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Rapid analysis of surrendered or seized drug samples provides important intelligence for health (e.g. treatment or harm reduction), and custodial services. Herein, three in-situ techniques, GC-MS, 1H NMR and FT-IR spectroscopy, with searchable libraries, are used to analyse 318 samples qualitatively, using technique specific library-based searches, obtained over the period 24th – 29th August 2019. 259 samples were identified as consisting of a single component, of which cocaine was the most prevalent (n = 158). Median match scores for all three techniques were ≥ 0.84 and showed agreement except for metformin (n = 1), oxandrolone (identified as vitamin K by IR (n = 4)), diazepam (identified as zolpidem by FT-IR (n = 2)) and 2-Br-4,5-DMPEA (n = 1), a structural isomer of 2C-B identified as a polymer of cellulose (cardboard) by FT-IR. 51 samples were found to consist of two or more components, of which 49 were adulterated cocaine samples (45 binary and 4 tertiary samples). GC-MS identified all components present in the 49 adulterated cocaine samples, whereas IR identified only cocaine in 88 % of cases (adulterant only = 12 %). The breakdown for 1H NMR spectroscopy was all components identified (51 %), cocaine only (33 %), adulterant only (10 %), cocaine and one adulterant (tertiary mixtures only, 6 %).

Amphetamine and cathinone derivatives are abused recreationally due to the sense of euphoria they provide to the user. Methodologies for the rapid detection of the drug derivative present in a seized sample, or an indication of the drug class, are beneficial to law enforcement and healthcare providers. Identifying the drug class is prudent because derivatisation of these drugs, to produce regioisomers, for example, occurs frequently to circumvent global and local drug laws. Thus, newly encountered derivatives might not be present in a spectral library. Employment of benchtop nuclear magnetic resonance (NMR) could be used to provide rapid analysis of seized samples as well as identifying the class of drug present. Discrimination of individual amphetamine-, methcathinone-, N-ethylcathinone and nor-ephedrine-derived fluorinated and methylated regioisomers is achieved herein using qualitative automated 1 H NMR analysis and compared to gas chromatography-mass spectrometry (GC-MS) data. Two seized drug samples, SS1 and SS2, were identified to contain 4-fluoroamphetamine by 1 H NMR (match score median = 0.9933) and GC-MS (RRt = 5.42-5.43 min). The amount of 4-fluoroamphetamine present was 42.8%-43.4% w/w and 48.7%-49.2% w/w for SS1 and SS2, respectively, from quantitative 19 F NMR analysis, which is in agreement with the amount determined by GC-MS (39.9%-41.4% w/w and 49.0%-49.3% w/w). The total time for the qualitative 1 H NMR and quantitative 19 F NMR analysis is ~10 min. This contrasts to ~40 min for the GC-MS method. The NMR method also benefits from minimal sample preparation. Thus, benchtop NMR affords rapid, and discriminatory, analysis of the drug present in a seized sample.

60 MHz proton NMR spectroscopy was used to analyse extracts from saffron spice and a range of potential adulterants and mixtures. Using a simple extraction procedure, good quality spectra were obtained which contain peaks from the characteristic metabolites picrocrocin and crocins, fatty acids and kaempferol. The spectra of samples from trusted suppliers were used to train one-class classification models by SIMCA, nearest neighbour and isolation forest methods. Applying these to spectra of saffron samples purchased from the online marketplace, it was found that 7 out of 33 samples were highly anomalous. From comparison with the spectra of known mixtures and confirmatory spectral analysis using 600 MHz NMR, it is probable that these contain considerable amounts of undisclosed foreign matter.

