ArticleSensors (Basel, Switzerland)2018
Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose.
Article in Sensors (Basel, Switzerland), 2018. 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.
- Effects of drying methods on the functional components and volatile organic compounds of Atractylodes chinensis.BMC plant biology · 2026Article
- An integrated machine learning framework for TCM five-flavor classification based on E-tongue profiling and SHAP analysis.Chinese medicine · 2026Article
- Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.IEEE transactions on bio-medical engineering · 2022Article
- Determination of the geographical origin ofHeliyon · 2022Article
- AI-based analysis of CT images for rapid triage of COVID-19 patients.NPJ digital medicine · 2021Article
- Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Artificial Noses: A Review.Sensors (Basel, Switzerland) · 2020Review
- Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.
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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.