Texture Prediction Models

Texture Prediction Models is a computational framework that belongs to the class of predictive modeling techniques in food science, specifically designed to quantitatively estimate the mechanical and sensory attributes of meat texture using physicochemical and instrumental input variables.

264

Publications

14.3K

Citations

967

Authors

363

Institutions

Publications per year

2017–2026

90

Authors

967

  1. JM
    Jean‐Francois Meullenet

    University of Arkansas at Fayetteville, United States

    8 publications · 291 citations

    8

    h-index

  2. TA
    Teresa Antequera

    Universidad de Extremadura, Spain

    8 publications · 224 citations

    8

    h-index

  3. DS
    Da‐Wen Sun

    University College Dublin, Ireland

    8 publications · 592 citations

    8

    h-index

  4. TP
    Trinidad Pérez‐Palacios

    Universidad de Extremadura, Spain

    6 publications · 144 citations

    6

    h-index

  5. PA
    Paul Allen

    Teagasc - The Irish Agriculture and Food Development Authority, Ireland

    6 publications · 391 citations

    6

    h-index

1–5 of 967

Venues

  1. Journal of Food Engineering

    25 publications · 1.4K citations

    20

    h-index

  2. Meat Science

    22 publications · 1.4K citations

    19

    h-index

  3. Journal of Food Science

    JFS · 15 publications · 661 citations

    13

    h-index

  4. Journal of Texture Studies

    14 publications · 458 citations

    12

    h-index

  5. Cereal Chemistry

    7 publications · 480 citations

    7

    h-index

1–5