HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits.

The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA); (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated; and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of ±2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

Combined untargeted and targeted fingerprinting by comprehensive two-dimensional gas chromatography: revealing fructose-induced changes in mice urinary metabolic signatures.

This study exploits the information potential of comprehensive two-dimensional gas chromatography configured with a parallel dual secondary column-dual detection by mass spectrometry and flame ionization (GC×2GC-MS/FID) to study changes in urinary metabolic signatures of mice subjected to high-fructose diets. Samples are taken from mice fed with normal or fructose-enriched diets provided either in aqueous solution or in solid form and analyzed at three stages of the dietary intervention (1, 6, and 12 weeks). Automated Untargeted and Targeted fingerprinting for 2D data elaboration is adopted for the most inclusive data mining of GC×GC patterns. The UT fingerprinting strategy performs a fully automated peak-region features fingerprinting and combines results from pre-targeted compounds and unknowns across the sample-set. The most informative metabolites, with statistically relevant differences between sample groups, are obtained by unsupervised multivariate analysis (MVA) and cross-validated by multi-factor analysis (MFA) with external standard quantitation by GC-MS. Results indicate coherent clustering of mice urine signatures according to dietary manipulation. Notably, the metabolite fingerprints of mice fed with liquid fructose exhibited greater derangement in fructose, glucose, citric, pyruvic, malic, malonic, gluconic, cis-aconitic, succinic and 2-keto glutaric acids, glycine acyl derivatives (N-carboxy glycine, N-butyrylglycine, N-isovaleroylglycine, N-phenylacetylglycine), and hippuric acid. Untargeted fingerprinting indicates some analytes which were not a priori pre-targeted which provide additional insights: N-acetyl glucosamine, N-acetyl glutamine, malonyl glycine, methyl malonyl glycine, and glutaric acid. Visual features fingerprinting is used to track individual variations during experiments, thereby extending the panorama of possible data elaboration tools. Graphical abstract ᅟ.

Alignment for comprehensive two-dimensional gas chromatography with dual secondary columns and detectors.

In each sample run, comprehensive two-dimensional gas chromatography with dual secondary columns and detectors (GC × 2GC) provides complementary information in two chromatograms generated by its two detectors. For example, a flame ionization detector (FID) produces data that is especially effective for quantification and a mass spectrometer (MS) produces data that is especially useful for chemical-structure elucidation and compound identification. The greater information capacity of two detectors is most useful for difficult analyses, such as metabolomics, but using the joint information offered by the two complex two-dimensional chromatograms requires data fusion. In the case that the second columns are equivalent but flow conditions vary (e.g., related to the operative pressure of their different detectors), data fusion can be accomplished by aligning the chromatographic data and/or chromatographic features such as peaks and retention-time windows. Chromatographic alignment requires a mapping from the retention times of one chromatogram to the retention times of the other chromatogram. This paper considers general issues and experimental performance for global two-dimensional mapping functions to align pairs of GC × 2GC chromatograms. Experimental results for GC × 2GC with FID and MS for metabolomic analyses of human urine samples suggest that low-degree polynomial mapping functions out-perform affine transformation (as measured by root-mean-square residuals for matched peaks) and achieve performance near a lower-bound benchmark of inherent variability. Third-degree polynomials slightly out-performed second-degree polynomials in these results, but second-degree polynomials performed nearly as well and may be preferred for parametric and computational simplicity as well as robustness.

Sedentariness and Urinary Metabolite Profile in Type 2 Diabetic Patients, a Cross-Sectional Study.

Recent findings indicate a significant association between sedentary (SED)-time and type 2 diabetes mellitus(T2DM). The aim of this study was to investigate whether different levels of SED-time could impact on biochemical and physiological processes occurring in sedentary and physically inactive T2DM patients. In particular, patients from the "Italian Diabetes and Exercise Study (IDES)_2 trial belonging to the first and fourth quartile of SED-time were compared. Urine samples were analyzed by comprehensive two-dimensional gas chromatography(GC×GC) with parallel detection by mass spectrometry and flame ionization detection(GC×2GC-MS/FID). This platform enables accurate profiling and fingerprinting of urinary metabolites while maximizing the overall information capacity, quantitation reliability, and response linearity. Moreover, using advanced pattern recognition, the fingerprinting process was extended to untargeted and targeted features, revealing diagnostic urinary fingerprints between groups. Quantitative metabolomics was then applied to analytes of relevance for robust comparisons. Increased levels of glycine, L-valine,L-threonine, L-phenylalanine, L-leucine, L-alanine, succinic acid, 2-ketoglutaric acid, xylitol, and ribitol were revealed in samples from less sedentary women. In conclusion, SED-time is associated with changes in urine metabolome signatures. These preliminary results suggest that reducing SED-time could be a strategy to improve the health status of a large proportion of diabetic patients.

Chemometric Modeling of Coffee Sensory Notes through Their Chemical Signatures: Potential and Limits in Defining an Analytical Tool for Quality Control.

Aroma is a primary hedonic aspect of a good coffee. Coffee aroma quality is generally defined by cup tasting, which however is time-consuming in terms of panel training and alignment and too subjective. It is challenging to define a relationship between chemical profile and aroma sensory impact, but it might provide an objective evaluation of industrial products. This study aimed to define the chemical signature of coffee sensory notes, to develop prediction models based on analytical measurements for use at the control level. In particular, the sensory profile was linked with the chemical composition defined by HS-SPME-GC-MS, using a chemometric-driven approach. The strategy was found to be discriminative and informative, identifying aroma compounds characteristic of the selected sensory notes. The predictive ability in defining the sensory scores of each aroma note was used as a validation tool for the chemical signatures characterized. The most reliable models were those obtained for woody, bitter, and acidic properties, whose selected volatiles reliably represented the sensory note fingerprints. Prediction models could be exploited in quality control, but compromises must be determined if they are to become complementary to panel tasting.

