Smart and Sustainable: Review of AI-Driven Approaches in Biodegradable Food Packaging Systems

Biodegradable materials for food preservation are becoming more popular as a result of the growing need for environmentally friendly and sustainable packaging solutions. However, because biopolymers have a wide range of characteristics and behaviors, improving the sustainability, safety, and performance of such materials is still a difficult task. One promising way to speed up research and innovation in this field is through artificial intelligence (AI). The use of AI in the creation, formulation, and improvement of biodegradable packaging systems is examined in this review. Predicting material properties, designing polymer blends in reverse, integrating sensors into smart packaging, and automated quality control are some of the main uses. Case studies demonstrating effective applications of machine learning and deep learning methodologies in actual biodegradable packaging situations are also covered in the review. Limitations are also critically analysed, including regulatory barriers, model interpretability, and data scarcity. The study concludes by outlining potential future developments, highlighting how AI-driven technologies, digital twins, and autonomous labs could revolutionise the sustainable packaging sector. This review offers a thorough grasp of how AI can facilitate the shift to intelligent, secure, and environmentally friendly food packaging systems by highlighting recent developments and potential future paths.

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Publication Details

Journal
European Journal of Nutrition & Food Safety
Published
2025-08-18
DOI
https://doi.org/10.9734/ejnfs/2025/v17i81815
Citations
4
Primary Topic
Nanocomposite Films for Food Packaging
Type
article
Field-Weighted Citation Impact
2.39
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article

Smart and Sustainable: Review of AI-Driven Approaches in Biodegradable Food Packaging Systems

Gyan Prakash, Shivbilas Maurya, Virendra N. Pandey, Devendra Pandey
4 citations
European Journal of Nutrition & Food Safety
Nanocomposite Films for Food Packaging
2.39
article

Smart and Sustainable: Review of AI-Driven Approaches in Biodegradable Food Packaging Systems

Gyan Prakash, Shivbilas Maurya, Virendra N. Pandey, Devendra Pandey
article en
4 citations

Abstract

Biodegradable materials for food preservation are becoming more popular as a result of the growing need for environmentally friendly and sustainable packaging solutions. However, because biopolymers have a wide range of characteristics and behaviors, improving the sustainability, safety, and performance of such materials is still a difficult task. One promising way to speed up research and innovation in this field is through artificial intelligence (AI). The use of AI in the creation, formulation, and improvement of biodegradable packaging systems is examined in this review. Predicting material properties, designing polymer blends in reverse, integrating sensors into smart packaging, and automated quality control are some of the main uses. Case studies demonstrating effective applications of machine learning and deep learning methodologies in actual biodegradable packaging situations are also covered in the review. Limitations are also critically analysed, including regulatory barriers, model interpretability, and data scarcity. The study concludes by outlining potential future developments, highlighting how AI-driven technologies, digital twins, and autonomous labs could revolutionise the sustainable packaging sector. This review offers a thorough grasp of how AI can facilitate the shift to intelligent, secure, and environmentally friendly food packaging systems by highlighting recent developments and potential future paths.

European Journal of Nutrition & Food SafetyVol. 17(8)
Amicable Knowledge Solution University (IN)
Openalex Percentile: Top 11%
Nanocomposite Films for Food Packaging
2.39
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Smart and Sustainable: Review of AI-Driven Approaches in Biodegradable Food Packaging Systems — Gyan Prakash, Shivbilas Maurya, et al. · European Journal of Nutrition & Food Safety (2025) | TGRS Research Map | TGRS