Quantitation of Compounds in Wine Using (1)H NMR Spectroscopy: Description of the Method and Collaborative Study.

To examine whether NMR analysis is a suitable method for the quantitative determination of wine components, an international collaborative trial was organized to evaluate the method according to the international regulations and guidelines of the German Institute for Standardization/International Organization for Standardization, AOAC INTERNATIONAL, the International Union of Pure and Applied Chemistry, and the International Organization of Vine and Wine. Sugars such as glucose; acids such as malic, acetic, fumaric, and shikimic acids (the latter two as minor components); and sorbic acid, a preservative, were selected for the exemplary quantitative determination of substances in wine. Selection criteria for the examination of sample material included different NMR spectral signal types (singlet and multiplet), as well as the suitability of the proposed substances for manual integration at different levels of challenge (e.g., interference as a result of the necessary suppression of a water signal or the coverage of different typical wine concentration ranges for a selection of major components, minor components, and additives). To show that this method can be universally applied, NMR measurement and the method of evaluation were not strictly elucidated. Fifteen international laboratories participated in the collaborative trial and determined six parameters in 10 samples. The values, in particular the reproducibility SD (SR), were compared with the expected Horwitz SD (SH) by forming the quotient SR/SH (i.e., the HorRat value). The resulting HorRat values of most parameters were predominantly between 0.6 and 1.5, and thus of an acceptable range.

Benzaldehyde in cherry flavour as a precursor of benzene formation in beverages.

During sampling and analysis of alcohol-free beverages for food control purposes, a comparably high contamination of benzene (up to 4.6µg/L) has been detected in cherry-flavoured products, even when they were not preserved using benzoic acid (which is a known precursor of benzene formation). There has been some speculation in the literature that formation may occur from benzaldehyde, which is contained in natural and artificial cherry flavours. In this study, model experiments were able to confirm that benzaldehyde does indeed degrade to benzene under heating conditions, and especially in the presence of ascorbic acid. Analysis of a large collective of authentic beverages from the market (n=170) further confirmed that benzene content is significantly correlated to the presence of benzaldehyde (r=0.61, p<0.0001). In the case of cherry flavoured beverages, industrial best practices should include monitoring for benzene. Formulations containing either benzoic acid or benzaldehyde in combination with ascorbic acid should be avoided.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine.

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.

Synergistic effect of the simultaneous chemometric analysis of ¹H NMR spectroscopic and stable isotope (SNIF-NMR, ¹8O, ¹³C) data: application to wine analysis.

It is known that (1)H NMR spectroscopy represents a good tool for predicting the grape variety, the geographical origin, and the year of vintage of wine. In the present study we have shown that classification models can be improved when (1)H NMR profiles are fused with stable isotope (SNIF-NMR, (18)O, (13)C) data. Variable selection based on clustering of latent variables was performed on (1)H NMR data. Afterwards, the combined data of 718 wine samples from Germany were analyzed using linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), factorial discriminant analysis (FDA) and independent components analysis (ICA). Moreover, several specialized multiblock methods (common components and specific weights analysis (ComDim), consensus PCA and consensus PLS-DA) were applied to the data. The best improvement in comparison with (1)H NMR data was obtained for prediction of the geographical origin (up to 100% for the fused data, whereas stable isotope data resulted only in 60-70% correct prediction and (1)H NMR data alone in 82-89% respectively). Certain enhancement was obtained also for the year of vintage (from 88 to 97% for (1)H NMR to 99% for the fused data), whereas in case of grape varieties improved models were not obtained. The combination of (1)H NMR data with stable isotope data improves efficiency of classification models for geographical origin and vintage of wine and can be potentially used for other food products as well.

Can resveratrol in wine protect against the carcinogenicity of ethanol? A probabilistic dose-response assessment.

Resveratrol, which may occur in wine, was suggested to act as a chemopreventive agent against the carcinogenic effects of ethanol. The assumption was based on data from experimental animals, which have shown that resveratrol above certain thresholds may reduce the incidence of tumours in several of the alcohol-related cancer sites (colon, liver and female breast). Using a probabilistic Monte Carlo type methodology, we estimated daily intake based on chemical analysis of resveratrol (n = 672) and ethanol (n = 867). Benchmark dose (BMD)-response modelling was conducted for resveratrol based on eight animal experiments, whereas BMD data for ethanol were taken from the literature. The margin of exposure (MOE) was calculated for both substances as an indicator if the intake may reach effective dosages. For intake of one 100-mL glass of wine, the average MOE was found to be 4.1 for ethanol and 459,937 for resveratrol. In the best-case scenario for resveratrol (e.g., very high contents and assuming a low effective dosage), the minimum MOE would be 111, which means that 111 glasses of wine need to be consumed daily to reach the BMD. The MOE ratio between resveratrol and ethanol is 166,128 on average, meaning that per glass of wine, ethanol is more than 100,000 times more potent than resveratrol. As resveratrol intake may not optimally reach the effective dosage, our study excludes a preventive effect of this substance on alcohol-related cancer. Commercial information about cancer-preventive or -protective effects of resveratrol in wine is misleading and must be prohibited.

Targeted and nontargeted wine analysis by (1)h NMR spectroscopy combined with multivariate statistical analysis. Differentiation of important parameters: grape variety, geographical origin, year of vintage.

