Nutritive evaluation of herbage from permanent meadows by near‐infrared reflectance spectroscopy: 1. Prediction of chemical composition and <i>in vitro</i> digestibility

Andrés, Sonia; Giráldez, Francisco Javier; López, Secundino; Mantecón, Ángel R.; Calleja, Alfredo

doi:10.1002/jsfa.2138

Cited by 32 publications

(38 citation statements)

References 35 publications

(35 reference statements)

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“…The performance of our prediction models of NDF and ADF are comparable to previous studies on solid rumen content samples (Redjadj et al 2014), liquid rumen samples (Jean et al 2016) of herbivores, a wide range of feeds for ruminants (Belanche et al 2014) and meadow herbage (Andrés et al 2005). For the prediction of microbial-N (R 2 = 0.81; RMSEP = 1.25; RPD = 2.32) and lignin (R 2 = 0.84; RMSEP = 1.05; RPD = 2.57), the model performance was comparably lower than that of protein and NDF.…”

Section: Discussionsupporting

confidence: 79%

“…For free-browsing ungulates, like moose, it is legitimate to expect heterogeneity in chemical fractions of lignin in the diet due to spatio-temporal variation in the type, availability and maturity of the feed. Anyway, the model parameters for the prediction of microbial-N and lignin concentrations in our study are comparable or even better than those observed for duodenal digesta (microbial-N: R 2 = 0.88, SEcv = 3.80; Atanassova et al 1998) and other herbage (Lignin: R 2 adj = 0.66, SEP = 7.18 g kg −1 DM; Andrés et al 2005).…”

Section: Discussionsupporting

confidence: 75%

“…In addition, lignin is composed of a large group of aromatic compounds with marked variation in chemical fractions among samples depending on the type of plant species the animal consumed and the stage of maturity of the plant species (Van Soest 1994). Andrés et al (2005) also suggested that the relatively low prediction accuracy of lignin in meadow herbage by NIRS could be partly attributed to heterogeneity of chemical fractions of lignin in the herbage. For free-browsing ungulates, like moose, it is legitimate to expect heterogeneity in chemical fractions of lignin in the diet due to spatio-temporal variation in the type, availability and maturity of the feed.…”

Section: Discussionmentioning

confidence: 99%

“…Particularly, the 2148-2200 nm band has been demonstrated as useful to predict protein in forage (Andrés et al 2005) and agricultural products (Shenk et al 2001). Similar to our results, Atanassova et al (1998) found absorption bands in 1720-1891 nm, 1930-2090 nm and 2090-2500 nm wavelength regions useful to predict microbial-N content of in sacco feed residues and duodenal digesta of sheep.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have demonstrated the usefulness of fecal NIRS profiling to determine the nutritional status of free ranging ruminants (Landau et al 2006;Showers et al 2006;Galvez-Ceron et al 2013;Gil-Jemenez et al 2015;Jean et al 2015;Villamuelas et al 2017). NIRS has also proven its utility for nutritive evaluation of forage and diets in a wide range of applications (Andrés et al 2005;Belanche et al 2014;Foskolos et al 2015). However, direct analyses of rumen contents by NIRS are largely missing (Redjadj et al 2014;Jean et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Tigabu¹

2018

Silva Fenn.

