Sweetener identification using transfer learning and attention mechanism
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 incorpora...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | CyTA - Journal of Food |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19476337.2024.2341812 |
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| _version_ | 1850052994730557440 |
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| author | Fanchao Lin Yuan Ji Shoujiang Xu |
| author_facet | Fanchao Lin Yuan Ji Shoujiang Xu |
| author_sort | Fanchao Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b8e5fa0bd721413fb66829596c2096a4 |
| institution | DOAJ |
| issn | 1947-6337 1947-6345 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | CyTA - Journal of Food |
| spelling | doaj-art-b8e5fa0bd721413fb66829596c2096a42025-08-20T02:52:41ZengTaylor & Francis GroupCyTA - Journal of Food1947-63371947-63452024-12-0122110.1080/19476337.2024.2341812Sweetener identification using transfer learning and attention mechanismFanchao Lin0Yuan Ji1Shoujiang Xu2School of Artificial Intelligence, Jiangsu Food and Pharmaceutical Science College, Huaian, ChinaSchool of Artificial Intelligence, Jiangsu Food and Pharmaceutical Science College, Huaian, ChinaSchool of Artificial Intelligence, Jiangsu Food and Pharmaceutical Science College, Huaian, ChinaAccurate 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.https://www.tandfonline.com/doi/10.1080/19476337.2024.2341812Sweetener identificationtransfer learningattention mechanism |
| spellingShingle | Fanchao Lin Yuan Ji Shoujiang Xu Sweetener identification using transfer learning and attention mechanism CyTA - Journal of Food Sweetener identification transfer learning attention mechanism |
| title | Sweetener identification using transfer learning and attention mechanism |
| title_full | Sweetener identification using transfer learning and attention mechanism |
| title_fullStr | Sweetener identification using transfer learning and attention mechanism |
| title_full_unstemmed | Sweetener identification using transfer learning and attention mechanism |
| title_short | Sweetener identification using transfer learning and attention mechanism |
| title_sort | sweetener identification using transfer learning and attention mechanism |
| topic | Sweetener identification transfer learning attention mechanism |
| url | https://www.tandfonline.com/doi/10.1080/19476337.2024.2341812 |
| work_keys_str_mv | AT fanchaolin sweeteneridentificationusingtransferlearningandattentionmechanism AT yuanji sweeteneridentificationusingtransferlearningandattentionmechanism AT shoujiangxu sweeteneridentificationusingtransferlearningandattentionmechanism |