Using HS-GC-MS and flash GC e-nose in combination with chemometric analysis and machine learning algorithms to identify the varieties, geographical origins and production modes of Atractylodes lancea-Reference-Cited by-同舟云学术

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Using HS-GC-MS and flash GC e-nose in combination with chemometric analysis and machine learning algorithms to identify the varieties, geographical origins and production modes of Atractylodes lancea

Published:2024-03 Issue: Volume:209 Page:117955
ISSN:0926-6690
Container-title:Industrial Crops and Products
language:en
Short-container-title:Industrial Crops and Products

Author:

Gan YiFu,Yang Tao,Gu Wei,Guo LanPing,Qiu Rongli,Wang Sheng,Zhang Yan,Tang Min,Yang Zengcai

Publisher

Elsevier BV

Subject

Agronomy and Crop Science

Reference39 articles.

1. Detection of fraud in sesame oil with the help of artificial intelligence combined with chemometrics methods and chemical compounds characterization by gas chromatography–mass spectrometry;Aghili;LWT,2022

2. Analysis of oligosaccharide mapping of atractylodis rhizoma from different habitats;Chang;Chin. J. Exp. Tradit. Med. Formula,2022

3. Integrative analysis of transcriptome and metabolome reveals the sesquiterpenoids and polyacetylenes biosynthesis regulation in Atractylodes lancea (Thunb.) DC;Chen;Int. J. Biol. Macromol.,2023

4. Comparison of volatile compounds in different parts of fresh Amomum villosum Lour. from different geographical areas using cryogenic grinding combined HS-SPME-GC-MS;Chen;Chin. Med,2020

5. Correlation between active component content and color of Atractylodes Lancea and A. chinensis based on color difference principle;Feng;China J. Chin. Mater. Med.,2023

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