ArticleJournal of fluorescence2025
Identification of Camellia Oil Adulteration With Excitation-Emission Matrix Fluorescence Spectra and Deep Learning.
Article in Journal of fluorescence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Adulteration Detection of Multi-Species Vegetable Oils in Camellia Oil Using SICRIT-HRMS and Machine Learning Methods.Foods (Basel, Switzerland) · 2026Article
- Non-destructive detection of microplastics stress in rice seedling: an interpretable deep learning approach using excitation emission matrix fluorescence spectra of root exudates.Frontiers in plant science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Camellia oil (CAO), known for its high nutritional and commercial value, has raised increasing concerns about adulteration. Developing an accurate and non-destructive method to identify CAO adulterants is crucial for safeguarding public health and well-being. This study simulates potential real-world adulteration cases by designing representative adulteration scenarios, followed by the acquisition and analysis of corresponding excitation-emission matrix fluorescence (EEMF) spectra. Parallel factor analysis (PARAFAC) was employed to characterize and explore the variations of fluorophores in the EEMF spectra of different adulterated scenarioss, which showed a linear correlation between the relative concentration of PARAFAC components and adulteration levels. A deep learning model named ResTransformer, which combines residual modules with Transformer, was proposed for both the qualitative detection of adulteration types and the quantitative detection of adulteration concentrations from local and global perspectives. The global ResTransformer qualitative models achieved accuracies of over 96.92% based on EEMF spectra and PARAFAC, and quantitative models showed determination coefficient of validation ([Formula: see text]) > 0.978, root mean square error of validation ([Formula: see text]) < 3.0643%, and the ratio performance deviation (RPD) > 7.6741. Compared to traditional chemometric models, the ResTransformer model demonstrated superior performance. The integration of EEMF and ResTransformer presents a highly promising strategy for rapid and reliable detection of CAO adulteration.
Indexed as
Identifiers
40063235What Socratic 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 Socratic graph.