ArticleNPJ science of food2026
Tracing origin and cultivation practice of Lithocarpus litseifolius via multi-data fusion and machine learning approaches.
Article in NPJ science of food, 2026. 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
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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.
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Who cites it
2 citing papers in PubMed.
- Stable isotopes from Earth science to bedside medicine: beyond tracers and toward mechanistic biomarkers in medical oncology.Medical oncology (Northwood, London, England) · 2026Review
- Geographical Origin Traceability of Tea (Foods (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Lithocarpus litseifolius (sweet tea) is a medicinal and edible plant rich in flavonoids and essential nutrients, with potential as a hepatoprotective beverages and natural sweetener. Although widely cultivated across several provinces in China, the quality and consistency of its raw material remain poorly regulated. To address this, 163 samples (n ≥ 18) from 7 main producing regions were analyzed for 22 functional compounds, 4 stable isotope ratios, and 49 multi-element to discriminate cultivation practices and geographical origins. Orthogonal partial least squares discriminant analysis (OPLS-DA) successfully generated prediction models across two cultivation regions. Integrating 8 machine-learning algorithms with multi-level data fusion identified 6 key variables-caffeine, Rb, Ce, δ¹⁵N, Sr, and 3"-O-acetylphlorizin. Five base learners built on these variables were then combined via soft-voting ensemble learning, yielding an optimal origin classifier with 100.00% accuracy. Additionally, the study delivered the first comprehensive analysis of quality variations in sweet tea and identified seven primary influenced environmental factors, offering insights into cultivation strategies and quality formation mechanisms.
Identifiers
What 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.