Evgeny Polshin

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.

Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: Reflectance or transmittance?

The composition of produced milk has great value for the dairy farmer. It determines the economic value of the milk and provides valuable information about the metabolism of the corresponding cow. Therefore, online measurement of milk components during milking 2 or more times per day would provide knowledge about the current health and nutritional status of each cow individually. This information provides a solid basis for optimizing cow management. The potential of visible and near-infrared (Vis/NIR) spectroscopy for predicting the fat, crude protein, lactose, and urea content of raw milk online during milking was, therefore, investigated in this study. Two measurement modes (reflectance and transmittance) and different wavelength ranges for Vis/NIR spectroscopy were evaluated and their ability to measure the milk composition online was compared. The Vis/NIR reflectance measurements allowed for very accurate monitoring of the fat and crude protein content in raw milk (R(2)>0.95), but resulted in poor lactose predictions (R(2)<0.75). In contrast, Vis/NIR transmittance spectra of the milk samples gave accurate fat and crude protein predictions (R(2)>0.90) and useful lactose predictions (R(2)=0.88). Neither Vis/NIR reflectance nor transmittance spectroscopy lead to an acceptable prediction of the milk urea content. Transmittance spectroscopy can thus be used to predict the 3 major milk components, but with lower accuracy for fat and crude protein than the reflectance mode. Moreover, the small sample thickness (1mm) required for NIR transmittance measurement considerably complicates its online use.

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.

Measurement Of Beer Taste Attributes Using An Electronic Tongue

The present work deals with the results of the application of an electronic tongue system as an analytical tool for rapid assessment of beer flavour. Fifty samples of Belgian and Dutch beers of different types, characterized with respect to sensory properties and bitterness, were analyzed using the electronic tongue (ET) based on potentiometric chemical sensors. The ET was capable of predicting 10 sensory attributes of beer with good precision including sweetness, sourness, intensity, body, etc., as well as the most important instrumental parameter—bitterness. These results show a good promise for further progressing of the ET as a new analytical technique for the fast assessment of taste attributes and bitterness, in particular, in the food and brewery industries.