical information and knowledge related to the risk factors, virulence, and understanding of the distribution and transmission routes associated with different strains of foodborne Campylobacter spp.

Human pharmaceuticals have been detected in wastewater treatment plants, rivers, and estuaries throughout Europe and the United States. It is widely acknowledged that there is insufficient information available to determine whether prolonged exposure to low levels of these substances is having an impact on the microbial ecology in such environments. In this study we attempt to measure the effects of exposing cultures of Pseudomonas putida KT2440 (UWC1) to six pharmaceuticals by looking at differences in metabolite levels. Initially, we used Fourier transform infrared (FT-IR) spectroscopy coupled with multivariate analysis to discriminate between cell cultures exposed to different pharmaceuticals. This suggested that on exposure to propranolol there were significant changes in the lipid complement of P. putida. Metabolic profiling with gas chromatography-mass spectrometry (GC-MS), coupled with univariate statistical analyses, was used to identify endogenous metabolites contributing to discrimination between cells exposed to the six drugs. This approach suggested that the energy reserves of exposed cells were being expended and was particularly evident on exposure to propranolol. Adenosine triphosphate (ATP) concentrations were raised in P. putida exposed to propranolol. Increased energy requirements may be due to energy dependent efflux pumps being used to remove propranolol from the cell.

In this study we have employed metabolomics approaches to understand the metabolic effects of producing enhanced green fluorescent protein (eGFP) as a recombinant protein in Escherichia coli cells. This metabolic burden analysis was performed against a number of recombinant expression systems and control strains and included: (i) standard transcriptional recombinant expression control system BL21(DE3) with the expression plasmid pET-eGFP, (ii) the recently developed dual transcriptional–translational recombinant expression control strain BL21(IL3), with pET-eGFP, (iii) BL21(DE3) with an empty expression plasmid pET, (iv) BL21(IL3) with an empty expression plasmid, and (v) BL21(DE3) without an expression plasmid; all strains were cultured under various induction conditions. The growth profiles of all strains together with the results gathered by the analysis of the Fourier transform infrared (FT-IR) spectroscopy data, identified IPTG-dependent induction as the dominant factor hampering cellular growth and metabolism, which was in general agreement with the findings of GC-MS analysis of cell extracts and media samples. In addition, the exposure of host cells to the synthetic inducer ligand, pyrimido[4,5-d] pyrimidine-2,4-diamine (PPDA), of the orthogonal riboswitch containing expression system (BL21(IL3)) did not display any detrimental effects, and its detected levels in all the samples were at similar levels, emphasising the inability of the cells to metabolise PPDA. The overall results obtained in this study suggested that although the BL21(DE3)-EGFP and BL21(IL3)-EGFP strains produced comparable levels of recombinant eGFP, the presence of the orthogonal riboswitch seemed to be moderating the metabolic burden of eGFP production in the cells enabling higher biomass yield, whilst providing a greater level of control over protein expression.

Orange juice is a hugely popular, widely consumed, and high price commodity typically traded in a concentrate form making it highly susceptible to adulteration. It has been consistently shown to be one of the leading food categories of reported cases of food fraud. One of the many forms of adulteration is dilution which can then be disguised with sugar solutions, or juices from other fruits or vegetables, which mimic the natural fruit sugars in this juice. Here, we demonstrate Fourier transform infrared (FT-IR) spectroscopy as a rapid, high-throughput and quantitative method for the determination of orange juice adulteration. Initial experiments involved the simple adulteration of pure orange juice with 0.5–20.0% water disguised with glucose, fructose or sucrose individually. This was followed by more complex mixtures of these three sugars at appropriate concentrations found in freshly prepared orange juice established using GC-MS; a total of 41 samples were prepared and all experiments undertaken in triplicate. Principal components-discriminant function analysis (PC-DFA) was undertaken on raw spectral data followed by partial least squares regression (PLSR) for quantification of the level of adulteration. Results from these chemometric analyses showed that infrared spectra contained information allowing for the discrimination and quantification between the three naturally occurring sugars in orange juice to disguise adulteration via dilution. Furthermore, it was clearly demonstrated that FT-IR in combination with PLSR is able to predict the levels of adulteration with excellent accuracy; the typical error on these predictions for test samples was 1.7%. We believe that the further development of these and other rapid methods could have an important role to play in the area of food authenticity and integrity, and food analysis in general.

There is no doubt that the contribution of microbially mediated bioprocesses toward maintenance of life on earth is vital. However, understanding these microbes in situ is currently a bottleneck, as most methods require culturing these microorganisms to suitable biomass levels so that their phenotype can be measured. The development of new culture-independent strategies such as stable isotope probing (SIP) coupled with molecular biology has been a breakthrough toward linking gene to function, while circumventing in vitro culturing. In this study, for the first time we have combined Raman spectroscopy and Fourier transform infrared (FT-IR) spectroscopy, as metabolic fingerprinting approaches, with SIP to demonstrate the quantitative labeling and differentiation of Escherichia coli cells. E. coli cells were grown in minimal medium with fixed final concentrations of carbon and nitrogen supply, but with different ratios and combinations of 13C/12C glucose and 15N/14N ammonium chloride, as the sole carbon and nitrogen sources, respectively. The cells were collected at stationary phase and examined by Raman and FT-IR spectroscopies. The multivariate analysis investigation of FT-IR and Raman data illustrated unique clustering patterns resulting from specific spectral shifts upon the incorporation of different isotopes, which were directly correlated with the ratio of the isotopically labeled content of the medium. Multivariate analysis results of single-cell Raman spectra followed the same trend, exhibiting a separation between E. coli cells labeled with different isotopes and multiple isotope levels of C and N.

•PLS-DA, PC-DFA, SVM and RF analyses were compared for metabolomics analyses.•Parsimonious models for feature selection and data reduction were presented.•Comparisons include generally recognized pros along with specific caveats for each of the methods.•Statistical models applied in the analysis of metabolomics data were shown.•Pros and cons of common analytical techniques used in metabolomics studies are highlighted.The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.

