This work was designed to investigate the dynamic changes process of non-volatile organic compounds (n-VOCs) and volatile organic compounds (VOCs) in mulberries during different growth periods using UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS, and HS-GC-IMS. A total of 166 compounds were identified, including 68 n-VOCs and 98 VOCs. Furthermore, principal component analysis (PCA), random forest analysis (RFA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze differences in mulberries at different ripening stages. A total of 74 compounds appeared or disappeared at different ripening periods and 24 compounds were presented throughout the growth process. Quantitative analysis and antioxidant experiments revealed that as the mulberries continued to mature, flavonoids and phenolic acids continued to increase, and the best antioxidant activity occurred from stage IV. Conclusively, an effective strategy was established for analyzing the composition change process during different growth periods, which could assist in achieving dynamic change process analysis and quality control.

The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.