We use 60‐MHz benchtop nuclear magnetic resonance (NMR) to acquire 1H spectra from argan oils of assured origin. We show that the low‐field NMR spectrum of neat oil contains sufficient information to make estimates of compositional parameters and to inform on the presence of minor compounds. A screening method for quality and authenticity is presented based on nearest‐neighbour outlier detection. A variety of oil types are used to challenge the method. In a survey of retail‐purchased oils, several instances of fraud were found., We use 60‐MHz benchtop nuclear magnetic resonance (NMR) to acquire 1H spectra from argan oils of assured origin. We show that the low‐field NMR spectrum of neat oil contains information sufficient for obtaining composition values and informing on the presence of minor compounds. A screening method for quality and authenticity is presented based on nearest‐neighbour outlier detection. A variety of oil types are used to challenge the method. In a survey of retail‐purchased oils, several instances of fraud were found.

Rapid analysis of surrendered or seized drug samples provides important intelligence for health (e.g. treatment or harm reduction), and custodial services. Herein, three in-situ techniques, GC-MS

60 MHz proton NMR spectroscopy was used to analyse extracts from saffron spice and a range of potential adulterants and mixtures. Using a simple extraction procedure, good quality spectra were obtained which contain peaks from the characteristic metabolites picrocrocin and crocins, fatty acids and kaempferol. The spectra of samples from trusted suppliers were used to train one-class classification models by SIMCA, nearest neighbour and isolation forest methods. Applying these to spectra of saffron samples purchased from the online marketplace, it was found that 7 out of 33 samples were highly anomalous. From comparison with the spectra of known mixtures and confirmatory spectral analysis using 600 MHz NMR, it is probable that these contain considerable amounts of undisclosed foreign matter.

Cold pressed rapeseed oil (CPRO) is a relatively recent development in rapeseed processing, which produces a quality product with a high market value. High field NMR (400 MHz) is a well-established tool in food analysis, while low-field NMR (60 MHz) is much less studied. This study aims to establish the effectiveness of both techniques in identifying binary adulteration in CPRO. Three adulteration scenarios were investigated: a) CPRO and refined rapeseed oil (RRO); b) CPRO and refined sunflower oil (RSO); and c) CPRO and RRO or RSO. A range of classification techniques were trialled as well as partial least squares regression to gauge predictive quantification performance. The 400 MHz NMR achieved classification rates of 100% in the scenarios with a single adulterant, and 93% in the multiple adulterant scenario. The 60 MHz NMR produced lower but still encouraging classification rates (RSO 92%; RRO 85%; both RRO and RSO 87%).

[Image: see text] An automated approach to the collection of (1)H NMR (nuclear magnetic resonance) spectra using a benchtop NMR spectrometer and the subsequent analysis, processing, and elucidation of components present in seized drug samples are reported. An algorithm is developed to compare spectral data to a reference library of over 300 (1)H NMR spectra, ranking matches by a correlation-based score. A threshold for identification was set at 0.838, below which identification of the component present was deemed unreliable. Using this system, 432 samples were surveyed and validated against contemporaneously acquired GC–MS (gas chromatography–mass spectrometry) data. Following removal of samples which possessed no peaks in the GC–MS trace or in both the (1)H NMR spectrum and GC–MS trace, the remaining 416 samples matched in 93% of cases. Thirteen of these samples were binary mixtures. A partial match (one component not identified) was obtained for 6% of samples surveyed whilst only 1% of samples did not match at all.

Low field (60 MHz)

Amphetamine and cathinone derivatives are abused recreationally due to the sense of euphoria they provide to the user. Methodologies for the rapid detection of the drug derivative present in a seized sample, or an indication of the drug class, are beneficial to law enforcement and healthcare providers. Identifying the drug class is prudent because derivatisation of these drugs, to produce regioisomers, for example, occurs frequently to circumvent global and local drug laws. Thus, newly encountered derivatives might not be present in a spectral library. Employment of benchtop nuclear magnetic resonance (NMR) could be used to provide rapid analysis of seized samples as well as identifying the class of drug present. Discrimination of individual amphetamine-, methcathinone-, N-ethylcathinone and nor-ephedrine-derived fluorinated and methylated regioisomers is achieved herein using qualitative automated

