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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2512.11836 |
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| _version_ | 1866912761239306240 |
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| author | Freudenberg, Dayne R. Haughian, Daniel G. Klusty, Mitchell A. Leach, Caroline N. Black, W. Scott Woltenberg, Leslie N. Hallock, Rowan Solie, Elizabeth Collier, Emily B. Armstrong, Samuel E. Bumgardner, V. K. Cody |
| author_facet | Freudenberg, Dayne R. Haughian, Daniel G. Klusty, Mitchell A. Leach, Caroline N. Black, W. Scott Woltenberg, Leslie N. Hallock, Rowan Solie, Elizabeth Collier, Emily B. Armstrong, Samuel E. Bumgardner, V. K. Cody |
| contents | Accurate nutritional assessment is critical for public health, but existing profiling systems require detailed data often unavailable or inaccessible from colloquial text descriptions of food. This paper presents a machine learning pipeline that predicts the comprehensive Food Compass Score 2.0 (FCS) from text descriptions. Our approach uses multi-headed neural networks to process hybrid feature vectors that combine semantic text embeddings, lexical patterns, and domain heuristics, alongside USDA Food and Nutrient Database for Dietary Studies (FNDDS) data. The networks estimate the nutrient and food components necessary for the FCS algorithm. The system demonstratedstrong predictive power, achieving a median R^2 of 0.81 for individual nutrients. The predicted FCS correlated strongly with published values (Pearson's r = 0.77), with a mean absolute difference of 14.0 points. While errors were largest for ambiguous or processed foods, this methodology translates language into actionable nutritional information, enabling scalable dietary assessment for consumer applications and research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic Nutrition Estimation: Predicting Food Healthfulness from Text Descriptions Freudenberg, Dayne R. Haughian, Daniel G. Klusty, Mitchell A. Leach, Caroline N. Black, W. Scott Woltenberg, Leslie N. Hallock, Rowan Solie, Elizabeth Collier, Emily B. Armstrong, Samuel E. Bumgardner, V. K. Cody Machine Learning Artificial Intelligence Accurate nutritional assessment is critical for public health, but existing profiling systems require detailed data often unavailable or inaccessible from colloquial text descriptions of food. This paper presents a machine learning pipeline that predicts the comprehensive Food Compass Score 2.0 (FCS) from text descriptions. Our approach uses multi-headed neural networks to process hybrid feature vectors that combine semantic text embeddings, lexical patterns, and domain heuristics, alongside USDA Food and Nutrient Database for Dietary Studies (FNDDS) data. The networks estimate the nutrient and food components necessary for the FCS algorithm. The system demonstratedstrong predictive power, achieving a median R^2 of 0.81 for individual nutrients. The predicted FCS correlated strongly with published values (Pearson's r = 0.77), with a mean absolute difference of 14.0 points. While errors were largest for ambiguous or processed foods, this methodology translates language into actionable nutritional information, enabling scalable dietary assessment for consumer applications and research. |
| title | Semantic Nutrition Estimation: Predicting Food Healthfulness from Text Descriptions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.11836 |