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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Small Grain and Food Crops Quality Research » Research » Publications at this Location » Publication #405290
Research Project: Identification and Characterization of Quality Parameters for Enhancement of Marketability of Hard Spring Wheat, Durum, and Oat

Location: Small Grain and Food Crops Quality Research

Title: Development of Fourier transform-near infrared and -mid infrared models to predict oat quality

Author
item Ohm, Jae-Bom

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/13/2023
Publication Date: 9/13/2023
Citation: Ohm, J. 2023. Development of Fourier transform-near infrared and -mid infrared models to predict oat quality. Meeting Abstract. Cereals & Grains 23. P-45.

Interpretive Summary:

Technical Abstract: Oats are among the healthiest grains on earth with well-balanced nutrient composition. Health benefits of oats include weight loss, lower blood sugar levels and reduced risk of heart disease. Plant breeding programs usually require rapid and cost-effective evaluation of many samples in compressed time. Therefore, it is essential to develop high-throughput phenotyping platforms to facilitate screening of quality oat varieties. Near-infra red spectroscopy (NIRS) is ideal for plant breeding applications as it provides efficient evaluation of many samples in compressed time. Mid-infrared spectroscopy (MIRS) is a great tool for chemical identification of individual biochemical components in food. However, MIRS has not been extensively used for quality evaluation of cereal crops, probably because of labor-intensive and time-consuming sample preparation procedures. Recently, the development of instrumentation in MIRS provides the opportunity to analyze quality components in cereal crops. Therefore, this research aimed to develop enhanced evaluation of protein, oil, ß-glucan and total dietary fiber content in oat using the Fourier transform (FT)-NIRS and FT-MIRS. Total 294 spring oat samples collected from four state in US were tested in this experiment. Among 294 samples, 176 samples were used for model calibration, and the other 118 samples were used to test the models for validation. Oat groat and groat flour samples were scanned for reflectance measurement using an FT-NIRS instrument. In MIRS, groat flour samples were scanned using an FT-IR spectrometer equipped with an attenuated total reflection (ATR) accessory. FT-NIRS prediction models fitted very well for protein and oil as indicated by coefficient of determination (R2) values over 0.9 for calibration and validation sets. The R2 values were lower for beta-glucan and total dietary fiber models than those of protein and oil. But those figures still suggested that the NIRS models can be used to screen oat varieties. FT-MIR-ATR calibration models of protein and oil showed R2 values over 0.8 for calibration and validation sets. However, the R2 values were low for beta-glucan and total dietary fiber models indicating poor performance. Overall, the results indicated that the FT-NIR and FT-IR-ATR applications will help to rapidly evaluate and segregate oat genotypes for the quality traits in oat breeding programs and industry.