Compositional diversity and antioxidant properties of essential oils: Predictive models. (March 2021)

• Record Type: Journal Article
• Authors: Khodaei, Nastaran; Nguyen, Marina Minh; Mdimagh, Asma; Bayen, Stéphane; Karboune, Salwa
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 138(2021)
• Publication Date: 2021-03
• Subjects: Volatile constituents -- Essential oils -- Natural antioxidants -- Synergistic effect -- Principal component analysis -- Statistical models -- ORAC assay -- DPPH assay; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2020.110684
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: The contribution of the chemical diversity of common essential oils to the antioxidant properties was investigated. Principal component analyses were performed to cluster essential oils with similar compositions and antioxidant properties, and to correlate their chemical profiles with their antioxidant properties. Moreover, mathematical models were developed for the prediction of antioxidant properties. Oregano, cinnamon, clove, pimento, thyme, and sage exhibited the highest antioxidant properties. When clustering based on the chemical profile, onion, cranberry, melissa, clove, cinnamon, and garlic oils were clustered into the same group. Correlation analysis showed concurrent abundance of some compounds in essential oils, for example, monoterpenes and alcohols contents increase simultaneously, and phenol-rich samples had higher sesquiterpenes content. The statistically significant predictive models were developed. These models revealed that the effects of monoterpenes, ketones and phenols concentration on antioxidant capacity were more significant. Phenols paired with esters and alcohols showed synergistic effects on hydrogen atom transfer-based ORAC value, whereas the opposite was true on single electron transfer-based IC50 when paired with monoterpenes and ketones. The developed predictive models are expected to provide the capability to predict the antioxidant properties of essential oils based on their chemical compositions and to identify combinations of oils that can act synergistically. Highlights: Chemical composition and antioxidant properties of 38 essential oils were investigated. Principal component analyses were used to categorize oils with similar chemical profiles. Correlations between chemical profile and antioxidant activity were identified. Mathematical models were developed to predict antioxidant properties based on chemical profile. Essential oil constituents with significant positive effect on antioxidant properties were identified.