Establishment of micromethods for macronutrient contents analysis in breast milk
Bibliographic record
Abstract
Commercially available milk analysers were originally developed for use in the dairy industry, but they are now used to analyse macronutrient content of breast milk in clinical studies and routine care of the premature or very low birthweight (VLBW) infants. Due to the different composition of cow and breast milk, these devices need to be validated against reference methods before they can be used in daily routine. However, current reference methods require a sample volume of 30-100 mL to analyse fat, protein and lactose. It is not feasible to obtain this volume of milk for research purposes, especially from VLBW infants as lactation may be delayed or impaired and the limited volume of breast milk must be provided to the infant. To support validation of milk analysers in both clinical and research settings, the aim of this study is to establish and validate micromethods for precise macronutrient analysis in small volume of breast milk and conduct a feasibility study of the micromethods as a post-validation. Methods include a modified Mojonnier ether extraction (fat), elemental analysis (protein) and ultra-performance liquid chromatography-tandem mass spectrometry (lactose). We were able to downsize volumes required for analysis of fat, protein and lactose to 1 mL, 260 μL and 100 μL; corresponding coefficients of variation are 1.7, 1.8 and 2.3%, respectively. The presented methods allow for reliable and precise analyses of macronutrients in ≤1.5 mL of breast milk and will be used to validate milk analysers.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".