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

• Published in: Applied system innovation 2022-10, Vol.5 (5), p.104
• Main Authors: Kazak, Anatoliy, Plugatar, Yurij, Johnson, Joel, Grishin, Yurij, Chetyrbok, Petr, Korzin, Vadim, Kaur, Parminder, Kokodey, Tatiana
• Format: Article
• Language: English
• Subjects: amperometric method; antioxidant activity; Antioxidants; Calibration; Chemiluminescence; chemiluminescent method; Chromatography; Comparative analysis; Electric currents; Electrical measurement; Food products; Free radicals; Gallic acid; Life expectancy; Machine learning; Methods; Molybdenum; Neural networks; Nutrients; Oxidation; phenolic substances; Reagents; Regression analysis; Regression models; Spectrum analysis; Theories of aging; Wines
• ISSN: 2571-5577; 2571-5577
• DOI: 10.3390/asi5050104

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.