Differences between two plants fruits: Amomum tsaoko and Amomum maximum, using the SPME-GC–MS and FT-NIR to classification

化学 萜烯 气相色谱-质谱法 传统医学 色谱法 药用植物 气相色谱法 质谱法 食品科学 有机化学 医学
作者
Fengjiao Li,Weize Yang,Meiquan Yang,Yuanzhong Wang,Jinyu Zhang
出处
期刊:Arabian Journal of Chemistry [Elsevier]
卷期号:17 (4): 105665-105665 被引量:3
标识
DOI:10.1016/j.arabjc.2024.105665
摘要

Also known as Amomum tsaoko Crevost et Lemaire (A. tsaoko), is a spice and medicinal fruit plant that can be used in daily human life for cooking and health purposes. In Xishuangbanna (Yunnan Province, China) there is an interesting phenomenon where the Amomum maximum Roxb. (A. maximum) is consumed as a fruit by the local people. The spice trade played a significant role in the development of human civilization and the histories of nations. Both of them have high edible and medicinal value. The identification and classification of the metabolic components of both are key to driving the development of related industries. We used SPME-GC–MS (Non-targeted headspace-solid phase microextraction-gas chromatography-mass spectrometry) for its accurate qualitative and quantitative performance, combined with multiple analytical methods, to explain the metabolic components present in the fruits of these two plants. Next, near-infrared (NIR) technology offers researchers the advantage of saving time and being cost-effective. We analyzed the characteristic spectral chemical information of the NIR data. Identified the relevant base group and vibration mode. After analyzing the data of both, it was determined that there are a total of 66 metabolites, with 64 from cardamom and 29 from the substance, showing 37 differences. Among the metabolites, 35 had a significance level of P ≤ 0.05 and VIP ≥ 1, totaling 101 substances. This includes terpenes (37.9 %), aldehydes (12.1 %), organic heterocyclic compounds (7.6 %), alcohols (6.1 %), aromatic compounds (4.5 %), esters (4.5 %), and ketones (4.5 %). This study validates the metabolic components of the two species and provides a clear classification, which will be beneficial for future data-based applications in the fields of food and medicine.

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