Decision Rules for Computer-Vision Quality Classification of Wine Natural Cork Stoppers

Abstract

Image-analysis techniques were applied to the surface of wine cork stoppers (tops and bodies) of the standard seven commercial quality classes to characterize their porosity. Canonical discriminant analysis (CDA) and stepwise discriminant analysis (SDA) were used to differentiate quality class and to identify the best features to select these classes. The accuracy of classification using CDA functions was on average greater than 50% for the seven commercial classes and was greater than 67% for a simplified three-grade classification. Based on the independent variables of the first CDA function determined by the stepwise method, a set of features was selected for use in decision rules for cork stopper classification: porosity coefficient and maximum pore dimensions (length and area) for bodies and porosity coefficient and number of pores for tops. Threshold limits for each feature were established for each quality class and a classification algorithm was applied. Results showed an overall match in class yield of 86% and better class homogeneity and separation. These are proposed as a foundation for future standardization of cork stopper classification based on image analysis and computerized vision systems selection of quantified features to ensure uniformity and transparency in trade while maintaining the overall economical feasibility in industrial processing.