The ever increasing numbers and users of designer drugs means that analytical techniques have to evolve constantly to facilitate their identification and detection. We report that surface enhanced Raman scattering (SERS) offers a relatively fast and inexpensive method for the detection of MDAI at low concentrations. Careful optimisation of the silver sol, and salt concentrations was undertaken to ensure the SERS analysis was both reproducible and sensitive. The optimised system demonstrated acceptable peak variations of less than 15% RSD and resulted in a detection limit of just 8 ppm (5.4 × 10−5 M).

There is an on going requirement for the detection and quantification of illicit substances. This is in particular the case for law enforcement where portable screening methods are needed and there has been recent interest in breath tests for a range of narcotics. In this study we first developed surface enhanced Raman scattering (SERS) for the detection of tramadol in water and establish robust and reproducible methods based on silver hydroxylamine colloid. We used 0.5 M NaCl as the aggregating agent, with the pH ∼ 7.0 and SERS data were collected immediately (i.e., the analyte association and colloid aggregation times were zero). The limit of detection was rather high and calculated to be 5 × 10−4 M which would not be practical in the field. Undeterred we continued with spiking tramadol in artificial urine and found that no aggregating agent or modification of pH was necessary. Indeed aggregation occurred spontaneously due to the complexity of the medium which is rich in multiple salts, which are commonly used for SERS. We estimated the limit of detection in artificial urine to be 2.5 × 10−6 M which is equivalent to 657.5 ng mL−1 and very close to the levels typically found in individuals who use tramadol for pain relief. We believe this opens up opportunities for testing SERS in real world samples and this will be an area of future study.

Psoriasis is a common, immune-mediated inflammatory skin disease characterized by red, heavily scaled plaques. The disease affects over one million people in the UK and causes significant physical, psychological and societal impact. There is limited understanding regarding the exact pathogenesis of the disease although it is believed to be a consequence of genetic predisposition and environmental triggers. Treatments vary from topical therapies, such as dithranol, for disease of limited extent (<5% body surface area) to the new immune-targeted biologic therapies for severe psoriasis. Dithranol (also known as anthralin) is a topical therapy for psoriasis believed to work by inhibiting keratinocyte proliferation. To date there have been no metabolomic-based investigations into psoriasis. The HaCaT cell line is a model system for the epidermal keratinocyte proliferation characteristic of psoriasis and was thus chosen for study. Dithranol was applied at therapeutically relevant doses to HaCaT cells. Following the optimisation of enzyme inactivation and metabolite extraction, gas chromatography-mass spectrometry was employed for metabolomics as this addresses central metabolism. Cells were challenged with 0–0.5 μg mL−1 in 0.1 μg mL−1 steps and this quantitative perturbation generated data that were highly amenable to correlation analysis. Thus, we used a combination of traditional principal components analysis, hierarchical cluster analysis, along with correlation networks. All methods highlighted distinct metabolite groups, which had different metabolite trajectories with respect to drug concentration and the interpretation of these data established that cellular metabolism had been altered significantly and provided further clarification of the proposed mechanism of action of the drug.

Phenotyping of 1,200 ‘healthy’ adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography–mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the ‘normal’ relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

Raman spectroscopy has been of interest as a detection method for liquid chromatographic separations for a significant period of time, due to the structural information it can provide, allowing the identification and distinction of coeluting analytes. Combined with the rapidly advancing field of enhanced Raman techniques, such as surface-enhanced Raman scattering (SERS), the previous low sensitivity of Raman measurements has also been alleviated. At-line LC-SERS analyses, where SERS measurements are taken of fractions collected during or after HPLC separation have been shown to be sensitive and applicable to a wide variety of analytes; however, quantitative, real-time, online LC-SERS analysis at comparable sensitivity to existing methods, applicable to high-throughput experiments, has not been previously demonstrated. Here we show that by introducing silver colloid, followed by an aggregating agent into the postcolumn flow of an HPLC system, we can quantitatively and reproducibly analyze mixtures of purine bases, with limits of detection in the region of 100–500 pmol. The analysis is performed without the use of a flow cell, thereby eliminating previously detrimental memory effects.

•LDA, PLS-DA, SVM and RF analyses were applied to MS data.•Double cross-validation using bootstrapping was employed to assess models.•For all classifications, all bacteria were assessed with ∼95% accuracy.•Parsimonious modelling was used on a reduced set of mass ions and was more robust.•The approaches developed are equally applicable to any multivariate 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.

No single analytical method can cover the whole metabolome and the choice of which platform to use may inadvertently introduce chemical selectivity. In order to investigate this we analysed a collection of uropathogenic Escherichia coli. The selected strains had previously undergone extensive characterisation using classical microbiological methods for a variety of metabolic tests and virulence factors. These bacteria were analysed using Fourier transform infrared (FT-IR) spectroscopy; gas chromatography mass spectrometry (GC-MS) after derivatisation of polar non-volatile analytes; as well as reversed-phase liquid chromatography mass spectrometry in both positive (LC-MS+ve) and negative (LC-MS−ve) electrospray ionisation modes. A comparison of the discriminatory ability of these four methods with the metabolic test and virulence factors was made using Procrustes transformations to ascertain which methods produce congruent results. We found that FT-IR and LC-MS−ve, but not LC-MS+ve, were comparable with each other and gave highly similar clustering compared with the virulence factors tests. By contrast, FT-IR and LC-MS−ve were not comparable to the metabolic tests, and we found that the GC-MS profiles were significantly more congruent with the metabolic tests than the virulence determinants. We conclude that metabolomics investigations may be biased to the analytical platform that is used and reflects the chemistry employed by the methods. We therefore consider that multiple platforms should be employed where possible and that the analyst should consider that there is a danger of false correlations between the analytical data and the biological characteristics of interest if the full metabolome has not been measured.

