[Comparison of volatile components between ginger (Zingiberis Rhizoma Recens) dried with different methods based on GC-IMS/GC-MS data fusion and multivariate statistics].

Authors

Jie Yong, Tong Wu, Qi-Lan Xu, Xiao Han, Jia-Xin Yin, Shi-Xin Cen, He-Shui Yu, Zheng Li

Published in

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica. Volume 51. Issue 15. Pages 4341-4349.

Abstract

Ginger volatile oil(GVO) is the key component responsible for the unique aroma and flavor of ginger. To evaluate the impact of different drying methods(sun drying, shade drying, hot-air drying, vacuum drying, and vacuum freeze-drying) on GVO, this study employed a combination of headspace gas chromatography-ion mobility spectrometry(HS-GC-IMS) and headspace gas chromatography-mass spectrometry(HS-GC-MS) techniques to characterize the changes in characteristic volatile organic compounds(VOCs) in ginger under different drying methods. Additionally, chemometric methods and machine learning were incorporated to classify and analyze the GVO obtained through different drying methods. The results showed that a total of 60 volatile components were identified in GVO based on GC-IMS, and 59 volatile components were identified based on HS-GC-MS. Principal component analysis indicated that different drying methods significantly affected the composition and relative content of VOCs in GVO. From the GC-IMS, HS-GC-MS, and fused data, 14, 10, and 23 VOCs with strong discriminatory power were selected, including key components such as β-myrcene, hexanal, and citral. Based on these features, machine learning models were used for further classification analysis. The core purpose of this research classification is to establish intelligent identification models for different drying process types based on VOCs fingerprint characteristics, in order to achieve precise traceability of the processing methods of dried ginger raw materials. The results indicated that both random forest(RF) and convolutional neural network(CNN) exhibited high accuracy and stability in GVO classification and discrimination. In summary, this study revealed the impact of different drying methods on the composition and distribution characteristics of volatile components in GVO from the perspective of volatile components, providing a scientific basis and technical support for the study of differences in ginger volatile components and the optimization of drying processes.

PMID:
42693048
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

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