The possibility of using the potentiometric titration method to determine the antioxidant properties of wines

Grishin, Yuriy V.; Kazak, Anatoliy N.; N, Oleinikov N; Chetyrbok, Petr V.

doi:10.1088/1742-6596/1703/1/012048

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The amperometric method for evaluating AOA consists in measuring the strength of the electric current that occurs during the oxidation of the test substance (or mixture of substances) on the surface of the working electrode at a certain potential and then comparing the received signal with the signal from the standard (quercetin) under the same measurement conditions. This method includes sample preparation of the analyzed and standard substances, their electro-oxidation in the cell of an amperometric detector, amplification of electrical signals, and their registration and calculation of AOA according to the proposed mathematical dependence which is then used for the analysis of multicomponent systems without their preliminary separation [22][23][24][25][26][27][28]. The amperometric method allows us to estimate the total AOA with high accuracy and reproducibility using the equipment of domestic production and the amperometric flow analyzer "Tsvet-Yauza-01-AA".…”

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The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines

Kazak

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Johnson

et al. 2022

ASI

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This paper presents an analysis of modern methods used to determine antioxidant activity. According to research by the World Health Organization, the deficiency of such important nutrients as antioxidants leads to a decrease in body resistance and the development of chronic diseases. When it comes to diet, the inclusion of foods with a high content of antioxidants helps to increase life expectancy. As a result of this research, the mass concentration of phenolic substances and the antioxidant activity of phenolic antioxidants in young white and red table wine materials were determined using amperometric and chemiluminescent methods in order to determine antioxidant activity. Regression equations reflecting the relationship between the indicator of antioxidant activity and the value of the mass concentration of phenolic substances in young table wine materials were derived. The conversion coefficient for determining the mass concentration of phenolic substances when using Trolox-C and gallic acid as standards was established, which was—3.75. Based on a multiple linear regression model, the total antioxidant activity of the samples (F9.5 = 19.10 and p = 0.0023) can be fairly accurately predicted with an R2 of 0.921 for the calibration data set. A neural network regression model (NNRM) was chosen for the machine-learning regression analysis of the antioxidant activity of the wine samples due to its effectiveness in predicting outcomes in various applications. The implementation was performed using the fitrnet function provided in the Statistics and Machine Learning Toolbox in MATLAB R2021b. The MSE of the calibration model was 0.056; however, the MSE for the three validation samples was much higher, at 0.272.

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confidence: 99%

The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines

Kazak

Plugatar

Johnson

et al. 2022

ASI

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Introduced clones of red grape varieties perspective for the Crimea

et al. 2021

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The research has shown that clones of the Cabernet Sauvignon variety R-5 and R-8 are distinguished by higher rates of fruiting. The average bunch weight in clones reaches 188.7 ± 0.02 g (R-5) and 177.3 ± 2.6 g (R-8), exceeding the control. The yield per bush is on average 5.34 ± 0.21 kg and 4.25 ± 0.07 kg, exceeding the control by 1.54 - 1.23 times. Shoot productivity in terms of bunch wet weight in the Cabernet-Sauvignon R-5 clone is characterized as “high”. The clone of the Merlot R-3 variety has a high fruiting coefficient and a high fertility coefficient. The bunch weight of the clone reaches an average of 227.6 ± 4.3 g, the yield per bush is 6.3 ± 0.1 kg, exceeding the control. Shoot productivity in terms of bunch wet weight in the Merlot R-3 clone is characterized as “very high", in the control it is “high”. The clone of the variety Bastardo Magarachsky VCR-1 has a higher fruiting coefficient than in the control. The average weight of a bunch of a clone reaches 247.7 ± 1.4 g and exceeds the control (186.3 ± 9.14 g). The yield per bush is within 4.62 ± 0.2 kg, exceeding the standard variety by 0.85 kg. Shoot productivity in terms of bunch wet weight in Bastardo Magarachsky VCR-1 clone is characterized as “very high", in control this indicator is defined as “high”. The degree of variability of most traits in populations of standard varieties is characterized as average, which indicates the heterogeneity of the population and the possibility of further identification of new biotypes. According to the technological assessment, the indicators of wine materials in terms of physicochemical and organoleptic indicators, the introduced clones of the studied varieties were superior to the control varieties. In terms of the volume fraction of ethyl alcohol, mass concentration of volatile and titratable acids, all wine materials met the requirements of regulatory documents. Tasting scores of Cabernet Sauvignon wine materials were 7.7-7.9 points. The highest marks were obtained for the sample generated from clone R-8. Wine materials produced from the Merlot variety were noted as high quality, with pronounced varietal characteristics.

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Methodology for Applying IIoT Architecture to Design IIOT Systems in the Agricultural Sector of the Russian Federation

Makoveichuk,

Olifirov,

Petrenko

et al. 2023

2023 Seminar on Information Computing and Processing (ICP)

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The possibility of using the potentiometric titration method to determine the antioxidant properties of wines

Cited by 3 publications

References 11 publications

The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines

The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines

Introduced clones of red grape varieties perspective for the Crimea

Methodology for Applying IIoT Architecture to Design IIOT Systems in the Agricultural Sector of the Russian Federation

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