Recreational MDMA use is a worldwide problem. Tablet dosage varies, thus entailing a requirement for quantitative analysis. The quantification of MDMA in tablets using benchtop 1H NMR spectroscopy via either linear regression (‘manual’ method) or partial least square regression (‘automated’ method) approaches are reported, without the need for an internal standard, and compared against contemporaneously obtained GC–MS data. Twenty samples were evaluated of which 15 were proven to contain MDMA, via qualitative NMR (hit score ≥ 0.97) and GC–MS (Rt = 5.6 min) analysis. Quantitative NMR analysis showed that the mean value of MDMA content was 42.6% w/w by the manual method and 45.9% w/w by the automated method. The mean value obtained from GC analysis was 44.0% w/w. A substantial proportion (n = 9) of the tablets tested possessed > 190 mg of MDMA (range 133–223 mg, average of all techniques’ calculations for each tablet). This value is higher than the reported average MDMA content of tablets by the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), which was ca. 125 mg of MDMA per tablet in 2016.

Colorectal cancer (CRC), a primary cause of morbidity and mortality worldwide is expected to rise in the coming years. A better understanding of the metabolic changes taking place during the disease progression is needed for effective improvements of screening strategies and treatments. In the present study, Nuclear Magnetic Resonance (NMR) metabolomics was used to quantify the absolute concentrations of metabolites in faecal extracts from two cohorts of CRC patients and healthy controls. The quantification of over 80 compounds revealed that patients with CRC had increased faecal concentrations of branched chain fatty acids (BCFA), isovalerate and isobutyrate plus valerate and phenylacetate but diminished concentrations of amino acids, sugars, methanol and bile acids (deoxycholate, lithodeoxycholate and cholate). These results suggest that alterations in microbial activity and composition could have triggered an increase in utilisation of host intestinal slough cells and mucins and led to an increase in BCFA, valerate and phenylacetate. Concurrently, a general reduction in the microbial metabolic function may have led to reduced levels of other components (amino acids, sugars and bile acids) normally produced under healthy conditions. This study provides a thorough listing of the most abundant compounds found in human faecal waters and presents a template for absolute quantification of metabolites. The production of BCFA and phenylacetate in colonic carcinogenesis warrants further investigations.

Low field (60 MHz) 1H NMR spectroscopy was used to analyse a large (n = 410) collection of edible oils, including olive and argan, in an authenticity screening scenario. Experimental work was carried out on multiple spectrometers at two different laboratories, aiming to explore multivariate model stability and transfer between instruments. Three modelling methods were employed: Partial Least Squares Discriminant Analysis, Random Forests, and a One Class Classification approach. Clear inter-instrument differences were observed between replicated data collections, sufficient to compromise effective transfer of models based on raw data between instruments. As mitigations to this issue, various data pre-treatments were investigated: Piecewise Direct Standardisation, Standard Normal Variates, and Rank Transformation. Datasets comprised both phase corrected and magnitude spectra, and it was found that that the latter spectral form may offer some advantages in the context of pattern recognition and classification modelling, particularly when used in combination with the Rank Transformation pre-treatment.

Highlights • Lipophilic extracts of ground roast Arabica coffees were authenticated by benchtop NMR. • Small amounts of esterified 16-O-methylcafestol were found in Arabica coffees. • The compound identity was confirmed by NMR and MS experiments. • 16-OMC remains a useful marker for non-Arabicas as these contain much higher amounts. • 6 out of 60 retail Arabicas contained significant amounts of non-Arabica species., High-field and low-field proton NMR spectroscopy were used to analyse lipophilic extracts from ground roast coffees. Using a sample preparation method that produced concentrated extracts, a small marker peak at 3.16 ppm was observed in 30 Arabica coffees of assured origin. This signal has previously been believed absent from Arabicas, and has been used as a marker for detecting adulteration with robusta. Via 2D 600 MHz NMR and LC-MS, 16-O-methylcafestol and 16-O-methylkahweol were detected for the first time in Arabica roast coffee and shown to be responsible for the marker peak. Using low-field NMR, robusta in Arabica could be detected at levels of the order of 1–2% w/w. A surveillance study of retail purchased “100% Arabica” coffees found that 6 out of 60 samples displayed the 3.16 ppm marker signal to a degree commensurate with adulteration at levels of 3–30% w/w.

