Vision transformer for quality identification of sesame oil with stereoscopic fluorescence spectrum image
Zhilei Zhao, Xijun Wu, Hailong Liu
Abstract
Sesame oil (SO), as a high-priced edible oil, is often counterfeited and adulterated. A new method for SO quality identification using Vision Transformer (ViT) network based on stereoscopic images of Excitation-emission matrix fluorescence (EEMF) and Total synchronous fluorescence (TSyF) spectroscopy was proposed. The basic samples including pure, counterfeit and adulterated SOs were characterized by fluorescence spectroscopy. A data augmentation strategy including linear interpolation, shift and noise injection was selected for few sample learning. All fluorescence spectral data were visualized as stereoscopic images with rich spectral characteristics. The ViT network architecture based on attention mechanism was designed and trained to establish four SO quality identification models. The macro averages of precision, recall and F1-score on the validation set were greater than 0.99. The values of these indicators on the test samples were equal to one. In conclusion, deep learning based on ViT using stereoscopic fluorescence spectrum image provided a new method for sesame oil identification.