Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy

Cozzolino, Daniel; Fassio, A.; Fernandez, E.; Restaino, E.; Manna, Alejandro La

doi:10.1016/j.anifeedsci.2006.01.025

Cited by 58 publications

(46 citation statements)

References 15 publications

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“…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).…”

Section: Discussionsupporting

confidence: 66%

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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: Discussionsupporting

confidence: 66%

“…In relation to the RPD CV , Cozzolino et al (2006) found the same estimation for CP (4.1) than that presented in Table 3 and poorer values for the traits ADF (2.0), NDF (1.6) and IVOMD (1.0). The results of our study showed an RPD CV value of 2.2 for IVOMD below the threshold value of 2.5.…”

Section: Discussionmentioning

confidence: 63%

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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show abstract

“…The lowest correlation was obtained for OMD (Table 4). Cozzolino et al (2006) also showed low correlation between NIR spectra and OMD. The result obtained in our study was much higher than that presented by Cozzolino et al (2006) for whole-crop maize silage, equaling 0.40.…”

Section: The High Rmentioning

confidence: 99%

“…Cozzolino et al (2006) also showed low correlation between NIR spectra and OMD. The result obtained in our study was much higher than that presented by Cozzolino et al (2006) for whole-crop maize silage, equaling 0.40. The low correlation between spectra data and OMD is difficult to explain, but in the current study it could be a result of the indirect (mathematical) OMD calculation and the mixing of sorghum silage with other feeds.…”

Section: The High Rmentioning

confidence: 99%

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Prediction of the nutritive value of wet whole-crop sorghum silage according to the INRA feeding system by near-infrared spectroscopy

Kański¹,

Pys²,

Górka³

2013

J. Anim. Feed Sci.

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Prediction of browse nutritive attributes in a Pinus radiata D. Don silvopastoral system based on visible-near infrared spectroscopy

et al. 2018

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Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy

Cited by 58 publications

References 15 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 nutritive value of wet whole-crop sorghum silage according to the INRA feeding system by near-infrared spectroscopy

Prediction of browse nutritive attributes in a Pinus radiata D. Don silvopastoral system based on visible-near infrared spectroscopy

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