Effect of Cereal Product Residual Moisture Content on Total Dietary Fiber Determined by Near-Infrared Reflectance Spectroscopy

1997 ◽  
Vol 45 (1) ◽  
pp. 140-144 ◽  
Author(s):  
W. R. Windham ◽  
Sandra E. Kays ◽  
Franklin E. Barton
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Residual Moisture Content ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Residual Moisture ◽  
Total Dietary Fiber ◽  
Cereal Product

scholarly journals Dry Matter Estimation of Standing Corn with Near-Infrared Reflectance Spectroscopy

2021 ◽  
Vol 37 (5) ◽  
pp. 775-781
Author(s):  
Matthew F. Digman ◽  
Jerry H. Cherney ◽  
Debbie J. Cherney
Keyword(s):  
Moisture Content ◽  
Prediction Model ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Whole Plant ◽  
Plant Moisture

HIGHLIGHTSQuadratic relationships were established to relate ear moisture or stover moisture to whole plant moisture, and they explained 90% and 84% of whole plant moisture, respectively. Based on our observations, the moisture content of a corn field can be estimated within +1% w.b. in 19 out of 20 fields by sampling 5-10 plants. The calibration offered by SCiO was successful at predicting oven-dried moisture content based on traditional NIRS metrics of R2 = 0.92, RMSE = 3.6, RPD = 3.2, and RER = 15. However, the 95% prediction bands were +6.9% w.b., which would indicate little utility in estimating ear moisture content. Based on a prediction model that was developed using the data collected for this study, a significant instrument-to-instrument bias was observed, indicating the necessity of including multiple SCiO devices in calibration spectra collection. ABSTRACT. Determining the appropriate time to harvest whole-plant corn is an essential factor driving the successful preservation via anaerobic fermentation (ensiling). The current options for timely on-farm monitoring of corn moisture in the field include selecting a set of representative plants, chopping and drying a subsample, or harvesting a portion of the field using a harvester equipped with an on-board moisture sensing system. Both methods are time-consuming and expensive, limiting their practicality for harvest decision-making. This work’s objective was to develop a practical solution that utilizes the moisture content of the ear to estimate whole-plant moisture. An improvement of this method was also considered that utilized a hand-held near-infrared reflectance spectroscopy (NIRS) device to predict ear moisture in situ. Based on the data collected during this work, a quadratic relationship was developed where ear moisture explained 90% of the variability in whole-plant corn moisture. However, based on our observations, the hand-held NIRS evaluated would have little utility in predicting whole-plant corn moisture with either the calibration developed here or provided by the manufacturer. The manufacturer’s prediction model yielded the best result with an R2 of 0.92, and a ratio of performance to deviation of 3.19. However, the 95% prediction band was +6.85% w.b. Finally, we determined that for a corn field uniform in appearance, sampling five to ten plants is likely to provide a reasonable estimate of field moisture. Keywords: Corn silage, Forage analysis, Harvest timing, Moisture content, NIRS.

Standardizing near infrared reflectance spectra by detrending against a water spectrum, for calibrating the voluntary intake of barley straw by sheep

1998 ◽  
Vol 22 ◽  
pp. 315-318
Author(s):  
A. V. Goodchild ◽  
F. J. El Haramein
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Simple Technique ◽  
Reflectance Spectroscopy ◽  
Single Sample ◽  
Barley Straw ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Voluntary Intake

The moisture content of samples affects the accuracy of predictions made using near infrared reflectance spectroscopy (NIRS). One way to prevent the problem is to develop a repeatability file, which summarizes variation in the spectra of a single sample resulting from variation in conditions at the time of scanning (Murray, 1993). Another method is to re-read the spectra of all samples on a second occasion when their moisture content has been deliberately altered (Murray, 1993). The present study, using straws with known voluntary intakes, compares these methods with a simple technique that compensates the spectra for moisture content before NIRS calibration (or prediction) begins.

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