A SVM Based Wine Superiority Estimatation Using Advanced ML Techniques

Sirivanth, Paladugu; Rao, N. V. Subba; Manduva, Jenvith; Sekhar, G Chandra; Tajeswi, M.; Veeresh, Ch.; Kaushik, J V

doi:10.1109/icac3n53548.2021.9725492

Cited by 2 publications

(1 citation statement)

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“…In contrast to this study, researchers have found that SVM method is the most suitable technique for predicting the quality of red wine from data generated over 1000 years [50], but in another study in the same field where different methods were compared, the Gradient Boosting Regressor (GBR) and its variants surpassed all other models [26,51]. Atasoy and Er [52] found that when the quality classification of red and white wine is needed, the most successful method was the random forest algorithm with a 99.5% accuracy, which is the same as Patkar and Balaganesh [53], who found more than 90% accuracy in the wine quality prediction.…”

Section: Turbidity Prediction Model With Regression Learnercontrasting

confidence: 64%

An Artificial Intelligence Approach for Estimating the Turbidity of Artisanal Wine and Dosage of Clarifying Agents

De La Cruz Rojas,

Nuñez-Pérez,

Lara-Fiallos

et al. 2024

Applied Sciences

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Red wine is a beverage consumed worldwide and contains suspended solids that cause turbidity. The study’s purpose was to mathematically model estimated turbidity in artisanal wines concerning the dosage and types of fining agents based on previous studies presenting positive results. Burgundy grape wine (Vitis lambrusca) was made and clarified with ‘yausabara’ (Pavonia sepium) and bentonite at different concentrations. The system was modelled using several machine learning models, including MATLAB’s Neural Net Fitting and Regression Learner applications. The results showed that the validation of the neural network trained with the Levenberg–Marquardt algorithm obtained significant statistical indicators, such as the coefficient of determination (R2) of 0.985, mean square error (MSE) of 0.004, normalized root mean square error (NRSME) of 6.01 and Akaike information criterion (AIC) of −160.12, selecting it as the representative model of the system. It presents an objective and simple alternative for measuring wine turbidity that is useful for artisanal winemakers who can improve quality and consistency.

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