Analysis of Various Quality Attributes of Sunflower and Soybean Plants by Near Infrared Reflectance Spectroscopy: Development and Validation Calibration Models

Soybean and sunflower are summer annuals that can be grown as an alternative to corn and may be particularly useful in organic production systems for forage in addition to their traditional use as protein and/or oil yielding crops. Rapid and low cost methods of analyzing plant forage quality would be helpful for nutrition management of livestock. We developed and validated calibration models using Near-infrared Reflectance Spectroscopic (NIRS) analysis for 27 different forage quality parameters of organically grown sunflower and soybean leaves or reproductive parts. Crops were managed under conventional tillage or no-till with a cover crop of wheat before soybean and ryecrimson clover before sunflower. From a population of 120 samples from both crops, covering multiple sampling dates within the treatments, calibration models were developed utilizing spectral information covering both visible and NIR region of 61 85 randomly chosen samples using modified partial leastsquares (MPLS) regression with internal cross validation. Within MPLS protocol, we compared nine different math treatments on the quality of the calibration models. The math treatment “2,4,4,1” yielded the best quality models for all but starch and simple sugars (r = 0.699 0.999; where the 1st digit is the number of the derivative with 0 for raw spectra, 1 for first derivative, and 2 for second derivative, the 2nd digit is the gap over which the derivative is calculated, the 3rd digit is the number of data points in a running average or smoothing, and the 4th digit is the second smoothing). Prediction of How to cite this paper: Saha, U., Endale, D., Tillman, P.G., Johnson, W.C., Gaskin, J., Sonon, L., Schomberg, H. and Yang, Y.G. (2017) Analysis of Various Quality Attributes of Sunflower and Soybean Plants by Near Infrared Reflectance Spectroscopy: Development and Validation Calibration Models. American Journal of Analytical Chemistry, 8, 462-492. https://doi.org/10.4236/ajac.2017.87035 Received: May 26, 2017 Accepted: July 4, 2017 Published: July 7, 2017 Copyright © 2017 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access