Machine Learning Applications in Multi-Category Food Nutritional Assessment

Precise detection of key nutrients, such as protein, fat, and carbohydrates in food is essential for public health protection, clinical nutrition guidance, and the development of the food industry. Traditional nutrient content detection methods mainly rely on chemical analysis and laboratory techniques, which are cumbersome, time-consuming, and costly. These limitations become even more pronounced when dealing with multi-category and compositionally complex foods. With the rapid development of big data technology and artificial intelligence, machine learning (ML) has brought entirely new solutions to food nutrient detection. By deeply learning from large volumes of multi-source data, such as food composition, spectral information, and images, machine learning models can efficiently and automatically detect and predict the major nutrients contents—including protein, fat, and carbohydrates in foods. This significantly improves the accuracy and scalability of nutrient assessment across various food categories. As the demand for personalized diets and nutrition monitoring scenarios continues to grow, machine learning-driven food nutrient detection and evaluation will play an increasingly important role in health management and food safety.

The primary goal of this Research Topic is to develop an efficient, accurate, and scalable method for detecting and quantifying core nutrients—such as protein, fat, and carbohydrates in diverse food products using advanced machine learning techniques. By leveraging multi-source data, including food composition, spectral analysis, and image information, we aim to overcome the limitations of traditional chemical and laboratory-based detection methods, which are often time-consuming, costly, and less adaptable to complex or multi-category food matrices. Ultimately, the Research Topic seeks to create a machine learning-driven platform that enables rapid, automated, and precise nutritional analysis, supporting various applications, such as public health monitoring, clinical nutrition guidance, and industrial food quality control. This approach will also lay the groundwork for individualized nutrition management and broader adoption of intelligent food safety tools in an increasingly health-conscious society.

We welcome submissions of Original Research Articles and Review Articles related to machine learning applications in food nutrient quantitative detection. The scope of this Research Topic includes, but is not limited to, the following themes:

● Development and validation of machine learning models for the detection and quantification of core nutrients (protein, fat, carbohydrates, et al ) in foods.

● Integration of multi-source data (spectral, compositional, image-based, etc.) for improved nutrient analysis.

● Novel data preprocessing, feature extraction, and dimensionality reduction methods for complex food matrices.

● Case studies applying AI-driven methodologies to multi-category or complex foods.

●Comparison of machine learning-based nutrient analysis with conventional chemical or laboratory techniques.

● Real-time or portable machine learning systems for on-site food analysis.

We encourage interdisciplinary submissions and novel insights that address the advancement of intelligent food nutrient detection and health management technologies.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Nutritional Analysis, Machine Learning Algorithms, Analytical Techniques, Food Composition, Detection Methods

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.