Absorption, scattering, and refractive index of blood and its components: a review
Bibliographic record
Abstract
Blood is a complex biofluid with distinct optical characteristics that underpin a range of diagnostic and monitoring technologies. This review examines the absorption, scattering, and refractive index properties of whole blood and its components across the visible and near-infrared spectrum. Blood’s optical properties are determined primarily by water, hemoglobin, and its encapsulation in red blood cells. Hemoglobins dominate blood’s light absorption in the 400–1,100 nm range, with sharp spectral differences between oxygenated and deoxygenated forms. Scattering in whole blood is primarily due to red blood cells and is influenced by hematocrit, oxygenation, shear rate, and osmolarity. Reduced scattering coefficients are close to 13 cm −1 in the whole visible range of the spectrum, and the anisotropy factor is close to unity, indicating highly forward-directed scattering. While other blood cells (white blood cells and platelets) do not contribute significantly to blood’s optical properties, their scattering properties are used in many biomedical applications. We also highlight the role of the geometry of experiment—including detour, sieve, and self-shielding phenomena—in shaping blood’s optical response. Multiple clinical technologies, such as pulse oximetry, are based on blood’s optical properties. Recently reported discrepancies between consumer and clinical devices highlight the need for more accurate models of blood optics for emerging biomedical and wearable sensing applications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".