Hostname: page-component-7479d7b7d-fwgfc Total loading time: 0 Render date: 2024-07-10T14:14:09.088Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of CP and starch concentrations in ruminal in situ studies and ruminal degradation of cereal grains using NIRS

Published online by Cambridge University Press:  03 August 2017

J. Krieg ,
E. Koenzen ,
N. Seifried ,
H. Steingass ,
H. Schenkel  and
M. Rodehutscord
J. Krieg
Affiliation:
Institut für Nutztierwissenschaften, Universität Hohenheim, Emil-Wolff-Straße 10, 70599 Stuttgart, Deutschland
E. Koenzen
Affiliation:
Core Facility Hohenheim, Universität Hohenheim, Emil-Wolff-Straße 12, 70599 Stuttgart, Deutschland
N. Seifried
Affiliation:
Institut für Nutztierwissenschaften, Universität Hohenheim, Emil-Wolff-Straße 10, 70599 Stuttgart, Deutschland
H. Steingass
Affiliation:
Institut für Nutztierwissenschaften, Universität Hohenheim, Emil-Wolff-Straße 10, 70599 Stuttgart, Deutschland
H. Schenkel
Affiliation:
Institut für Nutztierwissenschaften, Universität Hohenheim, Emil-Wolff-Straße 10, 70599 Stuttgart, Deutschland
M. Rodehutscord*
Affiliation:
Institut für Nutztierwissenschaften, Universität Hohenheim, Emil-Wolff-Straße 10, 70599 Stuttgart, Deutschland
*
†E-mail: inst450@uni-hohenheim.de
Get access

Abstract

Ruminal in situ incubations are widely used to assess the nutritional value of feedstuffs for ruminants. In in situ methods, feed samples are ruminally incubated in indigestible bags over a predefined timespan and the disappearance of nutrients from the bags is recorded. To describe the degradation of specific nutrients, information on the concentration of feed samples and undegraded feed after in situ incubation (‘bag residues’) is needed. For cereal and pea grains, CP and starch (ST) analyses are of interest. The numerous analyses of residues following ruminal incubation contribute greatly to the substantial investments in labour and money, and faster methods would be beneficial. Therefore, calibrations were developed to estimate CP and ST concentrations in grains and bag residues following in situ incubations by using their near-infrared spectra recorded from 680 to 2500 nm. The samples comprised rye, triticale, barley, wheat, and maize grains (20 genotypes each), and 15 durum wheat and 13 pea grains. In addition, residues after ruminal incubation were included (at least from four samples per species for various incubation times). To establish CP and ST calibrations, 620 and 610 samples (grains and bag residues after incubation, respectively) were chemically analysed for their CP and ST concentration. Calibrations using wavelengths from 1250 to 2450 nm and the first derivative of the spectra produced the best results (R2Validation=0.99 for CP and ST; standard error of prediction=0.47 and 2.10% DM for CP and ST, respectively). Hence, CP and ST concentration in cereal grains and peas and their bag residues could be predicted with high precision by NIRS for use in in situ studies. No differences were found between the effective ruminal degradation calculated from NIRS estimations and those calculated from chemical analyses (P>0.70). Calibrations were also calculated to predict ruminal degradation kinetics of cereal grains from the spectra of ground grains. Estimation of the effective ruminal degradation of CP and ST from the near-infrared spectra of cereal grains showed promising results (R2>0.90), but the database needs to be extended to obtain more stable calibrations for routine use.

