Oat Evaluation at USDA-ARS Quality Lab, Fargo ND

Jae-Bom Ohm, Ph.D.

Hard Spring and Quality Laboratory USDA-ARS, Red River Valley Research Center, Crops Research Unit, Fargo, ND, USA

Oat Quality Evaluation

1. Introduction 2. Status of Oat Quality Evaluation at USDA Lab, Fargo, ND 3. Application of Near Infrared Spectroscopy (NIRS) 4. Single Kernel Characterization System 5. Future

Introduction

Physical quality characteristics - Important factor to determine value of oat variety

• Dehulling efficiency: ease of hull removal -high milling () yield • Test weight: heavy test weight - storage and transportation • Kernel size: large kernel - high groat yield and flaking • Kernel uniform size distribution - processing control • Groat breakage: low breakage (hardness ?) - high groat yield, low rancidity Introduction - contain high amounts of valuable nutrients

Protein: 15–23 % -Relatively high methionine and lysine concentrations Oil : 3-9 % • High energy diet for animals • Oil is relatively healthful, - Comparatively high in monounsaturated fatty acids, and tocopherols • Concern for interests in human food -Emphasis on fat-conscious diets -Stability (oxidation) problems β-Glucan: 3-7 % (soluble fiber) The consumption of oat β-glucan is leading to: -Decrease blood postprandial glycemic; -Lower blood total cholesterol; -Reduce low-density lipoprotein (LDL) cholesterol; -Improve high-density lipoprotein (HDL) cholesterol; -Maintain body weight; -Control type two diabetes; and -Prevent cancer.

Antioxidants: only source of avenanthramides 10-30 fold higher antioxidant activity than other phenols Status of Oat Quality Evaluation (USDA-ARS Laboratory, Fargo ND)

Uniform Oat Trial Samples -- Provides protein, oil, and β-glucan data

a) Year 2015 • Uniform Winter Oat Trial (32 composite samples) • Uniform Spring Early Season Trial (24 varieties x 2 locations: MN and SD) • Uniform Spring Mid Season Trial (30 varieties x 4 locations: MN,ND, and SD)

b) Year 2016 • Uniform Spring Early Season Trial (20 varieties x 4 locations: Waseca IL SD WI) • Uniform Spring Mid Season Trial (36 varieties x 6 locations: MN, IL ND, SD, and WI)

NDSU oat breeding program -- Protein and oil contents using NIRS for ~ 1000 samples each year

Analytical Methods

• Oat groat samples were steamed and then ground by a Retsch mill (ZM2, Retsch) with 0.5 mm sieve.

• Protein: Combustion analysis with a nitrogen analyzer (FP-428, Leco Corp., St. Joseph, MI) at N × 6.25 (AACC International Approved Method 46-30.01).

• Total β-D-glucan (β-glucan) is determined by the method of McCleary and Glennie-Holmes (1985).

• Oil: Petroleum ether

McCleary, B. V., and Glennie-Holmes, M. 1985. Enzymatic quantification of (1-3), (1-4)-β-glucan in and malt. J. Inst. Brew. 91:285-295. Near-Infra Red Spectroscopy (NIRS) a) Advantages of NIRS • Provides non-destructive tool for the determination of constituent concentrations and quality parameters in agricultural products • Provides efficient (rapid and cost effective) analysis of many samples in compressed time • Can be used by less experienced operators b) NIRS is ideal for plant breeding applications c) Need to develop calibration models based on reference methods (traditional analytical methods) Development of NIRS Calibration Models (Protein, oil, and β-glucan)

a) Materials: Groat and groat sample ● Sample No- 291 samples (UEOPN- 20 x 4 and UMOPN 36 x 6) ● Calibration-194, Validation- 97 b) Sample quantity ● Regular Ring: ~8 g ● Micro insert: ~1 g c) Instrument: NIRSystems 6500 (2184 and 2663) d) Wavelength: 400-2500 nm, 2 nm interval, Reflectance Development of NIRS Calibration Models (Protein, oil, and β-glucan)

e) Model calibration (WinISI, v.4.6.8, Foss Analytical A/S) o Math treatment: 2 4 4 1 (2nd derivative) o Scatter correction: SNV and Detrend o Regression method: Modified partial least square f) Evaluation of models: o Coefficient of determination (R2) o Standard error (SE) values ● Calibration (SEC) ● Cross validation (SECV) ● Validation (SEP) NIRS Calibration: Protein

Mean, and Ranges for Calibration and Validation Samples Calibration Sample (n=194) Validation Sample (n=97) Mean SD Min Max Mean SD Min Max 17.2 1.8 13.6 22.7 17.1 1.7 13.6 20.6

Calibration Results: Protein

Ring NIRS Calibration Cross Validation Validation Sample Type Systems SEC R2 SECV R2 SEP R2 Groat Regular 2184 0.25 0.98 0.28 0.97 0.36 0.96 Regular 2663 0.27 0.98 0.30 0.97 0.28 0.98 Micro 2184 0.31 0.97 0.36 0.96 0.40 0.95 Micro 2663 0.35 0.96 0.39 0.95 0.43 0.94 Groat Regular 2184 0.23 0.98 0.26 0.98 0.31 0.97 Flour Regular 2663 0.25 0.98 0.29 0.97 0.32 0.96 Micro 2184 0.25 0.98 0.29 0.97 0.30 0.97 Micro 2663 0.21 0.99 0.26 0.98 0.32 0.96 SEC=standard error of calibration, SECV=standard error of cross validation, and SEP=standard error of performance. NIRS Calibration: Oil

