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Kreft’s Dichromaticity Index

A paired CIELAB hue-shift measure comparing a transparent sample's maximal-chroma state with fourfold lighter/thinner and darker/thicker states to quantify dichromatism.

Version
v2 · 2026-09-06 · History
Domain-specific #
2144
Origin domain
physics
Subdomain
colorimetry
Aliases
Kreft dichromaticity index, DI L and DI D

Core Idea

Kreft’s dichromaticity index quantifies dichromatism: a transparent material's change in perceived hue as optical path length or concentration changes. A transmission spectrum is converted to CIELAB coordinates across a dilution or thickness series. The reference state is the concentration–path-length product \(x_*\) at which chroma \(C^*_{ab}\) is maximal. Two signed hue differences are then computed:

\[ DI_L=\Delta h_{ab}(x_*,x_*/4),\qquad DI_D=\Delta h_{ab}(x_*,4x_*), \]

for lighter/thinner and darker/thicker directions respectively.

Scope of Application

The measure applies to dyes, pigments in transparent media, oils, biological chromophores, and other samples whose visible spectra yield systematic hue change with optical density. Pumpkin seed oil is a canonical high-dichromatism example. Its spectral-to-perceptual translation follows standard color-science practice rather than direct wavelength labeling.

Application assumes reproducible spectral data and meaningful CIELAB conversion. Scattering, fluorescence, chemical change across dilution, aggregation, or departure from the optical model can mix other phenomena into the result.

Clarity

Report sample preparation, solvent, temperature, path length or concentration, spectral instrument, illuminant, observer, white reference, CIELAB convention, interpolation method, maximal-chroma estimate, hue unwrapping, and sign orientation. Publish both \(DI_L\) and \(DI_D\); a single unsigned magnitude loses information.

Manages Complexity

The paired index reduces a full spectral and color trajectory to two interpretable angular displacements while anchoring comparisons at the sample's most saturated state. Fixed fourfold changes support reproducibility across samples whose absolute concentration scales differ.

Abstract Reasoning

  1. Measure or model visible spectra across a sufficient optical-density range.
  2. Convert each spectrum under fixed colorimetric conditions.
  3. Compute chroma and circular hue angle.
  4. Locate or interpolate the maximal-chroma state \(x_*\).
  5. Evaluate colors at \(x_*/4\) and \(4x_*\).
  6. Compute signed shortest hue differences with consistent wrapping.
  7. Preserve the lighter and darker components separately.
  8. Quantify sensitivity to spectral noise, interpolation, and colorimetric settings.
  9. Check that chemistry and scattering remain stable across the series.

Knowledge Transfer

The portable pattern is anchor a nonlinear trajectory at its maximum salience, perturb the controlling scale symmetrically in log space, and retain directional response. It transfers to paired sensitivity indices, hysteresis-free path summaries, dose–response characterization, and multiscale feature change. The proposed immediate parent is Measurement.

Relationships to Other Abstractions

Local relationship map for Kreft’s Dichromaticity IndexParents appear above the current abstraction, mutual partners to the right, and children below. Node labels state whether each abstraction is prime or domain-specific; colors identify relation types.Kreft’sDichromaticity IndexDOMAINPrime abstraction: Measurement — is a kind ofMeasurementPRIME

Current abstraction Kreft’s Dichromaticity Index Domain-specific

Parents (1) — more general patterns this builds on

  • Kreft’s Dichromaticity Index is a kind of Measurement Prime

    Measurement is the proposed immediate parent.

Hierarchy path (1) — routes to 1 parentless root

Neighborhood in Abstraction Space

Kreft’s Dichromaticity Index sits in a sparse region of the domain-specific corpus (94th percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.

Family — Unclustered & Miscellaneous (1565 abstractions)

Nearest neighbors

Computed from structural-signature embeddings · 2026-09-08