Document Type

Thesis - Open Access

Award Date


Degree Name

Doctor of Philosophy (PhD)


Dairy Science

First Advisor

Padmanaban G. Krishnan


Oats is a unique cereal owing to its nutritional and health benefits. Near Infrared Reflectance Spectroscopy (NIRS) is an efficient tool for monitoring the quality of cereal foods. NIRS holds potential for non-destructive multicomponent oat analyses with advantage of large sample throughput, speed, and reduced cost. The purpose of this study was to develop predictive calibration models for estimating beta-glucan, protein, and oil content of US oat cultivars using NIRS and validated AACCI reference methods. A rapid, non-destructive (whole oat groat), and secondary NIRS method was developed to estimate beta-glucan, protein, and oil content based on the standard reference analyses procedures approved by AACCI. Samples were collected from the 2014 to 2016 crop years from various locations in the United States (South Dakota, North Dakota, Minnesota, Washington, Iowa, and Wisconsin) representing a large geographical region and diverse genetic range (N=500). Predictive calibration equations were developed based on Modified Partial Least Square (MPLS) regression technique. Reference analyses were done by the following standard methods approved by AACCI and AOCS (AACCI method 32-23.01 for beta-glucan, AACCI method 46-30.01 for crude protein, AOCS Am 5-04 for oil content and AACCI method 44-15.02 for moisture content). Calibration for the estimation of beta-glucan content for ground oat groats yielded coefficient of determination (RSQ), standard error of calibration (SEC), standard error of cross validation (SECV) and one minus variance ratio (1-VR) ratio of 0.94, 0.16, 0.22 and 0.88, respectively. Whole oat groats beta glucan calibrations showed excellent RSQ, SEC, SECV and 1-VR of 0.93, 0.18, 0.23 and 0.89, respectively. Protein calibration for ground oat groats showed RSQ, 1-VR, SEC and SECV values of 0.93, 0.93, 0.61 and 0.64, respectively. For protein calibrations of whole oat groats, RSQ, SEC, SECV and 1-VR values of 0.92, 0.70, 0.80 and 0.89, respectively were obtained. Calibration from ground oat groats for oil content estimation yielded higher RSQ and 1-VR values of 0.93 and 0.92 and lower SEC and SECV values of 0.23 and 0.26, respectively. Oil content calibration with whole oat groats, RSQ, SEC, SECV and 1-VR values were 0.90, 0.27, 0.30 and 0.88, respectively. Higher RSQ and 1-VR and lower SEC and SECV values provide evidence supporting the accuracy and precision of calibration models developed for beta-glucan, protein, and oil content estimation of oats. The study shows that NIRS is an efficient technology for oat quality measurement for large throughput breeding programs and in oat processing.


Includes bibliographical references (pages 72-85)



Number of Pages



South Dakota State University


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Food Science Commons