Integration of computer vision and electronic nose as non-destructive systems for saffron adulteration detection

This work deals with the development and evaluation of an integrated system based on computer vision system (CVS) and electronic nose (e-nose) for saffron adulteration detection. Ten saffron samples adulterated with two common illegal constituents, namely, Artificially Colored Safflower (ACS) and Artificially Colored Yellow Styles of Saffron (ACYSS) at levels ranging from 10 to 50% (w/w) were characterized in this work. First, the developed CVS and e-nose system were integrated to form a unit system. This set up was utilized to extract color and aroma characteristic variables of each sample. The extracted variables were processed using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vectors Machines (SVMs) to demonstrate the discrimination capability of the developed system. Two multilayer artificial neural network (ANN-MLP) models were also employed for saffron color and aroma strength prediction based on ISO standards. PCA and HCA results of the color and aroma datasets revealed that the adulterated samples have different color and aroma strength compared to authentic saffron and they can clearly be distinguished. SVMs classifier showed good agreement with the PCA results and reached 89% and 100% success rate in the recognition of the different saffron samples based on their color and aroma datasets, respectively. Results of the two ANN-MLP models proved that the developed system is capable of differentiating the authentic and adulterated saffron samples based on their color and aroma strength (RColoranalysis2⩾0.95 and RAromaanalysis2⩾0.97).