Mean and Range for Calibration and Validation Samples Calibration Sample (n=194) Validation Sample (n=97) Mean SD Min Max Mean SD Min Max 6.8 1.0 4.7 9.4 7.0 1.0 4.6 9.2

Calibration Results: Oil

Ring NIRS Calibration Cross Validation Validation Sample Type Systems SEC R2 SECV R2 SEP R2 Groat Regular 2184 0.22 0.94 0.25 0.93 0.31 0.91 Regular 2663 0.18 0.96 0.23 0.94 0.30 0.92 Micro 2184 0.26 0.92 0.30 0.90 0.36 0.88 Micro 2663 0.25 0.93 0.30 0.90 0.34 0.89

Groat Regular 2184 0.14 0.98 0.17 0.97 0.25 0.94 Flour Regular 2663 0.16 0.97 0.18 0.97 0.37 0.87 Micro 2184 0.17 0.97 0.19 0.96 0.25 0.94 Micro 2663 0.12 0.98 0.18 0.97 0.28 0.93 SEC=standard error of calibration, SECV=standard error of cross validation, and SEP=standard error of performance. NIRS Calibration: β-Glucan

Mean and Range for Calibration and Validation Samples Calibration Sample (n=194) Validation Sample (n=97) Mean SD Min Max Mean SD Min Max 4.8 0.6 3.4 6.4 4.9 0.6 3.6 6.6

Calibration Results: β-Glucan

Ring NIRS Calibration Cross Validation Validation Sample Type Systems SEC R2 SECV R2 SEP R2 Groat Regular 2184 0.22 0.84 0.33 0.65 0.43 0.48 Regular 2663 0.20 0.86 0.29 0.71 0.36 0.61 Micro 2184 0.28 0.74 0.43 0.42 0.45 0.44 Micro 2663 0.24 0.82 0.39 0.52 0.42 0.50

Groat Regular 2184 0.17 0.90 0.33 0.66 0.32 0.70 Flour Regular 2663 0.17 0.90 0.32 0.66 0.33 0.74 Micro 2184 0.26 0.78 0.41 0.45 0.43 0.44 Micro 2663 0.22 0.84 0.38 0.52 0.38 0.58 SEC=standard error of calibration, SECV=standard error of cross validation, and SEP=standard error of performance. Protein, Oil, and beta-Glucan Values Determined by Reference Methods and NIRS (Validation samples, n=97, NIR spectra: groat samples using a regular ring) Single Kernel Characterization System (SKCS) a) The SKCS determines the characteristics for individual kernels in the wheat sample b) Hardness, diameter and weight are analyzed for each individual kernel c) The SKCS makes it possible to objectively test for uniformity d) Almost no data on the application to oat quality evaluation for ND oat

Application of SKCS to Oat Quality Evaluation

a) Materials • Uniform Oat Midseason samples (2016 ) 36 varieties grown at two locations (Minot and Fargo, ND, USA)

b) Methods • One hundred oat groat samples were analyzed using the SKCS 4100 (Perten Instruments) Mean Square Values for Oat Groat SKCS Parameters and Quality Characteristics

Location (df=1) Genotype (df=35) Mean Mean Characteristics Mean Square F-Value Square F-Value SKCS parameters Hardness Index -43.0 50.9 9.8 *** 60.3 11.6 *** Weight 27.6 1.8 2.5 ns 5.2 7.5 *** Diameter 2.1 0.03 37.0 *** 0.01 15.1 *** Test Weight (lbs/bu) 39.7 43.9 57.8 *** 5.9 7.7 *** Groat Yield (%) 73.8 60.0 40.9 *** 9.7 6.6 *** Dehulling Efficiency (%) 96.9 50.0 26.3 *** 12.2 6.4 *** Protein (%, DB) 18.2 4.7 22.7 *** 3.2 15.3 *** Oil (%, DB) 7.1 2.5 68.1 *** 1.7 44.7 *** beta-Glucan (%, DB) 4.8 2.6 51.1 *** 0.5 9.4 *** Linear Correlation Coefficients Between Oat Groat SKCS Parameters and Quality Characteristics

SKCS Parameters Hardness Characteristics Index Weight Diameter Weight -0.538 *** Diameter -0.232 * 0.733 *** Test Weight -0.303 ** 0.254 * 0.485 *** Groat Yield -0.342 ** 0.477 *** 0.725 *** Dehulling Efficiency -0.094 NS 0.090 NS 0.351 ** Protein -0.265 * 0.042 NS -0.115 NS Oil -0.243 * -0.164 NS -0.266 * beta-Glucan 0.305 ** -0.025 NS -0.144 NS Plots SKCS Diameter vs. Groat Yield and Hardness Index vs. Groat Yield Future Research a) Application of SKCS to quality test • Association uniformity of oat characteristics with quality traits b) NIRS calibration • β-glucan determination and other quality traits c) Analyses of Chemical components • Protein composition and molecular weight distribution • Fatty acid composition (essential fatty acid concentration) • β -glucan molecular weight distribution • Antioxidant activity (avenanthramides) d) Identification and evaluation of traits related to processing and end-product quality Acknowledgement

Coordinators and breeders of Oat Uniform Trial, NDSU Oat Breeding Program, Ms. Steph Ness, Dr. Linda Dykes , and Quality Lab Personnel (USDA-ARS, Fargo ND),