Detection of safflower adulteration in saffron by textural features

Saffron, a highly valuable spice in global trade, is often intentionally or unintentionally mixed with safflower stamens. In this study, a machine vision system was utilized to capture the images of saffron samples at different mixture proportions to explore the authentication. Then, three feature extraction algorithms including gray level co-occurrence matrix, gray-level run-length matrix, and Local Binary Pattern were applied to extract the textural features of data. Discriminant Analysis, Support Vector Machine, and Artificial Neural Network algorithms as supervised classification models were applied to classify datasets. The models were applied for 3 class and 6 class datasets to explore classification ability. The best outcome for the 6-class dataset was with the Support Vector Machine model and with all features with an accuracy of 80 %. For 3 class datasets, Discriminant Analysis model had the best result with all features and with the accuracy of 97.78 %. Then, to explore the importance of features, two Minimum Redundancy Maximum Relevance and Chi-Square Test algorithms were applied. For the gray level co-occurrence matrix extracted features, Chi-Square Test algorithm with 10 features had the best accuracy with a test accuracy of 76.94 %. Therefore, because of the adulteration of some profiteer sellers, the results of the proposed approach can be utilized in designing a system for exploring the authenticity of saffron and satisfaction of buyers.