Near Infrared Spectroscopy for Rapid Estimation of Somatic Cell Counts in Human Breast Milk
Bibliographic record
Abstract
Elevated somatic cell counts (SCC) in human milk are associated with mastitis, an inflammation of the breast. However, the presence of fat globules can make the direct measurement of cells in milk challenging. We showed that near infrared (NIR) spectroscopy, a technique that has previously been used in the dairy industry for direct measurement of SCC in bovine milk, can be used for estimating SCC in human milk. Binary classification models were developed using multilinear regression with genetic algorithm searching for selection of wavelets. After correcting NIR frequency spectra for scatter contributions by fat globules and applying a Haar wavelet transform to the data, we found that multivariate classification allowed for separation of samples with low SCC (?150 K cells mL −1 ) from those with high SCC (?600 K cells mL −1 ). Sensitivity and specificity for cross-validated NIR estimates were 85% and 84%, respectively. The NIR method had very low rates of misclassification, with a model that used only two wavelets for classification. Additionally, this technique required no sample preparation and has potential as a rapid screening method for identifying elevated SCC in milk of nursing mothers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".