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Title: Analysis of various quality attributes of sunflower and soybean plants by near infra-red reflectance spectroscopy: Development and validation calibration models

Author
item SAHA, UTTAM - University Of Georgia
item Endale, Dinku
item Tillman, Patricia - Glynn
item Johnson, Wiley - Carroll
item GASKIN, JULIA - University Of Georgia
item SONON, LETICIA - University Of Georgia

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/21/2016
Publication Date: 11/6/2016
Citation: Saha, U., Endale, D.M., Tillman, P.G., Johnson, W.C., Gaskin, J., Sonon, L. 2016. [ABSTRACT] Analysis of various quality attributes of sunflower and soybean plants by near infra-red reflectance spectroscopy: Development and validation calibration models. Presented at American Society of Agronomy, Crop Science Society of America and Soil Science Society of America's annual meeting in Phoenix, AZ, 11/6-9/2016.

Interpretive Summary:

Technical Abstract: Sunflower and soybean are summer annuals that can be grown as an alternative to corn and may be particularly useful in organic production systems. Rapid and low cost methods of analyzing plant quality would be helpful for crop management. We developed and validated calibration models for Near-infrared Reflectance Spectroscopic (NIRS) analysis of 27 different parameters belonging to proximate and plant-nutrient composition of sunflower and soybean leaves or reproductive parts. The 120 samples came from two years of a small plot research study conducted in Tifton, GA. The oven-dried (60 oC), coarse-ground and sieved (1 mm) samples were scanned on a Foss NIRSystem model 6500 scanning monochromator in reflectance mode. Scanning covered both visible and near-infrared regions in the wavelength range from 400 to 2500 nm at 2 nm intervals to give a total of 1050 data points per sample. Calibration models were developed with 56-84 randomly chosen samples for various parameters using modified partial least-squares regression with internal cross validation. The models developed had low standard error of both calibration (SEC) and cross-validation (SECV) with high coefficient of determination in both calibration (R2 = 0.6993-0.9986) and cross validation (1-VR = 0.6181-0.9966) for all parameters except starch and simple sugars. Prediction of an independent validation set of 26-35 samples yielded excellent agreement between the NIRS predicted values and the reference values based on the low standard error of prediction (SEP), low bias, high coefficient of determination (r2 = 0.8260-0.9990), and high ratios of both performance to deviation (RPD = SD/SEP; 2.34-28.78) and performance to inter-quartile distance (RPIQ = SEP/IQ; 2.18-11.94) for all 27 parameters except starch, indicating the 26 calibration models had good quantitative information. The results suggest that precise, accurate, and rapid analysis of proximate and plant-nutrient composition of sunflower and soybean plants can be done using NIRS.