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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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).</p> <p>Conclusions</p> <p>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.</p

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 (R2 0.75–0.95) and 17 compounds in ice cream (R2 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

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

Targeted and Nontargeted Wine Analysis by ¹H 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 ¹H 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 (<i>n</i> = 318) and 2009 (<i>n</i> = 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