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  Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging

Buenafe, R., Tiozon, R. J. N., Boyd, L., Sartagoda, K., & Sreenivasulu, N. (2022). Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging. Food Chemistry Advances, 1: 100141. doi:10.1016/j.focha.2022.100141.

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Genre: Journal Article
Alternative Title : Food Chemistry Advances

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 Creators:
Buenafe, R.J.1, Author
Tiozon, R. Jr. N.2, Author           
Boyd, L.A.1, Author
Sartagoda, K.J.1, Author
Sreenivasulu, N.1, Author
Affiliations:
1external, ou_persistent22              
2Central Metabolism, Department Gutjahr, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_3396323              

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Language(s): eng - English
 Dates: 2022-11-162022-10
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.focha.2022.100141
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Title: Food Chemistry Advances
Source Genre: Journal
 Creator(s):
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Pages: - Volume / Issue: 1 Sequence Number: 100141 Start / End Page: - Identifier: -