Intelligent recognition of frying degree in Sanguisorbae Radix Carbonisata based on spectrophotometer, E-nose and NIRS technology combined with machine learning and data fusion strategy.
- DOI
- 10.1016/j.jpba.2026.117786
- Published
- 2026 Oct 27
- Container
- Journal of pharmaceutical and biomedical analysis
- Publisher
- Not recorded
- Open access
- unknown
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- 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.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1016/j.jpba.2026.117786,
title = {Intelligent recognition of frying degree in Sanguisorbae Radix Carbonisata based on spectrophotometer, E-nose and NIRS technology combined with machine learning and data fusion strategy.},
author = {Deng J and Tian F and Zhang Y and Cao H and Wu M and Ma Z},
year = {2026},
journal = {Journal of pharmaceutical and biomedical analysis},
doi = {10.1016/j.jpba.2026.117786},
url = {https://doi.org/10.1016/j.jpba.2026.117786}
}RIS
TY - JOUR TI - Intelligent recognition of frying degree in Sanguisorbae Radix Carbonisata based on spectrophotometer, E-nose and NIRS technology combined with machine learning and data fusion strategy. AU - Deng J AU - Tian F AU - Zhang Y AU - Cao H AU - Wu M AU - Ma Z PY - 2026 JO - Journal of pharmaceutical and biomedical analysis DO - 10.1016/j.jpba.2026.117786 UR - https://doi.org/10.1016/j.jpba.2026.117786 ER -
APA
J, D., F, T., Y, Z., H, C., M, W., & Z, M. (2026). Intelligent recognition of frying degree in Sanguisorbae Radix Carbonisata based on spectrophotometer, E-nose and NIRS technology combined with machine learning and data fusion strategy.. Journal of pharmaceutical and biomedical analysis. https://doi.org/10.1016/j.jpba.2026.117786
Source records
- pubmed · retrieved 2026-11-10T11:07:20.395Z