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Menesatti, P., Antonucci, F., Pallottino, F., Roccuzzo, G., Allegra, M. and Stagno, F. (2010) Estimation of Plant Nutritional Status by Vis-NIR Spectrophotometric Analysis on Orange Leaves[Citrus sinensis (L) Osbeck cv. tarocco]. Biosystem Engineering, 105, 448-454.

has been cited by the following article:

  • 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.