Application of Data Mining Classifiers on Sunflower Edible Oil Bleaching Process: A Comprehensive Comparative Analysis

Abstract

Sunflower oil is widely used as edible oil. It is commonly extracted by solvent extraction method from the sunflower seed. After extraction, crude sunflower oil is obtained. Crude sunflower oil has some undesirable impurities and dark colors. These impurities and dark colors require removal. The bleaching process is applied to remove the color. The bleaching earth is used in the refining and removes color. The specifications of crude sunflower oil such as impurity, free fatty acid ratio, wax, color index and the temperature of the process, the vacuum of the process, the amount of bleaching earth used affect the bleaching output color value. In this study, machine learning algorithms are used to predict the bleaching output color. In order to predict, Waikato Environment for Knowledge Analysis (WEKA), an open-source Data Mining workbench is run. 15 well-known machine learning classifier algorithms, suitable for our data such as k-nearest neighbors, multilayer perceptron and random forest are performed. Each algorithm is tested on a real dataset by a 10-fold cross-validation method. The correlation coefficient, mean absolute error and root mean squared error is calculated for each algorithm and benchmarked. Results show that Random Forest Classifier is the most effective classifier for our data. Additionally, Wilcoxon Signed-Rank statistical test is conducted whether Random Forest Classifier is the most effective classifier for some k-fold cross validation.