TITLE:
Analysis of Various Quality Attributes of Sunflower and Soybean Plants by Near Infrared Reflectance Spectroscopy: Development and Validation Calibration Models
AUTHORS:
Uttam Saha, Dinku Endale, P. Glynn Tillman, W. Carroll Johnson, Julia Gaskin, Leticia Sonon, Harry Schomberg, Yuangen Yang
KEYWORDS:
NIRS, Calibration, Soybean, Sunflower, Validation
JOURNAL NAME:
American Journal of Analytical Chemistry,
Vol.8 No.7,
July
7,
2017
ABSTRACT: 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 rye-crimson
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 least-squares (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 (r2 = 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 an independent validation set of 28-35
samples with these models yielded excellent agreement between the NIRS
predicted values and the reference values except for starch (r2 =
0.8260 - 0.9990). The results showed that the same model was able to adequately
quantify a particular forage quality of both crops managed under different
tillage treatments and at different stages of growth. Thus, these models can be
reliably applied in the routine analysis of soybean and sunflower forage
quality for the purposes of livestock nutrient management decisions.