目的 采用电子鼻联合化学计量学方法，系统分析不同产地及提取方式沉香Aquilariae Lignum Resinatum精油的挥发性成分差异，旨在构建一种快速鉴别沉香精油来源与提取工艺的新方法。方法 收集来自7个主要产地、2种提取方式的38批沉香精油样品，采用气相色谱-质谱联用（gas chromatography-mass spectrometry，GC-MS）技术解析其化学组成，并利用电子鼻获取其整体香气指纹信息。综合运用主成分分析（principal component analysis，PCA）、偏最小二乘-判别分析（partial least squares-discriminant analysis，PLS-DA）及正交偏最小二乘-判别分析（orthogonal partial least squares-discriminant analysis，OPLS-DA）对多源数据进行模式识别。结果 GC-MS分析表明所有样品的共有主要成分为苄基丙酮、沉香螺醇、去氢呋喃酮和2-(2-苯乙基)色酮。通过变量重要性投影（variable importance projection，VIP＞1）筛选出2-(2-苯乙基)色酮、6-甲氧基-2-(2-苯乙基)色酮等7种关键差异性化合物。电子鼻响应值数据的PCA结果显示，能较好地与沉香精油样本的产地或提取方法进行关联；所建立的OPLS-DA判别模型性能优异，产地判别模型R_X²＝0.839，R_Y²＝0.794，Q²＝0.572＞0.500，提取方式判别模型R_X²＝0.790，R_Y²＝0.647，Q²＝0.633＞0.500，能有效对沉香精油进行分类。结论 成功将电子鼻快速判别与GC-MS成分分析相结合，证实该联合策略能准确鉴别沉香精油的提取方法与产地，并明确了其物质基础，为沉香精油的分类鉴别提供了一种高效、可靠的新方法。

Objective To systematically analyze the differences in volatile components of Chenxiang (Aquilariae Lignum Resinatum) essential oil (AEO) from various origins and extraction methods using an electronic nose combined with chemometrics, aiming to establish a novel method for rapid identification of AEO origins and extraction processes. Methods A total of 38 batches of AEO samples were collected from seven major origins using two extraction methods. Their chemical compositions were analyzed using gas chromatography-mass spectrometry (GC-MS), while overall aroma fingerprint information was obtained via electronic nose. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were comprehensively applied for pattern recognition of multi-source data. Results GC-MS analysis revealed that the common major components in all samples were benzylacetone, agarospirol, dehydrofuranone, and 2-(2-phenylethyl)chromone. Seven key differential compounds, including 2-(2-phenylethyl)chromone and 6-methoxy-2-(2-phenylethyl)chromone, were screened through variable importance projection (VIP > 1). The PCA results of the electronic nose response values show a good correlation with the origin or extraction method of AEO samples. The established OPLS-DA discriminant model exhibited excellent performance: the origin discriminant model achieved R_X² = 0.839, R_Y² = 0.794, Q² = 0.572 > 0.500, and the extraction method discriminant model achieved R_X² = 0.790, R_Y² = 0.647, Q² = 0.633 > 0.500, which can effectively classify AEO samples. Conclusion This study successfully combined the rapid identification of electronic nose with GC-MS component analysis, which confirmed that the combined strategy could accurately identify the extraction method and geographical origins of AEO, and clarified its material basis, providing an efficient and reliable new method for the classification and identification of AEO.

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江西省“赣鄱英才”创新领军人才项目（gpyc20240037）；江西省自然科学基金资助项目（20242BAB20456）；国家自然科学基金资助项目（82404878）；海南省重点研发项目（ZDYF2025SHFZ055）；江西中医药大学博士科研启动基金项目（2024BSZR016）；江西中医药大学中药科研实践创新训练项目（24KYCX-ZD014）