The use of machine learning techniques to automate the sorting of Cape gooseberry fruits according to their visual ripeness has been reported to provide accurate classification results. Classifiers like artificial neural networks, support vector machines, decision trees, and nearest neighbors are commonly employed to discriminate fruit samples represented in different color spaces (e.g., RGB, HSV, and L*a*b*). Although these feature spaces are equivalent up to a transformation, some of them facilitate classification. In a previous work, authors showed that combining the three-color spaces through principal component analysis enhances classification performance at expenses of increased computational complexity. In this paper, two combination and two selection approaches are explored to find the best characteristics among the combination of the different color spaces (9 features in total). Experimental results reveal that selection and combination of color channels allow classifiers to reach similar levels of accuracy, but combination methods require increased computational complexity.