Volatile flavour identification and odour complexity of radix Angelicae sinensis by electronic nose, integrated gas chromatography–mass spectrometry/olfactometry and comprehensive two‐dimensional gas chromatography‐time‐of‐flight‐mass spectrometry

Radix Angelicae sinensis (Danggui, DG) is known as one of the typical traditional Chinese medicines. DG material consists of a variety of volatile substances, polysaccharides, organic acids, ceramides, amino acids, vitamins, microelements, among others, and thus has been used for medicinal and edible purposes in a long history. The fragrance is of importance to assessing the DG material quality.This study was to determine volatile flavour compositions of DG materials and to reveal the odour complexity.Electronic nose (E-nose), integrated gas chromatography-mass spectrometry/olfactometry (GC-MS/O) and comprehensive two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC × GC-TOF-MS), combined with solid-phase micro-extraction (SPME), were mainly used to address the flavour complexity of DG materials.Using the E-nose sensor responses, a total of 105 batches of DG samples cultivated in six provinces of China were categorised according to their odour differentiations, and a principal component analysis (PCA) model was established for evaluating the sample quality through a combination of Hotelling's T2 and Q-residual values in a statistical quantitative sense. By the GC-MS/O and GC × GC-TOF-MS analyses, 196 volatile flavour compounds were identified, 51 odour-active areas discerned and 39 odourants determined. It was terpenes and aromatics of the flavour compounds that mainly contributed to the odour attributes of DG herb.The SPME-GC × GC-TOF-MS method was the first time employed to analyse the volatile flavours of DG materials, and thus made a breakthrough in determining 196 flavour compounds, much more than those in any previous report. The work also made a significant step forward to link the flavour compositions and odour complexity of radix Angelicae sinensis by E-nose and GC-MS/O techniques. It not only provided a statistical PCA model that did not depend on any predetermined compositions or sensory properties for, but also a comprehensive insight into the quality evaluation of DG materials.