Application of artificial neural networks to the prediction of the antioxidant activity of essential oils in two experimental in vitro models
Abstract
Introduction: The antioxidant properties of essential oils (EOs) have been on the centre of intensive research for their potential use as preservatives or nutraceuticals by the food industry. The enormous amount of information already generated on this subject provides a rich field for data-miners as it is conceivable, with suitable computational techniques, to predict the antioxidant capacity of any essential oil just by knowing its particular chemical composition. To accomplish this task we here report on the design, training and validation of an Artificial Neural Network (ANN) able to predict the antioxidant activity of EOs of known chemical composition. Methods: A multilayer ANN with 30 input neurons, 42 in hidden layers (20[rightward arrow]15[rightward arrow]7) and one neuron in the output layer was developed and run using Fast Artificial Neural Network software. The chemical composition of 32 EOs and their antioxidant activity in the DPPH and linoleic acid models were extracted from the scientific literature and used as input values. From the initial set of around 80 compounds present in these EOs, only 30 compounds with relevant antioxidant capacity were selected to avoid excessive complexity of the neural network and minimise the associated structural problems. Results and discussion: The ANN could predict the antioxidant capacities of essential oils of known chemical composition in both the DPPH and linoleic acid assays with an average error of only 3.16% and 1.46%, respectively. We also discuss on the contribution of different compounds to these chemical activities. Conclusions: These results confirm that artificial neural networks are reliable, fast and cheap tools for predicting antioxidant activity of essential oils from some of its components and can be used to model biochemical properties of complex natural products including the prediction of parameters associated with nutraceutical properties of food ingredients.
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Cabrera and Prieto (2010) first showed application of artificial neural networks to predict antioxidant activity of essential oils in the DPPH and linoleic acid models, and the results confirmed that this computational technique was reliable for predicting antioxidant activity from some components.
“Recently, Cabrera and Prieto (2010) [153] were able, for the first time, to show the application of artificial neural networks (ANN) on the prediction of the antioxidant activity of essential oils in two experimental in vitro models: in the DPPH and linoleic acid models. They used about 30 components from 80 that are described as possessing antioxidant activity and the results confirmed that this”
From Antioxidant and Anti-Inflammatory Activities of Essential Oils: A Short Review (2010) · cites this paper as [153]
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Antioxidant and Anti-Inflammatory Activities of Essential Oils: A Short Review
Miguel, M. G. (2010) · Molecules · doi:10.3390/molecules15129252 · Cited by 933 · see citing works
“Recently, Cabrera and Prieto (2010) [153] were able, for the first time, to show the application of artificial neural networks (ANN) on the prediction of the antioxidant activity of essential oils in two experimental in vitro models: in the DPPH and linoleic acid models.”
Background
- Authors
- Cabrera, A. C., & Prieto, J. M.
- Venue
- Food Chemistry
- Publisher
- Elsevier BV
- Published
- 2010-01
- Volume
- 118
- Issue
- 1
- Pages
- 141-146
- ISSN
- 0308-8146 (ISSN-L)
- OpenAlex
- W2032096084
- Cited by
- 72
- References
- 36
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 10 · 2017: 3 · 2016: 3 · 2015: 8 · 2014: 5 · 2013: 5 · 2012: 9
- FWCI
- 10.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Food Chemistry (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Erasmus+
- Topics
- Life Sciences › Agricultural and Biological Sciences › Food Science › Essential Oils and Antimicrobial Activity
- Physical Sciences › Chemistry › Organic Chemistry › Free Radicals and Antioxidants
- Physical Sciences › Chemistry › Organic Chemistry › Edible Oils Quality and Analysis
- Keywords
- essential oils, antioxidant activity, artificial neural networks, chemical composition, DPPH radical scavenging, linoleic acid oxidation, nutraceuticals, food preservatives
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