The authenticity, the grape variety, the geographical origin, and the year of vintage of wines produced in Germany were investigated by (1)H NMR spectroscopy in combination with several steps of multivariate data analysis including principal component analysis (PCA), linear discrimination analysis (LDA), and multivariate analysis of variance (MANOVA) together with cross-validation (CV) embedded in a Monte Carlo resampling approach (MC) and others. A total of about 600 wines were selected and carefully collected from five wine-growing areas in the southern and southwestern parts of Germany. Simultaneous saturation of the resonances of water and ethanol by application of a low-power eight-frequency band irradiation using shaped pulses allowed for high receiver gain settings and hence optimized signal-to-noise ratios. Correct prediction of classification of the grape varieties of Pinot noir, Lemberger, Pinot blanc/Pinot gris, Müller-Thurgau, Riesling, and Gewürztraminer of 95% in the wine panel was achieved. The classification of the vintage of all analyzed wines resulted in correct predictions of 97 and 96%, respectively, for vintage 2008 (n = 318) and 2009 (n = 265). The geographic origin of all wines from the largest German wine-producing regions, Rheinpfalz, Rheinhessen, Mosel, Baden, and Württemberg, could be predicted 89% correctly on average. Each NMR spectrum could be regarded as the individual "fingerprint" of a wine sample, which includes information about variety, origin, vintage, physiological state, technological treatment, and others.

Identification of Imitation Cheese and Imitation Ice Cream Based on Vegetable Fat Using NMR Spectroscopy and Chemometrics.

Vegetable oils and fats may be used as cheap substitutes for milk fat to manufacture imitation cheese or imitation ice cream. In this study, 400 MHz nuclear magnetic resonance (NMR) spectroscopy of the fat fraction of the products was used in the context of food surveillance to validate the labeling of milk-based products. For sample preparation, the fat was extracted using an automated Weibull-Stoldt methodology. Using principal component analysis (PCA), imitation products can be easily detected. In both cheese and ice cream, a differentiation according to the type of raw material (milk fat and vegetable fat) was possible. The loadings plot shows that imitation products were distinguishable by differences in their fatty acid ratios. Furthermore, a differentiation of several types of cheese (Edamer, Gouda, Emmentaler, and Feta) was possible. Quantitative data regarding the composition of the investigated products can also be predicted from the same spectra using partial least squares (PLS) regression. The models obtained for 13 compounds in cheese (R (2) 0.75-0.95) and 17 compounds in ice cream (R (2) 0.83-0.99) (e.g., fatty acids and esters) were suitable for a screening analysis. NMR spectroscopy was judged as suitable for the routine analysis of dairy products based on milk or on vegetable fat substitutes.

Rapid and mobile determination of alcoholic strength in wine, beer and spirits using a flow-through infrared sensor.

BACKGROUND: Ever since Gay-Lussac's time, the alcoholic strength by volume (% vol) has been determined by using densimetric measurements. The typical reference procedure involves distillation followed by pycnometry, which is comparably labour-intensive and therefore expensive. At present, infrared (IR) spectroscopy in combination with multivariate regression is widely applied as a screening procedure, which allows one to determine alcoholic strength in less than 2 min without any sample preparation. The disadvantage is the relatively large investment for Fourier transform (FT) IR or near-IR instruments, and the need for matrix-dependent calibration. In this study, we apply a much simpler device consisting of a patented multiple-beam infrared sensor in combination with a flow-through cell for automated alcohol analysis, which is available in a portable version that allows for on-site measurements. RESULTS: During method validation, the precision of the infrared sensor was found to be equal to or better than densimetric or FTIR methods. For example, the average repeatability, as determined in 6 different wine samples, was 0.05% vol and the relative standard deviation was below 0.2%. Accuracy was ensured by analyzing 260 different alcoholic beverages in comparison to densimetric or FTIR results. The correlation was linear over the entire range from alcohol-free beers up to high-proof spirits, and the results were in substantial agreement (R = 0.99981, p < 0.0001, RMSE = 0.279% vol). The applicability of the device was further proven for the analysis of wines during fermentation, and for the determination of unrecorded alcohol (i.e. non-commercial or illicit products). CONCLUSIONS: The flow-through infrared device is much easier to handle than typical reference procedures, while time-consuming sample preparation steps such as distillation are not necessary. Therefore, the alcoholic strength can be economically and quickly controlled (requiring less than 60 s per sample). The device also gives the opportunity for mobile on-site control in the context of labelling control of wine, beer and spirits, the process monitoring of fermentations, or the evaluation of unrecorded alcohols.

HPLC analysis and safety assessment of coumarin in foods.

Coumarin is a component of natural flavourings including cassia, which is widely used in foods and pastries. The toxicity of coumarin has raised some concerns and food safety authorities have set a maximum limit of 2mg/kg for foods and beverages in general, and a maximum level of 10mg/l for alcoholic beverages. An efficient method for routine analysis of coumarin is liquid chromatography with diode array detection. The optimal sample preparation for foods containing cinnamon was investigated and found to be cold extraction of 15g sample with 50mL of methanol (80%, v/v) for 30min using magnetic stirring. In the foods under investigation, appreciable amounts of coumarin were found in bakery products and breakfast cereals (mean 9mg/kg) with the highest concentrations up to 88mg/kg in certain cookies flavoured with cinnamon. Other foods such as liqueurs, vodka, mulled wine, and milk products did not have coumarin concentrations above the maximum level. The safety assessment of coumarin containing foods, in the context of governmental food controls, is complicated as a toxicological basis for the maximum limits appears to be missing. The limits were derived at a time when a genotoxic mechanism was assumed. However, this has since been disproven in more recent studies. Our exposure data on coumarin in bakery products show that there is still a need for a continued regulation of coumarin in foods. A toxicological re-evaluation of coumarin with the aim to derive scientifically founded maximum limits should be conducted with priority.