Mid-infrared spectrometry of milk for dairy metabolomics: a comparison of two sampling techniques and effect of homogenization.

Milk production is a dominant factor in the metabolism of dairy cows involving a very intensive interaction with the blood circulation. As a result, the extracted milk contains valuable information on the metabolic status of the cow. On-line measurement of milk components during milking two or more times a day would promote early detection of systemic and local alterations, thus providing a great input for strategic and management decisions. The objective of this study was to investigate the potential of mid-infrared (mid-IR) spectroscopy to measure the milk composition using two different measurement modes: micro attenuated total reflection (µATR) and high throughput transmission (HTT). Partial least squares (PLS) regression was used for prediction of fat, crude protein, lactose and urea after preprocessing IR data and selecting the most informative wavenumber variables. The prediction accuracies were determined separately for raw and homogenized copies of a wide range of milk samples in order to estimate the possibility for on-line analysis of the milk. In case of fat content both measurement modes resulted in an excellent prediction for homogenized samples (R(2)>0.92) but in poor results for raw samples (R(2)<0.70). Homogenization was however not mandatory to achieve good predictions for crude protein and lactose with both µATR and HTT, and urea with µATR spectroscopy. Excellent results were obtained for prediction of crude protein, lactose and urea content (R(2)>0.99, 0.98 and 0.86 respectively) in raw and homogenized milk using µATR IR spectroscopy. These results were significantly better than those obtained by HTT IR spectroscopy. However, the prediction performance of HTT was still good for crude protein and lactose content (R(2)>0.86 and 0.78 respectively) in raw and homogenized samples. However, the detection of urea in milk with HTT spectroscopy was significantly better (R(2)=0.69 versus 0.16) after homogenization of the milk samples. Based on these observations it can be concluded that µATR approach is most suitable for rapid at line or even on-line milk composition measurement, although homogenization is crucial to achieve good prediction of the fat content.

Electronic tongue as a screening tool for rapid analysis of beer.

An electronic tongue (ET) comprising 18 potentiometric chemical sensors was applied to the quantitative analysis of beer. Fifty Belgian and Dutch beers of different types were measured using the ET. The same samples were analyzed using conventional analytical techniques with respect to the main physicochemical parameters. Only non-correlated physicochemical parameters were retained for further analysis, which were real extract, real fermentation degree, alcohol content, pH, bitterness, color, polyphenol and CO(2) content. Relationship between the ET and physicochemical datasets was studied using Canonical Correlation Analysis (CCA). Four significant canonical variates were extracted using CCA. Correlation was observed between 6 physicochemical variables (real extract and fermentation degree, bitterness, pH, alcohol and polyphenols' content) and 14 sensors of the ET. The feasibility of the ET for the quantification of bitterness in beer was evaluated in the aqueous solutions of isomerized hop extract and in the set of 11 beers with bitterness varying between 14 and 38 EBU (European Bitterness Units). Sensors displayed good sensitivity to isomerized hop extract and good prediction of the bitterness in beer was obtained. Calibration models with respect to the physicochemical parameters using ET measurements in 50 Belgian and Dutch beer samples were calculated by Partial Least Square regression. The ET was capable of predicting such parameters as real extract, alcohol and polyphenol content and bitterness, the latter with Root Mean Square Error of Prediction (RMSEP) of 2.5.

Instrumental measurement of beer taste attributes using an electronic tongue.

The present study deals with the evaluation of the electronic tongue multisensor system as an analytical tool for the rapid assessment of taste and flavour of beer. Fifty samples of Belgian and Dutch beers of different types (lager beers, ales, wheat beers, etc.), which were characterized with respect to the sensory properties, were measured using the electronic tongue (ET) based on potentiometric chemical sensors developed in Laboratory of Chemical Sensors of St. Petersburg University. The analysis of the sensory data and the calculation of the compromise average scores was made using STATIS. The beer samples were discriminated using both sensory panel and ET data based on PCA, and both data sets were compared using Canonical Correlation Analysis. The ET data were related to the sensory beer attributes using Partial Least Square regression for each attribute separately. Validation was done based on a test set comprising one-third of all samples. The ET was capable of predicting with good precision 20 sensory attributes of beer including such as bitter, sweet, sour, fruity, caramel, artificial, burnt, intensity and body.