ArticleScientific reports2021
Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning.Scientific reports · 2026Article
- Visual feature-based multi-scale hybrid attention network for fine-grained Hawthorn varieties identification.Scientific reports · 2025Article
- UPLC-ESI-QTRAP-MS/MS identification and antioxidant activity of hawthorn with different infestation levels.BMC chemistry · 2025Article
- LLMs and AI Life Models for Traditional Chinese Medicine-derived Geroprotector Formulation.Aging and disease · 2025Article
- Rapid identification of medicinal plants via visual feature-based deep learning.Plant methods · 2024Article
- Application of Data Fusion in Traditional Chinese Medicine: A Review.Sensors (Basel, Switzerland) · 2023Review
- A Novel Method for Quality Evaluation ofMolecules (Basel, Switzerland) · 2022Article
- Variations in volatile flavour compounds inFrontiers in nutrition · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Crataegi Fructus (CF) is widely used as a medicinal and edible material around the world. Currently, different types of processed CF products are commonly found in the market. Quality evaluation of them mainly relies on chemical content determination, which is time and money consuming. To rapidly and nondestructively discriminate different types of processed CF products, an electronic nose coupled with chemometrics was developed. The odour detection method of CF was first established by single-factor investigation. Then, the sensor array was optimised by a stepwise discriminant analysis (SDA) and analysis of variance (ANOVA). Based on the best-optimised sensor array, the digital and mode standard were established, realizing the odour quality control of samples. Meanwhile, mathematical prediction models including the discriminant formula and back-propagation neural network (BPNN) model exhibited good evaluation with a high accuracy rate. These results suggest that the developed electronic nose system could be an alternative way for evaluating the odour of different types of processed CF products.
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Registered trials
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