Sweetener identification using transfer learning and attention mechanism
| Autor: | Fanchao Lin, Yuan Ji, Shoujiang Xu |
|---|---|
| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | CyTA - Journal of Food, Vol 22, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 19476337 1947-6345 1947-6337 |
| DOI: | 10.1080/19476337.2024.2341812 |
| Popis: | ABSTRACTAccurate 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|