Antioxidant activity screening and chemical constituents of the essential oil from rosemary by ultra‐fast GC electronic nose coupled with chemical methodology

2020 
BACKGROUND: Traditional chemical methods were mainly used to evaluate the total antioxidant activity of essential oils. How to determine the bioactivity of each compound in mixtures is an interesting research topic. Nowadays, an ultra-fast gas chromatography electronic nose (E-nose) has been gradually used in the detection of volatile compounds, but the screening of the active components of essential oils has not been reported. E-nose coupled with chemical methodology was established using the essential oil from rosemary (EOR) as a specific application example. The proposed method can both identify the chemical constituents of EOR and quickly screen the antioxidant by comparing the change of chromatographic peak area of every component in EOR before and after reaction with free radicals. RESULTS: Among all chemical constituents in EOR, verbenone, eucalyptol and o-cymene showed the strongest scavenging abilities in 1,1'-diphenyl-2-picrylhydrazine (DPPH.), 2,2'-azino-bis(3-ethyl-benzothiazoline-6-sulphonate) (ABTS.(+) ) and hydroxyl (.OH) radicals, respectively, with scavenging rates of 67.9%, 39.5%, and 69.9%. The reliability and feasibility of using E-nose to identify chemical constituents of EOR were verified by gas chromatography-tandem mass spectrometry (GC-MS/MS). The GC-MS/MS results showed that the main components of EOR were alpha-pinene (422.2 mug g(-1) ), p-cymene (208.4 mug g(-1) ), camphor (203.5 mug g(-1) ), verbenone (160.2 mug g(-1) ), and eucalyptol (129.1 mug g(-1) ). CONCLUSIONS: The E-nose methods can be used as a complementary method to traditional spectrophotometric techniques. And this study will be of great significance for the rapid screening of antioxidant active components in essential oils from natural products. This article is protected by copyright. All rights reserved.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    23
    References
    13
    Citations
    NaN
    KQI
    []