Near Infrared Reflectance Spectroscopy for Analyzing Undried Silage

Reeves, James B.; Blosser, T.H.; Colenbrander, V.F.

doi:10.3168/jds.s0022-0302(89)79082-2

Cited by 50 publications

(26 citation statements)

References 9 publications

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“…These researchers concluded that NIRS can be used for estimating ADF, NDF and IVOMD with good accuracy; however they did not present external validation data for digestibility. Reeves et al (1989) found R 2 and RPD P estimates of 0.83 and 2.41 for CP, 0.87 and 2.72 for ADF, 0.82 and 2.61 for NDF and 0.82 and 1.46 for IVOMD, respectively, what indicates NIRS prediction parameters poorer than those obtained in this work.…”

Section: Discussioncontrasting

confidence: 92%

“…Similar values of R 2 for CP (0.90), and smaller values for IVOMD, ADF and NDF (0.40,0.81 and 0.84,respectively) were shown by Cozzolino et al (2006) in comparison with results of this work (Table 3). Reeves et al (1989) achieved similar R 2 values for CP (0.83), ADF (0.86) and NDF (0.85) and smaller values for IVOMD (0.53) than in this work (Table 3). Park et al (2005) also reported values of calibration parameters R 2 and SECV in silage maize (0.96 and 1.05 for ADF, 0.93 and 2.14 for NDF, and 0.88 and 0.29 for CP, respectively), which were similar to those obtained in this study (Table 3), whereas their estimates 0.92 and 1.73 for IVOMD were better than those found in our study.…”

Section: Discussionsupporting

confidence: 72%

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NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

Campo¹,

Monteagudo²,

Salleres³

et al. 2013

Span. j. agric. res.

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The aim of this work was to study the potential of near-infrared reflectance spectroscopy (NIRS) to predict non-structural carbohydrates (NSC), water soluble carbohydrates (WSC), in vitro organic dry matter digestibility (IVOMD), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and starch in samples of whole plant maize with a wide range of variability. The samples were analyzed in reflectance mode by a spectrophotometer FOSS NIRSystems 6500. Four hundred and fifty samples of wide spectrum from different origin were selected out of 3000 scanned for the calibration set, whereas 87 independent random samples were used in the external validation. The goodness of the calibration models was evaluated using the following statistics: coefficient of determination (R2), standard error of cross-validation (SECV), standard error of prediction for external validation (SEP) and the RPDCV and RPDP indexes [ratios of standard deviation (SD) of reference analysis data to SECV and SEP, respectively]. The smaller the SECV and SEP and the greater the RPDCV and RPDP, the predictions are better. Trait measurement units were g/100g of dry matter (DM), except for IVOMD (g/100g OM). The SECV and RPDCV statistics of the calibration set were 1.34 and 3.2 for WSC, 2.57 and 3 for NSC and 2.3 and 2.2 for IVOMD, respectively. The SEP and RPDP statistics for external validation were 0.74 and 4.7 for WSC, 2.14 and 2.5 for NSC and 1.68 and 1.6 for IVOMD respectively. It can be concluded that the NIRS technique can be used to predict WSC and NSC with good accuracy, whereas prediction of IVOMD showed a lesser accuracy. NIRS predictions of OM, CP, NDF, ADF and starch also showed good accuracy.

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Section: Discussioncontrasting

confidence: 92%

Section: Discussionsupporting

confidence: 72%

NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

Campo¹,

Monteagudo²,

Salleres³

et al. 2013

Span. j. agric. res.

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“…These problems can be overcome by grinding silages in frozen state with dry ice or liquid nitrogen, but such a procedure is time-consuming and inconvenient due to cleanup required between samples and the need to thaw the sample for subsequent use (Park et al, 2002;Reeves et al, 1989). Also, advances in chemometrics software, spectral data transformation for scatter correction and partial least squares regression have minimized some of the interference of particle size variation and water absorption presented by wet silage samples (Baker et al, 1994;Gordon et al, 1998;Shenk and Westerhaus, 1995).…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Chemical Composition of Fresh Whole Crop Barley Silages by Near Infrared Spectroscopy

Park¹,

Lee²,

Lim³

et al. 2013

Journal of The Korean Society of Grassland and Forage Science

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Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH, as well as lactic acid content with correlation coefficients of cross-validation (R 2 cv) of 0.96, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

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“…So, in recent years researchers prefer to do a NIRS-analysis on the undried silage, although there remain other problems like the heterogeneity of the samples and the effect of water. In order to overcome this problem, Reeves and Blosser (1989) used dry ice or liquid N to grind the silage sample, but there is also very complicated. It was concluded that when not using dry ice, the undried silage samples treated in a wiley grinder gave better calibration results than that treated in a Hobart chopper and intact materails, and even intact silage samples could be used for CP determination (Reeves and Blosser, 1991).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of near-infrared reflectance spectroscopy (NIRS) for predicting chemical composition of straw silage

Liu¹,

Han²

2006

J. Anim. Feed Sci.

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A representative population of 157 straw silages was used as database for studying the possibilities of NIRS to predict chemical composition. NIRS calibrations were developed by means of modified partial least-squares (MPLS) regression. NIRS analysis of dried straw silages provided accurate predictions of dry matter (DM), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), hemicellulose, ash and lactic acid content as well as pH, with correlation coefficients of crossvalidation (R

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Near Infrared Reflectance Spectroscopy for Analyzing Undried Silage

Cited by 50 publications

References 9 publications

NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

Prediction of the Chemical Composition of Fresh Whole Crop Barley Silages by Near Infrared Spectroscopy

Evaluation of near-infrared reflectance spectroscopy (NIRS) for predicting chemical composition of straw silage

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