Handling the Impact of Uncertainties on Predicting the Quality Aspects of Doogh

Support Vector Machines (SVMs) are a valuable tool in the food industry due to their ability to handle complex, nonlinear relationships between variables, even with limited datasets, high-dimensional data, and noisy data. This makes SVMs well-suited for applications such as food quality and safety assessment, sensory evaluation, process optimization, and food authentication. Accordingly, a new approach is introduced to predict different features of a traditional yogurt drink, also called doogh. The proposed model combines the principles of Support Vector Regression with fuzzy logic to handle uncertainty and approximate complex relationships between inputs (retentate, xanthan, and shelf-life) and target variables including viscosity, syneresis, color values, and total acceptability. The implemented approach is particularly useful when dealing with problems where the relationships are not easily captured by traditional mathematical models due to their non-linearity or imprecision. Also, it mitigates the limitations of data availability. The predictive ability of the proposed model has been evaluated in terms of MSE, R2, RMSE, and MAE when adding different noise levels. Additionally, the conditions necessary to attain optimized metric values have been found. At the optimum point, the viscosity, syneresis, L*, a*, b* and total acceptability are 19.70 mPa.s, 11.30%, 97.04, -1.43, 8.13, and 5.00, respectively. Besides, the findings indicate that samples containing 0.8% retentate, 0.4% xanthan, and a 31-day shelf-life exhibit the highest viscosity, while those with 0.6% retentate, 0.4% xanthan, and a 31-day shelf-life show the lowest syneresis. Moreover, samples with 0.7% retentate, 0.2% xanthan, and a 13-day shelf-life demonstrate the highest total acceptability.