Flavan-3-ols and methylxanthines have potential beneficial effects on human health including reducing cardiovascular risk. We performed a randomized controlled crossover intervention trial to assess the acute effects of consumption of flavan-3-ol-enriched dark chocolate, compared with standard dark chocolate and white chocolate, on the human metabolome. We assessed the metabolome in urine and blood plasma samples collected before and at 2 and 6 h after consumption of chocolates in 42 healthy volunteers using a nontargeted metabolomics approach. Plasma samples were assessed and showed differentiation between time points with no further separation among the three chocolate treatments. Multivariate statistics applied to urine samples could readily separate the postprandial time points and distinguish between the treatments. Most of the markers responsible for the multivariate discrimination between the chocolates were of dietary origin. Interestingly, small but significant level changes were also observed for a subset of endogenous metabolites. H-1 NMR revealed that flavan-3-ol-enriched dark chocolate and standard dark chocolate reduced urinary levels of creatinine, lactate, some amino acids, and related degradation products and increased the levels of pyruvate and 4-hydroxyphenylacetate, a phenolic compound of bacterial origin. This study demonstrates that an acute chocolate intervention can significantly affect human metabolism.

We address the detection and quantitation of bovine milk in ‘buffalo’ mozzarella cheese using multiple reaction monitoring (MRM) mass spectrometry (MS). Focussing on the abundant protein α s1 -casein, present in both species but with 10 amino acid sequence differences, we extract a list of marker peptides specific to each species. ‘Identical’ peptides, exactly the same in both species, are used for relative quantitation of α s1 -casein in each milk type, whereas ‘similar’ peptides, present in both species but differing typically by one amino acid, are used to demonstrate relative quantitation in binary cheese mixtures. In addition, we report a pilot survey of UK supermarket and restaurant products labelled as ‘buffalo mozzarella’, finding that 2/3 of restaurant meals and supermarket pizzas are either mislabelled or adulterated.

Graphical abstract, Highlights • A new screening method for identifying the species in ground roast coffees using low-field NMR spectroscopy. • A calibration-free statistical approach to detecting adulteration of arabica with robusta coffee. • A study of time-varying spectral changes in the 16-OMC component of lipophilic extracts following sample preparation., This work reports a new screening protocol for addressing issues of coffee authenticity using low-field (60 MHz) bench-top 1H NMR spectroscopy. Using a simple chloroform-based extraction, useful spectra were obtained from the lipophilic fraction of ground roast coffees. It was found that 16-O-methylcafestol (16-OMC, a recognized marker compound for robusta beans) gives rise to an isolated peak in the 60 MHz spectrum, which can be used as an indicator of the presence of robusta beans in the sample. A total of 81 extracts from authenticated coffees and mixtures were analysed, from which the detection limit of robusta in arabica was estimated to be between 10% and 20% w/w. Using the established protocol, a surveillance exercise was conducted of 27 retail samples of ground roast coffees which were labelled as “100% arabica”. None were found to contain undeclared robusta content above the estimated detection limit.

peer-reviewed A Visible/Near-infrared (Vis/NIR) spectrometer equipped with a fibre-optic probe was used to stimulate and measure chlorophyll production in potato tubers, at low levels that produce no visible greening in the skin. Subtle responses to changes in the light stimulus were also tracked. When used with a static experimental setup, these measurements are precise. However, the technique is very sensitive to the exact geometry of the tuber-probe arrangement, and careful positioning of the probe is crucial. Complementary studies established that tissue under the apical buds (‘eyes’) has greater capacity to produce chlorophyll than other locations on the tuber surface. A long-term study of multiple tubers suggested that different cultivars behave differently in terms of the rate of chlorophyll production. These behavioural differences may be related to the batch dormancy status; validating this potential relationship is the focus of ongoing work.

