The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection
Artificial intelligence (AI) tends to be extensively used to develop reliable, fast, and inexpensive tools for authenticity control. Initially applied for food differentiation as an alternative to statistical methods, AI tools opened a new dimension in adulteration identification based on images. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/10/1808 |
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| author | Dana Alina Magdas Ariana Raluca Hategan Maria David Camelia Berghian-Grosan |
| author_facet | Dana Alina Magdas Ariana Raluca Hategan Maria David Camelia Berghian-Grosan |
| author_sort | Dana Alina Magdas |
| collection | DOAJ |
| description | Artificial intelligence (AI) tends to be extensively used to develop reliable, fast, and inexpensive tools for authenticity control. Initially applied for food differentiation as an alternative to statistical methods, AI tools opened a new dimension in adulteration identification based on images. This comprehensive review aims to emphasize the main pillars for applying AI for food authentication: (i) food classification; (ii) detection of subtle adulteration through extraneous ingredient addition/substitution; and (iii) fast recognition tools development based on image processing. As opposed to statistical methods, AI proves to be a valuable tool for quality and authenticity assessment, especially for input data represented by digital images. This review highlights the successful application of AI on data obtained through laborious, highly sensitive analytical methods up to very easy-to-record data by non-experimented personnel (i.e., image acquisition). The enhanced capability of AI can substitute the need for expensive and time-consuming analysis to generate the same conclusion. |
| format | Article |
| id | doaj-art-a40f98dc0f5949d29afccd9357ae7af3 |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-a40f98dc0f5949d29afccd9357ae7af32025-08-20T03:47:58ZengMDPI AGFoods2304-81582025-05-011410180810.3390/foods14101808The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud DetectionDana Alina Magdas0Ariana Raluca Hategan1Maria David2Camelia Berghian-Grosan3National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaArtificial intelligence (AI) tends to be extensively used to develop reliable, fast, and inexpensive tools for authenticity control. Initially applied for food differentiation as an alternative to statistical methods, AI tools opened a new dimension in adulteration identification based on images. This comprehensive review aims to emphasize the main pillars for applying AI for food authentication: (i) food classification; (ii) detection of subtle adulteration through extraneous ingredient addition/substitution; and (iii) fast recognition tools development based on image processing. As opposed to statistical methods, AI proves to be a valuable tool for quality and authenticity assessment, especially for input data represented by digital images. This review highlights the successful application of AI on data obtained through laborious, highly sensitive analytical methods up to very easy-to-record data by non-experimented personnel (i.e., image acquisition). The enhanced capability of AI can substitute the need for expensive and time-consuming analysis to generate the same conclusion.https://www.mdpi.com/2304-8158/14/10/1808food authenticationprocessing strategiesartificial intelligencefood adulterationimage processing |
| spellingShingle | Dana Alina Magdas Ariana Raluca Hategan Maria David Camelia Berghian-Grosan The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection Foods food authentication processing strategies artificial intelligence food adulteration image processing |
| title | The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection |
| title_full | The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection |
| title_fullStr | The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection |
| title_full_unstemmed | The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection |
| title_short | The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection |
| title_sort | journey of artificial intelligence in food authentication from label attribute to fraud detection |
| topic | food authentication processing strategies artificial intelligence food adulteration image processing |
| url | https://www.mdpi.com/2304-8158/14/10/1808 |
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