ArticleFood science & nutrition2026
A CNN-Based Deep Learning Architecture for Discriminating Botanical Adulteration and Complexities Among Commercial Apiaceae Medicinal Species.
Article in Food science & nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a CNN-based deep learning framework to automate the accurate authentication of mericarp seeds from 15 commercially important medicinal Apiaceae species, which exhibit subtle morphological overlaps and face risks of toxic adulteration (e.g., hemlock contamination), threatening consumer safety and trade integrity. Six CNN models were evaluated, with DenseNet121 demonstrating superior performance in terms of accuracy, precision, recall, F1-score, and convergence stability, followed by MobileNetV2, InceptionV3, and VGG16. Confusion matrix analysis revealed that higher-resolution inputs significantly improved discrimination, particularly for similar seeds like
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.