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. 2020 Jun 9;10:9284. doi: 10.1038/s41598-020-65999-7

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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PLS image of a subset of KZ-RILs with diverse levels of grain chalk (A), linear regression of predicted values of a PLS-DA model using the 702–922 nm range as a function of actual % chalk observed in brown rice of the KZ-RIL population (B), and scanning electron microscope (SEM) images of low (C), intermediate (D), and high chalk KZ-RILs (E). a.u. in (A) stands for arbitrary unit. Black and yellow dots in (B) display calibrated and predicted values, respectively. Grey dotted line/red solid line, grey solid line/blue solid line, and black lines in (B) represent 95% prediction and confidence bands and regression of calibrated/predicted values, respectively.