Recent advances and applications of artificial intelligence and related technologies in the food industry
Open accessAbstract
In current scenario most of food processing industries are majorly focusing on quality of food, nutritional value, and method of processing as the consumers are demanding for foods lined up with qualities, sensory and shelf life of the products. Emergence of technology in artificial intelligence (AI) and machine learning (ML) helps to measure the drifting issues in food processing technology. AI is an interdisciplinary promising approach for promoting performances in different areas of food sectors. Tremendous changes were carried out to solve problems to grow food industries. This review emphasises the applications of AI in dairy, bakery, beverages, fruit and vegetable industries. To advance the technology multiple strategies were used in different food sectors. Relevant literature on scope of robotics in food and beverages have been reviewed and discussed critically. Further intense research in advancing 3D printing that helps to improve food business from manufacture to servicing has been discussed with future vision.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
During pre-processing, AI aids in predicting the rheological properties of food inks, ensuring selected materials possess necessary flowability, shear stability, and structural integrity for successful printing.
“During pre-processing, AI aids in predicting the rheological properties of food inks, ensuring that the selected materials possess the necessary flowability, shear stability, and structural integrity for successful printing [39].”
From Towards the development of foods 3D printer: Trends and technologies for foods printing (2024) · cites this paper as [39]
Cites
A review of biometric technology along with trends and prospects
Unar, J. A., Seng, W. C., & Abbasi, A. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.01.016 · Cited by 404 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Towards the development of foods 3D printer: Trends and technologies for foods printing
Neamah, H. A., & Tandio, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33882 · Cited by 43 · see citing works
“During pre-processing, AI aids in predicting the rheological properties of food inks, ensuring that the selected materials possess the necessary flowability, shear stability, and structural integrity for successful printing [39].”
BackgroundMethodInfluentialHarnessing Artificial Intelligence to Safeguard Food Quality and Safety
Singh, D. (2025) · Journal of Food Protection · doi:10.1016/j.jfp.2025.100621 · Cited by 37 · see citing works
- Authors
- Addanki, M., Patra, P., & Kandra, P.
- Affiliations
- GITAM University (IN)
- Corresponding author
- Kandra, Prameela
- ORCID
- Kandra, Prameela
- Venue
- Applied Food Research
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 2
- Issue
- 2
- Pages
- 100126
- ISSN
- 2772-5022 (ISSN-L)
- OpenAlex
- W4280505928
- Cited by
- 168
- References
- 1
- Citations by year
- 2026: 43 · 2025: 61 · 2024: 43 · 2023: 16 · 2022: 5
- FWCI
- 10.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Food Research (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Gandhi Institute of Technology and Management
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- machine learning, food processing, dairy industry, bakery, beverage industry, fruit and vegetable processing, 3D food printing, food quality, shelf life
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review