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

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 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 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 (r 2 = 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 463an 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 (r 2 = 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.

Analysis of moisture, oil, and fatty acid composition of olives by near‐infrared spectroscopy: development and validation calibration models

“…Therefore, the acceptance or rejection of a calibration model solely based on RPIQc or RPDc value remains questionable. Other judging criteria (such as R 2 , 1 − VR, SEC and SECV) should therefore be taken into consideration …”

“…These statistics were used to evaluate the predictability or reliability of the calibration models. An NIRS calibration model is considered robust and reliable when it produces lower bias (lower than the bias (limit)) and SEPc (lower than the SEPc (limit)) with r 2 and slope close to 1.0, and RPD and RPIQ values greater than 2.0 in external validation carried out with samples independent of the calibration sample set …”

“…This method will provide rapid and high‐throughput analysis, and will replace time‐consuming traditional laboratory methods. This method is also considered a ‘cheap’ and ‘green chemistry’ one because it does not involve any chemicals, nor does it generate any hazardous waste . To the best of our knowledge, this is the first report of original research aimed at developing NIRS calibration models to analyze fresh olives for oil, moisture and fatty acid profiles in the USA.…”

“…1 − VR is the coefficient of determination in cross‐validation. The following two ratios were also utilized to evaluate the quality of the models: RPDc , SD ÷ SECV, the ratio of SECV to deviation (SD, standard deviation of reference data in calibration set). For a good quantitative model, the RPDc should be at least 2.0. RPIQc , IQ ÷ SECV, the ratio of standard error of cross validation to inter‐quartile distance (IQ, inter‐quartile distance in reference data in the calibration set).…”

“…The standard error of prediction (SEP), bias, bias‐corrected SEP (SEP C ) and the associated r 2 were then used to evaluate the prediction performance of the models. The following two ratios were also utilized to evaluate the success of external (independent) validation of the models: RPDv , SD ÷ SEP, the ratio of performance (SEP) to deviation (SD of the reference data in the external validation set). For a satisfactory model validation, the RPD should be at least 2.0. RPIQv , IQ ÷ SEP, the ratio of performance (SEP) to inter‐quartile distance (IQ of the reference data in the external validation set).…”