Artificial intelligence across the food packaging chain: from design to safety monitoring.

Authors

Tiantong Lan, Xiyuan Ma, Hao Zhang, Jingsheng Liu

Published in

Food research international (Ottawa, Ont.). Volume 242. Issue Pt 1. Pages 119796. Oct 31, 2026. Epub Jun 22, 2026.

Abstract

In recent years, with the improvement of food production and processing technology, as well as consumption upgrades and increasingly stringent environmental regulations, it has become crucial to promote technological innovation in the food packaging industry. Machine learning, in particular, offers significant potential to address emerging challenges in this field, driving high-quality development that satisfies both market requirements and consumer expectations. By applying machine learning to analyze and process data/images across the packaging 'design-optimization-resource recovery' lifecycle, this approach significantly reduces both time and economic costs in research and development while comprehensively addressing multi-dimensional consumer demands for food packaging. In terms of technology, it can also improve the level of industrial automation and production efficiency of traditional packaging technology. Additionally, it improves the identification, classification, and quantification of microplastics in plastic packaging, while simultaneously enabling accurate packaging waste detection. These capabilities offer an efficient approach to mitigating plastic pollution and advancing packaging recycling. This review systematically examines the transformative potential of machine learning across the entire food packaging industry chain, including material design and selection, structural optimization, sensory-preference analysis, smart packaging, automated packaging machinery, microplastic detection, and waste recycling. Moreover, to further promote the sustainable and high-quality development of food packaging, this review further elaborates on future prospects for machine learning technology applications in the food packaging field.

PMID:
42629033
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

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