Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations

Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on ne...

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Autores principales:	Kamaranga H. S. Peiris, Xiaorong Wu, Scott R. Bean, Mayra Perez-Fajardo, Chad Hayes, Melinda K. Yerka, S. V. Krishna Jagadish, Troy Ostmeyer, Fadi M. Aramouni, Tesfaye Tesso, Ramasamy Perumal, William L. Rooney, Mitchell A. Kent, Brent Bean
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	near infrared spectroscopy sorghum starch amylose amylopectin high throughput phenotyping Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/07fdc48f5a8d4cf69bcf34c697f9bce1
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spelling	oai:doaj.org-article:07fdc48f5a8d4cf69bcf34c697f9bce12021-11-25T18:50:38ZNear Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations10.3390/pr91119422227-9717https://doaj.org/article/07fdc48f5a8d4cf69bcf34c697f9bce12021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1942https://doaj.org/toc/2227-9717Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on near infrared (NIR) spectra were developed to estimate starch and amylose contents in intact grain sorghum samples. Sorghum starch calibration model with a coefficient of determination (R<sup>2</sup>) = 0.87, root mean square error of cross validation (RMSECV) = 1.57% and slope = 0.89 predicted the starch content of validation set with R<sup>2</sup> = 0.76, root mean square error of prediction (RMSEP) = 2.13%, slope = 0.93 and bias = 0.20%. Amylose calibration model with R<sup>2</sup> = 0.84, RMSECV = 2.96% and slope = 0.86 predicted the amylose content in validation samples with R<sup>2</sup> = 0.76, RMSEP = 2.60%, slope = 0.98 and bias = −0.44%. Final starch and amylose cross validated calibration models were constructed combining respective calibration and validation sets and used to predict starch and amylose contents in 1337 grain samples from two diverse sorghum populations. Protein and moisture contents of the samples were determined using previously tested NIR spectroscopy models. The distribution of starch and protein contents in the samples of low amylose (<5%) and normal amylose (>15%) and the overall relationship between starch and protein contents of the sorghum populations were investigated. Percent starch and protein were negatively correlated, low amylose lines tended to have lower starch and higher protein contents than lines with high amylose. The results showed that NIR spectroscopy of whole grain can be used as a high throughput pre-screening method to identify sorghum germplasm with specific starch quality traits to develop hybrids for various end uses.Kamaranga H. S. PeirisXiaorong WuScott R. BeanMayra Perez-FajardoChad HayesMelinda K. YerkaS. V. Krishna JagadishTroy OstmeyerFadi M. AramouniTesfaye TessoRamasamy PerumalWilliam L. RooneyMitchell A. KentBrent BeanMDPI AGarticlenear infrared spectroscopysorghumstarchamyloseamylopectinhigh throughput phenotypingChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1942, p 1942 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	near infrared spectroscopy sorghum starch amylose amylopectin high throughput phenotyping Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	near infrared spectroscopy sorghum starch amylose amylopectin high throughput phenotyping Chemical technology TP1-1185 Chemistry QD1-999 Kamaranga H. S. Peiris Xiaorong Wu Scott R. Bean Mayra Perez-Fajardo Chad Hayes Melinda K. Yerka S. V. Krishna Jagadish Troy Ostmeyer Fadi M. Aramouni Tesfaye Tesso Ramasamy Perumal William L. Rooney Mitchell A. Kent Brent Bean Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations
description	Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on near infrared (NIR) spectra were developed to estimate starch and amylose contents in intact grain sorghum samples. Sorghum starch calibration model with a coefficient of determination (R<sup>2</sup>) = 0.87, root mean square error of cross validation (RMSECV) = 1.57% and slope = 0.89 predicted the starch content of validation set with R<sup>2</sup> = 0.76, root mean square error of prediction (RMSEP) = 2.13%, slope = 0.93 and bias = 0.20%. Amylose calibration model with R<sup>2</sup> = 0.84, RMSECV = 2.96% and slope = 0.86 predicted the amylose content in validation samples with R<sup>2</sup> = 0.76, RMSEP = 2.60%, slope = 0.98 and bias = −0.44%. Final starch and amylose cross validated calibration models were constructed combining respective calibration and validation sets and used to predict starch and amylose contents in 1337 grain samples from two diverse sorghum populations. Protein and moisture contents of the samples were determined using previously tested NIR spectroscopy models. The distribution of starch and protein contents in the samples of low amylose (<5%) and normal amylose (>15%) and the overall relationship between starch and protein contents of the sorghum populations were investigated. Percent starch and protein were negatively correlated, low amylose lines tended to have lower starch and higher protein contents than lines with high amylose. The results showed that NIR spectroscopy of whole grain can be used as a high throughput pre-screening method to identify sorghum germplasm with specific starch quality traits to develop hybrids for various end uses.
format	article
author	Kamaranga H. S. Peiris Xiaorong Wu Scott R. Bean Mayra Perez-Fajardo Chad Hayes Melinda K. Yerka S. V. Krishna Jagadish Troy Ostmeyer Fadi M. Aramouni Tesfaye Tesso Ramasamy Perumal William L. Rooney Mitchell A. Kent Brent Bean
author_facet	Kamaranga H. S. Peiris Xiaorong Wu Scott R. Bean Mayra Perez-Fajardo Chad Hayes Melinda K. Yerka S. V. Krishna Jagadish Troy Ostmeyer Fadi M. Aramouni Tesfaye Tesso Ramasamy Perumal William L. Rooney Mitchell A. Kent Brent Bean
author_sort	Kamaranga H. S. Peiris
title	Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations
title_short	Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations
title_full	Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations
title_fullStr	Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations
title_full_unstemmed	Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations
title_sort	near infrared spectroscopic evaluation of starch properties of diverse sorghum populations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/07fdc48f5a8d4cf69bcf34c697f9bce1
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Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations

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