We describe a simple protocol for identifying and quantifying the two components in binary mixtures of species possessing one or more similar proteins. Central to the method is the identification of 'corresponding proteins' in the species of interest, in other words proteins that are nominally the same but possess species-specific sequence differences. When subject to proteolysis, corresponding proteins will give rise to some peptides which are likewise similar but with species-specific variants. These are 'corresponding peptides'. Species-specific peptides can be used as markers for species determination, while pairs of corresponding peptides permit relative quantitation of two species in a mixture. The peptides are detected using multiple reaction monitoring (MRM) mass spectrometry, a highly specific technique that enables peptide-based species determination even in complex systems. In addition, the ratio of MRM peak areas deriving from corresponding peptides supports relative quantitation. Since corresponding proteins and peptides will, in the main, behave similarly in both processing and in experimental extraction and sample preparation, the relative quantitation should remain comparatively robust. In addition, this approach does not need the standards and calibrations required by absolute quantitation methods. The protocol is described in the context of red meats, which have convenient corresponding proteins in the form of their respective myoglobins. This application is relevant to food fraud detection: the method can detect 1% weight for weight of horse meat in beef. The corresponding protein, corresponding peptide (CPCP) relative quantitation using MRM peak area ratios gives good estimates of the weight for weight composition of a horse plus beef mixture.

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods. Electronic supplementary material The online version of this article (doi:10.1007/s12263-012-0288-4) contains supplementary material, which is available to authorized users.

The activity of Cytochrome P450 3A4 (CYP3A4) enzyme is associated with many adverse or poor therapeutic responses to drugs. We used (1)H NMR-based metabonomics to identify a metabolic signature associated with variation in induced CYP3A4 activity. A total of 301 female twins, aged 45--84, participated in this study. Each volunteer was administered a potent inducer of CYP3A4 (St. John's Wort) for 14 days and the activity of CYP3A4 was quantified through the metabolism of the exogenously administered probe drug quinine sulfate (300 mg). Pre- and postintervention fasting urine samples were used to obtain metabolite profiles, using (1)H NMR spectroscopy, and were analyzed using UPLC--MS to obtain a marker for CYP3A4 induction, via the ratio of 3-hydroxyquinine to quinine (3OH-Q:Q). Multiple linear regression was used to build a predictive model for 3OH-Q:Q values based on the preintervention metabolite profiles. A combination of seven metabolites and seven covariates showed a strong (r = 0.62) relationship with log(3OH-Q:Q). This regression model demonstrated significant (p < 0.00001) predictive ability when applied to an independent validation set. Our results highlight the promise of metabonomics for predicting CYP3A4-mediated drug response.

Colorectal cancer (CRC), a primary cause of morbidity and mortality worldwide is expected to rise in the coming years. A better understanding of the metabolic changes taking place during the disease progression is needed for effective improvements of screening strategies and treatments. In the present study, Nuclear Magnetic Resonance (NMR) metabolomics was used to quantify the absolute concentrations of metabolites in faecal extracts from two cohorts of CRC patients and healthy controls. The quantification of over 80 compounds revealed that patients with CRC had increased faecal concentrations of branched chain fatty acids (BCFA), isovalerate and isobutyrate plus valerate and phenylacetate but diminished concentrations of amino acids, sugars, methanol and bile acids (deoxycholate, lithodeoxycholate and cholate). These results suggest that alterations in microbial activity and composition could have triggered an increase in utilisation of host intestinal slough cells and mucins and led to an increase in BCFA, valerate and phenylacetate. Concurrently, a general reduction in the microbial metabolic function may have led to reduced levels of other components (amino acids, sugars and bile acids) normally produced under healthy conditions. This study provides a thorough listing of the most abundant compounds found in human faecal waters and presents a template for absolute quantification of metabolites. The production of BCFA and phenylacetate in colonic carcinogenesis warrants further investigations.

