Cited by 168Artificial Intelligence ApplicationsSmart Agriculture and AIImpact of AI and Big Data on Business and Society

Recent advances and applications of artificial intelligence and related technologies in the food industry

Open access
Addanki, Mounika, Patra, Priyanka, Kandra, Prameela·2022·Applied Food Research

Abstract

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

Crossref · 10.1016/j.afres.2022.100126

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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].”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Addanki, M., Patra, P., & Kandra, P.
Affiliations
GITAM University (IN)
Corresponding author
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
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