Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines

In average all the models presented accuracy in relation to the commercial classification over 68% with a higher mismatch in the mid-quality range. Color showed an important discriminating power, increasing the accuracy in 10%. The main discriminant features were porosity coefficient and color variables, calculated for the lateral surface. A quality classification algorithm was presented based on a simplified model with an accuracy of 75%. The classification based on color vision systems can ensure improved quality class uniformity and a higher transparency in trade.