Coffee aroma: Chemometric comparison of the chemical information provided by three different samplings combined with GC-MS to describe the sensory properties in cup.

This study is part of a wider project aiming to correlate the chemical composition of the coffee volatile fraction to its sensory properties with the end-goal of developing an instrumental analysis approach complementary to human sensory profiling. The proposed investigation strategy compares the chemical information concerning coffee aroma and flavor obtained with HS-SPME of the ground coffee and in-solution SBSE/SPME sampling combined with GC-MS to evaluate their compatibility with the cupping evaluation for quality control purposes. Roasted coffee samples with specific sensory properties were analyzed. The chemical results obtained by the three samplings were compared through multivariate analysis, and related to the samples' sensory attributes. Despite the differences between the three sampling approaches, data processing showed that the three methods provide the same kind of chemical information useful for sample discrimination, and that they could be used interchangeably to sample the coffee aroma and flavor.

High-quality Italian rice cultivars: chemical indices of ageing and aroma quality.

The volatile fractions of six Italian high-quality rice cultivars were investigated by HS-SPME-GC-MS to define fingerprinting and identify chemical markers and/or indices of ageing and aroma quality. In particular, four non-aromatic (Carnaroli, Carnise, Cerere and Antares) and two aromatic (Apollo and Venere) rices, harvested in 2010 and 2011, were monitored over 12months. Twenty-five aroma components were considered and, despite considerable inter-annual variability, some of them showed similar trends over time, including 2-(E)-octenal as a marker of ageing for all cultivars, and heptanal, octanal and 2-ethyl hexanol as cultivar-specific indicators. The area ratios 2-acetyl-1-pyrroline/1-octen-3-ol, for Venere, and 3-methyl-1-butanol/2-methyl-1-butanol, for Apollo, were also found to act as ageing indices. Additional information on release of key-aroma compounds was also obtained from quantitation and its dependence on grain shape and chemical composition. Heptanal/1-octen-3-ol and heptanal/octanal ratios were also defined as characterising the aroma quality indices of the six Italian rice cultivars investigated.

Parallel dual secondary column-dual detection: a further way of enhancing the informative potential of two-dimensional comprehensive gas chromatography.

Comprehensive two-dimensional gas chromatography (GC×GC) coupled with Mass Spectrometry (MS) is one of today's most powerful analytical platforms for detailed analysis of medium-to-high complexity samples. The column set usually consists of a long, conventional-inner-diameter first dimension ((1)D) (typically 15-30m long, 0.32-0.25mm dc), and a short, narrow-bore second dimension ((2)D) column (typically 0.5-2m, 0.1mm dc) where separation is run in a few seconds. However, when thermal modulation is used, since the columns of a set are coupled in series, a flow mismatch occurs between the two dimensions, making it impossible to operate simultaneously at optimized flow conditions. Further, short narrow-bore capillaries can easily be overloaded, because of their lower loadability, limiting the effectiveness of (2)D separation. In this study, improved gas linear velocities in both chromatographic dimensions were achieved by coupling the (1)D column with two parallel (2)D columns, having identical inner diameter, stationary phase chemistry, and film thickness. In turn, these were connected to two detectors: a fast quadrupole Mass Spectrometer (MS) and a Flame Ionization Detector (FID). Different configurations were tested and performances compared to a conventional set-up; experimental results on two model mixtures (n-alkanes and fourteen medium-to-high polarity volatiles of interest in the flavor and fragrance field) and on the essential oil of Artemisia umbelliformis Lam., show the system provides consistent results, in terms of analyte identification (reliability of spectra and MS matching) and quantitation, also affording an internal cross-validation of quantitation accuracy.

Urinary metabolic fingerprinting of mice with diet-induced metabolic derangements by parallel dual secondary column-dual detection two-dimensional comprehensive gas chromatography.

This study investigates the potential of a parallel dual secondary column-dual detection two-dimensional comprehensive GC platform (GC×2GC-MS/FID) for metabolic profiling and fingerprinting of mouse urine. Samples were obtained from a murine model that mimics a typical unhealthy Western diet featuring both high fat and sugar (HFHS) intake, which induces obesity, dyslipidemia, and insulin resistance. Urines collected at different steps of the study were used to obtain pivotal and comparative data on the presence and relative distributions of early markers of metabolic disease. The data elaboration and interpretation work-flow includes an advanced untargeted fingerprinting approach, with peak-region features to locate relevant features to be quantified by external standard calibration. The reliability of untargeted fingerprinting is confirmed by quantitative results on selected relevant features that showed percentages of variations consistent with those observed by comparing raw data quantitative descriptors (2D peak-region volumes and percent of response). Analytes that were up-regulated with % of variation ranging from 30 to 1000, included pyruvic acid, glycerol, fructose, galactose, glucose, lactic acid, mannitol and valine. Down-regulation was evidenced for malonic acid, succinic acid, alanine, glycine, and creatinine. Advanced fingerprinting also is demonstrated for effectively evaluating individual variations during experiments, thus representing a promising tool for personalized intervention studies. In this context, it is interesting to observe that informative features that were not discriminant for the entire population may be relevant for individuals.