Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation.

Zia A, Husnain M, Buck S, Richetti J, Hulm E, Ral JP, Rolland V, Sirault X

DOI: 10.1016/j.crfs.2025.101030
Published: 2025
Container: Current research in food science
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1016/j.crfs.2025.101030,
  title = {Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation.},
  author = {Zia A and Husnain M and Buck S and Richetti J and Hulm E and Ral JP and Rolland V and Sirault X},
  year = {2025},
  journal = {Current research in food science},
  doi = {10.1016/j.crfs.2025.101030},
  url = {https://doi.org/10.1016/j.crfs.2025.101030}
}

RIS

TY  - JOUR
TI  - Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation.
AU  - Zia A
AU  - Husnain M
AU  - Buck S
AU  - Richetti J
AU  - Hulm E
AU  - Ral JP
AU  - Rolland V
AU  - Sirault X
PY  - 2025
JO  - Current research in food science
DO  - 10.1016/j.crfs.2025.101030
UR  - https://doi.org/10.1016/j.crfs.2025.101030
ER  -

APA

A, Z., M, H., S, B., J, R., E, H., JP, R., V, R., & X, S. (2025). Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation.. Current research in food science. https://doi.org/10.1016/j.crfs.2025.101030

Source records

pubmed · retrieved 2026-09-26T03:23:14.743Z