The detection and quantification of xenobiotics and their metabolites in man is important for drug dosing, therapy and for substance abuse monitoring where longer-lived metabolic products from illicit materials can be assayed after the drug of abuse has been cleared from the system. Raman spectroscopy offers unique specificity for molecular characterization and this usually weak signal can be significantly enhanced using surface enhanced Raman scattering (SERS). We report here the novel development of SERS with chemometrics for the simultaneous analysis of the drug nicotine and its major xenometabolites cotinine and trans-3′-hydroxycotinine. Initial experiments optimized the SERS conditions and we found that when these three determinands were analysed individually that the maximum SERS signals were found at three different pH. These were pH 3 for nicotine and pH 10 and 11 for cotinine and trans-3′-hydroxycotinine, respectively. Tertiary mixtures containing nicotine, cotinine and trans-3′-hydroxycotinine were generated in the concentration range 10−7–10−5 M and SERS spectra were collected at all three pH values. Chemometric analysis using kernel-partial least squares (K-PLS) and artificial neural networks (ANNs) were conducted and these models were validated using bootstrap resampling. All three analytes were accurately quantified with typical root mean squared error of prediction on the test set data being 5–9%; nicotine was most accurately predicted followed by cotinine and then trans-3′-hydroxycotinine. We believe that SERS is a powerful approach for the simultaneous analysis of multiple determinands without recourse to lengthy chromatography, as demonstrated here for the xenobiotic nicotine and its two major xenometabolites.

MON 87460 (D1) maize contains a gene that expresses the cold shock protein B (CSPB) from Bacillus subtilis to confer a yield advantage when yield is limited by water availability. This study evaluated the composition of grain from the D1-containing combined-trait maize hybrids D1 × NK603, D1 × MON 89034 × NK603, and D1 × MON 89034 × MON 88017. These stacks offer a combination of insect protection and herbicide tolerance traits. These hybrids were grown under well-watered and water-limited conditions at three replicated field sites across Chile during the 2006–2007 growing season. Compositional analyses included measurement of proximates, fibers, total amino acids, fatty acids, minerals, vitamins, raffinose, phytic acid, p-coumaric acid, and ferulic acid. The statistical analyses included an evaluation of the applicability of multiblock principal component analysis (MB-PCA) and ANOVA–simultaneous component analysis (ASCA) to studies when more than one experimental factor will contribute to compositional variability. Results from these multivariate procedures highlighted that water treatment was the greatest contributor to compositional variability and, as expected, confirmed that the grain of combined-trait drought-tolerant hybrids was compositionally equivalent to that of conventional comparators as established by traditional statistical significance testing.

Recently, it has been reported that anti-viral drugs, such as indinavir and lopinavir (originally targeted for HIV), also inhibit E6-mediated proteasomal degradation of mutant p53 in E6-transfected C33A cells. In order to understand more about the mode-of-action(s) of these drugs the metabolome of HPV16 E6 expressing cervical carcinoma cell lines was investigated using mass spectrometry (MS)-based metabolic profiling. The metabolite profiling of C33A parent and E6-transfected cells exposed to these two anti-viral drugs was performed by ultra performance liquid chromatography (UPLC)-MS and gas chromatography (GC)-time of flight (TOF)-MS. Using a combination of univariate and multivariate analyses, these metabolic profiles were investigated for analytical and biological reproducibility and to discover key metabolite differences elicited during anti-viral drug challenge. This approach revealed both distinct and common effects of these two drugs on the metabolome of two different cell lines. Finally, intracellular drug levels were quantified, which suggested in the case of lopinavir that increased activity of membrane transporters may contribute to the drug sensitivity of HPV infected cells.

Assessing the stability of proteins by comparing their unfolding profiles is a very important characterization and quality control step for any biopharmaceutical, and this is usually measured by fluorescence spectroscopy. In this paper we propose Raman spectroscopy as a rapid, noninvasive alternative analytical method and we shall show this has enhanced sensitivity and can therefore reveal very subtle protein conformational changes that are not observed with fluorescence measurements. Raman spectroscopy is a powerful nondestructive method that has a strong history of applications in protein characterization. In this work we describe how Raman microscopy can be used as a fast and reliable method of tracking protein unfolding in the presence of a chemical denaturant. We have compared Raman spectroscopic data to the equivalent samples analyzed using fluorescence spectroscopy in order to validate the Raman approach. Calculations from both Raman and fluorescence unfolding curves of [D]50 values and Gibbs free energy correlate well with each other and more importantly agree with the values found in the literature for these proteins. In addition, 2D correlation analysis has been performed on both Raman and fluorescence data sets in order to allow further comparisons of the unfolding behavior indicated by each method. As many biopharmaceuticals are glycosylated in order to be functional, we compare the unfolding profiles of a protein (RNase A) and a glycoprotein (RNase B) as measured by Raman spectroscopy and discuss the implications that glycosylation has on the stability of the protein.

A new optimization strategy for the SERS detection of mephedrone using a portable Raman system has been developed. A fractional factorial design was employed, and the number of statistically significant experiments (288) was greatly reduced from the actual total number of experiments (1722), which minimized the workload while maintaining the statistical integrity of the results. A number of conditions were explored in relation to mephedrone SERS signal optimization including the type of nanoparticle, pH, and aggregating agents (salts). Through exercising this design, it was possible to derive the significance of each of the individual variables, and we discovered four optimized SERS protocols for which the reproducibility of the SERS signal and the limit of detection (LOD) of mephedrone were established. Using traditional nanoparticles with a combination of salts and pHs, it was shown that the relative standard deviations of mephedrone-specific Raman peaks were as low as 0.51%, and the LOD was estimated to be around 1.6 μg/mL (9.06 × 10–6 M), a detection limit well beyond the scope of conventional Raman and extremely low for an analytical method optimized for quick and uncomplicated in-field use.

