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Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.

Marimuthu Murugesan¹, Prakash Pandurangan¹, Anitha Murugesan²

¹Department of Biotechnology, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu 600119, India.

Food Chemistry: X

|February 27, 2026

Summary

Artificial intelligence (AI) enhances bakery practices by using machine learning (ML) for better quality prediction and process optimization. AI integration leads to cost-effective, healthier products and reduced food waste.

Keywords:

Artificial intelligence Fermentation control Ingredient optimization Sensory profiling Shelf-life prediction Smart baking

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Area of Science:

Food Science and Technology
Artificial Intelligence in Food Processing
Machine Learning Applications in Baking

Background:

The bakery industry is increasingly adopting data-driven strategies to improve traditional practices.
Existing methods for quality prediction, process optimization, and shelf-life assessment have limitations.
There is a growing need for advanced analytical tools to manage complex interactions in food production.

Purpose of the Study:

To review the application of Artificial Intelligence (AI) and Machine Learning (ML) in the bakery industry.
To explore how AI models can enhance accuracy in quality prediction, process optimization, and shelf-life assessment.
To highlight AI's role in product innovation and sustainable bakery practices.

Main Methods:

Discussion of various ML algorithms, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests (RF), and Deep Neural Networks (DNNs).
Analysis of AI's capability to model complex interactions between formulation, processing, and quality attributes.
Integration of AI with advanced sensory analysis tools (e.g., GC-O, Texture Profiling, E-nose/E-tongue) and digital technologies (e.g., digital twins, IoT).

Main Results:

AI enables accurate prediction of key bread quality features like loaf volume, crumb structure, staling, and spoilage risk.
AI facilitates the optimization of product formulations for cost-effectiveness and nutritional enhancement without compromising sensory quality.
Combining AI with advanced methods and digital systems improves real-time analysis, operational performance, and sustainability.

Conclusions:

AI, particularly ML, offers significant potential to revolutionize the bakery industry through data-informed decision-making.
AI integration supports the development of sustainable, less wasteful, and personalized bakery products.
Future applications of AI in baking promise enhanced efficiency, improved product quality, and innovative product development.