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

Buenafe, R.J.; Tiozon, R. Jr. N.; Boyd, L.A.; Sartagoda, K.J.; Sreenivasulu, N.

doi:10.1016/j.focha.2022.100141

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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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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-B691-2 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-B693-0

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: Published Online: 2022-11-16Date issued: 2022-10

Publication Status: Issued

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Identifiers: DOI: 10.1016/j.focha.2022.100141

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Title: Food Chemistry Advances

Source Genre: Journal

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Pages: - Volume / Issue: 1 Sequence Number: 100141 Start / End Page: - Identifier: -