Portable rapid detection of pathogenic bacteria such as Bacillus is highly desirable for safety in food manufacture and under the current heightened risk of biological terrorism. Surface-enhanced Raman scattering (SERS) is becoming the preferred analytical technique for bacterial detection, due to its speed of analysis and high sensitivity. However in seeking methods offering the lowest limits of detection, the current research has tended toward highly confocal, microscopy-based analysis, which requires somewhat bulky instrumentation and precisely synthesized SERS substrates. By contrast, in this study we have improved SERS for bacterial analyses using silver colloidal substrates, which are easily and cheaply synthesized in bulk, and which we shall demonstrate permit analysis using portable instrumentation. All analyses were conducted in triplicate to assess the reproducibility of this approach, which was excellent. We demonstrate that SERS is able to detect and quantify rapidly the dipicolinate (DPA) biomarker for Bacillus spores at 5 ppb (29.9 nM) levels which are significantly lower than those previously reported for SERS and well below the infective dose of 104B. anthracis cells for inhalation anthrax. Finally we show the potential of multivariate data analysis to improve detection levels in complex DPA extracts from viable spores.

UV resonance Raman (UVRR) spectroscopy combined with chemometric techniques was investigated as a physiochemical tool for monitoring secreted recombinant antibody production in cultures of Chinese hamster ovary (CHO) cells. Due to the enhanced selectivity of the UVRR, spectral variations arising from protein, small molecule substrates, and nucleic acid medium components could be measured simultaneously and we have successfully determined antibody titre. Medium samples were taken during culture of three CHO cell lines: two antibody-producing cell lines and a non-producing cell line, and analysed by UVRR spectroscopy using an excitation laser of 244 nm. Principal component analysis (PCA) was applied to the spectral sets and showed a linear trend over time for the antibody-producing cell lines that was not observed in the non-producing cell line. Partial least squares regression (PLSR) was used to predict antibody titres, glucose utilization and lactate accumulation, and compared very favourably with gold standard data acquired with the much slower techniques of ELISA and liquid chromatography. Further analysis of the UVRR spectral sets using two-dimensional correlation moving windows also revealed that spectral variations due to protein and nucleic acid concentrations in the medium during cell culture varied between each of the three cell lines investigated.

Fourier transform infrared (FT-IR) spectroscopy is an established rapid whole-organism fingerprinting method that generates metabolic fingerprints from bacteria that reflect the phenotype of the microorganism under investigation. However, whilst FT-IR spectroscopy is fast (typically 10 s to 1 min per sample), the approaches for microbial sample preparation can be time consuming as plate culture or shake flasks are used for growth of the organism. We report a new approach that allows micro-cultivation of bacteria from low volumes (typically 200 μL) to be coupled with FT-IR spectroscopy. This approach is fast and easy to perform and gives equivalent data to the lengthier and more expensive shake flask cultivations (sample volume = 20 mL). With this micro-culture approach we also demonstrate high reproducibility of the metabolic fingerprints. The approach allowed separation of different isolates of Escherichia coli involved in urinary tract infection, including members of the globally disseminated ST131 clone, with respect to both genotype and resistance or otherwise to the antibiotic Ciprofloxacin.

Deposition of silver onto British 2p coins has been demonstrated as an efficient and cost effective approach to producing substrates capable of promoting surface enhanced Raman scattering (SERS). Silver application to the copper coins is undemanding taking just 20 s, and results in the formation of multiple hierarchial dendritic structures. To demonstrate that the silver deposition sites were capable of SERS the highly fluorescent Rhodamine 6G (R6G) probe was used. Analyses indicated that Raman enhancement only occurs at the silver deposition sites and not from the roughened copper surface. The robustness of the substrate in the identification and discrimination of illegal and legal drugs of abuse was then explored. Application of the drugs to the substrates was carried out using spotting and soaking methodologies. Whilst little or no SERS spectra of the drugs were generated upon spotting, soaking of the substrate in a methanolic solution of the drugs yielded a vast amount of spectral information. Excellent reproducibility of the SERS method and classification of three of the drugs, 4-methylmethcathinone (mephedrone), 5,6-methylenedioxy-2-aminoindane (MDAI) and 3,4-methylenedioxy-N-methylamphetamine (MDMA) were demonstrated using principal components analysis and partial least squares.

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.

Colloidal-based surface-enhanced Raman scattering (SERS) is a complex technique, where interaction between multiple parameters, such as colloid type, its concentration, and aggregating agent, is poorly understood. As a result SERS has so far achieved limited reproducibility. Therefore the aim of this study was to improve enhancement and reproducibility in SERS, and to achieve this, we have developed a multiobjective evolutionary algorithm (MOEA) based on Pareto optimality. In this MOEA approach, we tested a combination of five different colloids with six different aggregating agents, and a wide range of concentrations for both were explored; in addition we included in the optimization process three laser excitation wavelengths. For this optimization of experimental conditions for SERS, we chose the β-adrenergic blocker drug propranolol as the target analyte. The objective functions chosen suitable for this multiobjective problem were the ratio between the full width at half-maximum and the half-maximum intensity for enhancement and correlation coefficient for reproducibility. To analyze a full search of all the experimental conditions, 7785 experiments would have to be performed empirically; however, we demonstrated the search for acceptable experimental conditions of SERS can be achieved using only 4% of these possible experiments. The MOEA identified several experimental conditions for each objective which allowed a limit of detection of 2.36 ng/mL (7.97 nM) propranolol, and this is significantly lower (>25 times) than previous SERS studies aimed at detecting this β-blocker.

