Pistachio powder authenticity assessment using supervised machine learning algorithms.
- DOI
- 10.1080/19440049.2026.2713743
- Published
- 2026 Oct 29
- Container
- Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/19440049.2026.2713743,
title = {Pistachio powder authenticity assessment using supervised machine learning algorithms.},
author = {Lashgari M and Mohammadigol R},
year = {2026},
journal = {Food additives \& contaminants. Part A, Chemistry, analysis, control, exposure \& risk assessment},
doi = {10.1080/19440049.2026.2713743},
url = {https://doi.org/10.1080/19440049.2026.2713743}
}RIS
TY - JOUR TI - Pistachio powder authenticity assessment using supervised machine learning algorithms. AU - Lashgari M AU - Mohammadigol R PY - 2026 JO - Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment DO - 10.1080/19440049.2026.2713743 UR - https://doi.org/10.1080/19440049.2026.2713743 ER -
APA
M, L., & R, M. (2026). Pistachio powder authenticity assessment using supervised machine learning algorithms.. Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment. https://doi.org/10.1080/19440049.2026.2713743
Source records
- pubmed · retrieved 2026-11-10T14:18:01.830Z