Machine Learning Authentication

Machine Learning Authentication is a computational methodology that employs machine learning algorithms to verify the authenticity of meat products by distinguishing them from adulterated or mislabeled samples based on intrinsic biological or chemical signatures.

115

Publications

4.7K

Citations

475

Authors

204

Institutions

Publications per year

2017–2026

91

Authors

475

  1. KR
    Kim‐Kwang Raymond Choo

    The University of Texas at San Antonio, United States

    2 publications · 116 citations

    2

    h-index

  2. MT
    Milad Taleby Ahvanooey

    Nanjing University of Science and Technology, China

    2 publications · 112 citations

    2

    h-index

  3. AC
    Artur Carnicer

    Universitat de Barcelona, Spain

    2 publications · 69 citations

    2

    h-index

  4. HF
    He Fang

    Western University, Canada

    2 publications · 341 citations

    2

    h-index

  5. PC
    P. C. Panchariya

    Central Electronics Engineering Research Institute, India

    2 publications · 59 citations

    2

    h-index

1–5 of 475

Venues

  1. Trends in Food Science & Technology

    4 publications · 320 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 73 citations

    4

    h-index

  3. 3

    h-index

  4. Food Chemistry

    2 publications · 82 citations

    2

    h-index

  5. Scientific Reports

    2 publications · 68 citations

    2

    h-index

1–5