Characterization of saffron from different origins by HS-GC-IMS and authenticity identification combined with deep learning

With the rising demand of saffron, it is essential to standardize the confirmation of its origin and identify any adulteration to maintain a good quality led market product. However, a rapid and reliable strategy for identifying the adulteration saffron is still lacks. Herein, a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and convolutional neural network (CNN) was developed. Sixty-nine volatile compounds (VOCs) including 7 groups of isomers were detected rapidly and directly. A CNN prediction model based on GC-IMS data was proposed. With the merit of minimal data prepossessing and automatic feature extraction capability, GC-IMS images were directly input to the CNN model. The origin prediction results were output with the average accuracy about 90 %, which was higher than traditional methods like PCA (61 %) and SVM (71 %). This established CNN also showed ability in identifying counterfeit saffron with a high accuracy of 98 %, which can be used to authenticate saffron. • HS-GC-IMS with CNN was used for characterizing and authenticating of saffron. • Sixty-nine VOCs with seven groups of isomers were detected by incubation at 80 °C. • Significant differences and relationships among origins and VOCs were revealed. • A CNN model was developed for predicting saffron origins with about 90 % accuracy. • The built CNN model showed 98 % accuracy for authenticating counterfeit saffron.