دورية أكاديمية
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Innovative AI methods for monitoring front-of-package information: A case study on infant foods.

التفاصيل البيبلوغرافية
العنوان: Innovative AI methods for monitoring front-of-package information: A case study on infant foods.
المؤلفون: Kim D; Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daejeon, Republic of Korea., Kim SY; Advanced Institute of Convergence Technology, Suwon, Republic of Korea.; Department of Agricultural Biotechnology, Seoul National University, Seoul, Republic of Korea., Yoo R; Advanced Institute of Convergence Technology, Suwon, Republic of Korea., Choo J; Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daejeon, Republic of Korea., Yang H; Department of Food and Nutrition, Kookmin University, Seoul, Republic of Korea.
المصدر: PloS one [PLoS One] 2024 May 16; Vol. 19 (5), pp. e0303083. Date of Electronic Publication: 2024 May 16 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Artificial Intelligence* , Infant Food*, Humans ; Infant ; Food Labeling ; Food Packaging
مستخلص: Front-of-package (FOP) is one of the most direct communication channels connecting manufacturers and consumers, as it displays crucial information such as certification, nutrition, and health. Traditional methods for obtaining information from FOPs often involved manual collection and analysis. To overcome these labor-intensive characteristics, new methods using two artificial intelligence (AI) approaches were applied for information monitoring of FOPs. In order to provide practical implementations, a case study was conducted on infant food products. First, FOP images were collected from Amazon.com. Then, from the FOP images, 1) the certification usage status of the infant food group was obtained by recognizing the certification marks using object detection. Moreover, 2) the nutrition and health-related texts written on the images were automatically extracted based on optical character recognition (OCR), and the associations between health-related texts were identified by network analysis. The model attained a 94.9% accuracy in identifying certification marks, unveiling prevalent certifications like Kosher. Frequency and network analysis revealed common nutrients and health associations, providing valuable insights into consumer perception. These methods enable fast and efficient monitoring capabilities, which can significantly benefit various food industries. Moreover, the AI-based approaches used in the study are believed to offer insights for related industries regarding the swift transformations in product information status.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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تواريخ الأحداث: Date Created: 20240516 Date Completed: 20240516 Latest Revision: 20240519
رمز التحديث: 20240519
مُعرف محوري في PubMed: PMC11098498
DOI: 10.1371/journal.pone.0303083
PMID: 38753840
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0303083