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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Neural network in food analytics.

Peihua Ma¹, Zhikun Zhang², Xiaoxue Jia¹

¹Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA.

Critical Reviews in Food Science and Nutrition

|November 2, 2022

Summary

This summary is machine-generated.

Neural networks (NN) offer a powerful approach to enhance food safety and supply chain integrity. This review explores NN applications in food analysis, highlighting successes and challenges for future advancements.

Keywords:

Analytical chemistry deep learning food analytics machine intelligence neural network

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Area of Science:

Food Science
Artificial Intelligence
Data Analytics

Background:

Neural networks (NN), including deep learning, are increasingly vital for food supply chain integrity and safety.
NN applications span food recognition, supply chain security, and omics analysis within food science.
The global agriculture market is projected to reach $11.2 billion, with NN playing a key role.

Purpose of the Study:

To provide a comprehensive overview of neural network applications in food analysis.
To focus on domain-specific applications, fundamental methodologies, recent progress, and challenges.
To highlight the potential of NN in revolutionizing the food industry.

Main Methods:

Review of existing literature on neural network applications in food analysis.
Analysis of NN methodologies relevant to food science domains.
Discussion of challenges and future directions for NN implementation in food safety and analysis.

Main Results:

NN shows superiority in areas like food recognition, sensory evaluation, and pattern recognition for spectroscopy and chromatography.
NN has demonstrated significant potential in enhancing food safety and supply chain management.
Effective collaboration between food specialists and the broader community is crucial for NN success.

Conclusions:

Neural networks hold immense potential for revolutionizing food analysis and ensuring global food supply chain integrity.
Major challenges include the lack of user-friendly software, model interpretability, and handling multi-source data.
Continued research and interdisciplinary collaboration are essential to overcome these hurdles and unlock the full potential of NN in the food sector.