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Gravimetric Determination of Amylase-Treated Neutral Detergent Fiber in Feeds with Refluxing in Beakers or Crucibles: Collaborative Study
Abstract
As an important constituent of animal feeds, fiber represents the portion of feeds that is bulky and difficult to digest. The neutral detergent fiber (NDF) method, developed over 30 years ago, is the method of choice for measuring total fiber in forages and other feeds. Several modifications that were made to improve its general applicability to all feeds and others developed in individual laboratories often resulted in variability among laboratories in measuring NDF. The amylase-treated NDF (aNDF) method, therefore, was developed as an accurate and precise method of measuring total insoluble fiber in feeds. A collaborative study was conducted to evaluate the repeatability and reproducibility of the aNDF method over the full range of animal feed materials. Twelve laboratories representing research, feed company, regulatory, and commercial feed testing laboratories analyzed 11 materials as blind duplicates. The materials represented feed matrixes, including animal products; high-protein, high-fat, and high-pectin feeds; oil seeds; grains; heated by-product feeds; and legume and grass hays and silages. Materials selected varied in chemical composition and contained 0–90% aNDF, 1–16% ash, 1–20% crude fat, 1–40% crude protein, and 0–50% starch. Correcting results for changes in blanks and reporting results as ash-free aNDF organic matter (aNDFom) improved the repeatability and reproducibility of results when aNDF was <25%. The within-laboratory repeatability standard deviation (sr) for percentage aNDFom in feeds varied from 0.21 to 1.82 and among-laboratory reproducibility standard deviation (sR) varied from 0.37 to 2.24. The HORRAT was <2 for all materials except feed materials containing >10% fat. However, standard deviations of repeatability and reproducibility for feeds with >10% fat were similar to those of other materials. It is recommended that the aNDF method be accepted for Official First Action status.
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Casali, A. O., Detmann, E., Valadares Filho, S. D. C., Pereira, J. C., Henriques, L. T., Freitas, S. G. D., & Paulino, M. F. (2008) · Revista Brasileira de Zootecnia · doi:10.1590/s1516-35982008000200021 · Cited by 442
Triolo, J. M., Sommer, S. G., Møller, H. B., Weisbjerg, M. R., & Jiang, X. Y. (2011) · Bioresource Technology · doi:10.1016/j.biortech.2011.07.026 · Cited by 434
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Metadata
- Authors
- Mertens, D. R., Collaborators:, Allen, M., Carmany, J., Clegg, J., Davidowicz, A., Drouches, M., Frank, K., Gambin, D., Garkie, M., Gildemeister, B., Jeffress, D., Jeon, C.-S., Jones, D., Kaplan, D., Kim, G.-N., Kobata, S., Main, D., Moua, X., Paul, B., Robertson, J., Taysom, D., Thiex, N., Williams, J., & Wolf, M.
- Venue
- Journal of AOAC International
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-11-01
- Volume
- 85
- Issue
- 6
- Pages
- 1217-1240
- ISSN
- 1060-3271 (ISSN-L)
- 1944-7922
- OpenAlex
- W1529137672
- Cited by
- 2,622
- References
- 2
- FWCI
- 7.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Gravimetric Determination of Amylase-Treated Neutral Detergent Fiber in Feeds with Refluxing in Beakers or Crucibles: Collaborative Study. https://app.cochat.ai/papers/10.1093/jaoac/85.6.1217
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- Mertens, D. R., Collaborators:, Allen, M., Carmany, J., Clegg, J., Davidowicz, A., Drouches, M., Frank, K., Gambin, D., Garkie, M., Gildemeister, B., Jeffress, D., Jeon, C.-S., Jones, D., Kaplan, D., Kim, G.-N., Kobata, S., Main, D., Moua, X., . . . Wolf, M. (2002). Gravimetric Determination of Amylase-Treated Neutral Detergent Fiber in Feeds with Refluxing in Beakers or Crucibles: Collaborative Study. Journal of AOAC International. https://doi.org/10.1093/jaoac/85.6.1217
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.