Comparative assessment of amino acids and volatile compounds role in the formation of wines sensor properties by means of covariation analysis

The objects of study were 150 samples of natural dry red and white grape wines of Russian origin, obtained by traditional technologies from European and hybrid grape varieties grown in wineries in Krasnodar Krai in 2010–2016. Natural red (Cabernet, Merlot) and white (Aligote, Riesling, Pinot Noir) (alcohol content of 9–13 % by volume, acidity of 4–7 g/dm3), as well as blend wines based on Cabernet Sauvignon, Merlot and Pinot Noir wines made under experimental conditions were analyzed. Chromatographic and electrophoretic methods were used to determine the content of volatile components and amino acids in the studied samples. A sensory assessment of wine quality was carried out by wine specialists working in the wine industry and having professional experience in the field of sensory analysis. Using statistical modeling we carried out a comparative assessment of the role of amino acids - threonine, proline, arginine and volatile compounds - methanol, acetic acid, furfural in the perception of taste and aromatic properties of wines, a general indicator of which is the average tasting rating. High adequacy of the regression model constructed using covariance analysis indicates that mainly amino acids and volatile compounds determine the sensory properties of wines. The dominant role of amino acids in the perception of taste and aromatic characteristics compared to other wine components is mathematically justified in accordance with the criterion of one-dimensional significance. It has been shown that more than 82% of the sensory characteristics of the analyzed wines group fall on the amino acids and volatile compounds under consideration, and less than 18% - on all the others, including titrated acids, free amino acids, mineral components, phenols, etc.

Using neural networks to identify the regional and varietal origin of Cabernet and Merlot dry red wines produced in Krasnodar region

This paper shows a possibility of establishing the authenticity and geographic origin of wines by neural networks based on multi-element analysis. The study used 144 samples of Cabernet and Merlot dry red wines pro- duced in Krasnodar Region according to traditional technologies. The wines were provided by the producers or pur- chased in retail stores. The concentrations of 20 micro- and macroelements in red wines were determined by atomic emission spectroscopy with inductively coupled plasma. The analysis of average elemental contents showed a signi- ficant dependence of wine composition on the grape variety and place of origin, which enabled us to examine inter- relations between the elements and think of a way to identify them by means of classification models. The software STATISTICA Neural Networks was used to assess a possibility of determining the grape variety and geographical origin. The neural networks constructed in the study contained five variables corresponding to the elements with sta- tistically significant correlations between the names of the regions and the wine samples, namely Fe, Mg, Rb, Ti, and Na. These predictors were able to determine the grape variety and place of growth with a sufficiently high accuracy. In the test sample set, the accuracy reached 95.24% and 100% for variety and region identification, respectively. A software product was developed to automate the calculations based on the neural networks. The program can estab- lish the grape variety from a minimal set of microelements, and then, based on the variety and the same set of micro- elements, determine its place of origin.

Computer analysis of the sensory qualities of red wines as a method to optimize their blend formulation

Three high quality red wines – Merlot, Cabernet Sauvignon and Pinot Noir – were used for development of an optimized formulation of a new blended wine called Zvezda Kubani (“The Star of Kuban”). The experimental plan was implemented with the mixture designs and triangular surfaces module in the STATISTICA package. According to the experimental plan, we made and studied 31 variants of wines, including 3 monovariants, 3 mixtures of 2 wines and 25 mixtures of 3 wines. In addition, highly qualified specialists have studied the changes in the mixtures according to the results of a sensory assessment to model the connection between the sensory perception of wine mixtures and the new blended wine formulation. As a result, we developed a mathematically proved formulation of a new blended wine, Zvezda Kubani, containing 48% Merlot, 35% Cabernet Sauvignon and 17% Pinot Noir. The experimental verification of the suggested composition of the blend proved to be a strong indicator of the experts' sensory assessment.