A fast and cost-effective approach for the synthesis of substrates used in surface enhanced Raman scattering (SERS) has been developed using galvanic displacement. Deposition of silver onto commercially available Cu foil has resulted in the formation of multiple hierarchical structures, whose morphology show dependence on deposition time and temperature. Analysis of the surface structure by scanning electron microscopy revealed that the more complex silver structures correlated well with increased deposition time and temperature. Using Rhodamine 6G (R6G) as a model Raman probe it was also possible to relate the substrate morphology directly with subsequent SERS intensity from the R6G analyte as well as the reproducibility across a total of 15 replicate Raman maps (20 × 20 pixels) consisting of 400 spectra at a R6G concentration of 10−4 M. The substrate with the highest reproducibility was then used to explore the limit of detection and this compared very favourably with colloidal-based SERS assessments of the same analyte.

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 (Q2) and training set (R2) 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.

Protein-based biopharmaceuticals are becoming increasingly widely used as therapeutic agents, and the characterization of these biopharmaceuticals poses a significant analytical challenge. In particular, monitoring posttranslational modifications (PTMs), such as glycosylation, is an important aspect of this characterization because these glycans can strongly affect the stability, immunogenicity, and pharmacokinetics of these biotherapeutic drugs. Raman spectroscopy is a powerful tool, with many emerging applications in the bioprocessing arena. Although the technique has a relatively rich history in protein science, only recently has Raman spectroscopy been investigated for assessing posttranslational modifications, including phosphorylation, acetylation, trimethylation, and ubiquitination. In this investigation, we develop for the first time Raman spectroscopy combined with multivariate data analyses, including principal components analysis and partial least-squares regression, for the determination of the glycosylation status of proteins and quantifying the relative concentrations of the native ribonuclease (RNase) A protein and RNase B glycoprotein within mixtures.

Spoilage in meat is the result of the action of microorganisms and results in changes of meat and microbial metabolism. This process may include pathogenic food poisoning bacteria such as Salmonella typhimurium, and it is important that these are differentiated from the natural spoilage process caused by non-pathogenic microorganisms. In this study we investigated the application of metabolic profiling using gas chromatography-mass spectrometry, to assess the microbial contamination of pork. Metabolite profiles were generated from microorganisms, originating from the natural spoilage process and from the artificial contamination with S. typhimurium. In an initial experiment, we investigated changes in the metabolic profiles over a 72 hour time course at 25 °C and established time points indicative of the spoilage process. A further experiment was performed to provide in-depth analysis of the metabolites characteristic of contamination by S. typhimurium. We applied a three-way PARAllel FACtor analysis 2 (PARAFAC2) multivariate algorithm to model the metabolic profiles. In addition, two univariate statistical tests, two-sample Wilcoxon signed rank test and Friedman test, were employed to identify metabolites which showed significant difference between natural spoiled and S. typhimurium contaminated samples. Consistent results from the two independent experiments were obtained showing the discrimination of the metabolic profiles of the natural spoiled pork chops and those contaminated with S. typhimurium. The analysis identified 17 metabolites of significant interest (including various types of amino acid and fatty acid) in the discrimination of pork contaminated with the pathogenic microorganism.

Metabolomics allows the simultaneous and relative quantification of thousands of different metabolites within a given sample using sensitive and specific methodologies such as gas or liquid chromatography coupled to mass spectrometry, typically in discovery phases of studies. Biomarkers are biological characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes or pharmacologic responses to a therapeutic intervention. Biomarkers are widely used in clinical practice for the diagnosis, assessment of severity and response to therapy in a number of clinical disease states. In human studies, metabolomics has been applied to define biomarkers related to prognosis or diagnosis of a disease or drug toxicity/efficacy and in doing so hopes to provide greater pathophysiological understanding of disease or therapeutic toxicity/efficacy. This review discusses the application of metabolomics in the discovery and subsequent application of biomarkers in the diagnosis and management of inborn errors of metabolism, cardiovascular disease and cancer. We critically appraise how novel biomarkers discovered through metabolomic analysis may be utilized in future clinical practice by addressing the following three fundamental questions: (1) Can the clinician measure them? (2) Do they add new information? (3) Do they help the clinician to manage patients? Although a number of novel biomarkers have been discovered through metabolomic studies of human diseases in the last decade, none have currently made the transition to routine use in clinical practice. Metabolites identified from these early studies will need to form the basis of larger, prospective, externally validated studies in clinical cohorts for their future use as biomarkers. At this stage, the absolute quantification of these biomarkers will need to be assessed epidemiologically, as will the ultimate deployment in the clinic via routine biochemistry, dip stick or similar rapid at- or near-patient care technologies.

The extensive consumption of milk and dairy products makes these foodstuffs targets for potential adulteration with financial gains for unscrupulous producers. Such practices must be detected as these can impact negatively on product quality, labelling and even health. Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF-MS) is a potentially useful technique, with proven abilities in protein identification and more recently through the use of internal standards for quantification purposes of specific proteins or peptides. In the current work, we therefore aim to explore the accuracy and attributes of MALDI-ToF-MS with chemometrics for the detection and quantification of milk adulteration. Three binary mixtures containing cows' and goats', cows' and sheep's, and goats' and sheep's milk and a fourth tertiary mixture containing all types of milk were prepared and analysed directly using MALDI-ToF-MS. In these mixtures, the milk concentrations of each milk varied from 0% to 100% in 5% steps. Multivariate statistical methods including partial least squares (PLS) regression and non-linear Kernel PLS regression were employed for multivariate calibration and final interpretation of the results. The results for PLS and KPLS were encouraging with between 2% and 13% root mean squared error of prediction on independent data; KPLS slightly outperformed PLS. We believe that these results show that MALDI-ToF-MS has excellent potential for future use in the dairy industry as a rapid method of detection and enumeration in milk adulteration.

