What question did this study set out to answer?

To evaluate the efficacy of near-infrared reflectance spectroscopy for rapid determination of chemical composition and nutritional value across various temperate and tropical forages.

October 1, 1976Journal of Animal Science

Predicting Forage Quality by Infrared Replectance Spectroscopy

View Full Paper

Ask AI

Bookmark

Authors

KNKarl H. NorrisCranfield University RBR. F. BarnesRockefeller Foundation JMJohn E. MooreQueen's University Belfast

Discussion

Member takes

Implication

Spectroscopic analysis demonstrates accurate prediction of nutritional quality in forage samples, indicating potential for rapid feed evaluation.

Key Points

To evaluate the efficacy of near-infrared reflectance spectroscopy for rapid determination of chemical composition and nutritional value across various temperate and tropical forages.
Recorded near-infrared reflectance spectra (1.4 to 2.4 µm) for N=87 ground dry forage samples across multiple temperate (alfalfa, tall fescue, bromegrass) and tropical (bermudagrass, pangola digitgrass) species.
Measured crude protein, fiber fractions (ADF, NDF), lignin, in vitro dry matter disappearance (IVDMD), and in vivo metrics (DMD, DMI, DEI).
Applied second derivative transformations to spectral data and developed calibration equations using multiple linear regression on odd-numbered samples to validate on even-numbered samples.
Prediction models using up to nine wavelength points achieved correlation coefficients of r = 0.99 for crude protein, r = 0.98 for NDF, r = 0.96 for ADF, r = 0.96 for lignin, and r = 0.95 for IVDMD.
Correlations for in vivo measures reached r = 0.88 for dry matter digestibility, r = 0.80 for dry matter intake, and r = 0.85 for digestible energy intake.
Validation on test samples showed standard errors of prediction of ±0.95% for crude protein, ±3.1% for NDF, ±5.1% for DMD, and ±7.9 g for DMI.

Cite This Study

Norris et al. (1976) studied this question.

synapsesocial.com/papers/6a79047e70e13102dc673f47 https://doi.org/10.2527/jas1976.434889x

View Full Paper

Ask AI

Bookmark

October 1, 1976Journal of Animal Science

Predicting Forage Quality by Infrared Replectance Spectroscopy

View Full Paper

Ask AI

Bookmark

Authors

KNKarl H. NorrisCranfield University RBR. F. BarnesRockefeller Foundation JMJohn E. MooreQueen's University Belfast

Discussion

Member takes

Implication

Spectroscopic analysis demonstrates accurate prediction of nutritional quality in forage samples, indicating potential for rapid feed evaluation.

Key Points

To evaluate the efficacy of near-infrared reflectance spectroscopy for rapid determination of chemical composition and nutritional value across various temperate and tropical forages.
Recorded near-infrared reflectance spectra (1.4 to 2.4 µm) for N=87 ground dry forage samples across multiple temperate (alfalfa, tall fescue, bromegrass) and tropical (bermudagrass, pangola digitgrass) species.
Measured crude protein, fiber fractions (ADF, NDF), lignin, in vitro dry matter disappearance (IVDMD), and in vivo metrics (DMD, DMI, DEI).
Applied second derivative transformations to spectral data and developed calibration equations using multiple linear regression on odd-numbered samples to validate on even-numbered samples.
Prediction models using up to nine wavelength points achieved correlation coefficients of r = 0.99 for crude protein, r = 0.98 for NDF, r = 0.96 for ADF, r = 0.96 for lignin, and r = 0.95 for IVDMD.
Correlations for in vivo measures reached r = 0.88 for dry matter digestibility, r = 0.80 for dry matter intake, and r = 0.85 for digestible energy intake.
Validation on test samples showed standard errors of prediction of ±0.95% for crude protein, ±3.1% for NDF, ±5.1% for DMD, and ±7.9 g for DMI.

Cite This Study

Norris et al. (1976) studied this question.

synapsesocial.com/papers/6a79047e70e13102dc673f47 https://doi.org/10.2527/jas1976.434889x

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark