Accelerating the Classification of NOVA Food Processing Levels Using a Fine-Tuned Language Model: A Multi-Country Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Food Composition Databases
2.2. NOVA Food Classification System
2.3. Data Preparation
2.4. Food Representations
2.5. Fine-Tuning Language Model
2.6. Statistical Analyses
3. Results
3.1. Different Machine Learning Algorithms Reached Moderate to High Accuracy in NOVA Food Processing Levels Classification
3.2. Fine-Tuned Language Model Performed Well in NOVA Food Processing Levels Classification
3.3. The Generalization Ability of the Fine-Tuned Language Model in NOVA Food Processing Levels Classification
3.4. Using Fine-Tuned Language Model to Estimate NOVA Food Processing Levels in the Food Supply across Different Countries
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature * | Model † | NOVA Classification Performance | ||
---|---|---|---|---|
Accuracy | Balanced Accuracy | F1 Score | ||
Structured data | XGBoost | 0.890 | 0.797 | 0.882 |
Bag-of-words | XGBoost | 0.970 | 0.938 | 0.970 |
Pre-trained embeddings | XGBoost | 0.940 | 0.882 | 0.940 |
Fine-tuned language models | BERT-Base | 0.978 | 0.955 | 0.978 |
DistilBERT-Base | 0.979 | 0.958 | 0.979 | |
MPNet-Base | 0.979 | 0.954 | 0.979 | |
all-MiniLM-L6 | 0.979 | 0.956 | 0.979 | |
multi-qa-MiniLM-L6-cos | 0.979 | 0.959 | 0.979 |
Method * | Database † | NOVA Classification Performance | ||
---|---|---|---|---|
Accuracy | Balanced Accuracy | F1 Score | ||
Structured data | FLIP-Canada | 0.872 | 0.737 | 0.862 |
Bag-of-words | FLIP-Canada | 0.937 | 0.870 | 0.936 |
Pre-trained embedding | FLIP-Canada | 0.921 | 0.831 | 0.917 |
Fine-tuned model | FLIP-Canada | 0.941 | 0.896 | 0.940 |
Structured data | FLIP-LAC | 0.857 | 0.726 | 0.850 |
Bag-of-words | FLIP-LAC | 0.863 | 0.681 | 0.858 |
Pre-trained embedding | FLIP-LAC | 0.825 | 0.567 | 0.808 |
Fine-tuned model | FLIP-LAC | 0.891 | 0.654 | 0.889 |
Structured data | USDA-BFPD | 0.806 | 0.647 | 0.788 |
Bag-of-words | USDA-BFPD | 0.919 | 0.832 | 0.918 |
Pre-trained embedding | USDA-BFPD | 0.900 | 0.766 | 0.892 |
Fine-tuned model | USDA-BFPD | 0.948 | 0.881 | 0.947 |
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Hu, G.; Flexner, N.; Tiscornia, M.V.; L’Abbé, M.R. Accelerating the Classification of NOVA Food Processing Levels Using a Fine-Tuned Language Model: A Multi-Country Study. Nutrients 2023, 15, 4167. https://doi.org/10.3390/nu15194167
Hu G, Flexner N, Tiscornia MV, L’Abbé MR. Accelerating the Classification of NOVA Food Processing Levels Using a Fine-Tuned Language Model: A Multi-Country Study. Nutrients. 2023; 15(19):4167. https://doi.org/10.3390/nu15194167
Chicago/Turabian StyleHu, Guanlan, Nadia Flexner, María Victoria Tiscornia, and Mary R. L’Abbé. 2023. "Accelerating the Classification of NOVA Food Processing Levels Using a Fine-Tuned Language Model: A Multi-Country Study" Nutrients 15, no. 19: 4167. https://doi.org/10.3390/nu15194167