S<scp>hort</scp> C<scp>ommunication</scp>: Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs

ZijlstraR., T.; SwiftM., L.; WangL., F.; ScottT., A.; EdneyM., J.

doi:10.4141/cjas10063

Cited by 24 publications

(27 citation statements)

References 14 publications

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“…To our knowledge, there is no existing reference on faecal NIRS prediction of nutritional value in the pig, although some references exist on prediction with feed spectra (e.g. Zijlstra et al, 2011). The relatively low variation in digestibility in our data set makes it quite difficult to obtain good calibration models.…”

Section: Discussionmentioning

confidence: 99%

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Predicting feed digestibility from NIRS analysis of pig faeces

Bastianelli

Bonnal

Jaguelin-Peyraud

et al. 2015

Animal

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Digestibility is a key parameter in the evaluation of feeds; however, the measurements on animals require heavy experimental trials, which are hardly feasible when large numbers of determinations are required -for example, in genetic studies. This experiment aimed at investigating the possibility to predict digestibility from NIRS spectra measured on faeces. A total of 196 samples were available from a digestibility experiment investigating the effects of age and genetic background of Large White pigs fed the same diet, rich in fibre (NDF = 21.4% DM). Digestibility of dry matter (dDM), organic matter (dOM), nitrogen content (dN), energy (dE) and apparent digestible energy content (ADE) were calculated, as well as total N content of faeces (N). The faeces samples were submitted to reflectance NIRS analysis after freeze-drying and grinding. Calibration errors and validation errors were, respectively, 0.08 and 0.13% DM for total N in faeces, 0.97% and 1.08% for dDM, 0.79% and 1.04% for dOM, 1.04% and 1.47% for dN, 0.87% and 1.12% for dE and 167 and 213 kJ/kg DM for ADE. These results indicate that NIRS can account for digestibility differences due to animal factors, with an acceptable accuracy. NIRS appears to be a promising tool for large-scale evaluations of digestibility. It could also be used for the study of digestibility of different feeds, after appropriate calibration based on a wide range of feed types.

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Section: Discussionmentioning

confidence: 99%

“…It can also be performed by NIRS with calibration equations predicting nutritional value from feed spectrum within a specific family of ingredients (e.g. Zijlstra et al, 2011). The NIRS is based on the absorption of (IR) light by the chemical bonds of samples.…”

Section: Introductionmentioning

confidence: 99%

Predicting feed digestibility from NIRS analysis of pig faeces

Bastianelli

Bonnal

Jaguelin-Peyraud

et al. 2015

Animal

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“…The parallel relationships among these corn sources imply that factors limiting energy availability were similar across species; this suggests that DE values for swine of these corn sources can be predicted from AME N or NIT-AME N measurements. In contrast, Zijlstra et al (2011) were unable to model swine DE (R 2 = 0.03) based on the AME of barley measured in young broilers and growing pigs and concluded that broiler AME could not be used to predict swine DE. This likely was due to a difference among species in response to additional fiber or in their capacity to digest NDF components.…”

Section: Digestible Energy Prediction Based On Nitrogen-corrected Appmentioning

confidence: 98%

“…Existing prediction models developed from indirect predictions may be imprecise for individual feed ingredients as the models were derived using mixed diets (Morgan et al, 1987;Noblet and Perez, 1993). Previous studies (Campbell et al, 1983;Sibbald et al, 1983;Smith et al, 1987Smith et al, , 1988 have demonstrated that data from rats and roosters can be used to predict DE and apparent ME (AME) for swine across a variety of feedstuffs and mixed diets, but cross-species comparisons with a single feedstuff are limited (Charmley and Greenhalgh, 1987;Zijlstra et al, 2011). Predicting DE values for swine from measurements with broilers or roosters could reduce both grain quantity and assay time required.…”

Section: Introductionmentioning

confidence: 99%

Predicting the digestible energy of corn determined with growing swine from nutrient composition and cross-species measurements