Keywords

grainsruminantsstarchproteinNIRS
Type
Research Article
Information
animal , Volume 12 , Issue 3 , March 2018 , pp. 472 - 480
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bellon-Maurel, V, Fernandez-Ahumada, E, Palagos, B, Roger, J and McBratney, A 2010. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. Trends in Analytical Chemistry 29, 1073–1081.CrossRefGoogle Scholar
Bruno-Soares, AM, Murray, I, Paterson, RM and Abreu, JMF 1998. Use of near infrared reflectance spectroscopy (NIRS) for the prediction of the chemical composition and nutritional attributes of green crop cereals. Animal Feed Science and Technology 75, 15–25.Google Scholar
Dardenne, P 2009. Some considerations about NIR spectroscopy: closing speech at NIR-2009. NIR News 21, 8–14.Google Scholar
European Commission 2010. Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes. Official Journal of the European Union 276 53, 33–79.Google Scholar
Foskolos, A, Calsamiglia, S, Chrenková, M, Weisbjerg, M and Albanell, E 2015. Prediction of rumen degradability parameters of a wide range of forages and non-forages by NIRS. Animal 9, 1163–1171.Google Scholar
Garnsworthy, PC and Wiseman, J 2000. Rumen digestibility of starch and nitrogen in near-isogenic lines of wheat. Animal Feed Science and Technology 85, 33–40.CrossRefGoogle Scholar
Garnsworthy, PC, Wiseman, J and Fegeros, K 2000. Prediction of chemical, nutritive and agronomic characteristics of wheat by near infrared spectroscopy. Journal of Agricultural Science 135, 409–417.Google Scholar
Huhtanen, P and Sveinbjörnsson, J 2006. Evaluation of methods for estimating starch digestibility and digestion kinetics in ruminants. Animal Feed Science and Technology 130, 95–113.Google Scholar
Jancewicz, LJ, Penner, GB, Swift, ML, McKinnon, JJ, Waldner, CL and McAllister, TA 2016. Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy. Canadian Journal of Animal Science 96, 532–549.CrossRefGoogle Scholar
Kim, H and Williams, PC 1990. Determination of starch and energy in feed grains by near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry 38, 682–688.CrossRefGoogle Scholar
Kitessa, S, Flinn, PC and Irish, GG 1999. Comparison of methods used to predict the in vivo digestibility of feeds in ruminants. Australian Journal of Agricultural Research 50, 825–841.CrossRefGoogle Scholar
Kovalenko, IV, Rippke, GR and Hurburgh, CR 2006. Determination of amino acid composition of soybeans (Glycine max) by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry 54, 3485–3491.Google Scholar
Krieg, J, Seifried, N, Steingass, H and Rodehutscord, M 2017a. In situ and in vitro ruminal starch degradation of grains from different rye, triticale and barley genotypes. Animal, https://doi.org/10.1017/S1751731117000337.Google Scholar
Krieg, J, Seifried, N, Steingass, H and Rodehutscord, M 2017b. In situ and in vitro ruminal crude protein degradation characteristics of barley, rye, and triticale grains. Journal of Animal Physiology and Animal Nutrition, DOI: 10.1111/jpn.12767.CrossRefGoogle Scholar
Krieg, J, Ziegeler, J, Steingass, H, Seifried, N, Longin, F and Rodehutscord, M 2015. Ruminaler Stärke- und Rohproteinabbau von Hartweizen in situ. VDLUFA-Kongress – Kurzfassungen der Referate 127, 95.Google Scholar
Lin, C, Chen, X, Jian, L, Shi, C, Jin, X and Zhang, G 2014. Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley. Food Chemistry 162, 10–15.Google Scholar
Minasny, B and McBratney, A 2013. Why you don’t need to use RPD. Pedometron 33, 14–15.Google Scholar
Ørskov, ER and McDonald, I 1979. The estimation of protein degradability in the rumen from incubation measurements weighted according to rate of passage. Journal of Agricultural Science 92, 499–503.Google Scholar
Osborne, B 2008. NIR analysis of cereal products. In Handbook of near-infrared analysis (ed. DA Burns and EW Ciurczak), pp. 399–413. CRC Press, Boca Raton, FL, USA.Google Scholar
Plaizier, JC, Krause, DO, Gozho, GN and McBride, BW 2008. Subacute ruminal acidosis in dairy cows: the physiological causes, incidence and consequences. Veterinary Journal 176, 21–31.Google Scholar
Rodehutscord, M, Rückert, C, Maurer, HP, Schenkel, H, Schipprack, W, Bach Knudsen, KE, Schollenberger, M, Laux, M, Eklund, M, Siegert, W and Mosenthin, R 2016. Variation in chemical composition and physical characteristics of cereal grains from different genotypes. Archives of Animal Nutrition 70, 87–107.CrossRefGoogle ScholarPubMed
Sapienza, D, Berzaghi, P, Martin, N, Taysom, D, Owens, F, Mahanna, B, Sevenich, D and Allen, R 2008. NIRS White Paper. Near infrared spectroscopy for forage and feed testing. NIRS Consortium, University of Wisconsin-Extension, Hillsboro, WI, USA.Google Scholar
Seifried, N, Steingass, H, Hoffmann, N and Rodehutscord, M 2017. In situ starch and crude protein degradation in the rumen and in vitro gas production kinetics of wheat genotypes. Journal of Animal Physiology and Animal Nutrition 101, 779-790.CrossRefGoogle Scholar
Seifried, N, Steingass, H, Schipprack, W and Rodehutscord, M 2016. Variation in ruminal in situ degradation of crude protein and starch from maize grains compared to in vitro gas production kinetics and physical and chemical characteristics. Archives of Animal Nutrition 70, 333–349.Google Scholar
Tillmann, P 1996. Kalibrationsentwicklung für NIRS-Geräte: Eine Einführung, 1st edition. Cuvillier, Göttingen, Germany.Google Scholar
Verband Deutscher Landwirtschaftlicher Untersuchungs- und Forschungsanstalten (VDLUFA) 2007. Handbuch der Landwirtschaftlichen Versuchs-und Untersuchungsmethodik Bd. III: Die chemische Untersuchung von Futtermitteln. VDLUFA-Verlag, Darmstadt, Germany.Google Scholar
Williams, P and Norris, K 1987. Near-infrared technology in the agricultural and food industries, 1st edition. American Association of Cereal Chemists Inc., St. Paul, MN, USA.Google Scholar
Workman, J, Burns, D and Ciurczak, E 2001. NIR spectroscopy calibration basics. Practical Spectroscopy Series 27, 91–128.Google Scholar
Xiccato, G, Trocino, A, De Boever, JL, Maertens, L, Carabaño, R, Pascual, JJ, Perez, JM, Gidenne, T and Falcao-E-Cunha, L 2003. Prediction of chemical composition, nutritive value and ingredient composition of European compound feeds for rabbits by near infrared reflectance spectroscopy (NIRS). Animal Feed Science and Technology 104, 153–168.CrossRefGoogle Scholar
Supplementary material: File

Krieg supplementary material

Tables S1-S2

Download Krieg supplementary material(File)
File 24.2 KB