Graphical exploration of 600- and 60-MHz proton NMR spectral datasets from ground roast coffee extracts

This article uses a variety of graphical and mathematical approaches to analyse 600- and 60-MHz ('benchtop') proton NMR spectra acquired from lipophilic and hydrophilic extracts of roasted coffee beans. The collection of 40 authenticated samples comprised various coffee species, cultivars and hybrids. The spectral datasets were analysed by a combination of metabolomics approaches, cross-correlation and whole spectrum methods, assisted by visualisation and mathematical techniques not conventionally employed to treat NMR data. A large amount of information content was shared between the 600-MHz and benchtop datasets, including in its magnitude spectral form, suggesting the potential for a lower cost, lower tech route to conducting informative metabolomics studies.

Detection, discrimination and quantification of amphetamine, cathinone and nor-ephedrine regioisomers using benchtop 1 H and 19 F nuclear magnetic resonance spectroscopy

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 (RR_t  = 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.

Comparative study of the analysis of seized samples by GC-MS, 1H NMR and FT-IR spectroscopy within a Night-Time Economy (NTE) setting

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 %).

Mitigating instrument effects in 60 MHz 1H NMR spectroscopy for authenticity screening of edible oils

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.

High-throughput screening of argan oil composition and authenticity using benchtop 1 H NMR

We use 60-MHz benchtop nuclear magnetic resonance (NMR) to acquire 1 H 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.

Rapid Identification of Novel Psychoactive and Other Controlled Substances Using Low-Field 1H NMR Spectroscopy

An automated approach to the collection of ¹H 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 ¹H 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 ¹H 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.

Correction: Metabolite quantification of faecal extracts from colorectal cancer patients and healthy controls

[This corrects the article DOI: 10.18632/oncotarget.26022.].

Metabolite quantification of faecal extracts from colorectal cancer patients and healthy controls

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.

16-O-methylcafestol is present in ground roast Arabica coffees: Implications for authenticity testing

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Lipophilic extracts of ground roast Arabica coffees were authenticated by benchtop NMR.

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Small amounts of esterified 16-O-methylcafestol were found in Arabica coffees.

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The compound identity was confirmed by NMR and MS experiments.

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16-OMC remains a useful marker for non-Arabicas as these contain much higher amounts.

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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.

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

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.

Low-field (1)H NMR spectroscopy for distinguishing between arabica and robusta ground roast coffees

This work reports a new screening protocol for addressing issues of coffee authenticity using low-field (60MHz) bench-top (1)H 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 60MHz 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.

Avoiding overfitting in the analysis of high-dimensional data with artificial neural networks (ANNs)

Complex data analysis is becoming more easily accessible to analytical chemists, including natural computation methods such as artificial neural networks (ANNs). Unfortunately, in many of these methods, inappropriate choices of model parameters can lead to overfitting. This study concerns overfitting issues in the use of ANNs to classify complex, high-dimensional data (where the number of variables far exceeds the number of specimens). We examine whether a parameter ρ, equal to the ratio of the number of observations in the training set to the number of connections in the network, can be used as an indicator to forecast overfitting. Networks possessing different ρ values were trained using as inputs either raw data or scores obtained from principal component analysis (PCA). A primary finding was that different data sets behave very differently. For data sets with either abundant or scant information related to the proposed group structure, overfitting was little influenced by ρ, whereas for intermediate cases some dependence was found, although it was not possible to specify values of ρ which prevented overfitting altogether. The use of a tuning set, to control termination of training and guard against overtraining, did not necessarily prevent overfitting from taking place. However, for data containing scant group-related information, the use of a tuning set reduced the likelihood and magnitude of overfitting, although not eliminating it entirely. For other data sets, little difference in the nature of overfitting arose from the two modes of termination. Small data sets (in terms of number of specimens) were more likely to produce overfit ANNs, as were input layers comprising large numbers of PC scores. Hence, for high-dimensional data, the use of a limited number of PC scores as inputs, a tuning set to prevent overtraining and a test set to detect and guard against overfitting are recommended.

60 MHz 1H NMR spectroscopy for the analysis of edible oils

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We present the first results from a new 60 MHz ¹H NMR bench-top spectrometer.

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Using chemometrics, we detected hazelnut oil adulteration of olive oil at 11.2%w/w.

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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.

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

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.

Prediction of variability in CYP3A4 induction using a combined 1H NMR metabonomics and targeted UPLC-MS approach

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.