Smith

Hassen

Hinds

et al. 2015

Journal of Animal Science

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The DE values of corn grain for pigs will differ among corn sources. More accurate prediction of DE may improve diet formulation and reduce diet cost. Corn grain sources ( = 83) were assayed with growing swine (20 kg) in DE experiments with total collection of feces, with 3-wk-old broiler chick in nitrogen-corrected apparent ME (AME) trials and with cecectomized adult roosters in nitrogen-corrected true ME (TME) studies. Additional AME data for the corn grain source set was generated based on an existing near-infrared transmittance prediction model (near-infrared transmittance-predicted AME [NIT-AME]). Corn source nutrient composition was determined by wet chemistry methods. These data were then used to 1) test the accuracy of predicting swine DE of individual corn sources based on available literature equations and nutrient composition and 2) develop models for predicting DE of sources from nutrient composition and the cross-species information gathered above (AME, NIT-AME, and TME). The overall measured DE, AME, NIT-AME, and TME values were 4,105 ± 11, 4,006 ± 10, 4,004 ± 10, and 4,086 ± 12 kcal/kg DM, respectively. Prediction models were developed using 80% of the corn grain sources; the remaining 20% was reserved for validation of the developed prediction equation. Literature equations based on nutrient composition proved imprecise for predicting corn DE; the root mean square error of prediction ranged from 105 to 331 kcal/kg, an equivalent of 2.6 to 8.8% error. Yet among the corn composition traits, 4-variable models developed in the current study provided adequate prediction of DE (model ranging from 0.76 to 0.79 and root mean square error [RMSE] of 50 kcal/kg). When prediction equations were tested using the validation set, these models had a 1 to 1.2% error of prediction. Simple linear equations from AME, NIT-AME, or TME provided an accurate prediction of DE for individual sources ( ranged from 0.65 to 0.73 and RMSE ranged from 50 to 61 kcal/kg). Percentage error of prediction based on the validation data set was greater (1.4%) for the TME model than for the NIT-AME or AME models (1 and 1.2%, respectively), indicating that swine DE values could be accurately predicted by using AME or NIT-AME. In conclusion, regression equations developed from broiler measurements or from analyzed nutrient composition proved adequate to reliably predict the DE of commercially available corn hybrids for growing pigs.

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“…Chez le porc, quelques travaux ont été publiés sur la prédiction de la valeur énergétique (Energie digestible, ED) par SPIR (Zijstra et al, 2011 ;Li et al, 2016). Cependant ces études ont été réalisées sur des jeux de données assez restreint et la méthode ne peut actuellement pas être considérée comme validée (Święch, 2017).…”

Section: Prédiction De La Valeur éNergétiqueunclassified

La spectrométrie dans le proche infrarouge pour la caractérisation des ressources alimentaires

et al. 2019

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Le pilotage des systèmes d’élevage en vue d’une optimisation technique, économique et environnementale passe par une formulation des rations de plus en plus précise et nécessite donc une connaissance fine des aliments consommés par les animaux. La spectrométrie dans le proche infrarouge (SPIR), utilisée de façon croissante depuis les années 60, est une technique analytique permettant de caractériser rapidement des échantillons d’aliments ou de fourrages. Les premières applications concernaient la composition chimique des fourrages, mais il est également possible de développer des étalonnages pour une estimation de la valeur nutritive et de l’ingestion. Au-delà de ces mesures directes sur l’aliment, la SPIR peut être appliquée sur les fèces afin de caractériser l’alimentation d’animaux sur parcours. Le traitement conjoint des spectres des aliments et des fèces peut donner une vision de l’utilisation réellement faite des aliments par les animaux. D’autres utilisations possibles de la SPIR comme la traçabilité ou la classification des aliments ainsi que la reconnaissance botanique des végétaux dans les prairies sont aussi décrites. Les développements techniques actuels, et notamment la miniaturisation des spectromètres, permettent d’amener les instruments sur le terrain pour produire l’information au plus près de son utilisation. D’autres techniques spectroscopiques alternatives ou complémentaires à la SPIR comme la spectroscopie dans le moyen infrarouge, la spectroscopie Raman ou l’imagerie hyperspectrale permettront d’autres conditions de mesures et d’autres applications.

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Short Communication: Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs

Cited by 24 publications

References 14 publications

Predicting feed digestibility from NIRS analysis of pig faeces

Predicting feed digestibility from NIRS analysis of pig faeces

Predicting the digestible energy of corn determined with growing swine from nutrient composition and cross-species measurements

La spectrométrie dans le proche infrarouge pour la caractérisation des ressources alimentaires

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