Infrared analyzers for breast milk analysis: fat levels can influence the accuracy of protein measurements
Bibliographic record
Abstract
BACKGROUND: Currently, there is a growing interest in lacto-engineering in the neonatal intensive care unit, using infrared milk analyzers to rapidly measure the macronutrient content in breast milk before processing and feeding it to preterm infants. However, there is an overlap in the spectral information of different macronutrients, so they can potentially impact the robustness of the measurement. In this study, we investigate whether the measurement of protein is dependent on the levels of fat present while using an infrared milk analyzer. METHODS: Breast milk samples (n=25) were measured for fat and protein content before and after being completely defatted by centrifugation, using chemical reference methods and near-infrared milk analyzer (Unity SpectraStar) with two different calibration algorithms provided by the manufacturer (released 2009 and 2015). RESULTS: While the protein content remained unchanged, as measured by elemental analysis, measurements by infrared milk analyzer show a difference in protein measurements dependent on fat content; high fat content can lead to falsely high protein content. This difference is less pronounced when measured using the more recent calibration algorithm. CONCLUSIONS: Milk analyzer users must be cautious of their devices' measurements, especially if they are changing the matrix of breast milk using more advanced lacto-engineering.
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".