A novel headspace solid-phase microextraction method combined with gas chromatography–mass spectrometry was developed to extract volatiles from fresh Amomum tsaoko (FAT) from four districts. The single-factor experiment found an optimum extraction fiber (2 cm) coating with 50/30-μm divinylbenzene-carbon-polydimethylsiloxane, and the response surface design experiment determined that optimal extraction was achieved with equilibration and extraction times of 31 and 32 min at 42°C, respectively. Fifty compounds were identified in FATs from four regions, of which the main volatiles, including (E)-2-octenal, (E)-2-decenal, 2-isopropylbenzaldehyde, neral, geranial, and eucalyptol, accounted for a large proportion. Additionally, of the 50 compounds, 32 volatiles determined by orthogonal partial least squares-discriminant analysis contributed to the differentiation of FATs from the four distinct regions. Hierarchical clustering analysis revealed that four FATs were divided into three groups based on the volatiles identified. The four FATs had similar odor profiles, but the odor intensity of the Kongdang sample was weaker than that of the other samples. Therefore, this study not only developed a feasible method to extract volatiles from FAT but also established a theoretical basis for differentiating FATs from different regions.

开发了一种新的顶空固相微萃取结合气相色谱-质谱法从四个地区的新鲜草果(FAT) 中提取挥发物。单因素实验发现了50/30-μm二乙烯基苯-碳-聚二甲基硅氧烷涂层的最佳萃取纤维（2 cm），响应面设计实验确定在平衡和萃取时间分别为31和32 min时达到最佳萃取效果。 42°C，分别。在四个地区的 FAT 中鉴定出 50 种化合物，其中主要挥发物包括 ( E )-2-辛烯醛、( E)-2-癸烯醛、2-异丙基苯甲醛、香叶醛、香叶醛和桉树脑，占比较大。此外，在 50 种化合物中，通过正交偏最小二乘判别分析确定的 32 种挥发物有助于区分来自四个不同区域的 FAT。层次聚类分析表明，四个 FAT 根据所识别的挥发物分为三组。四种 FAT 具有相似的气味特征，但空当样品的气味强度较其他样品弱。因此，本研究不仅开发了一种从FAT中提取挥发物的可行方法，而且为区分不同地区的FAT奠定了理论基础。