Electromyographic responses to prescribed mastication

The aim was to understand between-volunteer differences in Electromyography (EMG) behaviour during chewing. EMG was used to record the electrical activity of the temporal and masseter muscles of volunteers, who carried out mastication movements by operating calibrated springs held between their incisors. The volunteers coordinated their jaw movements with the signal produced by a metronome, at four rates: 30, 60, 90 and 120 beats per minute (bpm). Raw data were analyzed to examine the distributions of the intervals between chews. For the highest prescribed chew rates, the volunteers' distributions were very similar. The distributions varied most for the 30 bpm data, suggesting that volunteers differed in their ability to carry out and maintain this prescribed chewing pattern. The data were Fourier transformed to give power spectra in the frequency domain. The low frequency (<10 Hz) region contained spectral features related to the prescribed chew rate. Principal component analysis of the power spectra revealed that readings from each volunteer clustered together, and the clusters could be largely separated. Such grouping was found irrespective of whether data from each chew rate were analyzed separately or simultaneously. This indicated that within-volunteer variance, arising from the different chew rates as well as between-session variance, is lower than between-volunteer variance; even when individuals are asked to make jaw movements in the same prescribed manner, they can nevertheless be uniquely distinguished by their muscle activity as recorded by EMG.

Detection of adulteration in cooked meat products by mid-infrared spectroscopy

Mid-infrared spectroscopy was used to discriminate between pure beef and beef containing 20% w/w of a range of potential adulterants (heart, tripe, kidney, and liver). Spectra were acquired from raw samples and from samples cooked using two different cooking regimes. Chemometric methods (principal component analysis, partial least squares regression, and linear discriminant analysis) applied to the spectra showed that discrimination between the pure and adulterated sample types was possible, irrespective of cooking regime. The cross-validated classification success rate obtained was approximately 97%. Discrimination between all five sample types (pure beef and beef containing one of each of the four adulterants) at each level of cook was also possible, but became more difficult as the cooking level increased.

Comparison of spectroscopic techniques for the determination of Kjeldahl and ammoniacal nitrogen content of farmyard manure

The feasibility of determining the nitrogen content of farmyard manure using infrared spectroscopy was investigated. Fifteen samples each of cattle, pig, and turkey manure were analyzed by three infrared techniques: Fourier transform mid-infrared (MIR), using attenuated total reflection (ATR); near-infrared reflectance (NIR-R); and near-infrared optothermal photoacoustic (NIR-OT). The near-infrared measurements were made at wavelengths determined respectively by four (NIR-OT) and five (NIR-R) band-pass filters. The total nitrogen (using the Kjeldahl method) and volatile (ammoniacal) nitrogen contents of all samples were measured by wet chemistry. Internally cross-validated (ICV) partial least-squares (PLS) regression was then used to obtain calibrations for the nitrogen content. The data sets obtained by each technique were treated separately. Within these sets, data from each manure type were treated both separately and combined: the best predictive ability was obtained by combining data from all three manure types. From the combined data set, the residual standard deviations and correlation coefficients for the ICV-predicted versus actual Kjeldahl nitrogen content were, respectively, 6772 mg/kg dry wt, 0.862 (MIR); 9434 mg/kg dry wt, 0.771 (NIR-OT); and 8943 mg/kg dry wt, 0.865 (NIR-R). For the ammoniacal nitrogen content, the residual standard deviations and correlation coefficients were 3869 mg/kg dry wt, 0.899 (MIR); 6079 mg/kg dry wt, 0.820 (NIR-OT); and 3498 mg/kg dry wt, 0.961 (NIR-R).

Factors affecting the release of flavor encapsulated in carbohydrate matrixes

The effects of water content and temperature variation on the release of flavor components into the headspace over flavors, encapsulated by an extrusion process, in low water content carbohydrate matrixes is studied. The largest amounts of release occurred when the matrix was above its glass transition temperature, whether this was due to increased water content or elevated temperature. Under these conditions up to 70% of the sucrose in the matrix crystallized over a period of 10 days, as quantified using Fourier transform Raman spectroscopy. Smaller amounts of headspace release occurred when the water content of the encapsulated flavor system was decreased from 3. 5 to 3.1% w/w. Small amounts of release occurred from the "as prepared" materials, which were associated with the presence of small amounts of unencapsulated flavor oil with direct access to the headspace. It was concluded that release due to matrix permeability was relatively slow as compared with the above mechanisms.

Mid-infrared spectroscopy and chemometrics for the authentication of meat products

Mid-infrared (MIR) spectroscopy is used to address certain issues connected with the authentication of beef and ox kidney and liver: is it possible to distinguish muscle from offal tissue; does the condition, cut of meat, or type of offal influence the distinction; can pure minced beef be distinguished from that adulterated with offal? Using partial least squares (PLS) and canonical variate analysis, predictive models are developed to identify MIR spectra of beef, kidney, and liver. Using modified SIMCA, the pure beef specimens are modeled as a single class; this model identifies spectra of unadulterated beef as such, with an acceptable error rate, while rejecting spectra of specimens containing 10-100% w/w kidney or liver. Finally, PLS regressions are performed to quantify the amount of added offal. The prediction errors obtained (+/-4.8 and +/-4.0% w/w, respectively, for the kidney and liver calibrations) are commensurate with the detection limits suggested by the SIMCA analysis.