Predicting Antioxidant Synergism via Artificial Intelligence and Benchtop Data-Reference-Cited by-同舟云学术

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Predicting Antioxidant Synergism via Artificial Intelligence and Benchtop Data

Published:2023-10-05 Issue:42 Volume:71 Page:15644-15655
ISSN:0021-8561
Container-title:Journal of Agricultural and Food Chemistry
language:en
Short-container-title:J. Agric. Food Chem.

Author:

Ayres Lucas¹,Benavidez Tomás²,Varillas Armelle³,Linton Jeb¹,Whitehead Daniel C.¹^ORCID,Garcia Carlos D.^ORCID

Affiliation:

1. Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States

2. INFIQC-CONICET, Department of Physical Chemistry, National University of Córdoba, Cordoba 5000, Argentina

3. South Carolina Governor’s School for Science and Mathematics, Hartsville, South Carolina 29550, United States

Funder

Division of Chemistry

South Carolina Department of Agriculture

Clemson University

Publisher

American Chemical Society (ACS)

Subject

General Agricultural and Biological Sciences,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jafc.3c05462

Reference108 articles.

1. Lipid-based formulations in cosmeceuticals and biopharmaceuticals

2. BHA and TBHQ Quantification in Cosmetic Samples

3. Examination of marine and vegetable oil oxidation data from a multi-year, third-party database

4. Lipid oxidation of vegetable oils

5. Assessment of quality of edible vegetable oils accessed in Gondar City, Northwest Ethiopia

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