Using portable near-infrared spectroscopy to predict pig subcutaneous fat composition and iodine value
Bibliographic record
Abstract
This study tested the potential of portable near-infrared spectroscopy (NIRS) to predict fatty acids (FA) composition and iodine value (IV) of pig subcutaneous fat. Following carcass splitting, the inner layer of subcutaneous fat at the shoulder from the left carcass side was scanned using a hand-held NIRS probe (350–2500 nm) and analysed for FA composition using gas chromatography. The NIRS successfully predicted the total polysaturated fatty acids (PUFA) and n-3 FA proportions, polyunsaturated/saturated fatty acids (PUFA/SFA) ratio, and IV (R2 = 0.90–0.95; root-mean-square error of prediction, RMSEP = 0.019%–1.03% total FA). This portable technology also met the requirements for a quick screening of the proportions of total SFA, monounsaturated fatty acids (MUFA) and n-6 FA, n-6/n-3 ratio, and some individual FA such as C18:2n-6 and C18:3n-3 (R2 = 0.80–0.89; RMSEP = 0.37%–1.11% total FA). However, unreliable predictions were found for other individual FA with low variability (coefficient of variation = 4%–8%) such as C16:0, C18:0, and C18:1 (R2 = 0.60–0.77; RMSEP = 0.76%–1.00% total FA). These results show portable NIRS as a suitable technology to predict pig fat quality. In addition, this study has been successful in implementing this portable NIRS technology in a research abattoir to collect spectra directly on the carcass, which would enable carcass sorting based on fat composition or hardness for marketing purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".