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Record W2123374744 · doi:10.3168/jds.2008-1599

Near-infrared reflectance spectroscopy prediction of neutral detergent-soluble carbohydrates in timothy and alfalfa

2009· article· en· W2123374744 on OpenAlexafffund
Zhi-Dong Nie, Gaëtan F. Tremblay, Gilles Bélanger, R. Berthiaume, Yves Castonguay, Annick Bertrand, R. Michaud, G. Allard, Jing Han

Bibliographic record

VenueJournal of Dairy Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaNovalaitMinistère de l'Agriculture, des Pêcheries et de l'AlimentationMinistry of Education, IndiaFonds Québécois de la Recherche sur la Nature et les TechnologiesMinistry of Education of the People's Republic of ChinaNorth Dakota Soybean Council
KeywordsStarchNeutral Detergent FiberChemistryNear infrared reflectance spectroscopyForageCarbohydrateFodderAnimal scienceFood scienceAgronomyNear-infrared spectroscopyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Carbohydrates in forage crops can be divided into neutral detergent-insoluble fiber and neutral detergent-soluble carbohydrates (NDSC); the latter includes organic acids (OA), total ethanol:water-soluble carbohydrates (TESC), starch, and neutral detergent-soluble fiber (NDSF). The accurate and efficient estimation of NDSC in forage crops is essential for improving the performance of dairy cattle. In the present study, visible and near-infrared reflectance spectroscopy (NIRS) were applied to evaluate the feasibility of predicting OA, TESC, starch, NDSF, NDSC, and all related constituents used to calculate these 5 carbohydrate fractions in timothy and alfalfa. Forage samples (n = 1,008) of timothy and alfalfa were taken at the first and second harvests at 2 sites in 2007; samples were dried, ground, and then scanned (400 to 2,500 nm) using an NIRSystems 6500 monochromator. A calibration (n = 60) and a validation (n = 15) set of samples were selected for each species and then chemically analyzed. Concentrations of TESC and NDSC in timothy, as well as starch in alfalfa, were successfully predicted, but many other carbohydrate fractions were not predicted accurately when calibrations were performed using single-species sample sets. Both sets of samples were combined to form new calibration (n = 120) and validation (n = 30) sets of alfalfa and timothy samples. Calibration and validation statistics for the combined sets of alfalfa and timothy samples indicated that TESC, starch, and NDSC were predicted successfully, with coefficients of determination of prediction of 0.92, 0.89, and 0.93, and a ratio of prediction to deviation (RPD) of 3.3, 3.1, and 3.6, respectively. The NDSF prediction was classified as moderately successful The NIRS prediction of OA was unsuccessful All related constituents were predicted successfully by NIRS except ethanol-insoluble residual OM, with Our results confirm the feasibility of using NIRS to predict NDSC, its fractions, and other related constituents, except for OA and ethanol-insoluble residual OM, in timothy and alfalfa forage samples.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.251
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations56
Published2009
Admission routes2
Has abstractyes

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