Near infrared reflectance spectroscopy and related technologies for the analysis of feed ingredients
Bibliographic record
Abstract
The feed industry needs an accurate, rapid, and inexpensive means for predicting the total and available nutrient and energy contents of feed ingredients and feeds for use in feed formulation and quality control programmes. Currently, so-called rapid bioassays are still very time consuming and in vitro, or proximate-analysis based systems, take at least 48 hours to complete. Near infrared reflectance spectroscopy (NIRS) provides a fast, inexpensive and safe means to estimate total and available nutrient contents in feed ingredients and feeds. NIRS may also be used to quantify contents of antinutritional factors, such as glucosinolates. NIRS relies on chemometrics, or the application of mathematics to analytical chemistry. Mathematical models are constructed that relate composition of active chemical groups, or molecules, in specific feed constituents to energy absorption in the near infrared region of the light spectrum (700-2500 nm). The disadvantages of NIRS are the initial capital cost of equipment and the ongoing effort required for equipment calibration. It has taken the feed industry about twenty years to accept NIRS as a routine method for forage analysis, and so its regular use in the animal feed industry for applications, other than proximate analyses, is probably still some years away. Related technologies, such as near infrared transmittance (NIT), far infrared reflectance spectroscopy (FIRS) and nuclear magnetic resonance (NMR) deserve to be considered as well and may overcome some of the limitations of NIRS.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.034 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".