This paper describes an adaptation of Ergon's 2PLS approach (Compression into two-component PLS factorizations. J. Chemom. 2003; 17: 303–312.) to represent a single predictor regression model in terms of a two-factor latent vector model. The purpose of this reduction is to aid model interpretation and diagnostics. Non-orthogonal score vectors are produced from two orthonormal loading vectors: one identical to the first PLS loading vector, and a second built from the regression vector. Using an invertible matrix, the factorization can be alternatively represented by two orthogonal score vectors, one of which is proportional to centred predictions. An auxiliary set of loadings is also calculated, which captures a different model space, but is provided since its associated residuals have useful properties. Identities connecting the two model spaces are provided. The latent vector regression coefficients are not always least-squares estimates but can be represented as the solution to a two-term generalized ridge regression. Consequences of this are addressed. The utility of TinyLVR is demonstrated with example models built using stepwise variate selection and ridge regression.

This article concerns two chemometric modeling methods – the well-known partial least squares regression and the comparatively recently-devised orthogonal projections to latent structures (OPLS). We discuss their similarities and differences with a focus on the usage of OPLS in the analytical-chemistry literature.

Electromyography (EMG) of the masticatory muscles was compared with puncture testing and sensory evaluation, in a study of the textural properties of Red Delicious apples. An aim of the work was to use EMG in predictive models of sensory mealiness, as this property is strongly negatively associated with apple acceptability. It was found that subjects’ EMG sensitivity and repeatability are as good as, and frequently better, than their sensory sensitivity and reliability. In addition to systematic effects associated with data recording session and subject, EMG data was also systematically affected by subjects’ behavioural changes, specifically, the sensory evaluation being undertaken. Once these unwanted sources of variance were removed using suitable data pre-treatment, frequency-domain EMG data were correlated with puncture test data, and a significant relationship obtained for six out of thirteen subjects. For five out of these six subjects, the strength of this relationship was improved by making use only of higher frequencies (>15 Hz) in the EMG spectrum. EMG and puncture test data were each correlated with the sensory data. The nature of these relationships, in which the EMG frequency dependence effect was also clearly exhibited, demonstrated substantial common information content between the techniques. Finally, EMG and puncture test data were collectively used to model sensory mealiness, using univariate, bivariate and stepwise regression. Highly significant models were obtained for all but one subject’s sensory mealiness scores. In most cases, the best models used combinations of puncture test and EMG data from up to two of the four available electrodes.

A rapid multiple reaction monitoring (MRM) mass spectrometric method for the detection and relative quantitation of the adulteration of meat with that of an undeclared species is presented. Our approach uses corresponding proteins from the different species under investigation and corresponding peptides from those proteins, or CPCP. Selected peptide markers can be used for species detection. The use of ratios of MRM transition peak areas for corresponding peptides is proposed for relative quantitation. The approach is introduced by use of myoglobin from four meats: beef, pork, horse and lamb. Focusing in the present work on species identification, by use of predictive tools, we determine peptide markers that allow the identification of all four meats and detection of one meat added to another at levels of 1% (w/w). Candidate corresponding peptide pairs to be used for the relative quantification of one meat added to another have been observed. Preliminary quantitation data presented here are encouraging.

Highlights • We present the first results from a new 60 MHz 1H NMR bench-top spectrometer. • Using chemometrics, we detected hazelnut oil adulteration of olive oil at 11.2%w/w. • Bench-top 60 MHz NMR performs at least as well as FTIR for this type of application., We report the first results from a new 60 MHz 1H nuclear magnetic resonance (NMR) bench-top spectrometer, Pulsar, in a study simulating the adulteration of olive oil with hazelnut oil. There were qualitative differences between spectra from the two oil types. A single internal ratio of two isolated groups of peaks could detect hazelnut oil in olive oil at the level of ∼13%w/w, whereas a whole-spectrum chemometric approach brought the limit of detection down to 11.2%w/w for a set of independent test samples. The Pulsar’s performance was compared to that of Fourier transform infrared (FTIR) spectroscopy. The Pulsar delivered comparable sensitivity and improved specificity, making it a superior screening tool. We also mapped NMR onto FTIR spectra using a correlation-matrix approach. Interpretation of this heat-map combined with the established annotations of the NMR spectra suggested a hitherto undocumented feature in the IR spectrum at ∼1130 cm−1, attributable to a double-bond vibration.

