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

ABSTRACT: 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 %, w

SUBMITTER: Lu Y 

PROVIDER: S-EPMC11754009 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature