Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia

Khalafyan, А. А.; Темердашев, З. А.; Akinshina, V. A.; Yakuba, Yuri

doi:10.1016/j.dib.2021.106992

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Experts evaluated each sample in triplicate. The sensory evaluation results of wine quality were expressed with a 100-point rating scales, as described previously (Etaio et al, 2012;Khalafyan et al, 2021), according to the following points: appearance: 10, odour/aroma: 30: taste: 40, and overall impression: 20.…”

Section: Sensory Analysismentioning

confidence: 99%

Quality study of PDO (Protected Designation of Origin) Greek white wines of the grape variety ‘Debina’

Tsiraki

SAVVAIDIS²

2022

Not Bot Horti Agrobo

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In this study ‘Debina’ (Zitsa, Ioannina, Greece) wines (WD: white dry and WDS: white dry sparkling) were examined in terms of their quality (chemical, physicochemical and sensory) parameters. The concentration of alcohol in both wines was practically stable (p>0.05) from the stage of fermentation to packaging and specifically, the rates were 12 and 11.4% vol for WD and WDS wines, respectively. Total sugar concentrations were equal to 1.4 g/l and 1.2 g/l in WD and WDS wines, respectively. WD wine had final total acidity, volatile acidity and pH values of 4.9 g/l tartaric acid, 0.19 g/l acetic acid and 3.15, respectively. While, WDS wine had final total acidity, volatile acidity and pH value of 6.0 g/l tartaric acid, 0.15 g/l acetic acid and 3.11, respectively. Τotal sulfite content of wine samples decreased and final values of 90.6 mg/l and 99.8 mg/l, in the last production stage, for the WD and WDS wines, were recorded, respectively. According to the data of the study, all wines received a high acceptability sensory score, as judged by the panellists. It must be reported that the sensory scores were on average, 1 point higher for the WDS wines, as compared to the WD wines.

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Section: Sensory Analysismentioning

confidence: 99%

Quality study of PDO (Protected Designation of Origin) Greek white wines of the grape variety ‘Debina’

Tsiraki

SAVVAIDIS²

2022

Not Bot Horti Agrobo

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“…This includes a variety of techniques, such as data collection from experts and physicochemical laboratory test findings for each of the wines. Financial evaluation of wine items, ensuring and guaranteeing wine quality, preventing wine corruption, and controlling refreshment preparation are some of the reasons (Khalafyan et al, 2021). The Waikato Environment for Knowledge Analysis (WEKA) is an application software developed at the University of Waikato in New Zealand to process data from agricultural fields.…”

Section: Introductionmentioning

confidence: 99%

Application of supervised learning algorithm to determine the quality of slippers in WEKA

Mina

2023

Int. j. adv. appl. sci.

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This study is driven by the objective of evaluating the effectiveness of various regression algorithms in the prediction of slipper quality. The selected regression algorithms were implemented within the Waikato Environment for Knowledge Analysis. The assessment of their performance was conducted through the analysis of correlation coefficients, providing insights into their predictive capabilities. Notably, the Random Forest algorithm demonstrated the highest predictive power with an impressive correlation coefficient (r=0.76), surpassing other models in the analysis. Following Random Forest, the k-nearest neighbor algorithm achieved a substantial correlation coefficient of (r=0.65), followed by the Decision Tree (r=0.53), Linear regression (r=0.51), and the Multi-layer perceptron (r=0.51). In contrast, the Support Vector Machine showed a notably lower correlation coefficient (r=0.51), indicating its comparatively weaker predictive performance. Furthermore, this study uncovered two variables, "Easy to Wash" and "Water Resistance," which displayed significant correlations of (r=0.49) and (r=-0.35), respectively, in relation to the predictive performance of the regression model. However, no significant correlation was observed for other variables. In light of these findings, future research endeavors may explore alternative predictive models to further assess and compare their performance against the outcomes presented in this study, contributing to the ongoing enhancement of slipper quality prediction methodologies.

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A Machine Learning Based Approach for Wine Quality Prediction

Anami

Mainalli

Kallur

et al. 2022

2022 2nd Asian Conference on Innovation in Technology (ASIANCON)

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Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia

Cited by 4 publications

References 12 publications

Quality study of PDO (Protected Designation of Origin) Greek white wines of the grape variety ‘Debina’

Quality study of PDO (Protected Designation of Origin) Greek white wines of the grape variety ‘Debina’

Application of supervised learning algorithm to determine the quality of slippers in WEKA

A Machine Learning Based Approach for Wine Quality Prediction

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