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Related Experiment Video

Updated: Jul 14, 2026

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

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Published on: June 16, 2018

AI-based processing of future prepared foods: Progress and prospects.

Jinjin Huang¹, Min Zhang², Arun S Mujumdar³

¹State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China; International Joint Laboratory on Food Safety, Jiangnan University, 214122 Wuxi, Jiangsu, China.

Food Research International (Ottawa, Ont.)

|January 24, 2025

Summary

Artificial intelligence (AI) is revolutionizing prepared food processing by enhancing sorting, cleaning, and cutting. AI applications promise increased efficiency, safety, and sustainability in the food industry.

Keywords:

Artificial neural network Classification Computer vision Cutting Deep learning Freezing

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Area of Science:

Food Science and Technology
Artificial Intelligence in Food Processing
Sustainable Food Systems

Background:

The prepared foods sector is rapidly expanding due to modern lifestyles and demand for convenience.
Current processing faces challenges like labor shortages, inefficient sorting, inadequate cleaning, and safety concerns.
Artificial intelligence (AI) presents a transformative solution for these industry challenges.

Purpose of the Study:

To review the progress and future prospects of AI applications in prepared food processing.
To cover AI's role in sorting, classification, cleaning, preprocessing, and freezing.
To highlight AI's potential to improve efficiency, safety, and sustainability.

Main Methods:

Review of AI techniques including mathematical modeling, chemometrics, machine learning, fuzzy logic, and adaptive neuro fuzzy inference systems.
Analysis of AI applications in specific food processing stages: sorting, cleaning, and cutting.
Examination of AI's impact on ingredient classification, microbial contamination detection, and resource optimization.

Main Results:

AI-powered sorting systems improve ingredient classification accuracy using computer vision.
Deep learning models enhance microbial contamination detection in cleaning processes via spectral imaging.
AI applications demonstrate potential for increased efficiency, accuracy, consistency, and reduced waste.

Conclusions:

AI significantly enhances prepared food processing efficiency, accuracy, and consistency.
AI adoption can reduce labor costs, improve hygiene, minimize waste, and lower environmental impact.
Future AI development will drive smarter, more sustainable practices in the food processing industry.