Near- and mid-infrared spectra of a set of 56 lyophilized coffee samples were collected. The sample set comprised 29 Arabica and 27 Robusta coffees and was prepared in the laboratory. Each spectral collection was used separately to develop mathematical models for varietal authentication of the coffees using factorial discriminant analysis and partial least squares techniques. Subsequently, data from both spectral regions were combined and the chemometric approaches repeated. The relative success of the separate and combined approaches is discussed, as is the basis for the observed discriminations.

(1)H NMR spectroscopy of aqueous fecal extracts has been used to investigate differences in metabolic activity of gut microbiota in patients with ulcerative colitis (UC) (n = 13), irritable bowel syndrome (IBS) (n = 10), and healthy controls (C) (n = 22). Up to four samples per individual were collected over 2 years giving a total of 124 samples. Multivariate discriminant analysis, based on NMR data from all three groups, was able to predict UC and C group membership with good sensitivity and specificity; classification of IBS samples was less successful and could not be used for diagnosis. Trends were detected toward increased taurine and cadaverine levels in UC with increased bile acid and decreased branched chain fatty acids in IBS relative to controls; changes in short chain fatty acids and amino acids were not significant. Previous PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis of the same fecal material had shown alterations of the gut microbiota when comparing UC and IBS groups with controls. Hierarchical cluster analysis showed that DGGE profiles from the same individual were stable over time, but NMR spectra were more variable; canonical correlation analysis of NMR and DGGE data partly separated the three groups and revealed a correlation between the gut microbiota profile and metabolite composition.

We report the first results from a new 60MHz 1H nuclear magnetic resonance (NMR) bench-top spectrometer, Pulsar, in a study simulating the adulteration of olive oil with hazelnut oil. There were qualitative differences between spectra from the two oil types. A single internal ratio of two isolated groups of peaks could detect hazelnut oil in olive oil at the level of ∼13%w/w, whereas a whole-spectrum chemometric approach brought the limit of detection down to 11.2%w/w for a set of independent test samples. The Pulsar’s performance was compared to that of Fourier transform infrared (FTIR) spectroscopy. The Pulsar delivered comparable sensitivity and improved specificity, making it a superior screening tool. We also mapped NMR onto FTIR spectra using a correlation-matrix approach. Interpretation of this heat-map combined with the established annotations of the NMR spectra suggested a hitherto undocumented feature in the IR spectrum at ∼1130cm−1, attributable to a double-bond vibration.

It is desirable to monitor the quality of fresh fruit and vegetables since it benefits both producers, by offering a competitive advantage, and consumers, by improving consistency and hence encouraging a more healthy and varied diet. Near-infrared (NIR) spectroscopy is a candidate technology for monitoring agricultural produce quality. Here there has been a recent trend toward transmission-based geometries which interrogates deeper into the sample. NIR tomography is the natural progression of this, offering the possibility of detecting internal defects. The aim of this study was to evaluate a NIR tomograph built from relatively low-cost components. This comprised a stabilised VIS/NIR broadband source; a diode-array NIR spectrometer and a sample turntable. The angular positions of the detector and turntable could be moved independently of each other using two stepper motors under computer control. An experimental approach was adopted to generate a linear ‘difference image’ reconstruction matrix using 47 mm diameter potato cores, with a nominal length of 65 mm, and a 10 mm diameter black rod acting as an internal absorbing perturbation. The reconstruction matrix was generated for a single wavelength (689 nm) using multiple linear regression and evaluated for the case of two perturbing rods. The reconstructed image was of comparable quality to that typically obtained from other so-called ‘soft-field’ tomographic techniques. Although conducted under highly simplified conditions, the results suggest NIR tomography has potential for monitoring internal defects in agricultural produce.