|Montilla-bascon, Garcia - Consejo Superior De Investigaciones Cientificas (CSIC)|
|Han, Rongkui - University Of California|
|Sorrells, Mark - Cornell University - New York|
Submitted to: Journal of Near Infrared Spectroscopy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/19/2017
Publication Date: 7/10/2017
Publication URL: http://handle.nal.usda.gov/10113/5883109
Citation: Montilla-Bascon, G., Armstrong, P.R., Han, R., Sorrells, M. 2017. Quantification of betaglucans, lipid and protein contents in whole oat groats (Avena sativa L.) using near infrared reflectance spectroscopy. Journal of Near Infrared Spectroscopy. 25(3):172-179. doi: 10.1177/0967033517709615.
Interpretive Summary: Oats are considered as an important healthy food for humans due to several beneficial nutritional components. In particular is a soluble fiber called beta-glucan. The inclusion of oats in a broader range of products due to consumer demand has provided increased interest in breeding improved varieties. Part of the process of improving oat varieties requires measuring the composition of numerous oat samples of varying genetics grown under different conditions. For this work near infrared spectroscopy (NIRS) was studied as a method to measure the oil, protein and beta-glucan content of single, intact groat (dehulled oat) kernels. Results indicate that NIRS measurement of single kernel composition could be successfully used in breeding programs as an accurate and non-destructive screening tool for beta-glucan, protein, and oil contents of grains samples.
Technical Abstract: Whole oat has been described as an important healthy food for humans due to its beneficial nutritional components. Near infrared reflectance spectroscopy (NIRS) is a powerful, fast, accurate and non-destructive analytical tool that can be substituted for some traditional chemical analysis. A total of 1728 single intact groats of six different oat varieties were scanned by NIR to develop non-destructive predictions for (1,3;1,4)-beta-D-glucan (beta-glucan), protein and oil content in groats. Prediction models for single kernels were developed by the partial least squares regression (PLSR) method. Regression parameters between the chemical values, determined by wet-lab reference methods, and the predicted values, determined by NIRS, were veri'ed by cross validation and external validation. The cross validation and external validation cofficients for beta-glucan were 0.81 and 0.83, for protein 0.70 and 0.77 and for oil 0.93 and 0.92, respectively. The data indicated that NIRS prediction of single kernel composition could be successfully used in breeding programs, as an accurate and non-destructive screening tool for beta-glucan, protein, and oil contents of dehulled oat grains.