Artificial Intelligence for Food Packaging: A Life Cycle-Oriented Review of Material Performance, Functionality, Safety, and Sustainability.
食品包装のための人工知能：材料性能、機能性、安全性、持続可能性に関するライフサイクル指向レビュー (AI 翻訳)
Kehao Huang, Zhenhao Lin, Yixiang Wang, Xiaoyin Wang
🤖 gxceed AI 要約
日本語
このレビューは、食品包装におけるAI応用をライフサイクル視点で整理し、素材設計からリサイクルまでの各段階でのAIの役割を分析する。持続可能性向上の可能性を示す一方、データ品質や規制受容などの課題を指摘している。
English
This review systematically analyzes AI applications in food packaging across the life cycle, covering material design, production, quality prediction, safety, smart labeling, and recycling. It proposes a framework linking AI paradigms to six domains, highlighting potential for sustainability improvements and noting challenges in data quality and regulatory acceptance.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では食品ロスや包装廃棄物が課題であり、本レビューはAIによる包装最適化が環境負荷低減に寄与する可能性を示す。ただしGXの観点では温室効果ガス排出削減との直接的なリンクは弱い。
In the global GX context
This review connects AI to sustainability in food packaging, relevant to global circular economy and waste reduction goals. However, it does not directly address climate disclosure, carbon accounting, or transition finance, limiting its applicability to GX reporting frameworks.
👥 読者別の含意
🔬研究者:Provides a structured overview of AI applications in food packaging life cycle, useful for researchers exploring AI-driven sustainability in packaging.
🏢実務担当者:Food packaging companies can use this to identify AI opportunities for improving material efficiency and recyclability.
🏛政策担当者:Insights on AI's role in packaging sustainability may inform waste management policies but have limited direct climate policy relevance.
📄 Abstract（原文）
Artificial intelligence (AI) has been increasingly applied to address challenges in food packaging, including food waste, sustainability, and real-time quality assurance. However, existing studies are often confined to specific applications, with limited integration across different stages of the packaging life cycle and insufficient linkage between material performance, functionality, and system-level outcomes. This review systematically analyzes peer-reviewed studies retrieved from the Web of Science Core Collection (2021-2025), selected based on their relevance to AI applications in food packaging, including material performance, safety, and life cycle management. A life cycle-oriented framework is proposed, linking major AI paradigms (supervised, unsupervised, reinforcement, deep learning, and hybrid models) to six key domains: material design, production optimization, food quality prediction, safety assurance, smart labeling and traceability, and recycling. Within this framework, AI supports data-driven prediction, monitoring, and decision-making, whereas hybrid models improve robustness in complex, multifactor systems. Despite challenges related to data quality, model generalization, and regulatory acceptance, AI-driven packaging systems may support a transition from passive containment toward more adaptive and data-informed solutions that improve efficiency, sustainability, and consumer trust.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1111/1541-4337.70486first seen 2026-07-16 06:06:08
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。