Recently, it has been reported that the anti-viral drug, lopinavir, which is currently used as a human immunodeficiency virus (HIV) protease inhibitor, could also inhibit E6-mediated proteasomal degradation of mutant p53 in E6-transfected C33A cells. In this study, C33A parent control cells and HPV16 E6-transfected cells were exposed to lopinavir at concentrations ranging from 0 to 30 µM. The phenotypic response was assessed by Fourier transform infrared (FT-IR) spectroscopy directly on cells (the metabolic fingerprint) and on the cell growth medium (the metabolic footprint). Multivariate analysis of the data using both principal components analysis (PCA) and canonical variates analysis (PC-CVA) showed trends in scores plots that were related to the concentration of the drug. Inspection of the PC-CVA loadings vector revealed that the effect was not due to the drug alone and that several IR spectral regions including proteins, nucleotides and carbohydrates contributed to the separation in PC-CVA space. Finally, partial least squares regression (PLSR) could be used to predict the concentration of the drug accurately from the metabolic fingerprints and footprints, indicating a dose related phenotypic response. This study shows that the combination of metabolic fingerprinting and footprinting with appropriate chemometric analysis is a valuable approach for studying cellular responses to anti-viral drugs.

Sudan-1 has been used for coloring food. However, recent alarms worldwide about the carcinogenic and mutagenic properties of azo-compounds have led to concerns over their human consumption. In the U.K. in 2005, over 570 products were found to be contaminated with the azo dye Sudan-1 and this and the health risks associated with this dye resulted in the subsequent international ban of this additive in all foodstuff, at all levels, relating to human consumption. These incidents have also necessitated the need for high throughput low cost reliable approaches for the detection and quantification of food contaminated by such azo compounds. While there are a small number of analytical techniques that can be considered portable, many lack sensitivity. By contrast, we show that employing a portable Raman spectrometer, using surface enhanced Raman scattering (SERS), can provide good sensitivity, such that Sudan-1 can be quantified in a complex food matrix reliably over the range of 10−3 to 10−4 mol L−1. We also demonstrate that a variety of multivariate approaches including principal components analysis (PCA), partial least-squares (PLS) regression, artificial neural networks (ANNs), and support vector regression (SVR) can be employed for the chemical analysis of this dye in a quantitative manner. Compared to the commonly used univariate approaches, where the area under a single band in assessed, the advantage of using multivariate approaches is that these algorithms can analyze the full spectra directly and the laborious task of selecting and integrating marker appropriate quantitative spectral bands can be avoided thus greatly simplifying and speeding up data analysis.

Monitoring enzyme kinetics is an important aspect of biochemistry and is essential when studying metabolism. In this study we demonstrate that succinate dehydrogenase activity of mitochondria can be analyzed quantitatively by surface enhanced Raman scattering (SERS). We used the artificial electron acceptor reporter molecule 2,6-dichlorophenolindophenol (DCPIP) that when oxidized is SERS active. On reduction this redox dye changes from blue to colorless and is SERS inactive. This color change allows UV/vis spectrophotometry to be used as a standard reference measurement as well as SERS. SERS analysis incorporated kinetic time course measurements and employed a portable laser Raman spectrometer using an excitation wavelength of 785 nm in conjunction with gold colloids. Good correlation coefficients and quantitative data were observed with the additional advantage that analysis by SERS also provided good fingerprint spectral information from vibrational band frequencies thus allowing the potential of multiplexing enzyme reactions. We believe that this is the first demonstration of monitoring succinate dehydrogenase activity with SERS and that SERS has considerable potential for being applied to the analysis of metabolic processes.

Discrimination of bacteria was investigated using pyrolysis-gas chromatography-differential mobility spectrometry (Py-GC-DMS). Three strains belonging to the genus Bacillus were investigated and these included two strains of Bacillus subtilis and a single Bacillus megaterium. These were chosen so as to evaluate the possibility of bacterial strain discrimination using Py-GC-DMS. The instrument was constructed in-house and the long-term reproducibility of the instrument was evaluated over a period of 60 days using a Scotch whisky quality control. To assess the reproducibility further each bacterium was cultured six times and each culture was analysed in replicate to give three analytical replicates. The DMS data were generated in both positive and negative modes, and the data in each mode were analysed independently of each other. The Py-GC-DMS data were pre-processed via correlation optimised warping (COW) and asymmetric least square (ALS) to align the DMS chromatograms and to remove any unavoidable baseline shifts, prior to normalisation. Processed chromatograms were analysed using principal component analysis (PCA) followed by supervised learning methodology using partial least squares for discriminant analysis (PLS-DA). It was found that the separations between B. subtilis and B. megaterium can be readily observed by PCA; however, strain discrimination within the two B. subtilis was only possible using supervised learning. As multiple biological replicates were analysed an exhaustive splitting of the training and test sets was undertaken and this allowed correct classification rates (CCRs) to be assessed for the 3375 test sets. It was found that with PLS-DA the negative ion mode DMS data were more discriminatory than the positive mode data.

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a recently developed technique that generates molecular profiles usually of peptide and protein signals directly from the surface of thin tissue sections and can be coupled with automation to generate two-dimensional ion density maps. This allows specific information to be obtained on the relative abundance and spatial distribution of the analytes of interest. The technique has potential for application in many diseases including cancer with respect to elucidating the molecular pathology and identifying potential biomarkers. In this proof-of-principle study we have evaluated inkjet printing of the sinapinic acid matrix used for MALDI-IMS directly onto the surface of human oral squamous cell carcinoma biopsy specimens. This MS profiling technique produced reproducible informative chemical images for clinical pathology. Analysis of the resulting protein profiles of highly expressed protein in squamous cell carcinoma of the tongue reveals spectral features at ∼4500 and ∼8360 Da.

