Sweetener identification using transfer learning and attention mechanism

Abstract

Accurate identification of the taste of compounds has helped in the screening and development of new sweeteners. This study proposes a deep learning model for sweetener identification based on transfer learning and attention mechanism. The Squeeze-and-Excitation (SE) attention mechanism is incorporated into the pre-trained Residual Network-50 (ResNet-50) model, resulting in SE-ResNet-50. Additionally, the Convolutional Block Attention Module (CBAM) is integrated to generate the CBAM-SEResNet-50 model for sweetener identification. This study divided the taste molecule dataset into two parts: Cross-Validation (CV) dataset and Hold-out test dataset. The effectiveness of the algorithm was verified using both the 5-fold CV and the Hold-out test methods. The experimental results demonstrate that the CBAM-SEResNet-50 model achieves an accuracy of 0.956 and an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.972 on the Hold-out test dataset. In the case of the 5-fold CV, the accuracy is 0.944 and the AUROC is 0.969.