ReviewFoods (Basel, Switzerland)2025
Advances in the Identification Methods of Food-Medicine Homologous Herbal Materials.
Review in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Integrative chemotaxonomic and micromorphological insights into Peucedanum (Apiaceae).Scientific reports · 2026Article
- Chemical Components and Hypouricemic Activity Monitoring ofFoods (Basel, Switzerland) · 2026Article
- Development of plastome-based HRM markers for the authentication of 12 major medicinal herbs in the Apiaceae family.Scientific reports · 2026Article
- NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Non-Destructive Geographical Traceability and Quality Control ofFoods (Basel, Switzerland) · 2026Article
- Flavonoids: biological activities, nutritional and immunological aspects, therapeutic potential, food applications, and human health benefits-A comprehensive review.Frontiers in nutrition · 2026Review
- Detection of Adulterants in Powdered Foods Using Near-Infrared Spectroscopy and Chemometrics: Recent Advances, Challenges, and Future Perspectives.Foods (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
As a key component of both traditional medicine and modern healthcare, Food-Medicine Homologous Herbal Materials have attracted considerable attention in recent years. However, issues related to the quality and authenticity of medicinal materials on the market often arise, not only compromising their efficacy but also presenting potential risks to consumer health. Therefore, the establishment of accurate and efficient identification methods is crucial for ensuring the safety and quality of Food-Medicine Homologous Herbal Materials. This paper provides a systematic review of the research progress on the identification methods for Food-Medicine Homologous Herbal Materials, starting with traditional methods such as morphological and microscopic identification, and focusing on the applications of modern techniques, including biomimetic recognition, chromatography, mass spectrometry, chromatography-mass spectrometry coupling, hyperspectral imaging, near-infrared spectroscopy, terahertz spectroscopy, and DNA barcoding. Moreover, it provides a comprehensive analysis of the fundamental principles, advantages, and limitations of these methods. Finally, the paper outlines the current challenges faced by identification methods and suggests future directions for improvement, aiming to offer a comprehensive technical perspective on identifying Food-Medicine Homologous Herbal Materials and foster further development in this field.
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.