Machine learning-assisted spectroscopic approaches for pesticide residue detection in food matrices
- DOI
- 10.1016/j.foodres.2026.120168
- Published
- 2026-10
- Container
- Food Research International
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.foodres.2026.120168,
title = {Machine learning-assisted spectroscopic approaches for pesticide residue detection in food matrices},
author = {Bonaventure Chidi Ezenwanne and Charles Obinwanne Okoye and Stanley Ebhohimhen Abhadiomhen and Emmanuel Chigozie Aham and Olufemi Olalowo Olasoji and Yanfang Wu and Jianxiong Jiang},
year = {2026},
journal = {Food Research International},
doi = {10.1016/j.foodres.2026.120168},
url = {https://doi.org/10.1016/j.foodres.2026.120168}
}RIS
TY - JOUR TI - Machine learning-assisted spectroscopic approaches for pesticide residue detection in food matrices AU - Bonaventure Chidi Ezenwanne AU - Charles Obinwanne Okoye AU - Stanley Ebhohimhen Abhadiomhen AU - Emmanuel Chigozie Aham AU - Olufemi Olalowo Olasoji AU - Yanfang Wu AU - Jianxiong Jiang PY - 2026 JO - Food Research International DO - 10.1016/j.foodres.2026.120168 UR - https://doi.org/10.1016/j.foodres.2026.120168 ER -
APA
Ezenwanne, B. C., Okoye, C. O., Abhadiomhen, S. E., Aham, E. C., Olasoji, O. O., Wu, Y., & Jiang, J. (2026). Machine learning-assisted spectroscopic approaches for pesticide residue detection in food matrices. Food Research International. https://doi.org/10.1016/j.foodres.2026.120168
Source records
- crossref · retrieved 2026-09-26T10:19:37.335Z