Determining the Arrhenius Kinetics of Avocado Oil: Oxidative Stability under Rancimat Test Conditions.

Clicks: 390
ID: 3714
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #17 of 115 articles by views in Foods (Basel, Switzerland)

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Avocado is a highly potential functional fruit with significant health benefits which has high demand for consumption with a preferable taste. The fruit is one of the oil sources that still needs further examination on its probable kinetic behavior and oxidative stability as well as some characteristic behavior to commercialize and increase the market demand as functional oil. Hence, this study was motivated primarily for obtaining the Arrhenius kinetic information about avocado oil to evaluate the oxidative stability and provide predictive information about the shelf life by using the Rancimat method which is an accelerated shelf life test. Specifically, this research paper presents the study of the physical, physicochemical, chemical, and oxidative stability tests with the shelf life expectancy and kinetic property of avocado oil. According to the analyses, avocado oil has 210 days of predicted shelf life at 25 °C. This gives it a greater chance to be considered a good alternative to other oils as well as its antioxidant and phenolic content. According to the findings presented in this study, avocado oil has a very similar profile to olive oil and can be used as an alternative functional oil source.
Reference Key
aktar2019determiningfoods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aktar, Tugba;Adal, Eda;
Journal Foods (Basel, Switzerland)
Year 2019
DOI
E236
URL
Keywords
Deep learning smart meter semi-supervised learning non-technical loss sensor system

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.