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Tigabu M., Felton A.M. (2018). Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents. Silva Fennica vol. 52 no. 1 article id 7822. 14 p. https://doi.org/10.14214/sf.7822 Highlights• Multivariate calibrations were established for predicting nutrient concentrations of solid moose rumen contents by near infrared spectroscopy (NIRS).• Crude protein, available protein and ash contents were accurately predicted.• Prediction of microbial nitrogen, ash, acid-detergent fiber, neutral detergent fiber and lignin were satisfactory.• The results demonstrate that NIRS offers quick and inexpensive procedure to quantify nutrient concentrations of solid rumen contents. AbstractThis study aimed at establishing calibrations to predict nutrient concentrations of solid moose (Alces alces L.) rumen content using near infrared spectroscopy (NIRS), as an alternative to expensive chemical analyses. NIR reflectance spectra of 148 dry pulverized samples were recorded. The scanned samples were then analyzed for crude protein, available protein, microbial nitrogen (N), ash, acid-detergent fiber (ADF), neutral detergent fiber (NDF) and lignin contents following standard chemical analysis procedures. The calibration models were derived by Orthogonal Projection to Latent Structure (OPLS) and validated using external prediction sets. The calibration models accurately predicted crude protein, available protein and ash contents (R 2 = 0.99, 0.96, and 0.92, prediction error = 0.39, 0.72 and 0.53% dry matter, respectively) while NDF (R 2 = 0.92; prediction error = 2.23% dry matter) and ADF (R 2 = 0.89; prediction error = 1.94% dry matter) were predicted with sufficient accuracy and that of microbial-N (R 2 = 0.81; prediction error = 1.25 mg yeast-RNA g -1 dry matter) and lignin (R 2 = 0.84; prediction error = 1.05% dry matter) were acceptable. The ratio of performance to deviation values were > 3.0 for crude protein and available protein, between 3.0 and 2.5 for ADF, NDF and lignin, and 2.32 for microbial-N; attesting the robustness of the calibration models. It can be concluded that NIR spectroscopy offers a quick and inexpensive procedure for prediction of nutrient concentrations of solid rumen contents in wild herbivores.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Tigabu¹

2018

Silva Fenn.

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Nutritive evaluation of herbage from permanent meadows by near‐infrared reflectance spectroscopy: 2. Prediction of crude protein and dry matter degradability

et al. 2005

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A total of 62 herbage samples, harvested in natural meadows located in the uplands of León (northwestern Spain) and characterised by a diverse botanical composition and different stages of maturity of the plants, were used to evaluate the ability of chemical composition and near-infrared reflectance spectroscopy to predict dry matter (DM) and crude protein (CP) ruminal degradability. Three nonproductive Holstein-Friesian cows fitted with rumen cannulae were used to incubate the herbage samples. Once the DM and CP disappearance rates had been calculated, the exponential model of McDonald was fitted to estimate the kinetic parameters, which were used to calculate the potential and effective ruminal degradability at different passage rates. A Bran+Luebbe InfraAlyzer 500 spectrophotometer was used to obtain the near-infrared (NIR) spectra corresponding to the 62 original herbage samples. Prediction equations for the estimation of the DM and CP degradability parameters were generated using the chemical composition data and the NIR spectra as independent variables. The results showed that the kinetic parameters were predicted with less accuracy than the potential or effective degradability of the chemical fractions. When NIR spectra were used as independent variables, the accuracy of the predictions of the potential or effective degradability of DM and CP was higher. Overall, the degradability of CP was predicted less successfully than the degradability of DM, probably owing to errors in the reference method, such as the microbial contamination of the incubation residues.

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Determining the mineral composition in Cucurbita pepo fruit using near infrared reflectance spectroscopy

et al. 2014

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NIRS combined with different spectral transformations by modified partial least-squares (MPLS) regression has shown to be useful in determining the mineral composition of summer squash fruit, being a fast and low-cost analytical technique. Components such as chlorophyll, starch and lipids were used by MPLS for modelling the predicting equations. The promotion of micronutrient-rich summer squash varieties could have a significant long-term beneficial impact on the health of mineral deficient human populations.

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Nutritive evaluation of herbage from permanent meadows by near‐infrared reflectance spectroscopy: 1. Prediction of chemical composition and in vitro digestibility

Cited by 32 publications

References 35 publications

Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Nutritive evaluation of herbage from permanent meadows by near‐infrared reflectance spectroscopy: 2. Prediction of crude protein and dry matter degradability

Determining the mineral composition in Cucurbita pepo fruit using near infrared reflectance spectroscopy

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