ArticleFoods (Basel, Switzerland)2023
Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.Nutrients · 2024Pooled it
- Prioritizing Artificial Intelligence Opportunities for Food as Health in the U.S. Agrifood Value Chain.Foods (Basel, Switzerland) · 2026Article
- Residual Derivative-Guided Spectral Fusion Module for Few-Shot Classification of Soybean Seed Varieties Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2026Article
- A comparative study of vision-language models for food ingredient recognition and nutrient estimation.Current research in food science · 2026Article
- Multi-View Edge Attention Network for Fine-Grained Food Image Segmentation.Foods (Basel, Switzerland) · 2025Article
- FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations.Patterns (New York, N.Y.) · 2025Article
- Improving Personalized Meal Planning with Large Language Models: Identifying and Decomposing Compound Ingredients.Nutrients · 2025Article
- Applications of Artificial Intelligence in Food Industry.Foods (Basel, Switzerland) · 2025Article
- Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques.Scientific reports · 2025Article
- Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes.Foods (Basel, Switzerland) · 2025Review
- Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with Chemometrics.Foods (Basel, Switzerland) · 2024Article
- Varroa Mite Counting Based on Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2024Article
- On Application of Lightweight Models for Rice Variety Classification and Their Potential in Edge Computing.Foods (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation.
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