Surface-enhanced resonance Raman scattering (SERRS) spectra of aqueous solutions of the triphenylmethane dye methyl green have been obtained for the first time by use of citrate-reduced silver colloids and a laser excitation wavelength of 632.8 nm. Given the highly fluorescent nature of the analyte, which precluded collection of normal Raman spectra of the dye in solution and powdered state, it was highly encouraging that SERRS spectra showed no fluorescence due to quenching by the silver sol. The pH conditions for SERRS were optimised over the pH range 0.5–10 and the biggest enhancement for SERRS of this charged dye was found to be at pH 2.02, thus this condition was used for quantitative analysis. SERRS was found to be highly sensitive and enabled quantitative determination of methyl green over the range 10−9 to 10−7 mol dm−3. Good fits to correlation coefficients were obtained over this range using the areas under the vibrational bands at 1615 and 737 cm−1. Finally, a limit of detection of 83 ppb was calculated, demonstrating the sensitivity of the technique.

The release of active pharmaceutical ingredients (APIs) into the environment is an ecologically important topic for study because, whilst APIs have been designed to have a wide range of biological properties for the target of interest (usually in man), little information on potential ecological risks is currently available regarding their effects on the organisms that inhabit the environment. In this study, the algae Micrasterias hardyi was exposed to propranolol, metoprolol (beta-adrenergic receptor agonist drugs) and mefenamic acid (a non steroidal anti-inflammatory drug), at concentrations ranging between 0.002–0.2 mM. Initial studies showed that Fourier transform infrared (FT-IR) spectroscopy on algal homogenates illustrated that all three APIs had a quantitative effect on the metabolism of the organisms and it was possible to estimate the level of API exposure from the FT-IR metabolic fingerprints using partial least squares (PLS) regression. From the inspection of the PLS loadings matrices it was possible to elucidate that all drugs caused effects on protein and lipid levels. Most strikingly propranolol had significant effects on the lipid components of the cell. These were dramatically reduced possibly as a consequence of loss of membrane integrity. In order to investigate this further, FT-IR microspectroscopy was used to generate detailed metabolic fingerprinting maps. These chemical maps revealed that all the drugs had a dramatic effect on the distribution of various chemical species throughout the algae, and that all drugs had an affect on protein and lipid levels. In particular, as noted in the PLS analyses for propranolol treated cells, the lipid complement found in the lipid storage areas in the processes of M. hardyi was greatly reduced. This illustrates the power of spatial metabolic fingerprinting for investigating abiotic stresses on complex biological species.

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 °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 105 cfu ml−1. By contrast, FT-IR HT provided more accurate results for lower viable bacterial counts down to 103 cfu ml−1 for whole milk and, 4 × 102 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.

The labelling of target biomolecules followed by detection using some form of optical spectroscopy has become common practice to aid in their detection. This approach has allowed the field of bioanalysis to dramatically expand; however, most methods suffer from the lack of the ability to discriminate between the components of a complex mixture. Currently, fluorescence spectroscopy is the method of choice but its ability to multiplex is greatly hampered by the broad overlapping spectra which are obtained. Surface enhanced resonance Raman scattering (SERRS) holds many advantages over fluorescence both in sensitivity and, more importantly here, in its ability to identify components in a mixture without separation due to the sharp fingerprint spectra obtained. Here the first multiplexed simultaneous detection of six different DNA sequences, corresponding to different strains of the Escherichia coli bacterium, each labelled with a different commercially available dye label (ROX, HEX, FAM, TET, Cy3, or TAMRA) is reported. This was achieved with the aid of multivariate analysis, also known as chemometrics, which can involve the application of a wide range of statistical and data analysis methods. In this study, both exploratory discriminant analysis and supervised learning, by partial least squares (PLS) regression, were used and the ability to discriminate whether a particular labelled oligonucleotide was present or absent in a mixture was achieved using PLS with very high sensitivity (0.98–1), specificity (0.98–1), accuracy (range 0.99–1), and precision (0.98–1).

The ability to diagnose the early onset of disease, rapidly, non-invasively and unequivocally has multiple benefits. These include the early intervention of therapeutic strategies leading to a reduction in morbidity and mortality, and the releasing of economic resources within overburdened health care systems. Some of the routine clinical tests currently in use are known to be unsuitable or unreliable. In addition, these often rely on single disease markers which are inappropriate when multiple factors are involved. Many diseases are a result of metabolic disorders, therefore it is logical to measure metabolism directly. One of the strategies employed by the emergent science of metabolomics is metabolic fingerprinting; which involves rapid, high-throughput global analysis to discriminate between samples of different biological status or origin. This review focuses on a selective number of recent studies where metabolic fingerprinting has been forwarded as a potential tool for disease diagnosis using infrared and Raman spectroscopies.

Comparatively few studies have explored the ability of Raman spectroscopy for the quantitative analysis of microbial secondary metabolites in fermentation broths. In this study we investigated the ability of Raman spectroscopy to differentiate between different penicillins and to quantify the level of penicillin in fermentation broths. However, the Raman signal is rather weak, therefore the Raman signal was enhanced using surface enhanced Raman spectroscopy (SERS) employing silver colloids. It was difficult by eye to differentiate between the five different penicillin molecules studied using Raman and SERS spectra, therefore the spectra were analysed by multivariate cluster analysis. Principal components analysis (PCA) clearly showed that SERS rather than the Raman spectra produced reproducible enough spectra to allow for the recovery of each of the different penicillins into their respective five groups. To highlight this further the first five principal components were used to construct a dendrogram using agglomerative clustering, and this again clearly showed that SERS can be used to identify which penicillin molecule was being analysed, despite their molecular similarities. With respect to the quantification of penicillin G it was shown that Raman spectroscopy could be used to quantify the amount of penicillin present in solution when relatively high levels of penicillin were analysed (>50 mM). By contrast, the SERS spectra showed reduced fluorescence, and improved signal to noise ratios from considerably lower concentrations of the antibiotic. This could prove to be advantageous in industry for monitoring low levels of penicillin in the early stages of antibiotic production. In addition, SERS may have advantages for quantifying low levels of high value, low yield, secondary metabolites in microbial processes.

