Academic Journal
Artificial intelligence across the food packaging chain: from design to safety monitoring.
| Τίτλος: | Artificial intelligence across the food packaging chain: from design to safety monitoring. |
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| Συγγραφείς: | Lan T; Corn Processing Laboratory of China Agriculture Research System, College of Food Science and Engineering, Jilin Agricultural University, Changchun, Jilin 130118, China., Ma X; Corn Processing Laboratory of China Agriculture Research System, College of Food Science and Engineering, Jilin Agricultural University, Changchun, Jilin 130118, China., Zhang H; Corn Processing Laboratory of China Agriculture Research System, College of Food Science and Engineering, Jilin Agricultural University, Changchun, Jilin 130118, China. Electronic address: zhanghao3318@sina.com., Liu J; Corn Processing Laboratory of China Agriculture Research System, College of Food Science and Engineering, Jilin Agricultural University, Changchun, Jilin 130118, China. Electronic address: liujs1007@vip.sina.com. |
| Πηγή: | Food research international (Ottawa, Ont.) [Food Res Int] 2026 Oct 31; Vol. 242 (Pt 1), pp. 119796. Date of Electronic Publication: 2026 Jun 22. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science Country of Publication: Canada NLM ID: 9210143 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7145 (Electronic) Linking ISSN: 09639969 NLM ISO Abbreviation: Food Res Int Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Ottawa, Ontario, Canada : Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science, c1992- |
| Ιατρικοί όροι (MeSH): | Food Packaging*/methods , Food Safety*/methods , Artificial Intelligence*, Plastics/analysis ; Food Contamination/analysis ; Machine Learning ; Recycling ; Humans |
| Περίληψη: | 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. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Deep learning; Food packaging; Machine learning; Plastic waste; Smart packaging |
| Substance Nomenclature: | 0 (Plastics) |
| Entry Date(s): | Date Created: 20260821 Date Completed: 20260821 Latest Revision: 20260821 |
| Update Code: | 20260822 |
| DOI: | 10.1016/j.foodres.2026.119796 |
| PMID: | 42629033 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-7145 |
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| DOI: | 10.1016/j.foodres.2026.119796 |