Muscle foods are an integral part of the human diet and during the last few decades consumption of poultry products in particular has increased significantly. It is important for consumers, retailers and food regulatory bodies that these products are of a consistently high quality, authentic, and have not been subjected to adulteration by any lower-grade material either by accident or for economic gain. A variety of methods have been developed for the identification and authentication of muscle foods. However, none of these are rapid or non-invasive, all are time-consuming and difficulties have been encountered in discriminating between the commercially important avian species. Whilst previous attempts have been made to discriminate between muscle foods using infrared spectroscopy, these have had limited success, in particular regarding the closely related poultry species, chicken and turkey. Moreover, this study includes novel data since no attempts have been made to discriminate between both the species and the distinct muscle groups within these species, and this is the first application of Raman spectroscopy to the study of muscle foods. Samples of pre-packed meat and poultry were acquired and FT-IR and Raman measurements taken directly from the meat surface. Qualitative interpretation of FT-IR and Raman spectra at the species and muscle group levels were possible using discriminant function analysis. Genetic algorithms were used to elucidate meaningful interpretation of FT-IR results in (bio)chemical terms and we show that specific wavenumbers, and therefore chemical species, were discriminatory for each type (species and muscle) of poultry sample. We believe that this approach would aid food regulatory bodies in the rapid identification of meat and poultry products and shows particular potential for rapid assessment of food adulteration.

Metabolomics can play a particularly important role in elucidating novel anabolic and catabolic pathways in bacteria and fungi, and in understanding the dynamics of metabolism. In these approaches, an isotopically labelled substrate, with an artificially high abundance of isotopic label, is fed to the microorganism under study. The products become isotopically labelled, and can be measured using a combination of mass spectrometry and nuclear magnetic resonance spectroscopy. This mass isotopomer analysis is referred to as time and relative differences in systems (TARDIS)-based analysis, as it measures and quantifies the temporal sequential emergence of these labelled products. In this review, we cover this topic from an experimental point of view in relation to the study of metabolism, and summarise how the application of radioactive and stable isotopes is being used in pathway elucidation and metabolic flux determination (fluxomics).

As the world progresses from a fossil-fuel based economy to a more sustainable one, synthetic biology will become increasingly important for the production of high-value fine chemicals as well as low-value commodities in bulk. The integration of metabolomics and fluxomics within synthetic biology projects will be vital at all levels, including the initial design of the pathways to be generated, through to the optimisation of those pathways so that more efficient conversion of low-cost starting materials into highly desirable products can be achieved. This review highlights these areas and details the most important and exciting advances being made in this area.Highlights► Non-targeted tracer fate detection (NTFD) adds new knowledge and a new dimension to metabolomics and synthetic bioprocesses. ► How multiple functional analyses can be integrated to help understand carbohydrate utilisation. ► The importance of biofuel production illustrated via the engineering of aquatic cyanobacteria. ► TARDIS-based mass isotopomer analysis for microbial metabolomics. ► Reverse engineering and inference of metabolic networks using computational approaches.

The UK Synthetic Biology Research Centre, SYNBIOCHEM, hosted by the Manchester Institute of Biotechnology at the University of Manchester is delivering innovative technology platforms to facilitate the predictable engineering of microbial bio-factories for fine and speciality chemicals production. We provide an overview of our foundry activities that are being applied to grand challenge projects to deliver innovation in bio-based chemicals production for industrial biotechnology.

The authenticity of milk and milk products is important and has extended health, cultural, and financial implications. Current analytical methods for the detection of milk adulteration are slow, laborious, and therefore impractical for use in routine milk screening by the dairy industry. Fourier transform infrared (FT-IR) spectroscopy is a rapid biochemical fingerprinting technique that could be used to reduce this sample analysis period significantly. To test this hypothesis we investigated 3 types of milk: cow, goat, and sheep milk. From these, 4 mixtures were prepared. The first 3 were binary mixtures of sheep and cow milk, goat and cow milk, or sheep and goat milk; in all mixtures the mixtures contained between 0 and 100% of each milk in increments of 5%. The fourth combination was a tertiary mixture containing sheep, cow, and goat milk also in increments of 5%. Analysis by FT-IR spectroscopy in combination with multivariate statistical methods, including partial least squares (PLS) regression and nonlinear kernel partial least squares (KPLS) regression, were used for multivariate calibration to quantify the different levels of adulterated milk. The FT-IR spectra showed a reasonably good predictive value for the binary mixtures, with an error level of 6.5 to 8% when analyzed using PLS. The results improved and excellent predictions were achieved (only 4–6% error) when KPLS was employed. Excellent predictions were achieved by both PLS and KPLS with errors of 3.4 to 4.9% and 3.9 to 6.4%, respectively, when the tertiary mixtures were analyzed. We believe that these results show that FT-IR spectroscopy has excellent potential for use in the dairy industry as a rapid method of detection and quantification in milk adulteration.

•Optimization of MALDI-TOF-MS for characterizing Bacillus and Brevibacillus species.•Development of a suitable chemometric workflow for processing raw MALDI-TOF-MS data.•Classification of 7 species from bacteria achieved high accuracy (∼90%).•Allowed to dry at room temperature (ca. 22 °C) for 1 h.Over the past few decades there has been an increased interest in using various analytical techniques for detecting and identifying microorganisms. More recently there has been an explosion in the application of matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) for bacterial characterization, and here we optimize this approach in order to generate reproducible MS data from bacteria belonging to the genera Bacillus and Brevibacillus. Unfortunately MALDI-TOF-MS generates large amounts of data and is prone to instrumental drift. To overcome these challenges we have developed a preprocessing pipeline that includes baseline correction, peak alignment followed by peak picking that in combination significantly reduces the dimensionality of the MS spectra and corrects for instrument drift. Following this two different prediction models were used which are based on support vector machines and these generated satisfactory prediction accuracies of approximately 90%.Figure optionsDownload full-size imageDownload as PowerPoint slide