How does colorimeter data relate to observed color?
Since colorimeters have different built-in color spaces, different color spaces will produce different measurement results. Users can select the color space that meets their needs to obtain the desired results.
LAB color difference calculation formula: △E=[(△L)2+(△a)2+(△b)2]1/2.
△L=L of the product under test-L of the standard sample (brightness/difference between black and white)
△a=a of the product under test-a of the standard sample (red/green difference)
△b=btested product-bstandard sample (yellow/blue difference)
Compare the ΔL values to determine the color depth. A positive ΔL deviation indicates a lighter color, while a negative ΔL deviation indicates a darker color.
Comparing the ΔA values, positive deviations are reddish and negative deviations are greenish.
The relationship between colorimeter data and color:
A colorimeter is a photoelectric integrating colorimeter that uses an internal standard light source to measure transmitted or reflected color. A colorimeter can directly measure the tristimulus values and chromaticity coordinates of an object’s color. It can also calculate the color difference between two objects using an analog circuit or a connected computer. Three factors determine the magnitude of color difference: the first is the L value, which primarily controls the lightness or darkness of a product’s color, commonly referred to as black or white. A colorimeter uses positive and negative values. The second factor is the a value, which represents red and green and is also expressed in positive and negative terms. The third factor is the b value, which represents yellow and blue in both positive and negative directions. If all three values on the colorimeter are close to 0 (the farthest point), the color difference is small; otherwise, it is large.
L: (brightness) axis represents black and white, 0 is black and 100 is one hundred.
a: (red-green) axis positive value is red, negative value is green, and 0 is neutral.
b: (yellow-blue) positive values on the axis are yellow, negative values are blue, and 0 is neutral.
All colors can be perceived and measured using the Lab color space. This data can also be used to express the color difference between a standard sample and a test sample, and is usually expressed as △Eab (total color difference) △L△a△b.
Colorimeter data indicates the size of color difference:
If ΔL is positive, it means the sample is lighter than the standard; if ΔL is negative, it means the sample is darker than the standard.
If Δa is positive, it means the sample is redder (or less green) than the standard; if it is negative, it means the sample is greener (or less red).
If Δb is positive, it means the sample is yellower (or less blue) than the standard; if it is negative, it means the sample is bluer (or less yellow).
The color difference between L, a, and b can also be expressed by a separate color difference symbol ΔE. ΔE is defined as the total color difference of the sample, but it cannot indicate the direction of the color difference of the sample. The larger the ΔE value, the greater the color difference. It is calculated by the following formula: ΔE*=[（△L*）2+（△a*）2+（△b*）2]1/2
What is Color Measurement Parameters
Color measurement parameters relate to specific details like the specifications and conditions which have to be followed to make sure the color measurements are accurate, precise, and repeatable. These parameters are set to explain the functions of instruments and how measurement data should be processed.
Color Scale
Every color measurement parameters have a data measurement range. In specific, color measurement parameters limit to only twelve data points to form a complete color measurement parameters color chart.
CIE Illuminant
Color scales that have been CIE coordinates and subdivisions still provide measures of relative distance between colors. Important scales such as CIE XYZ still have value and should be documented as having set foundations to many color spaces, RGB red, green, blue, Hunter Lab, preceeding CIE but still used in legacy applications.
CIE Standard Observer
Mathematically modeling an average human’s color vision utilizes the CIE Standard Observer functions. There are two standard observers: the “2° observer,” which is based on a 2° field of view, and the “10° observer,” which is based on a 10° field of view.
In colorimetric calculations, the choice of observer can be limiting, particularly for peripheral vision. The 10° observer is usually favored for large sample sets and is becoming the standard for most other color measurement applications.
Instrument Geometry
Instrument geometry pertains to illumination and collection angles, that is: how light illuminates the sample and the angle from which the light is collected by the detector. This parameter particularly affects measurement results for a sample with a textured, metallic, or pearlescent finish.
Common geometries include 45°/0° (45° illumination and 0° viewing), 0°/45° (0° illumination and 45° viewing), and diffuse/8°. Each geometry is appropriate for a given sample type and application.
Sample Preparation
Accurate and repeatable measurements rely on the precision of the sample prepared. This includes the surface cleanliness, surface finish, sample volume, sample and layer thickness uniformity (in the case of transmission measurements), and alignment to the measurement aperture.
Sample Presentation
Sample presentation is how the sample is held and positioned during the measurements. Standard sample presentation improves precision and repeatability, enhancing the reliability of the results.
Relevant factors include the sample backing (for the translucent materials), sample flatness, standard force, and alignment with the measuring device’s optics axis.
Color Difference Formula
Mathematical computations on color difference quantify the variation by computing the difference between two measurements. Different formulas put different weights on different factors of color difference.
The most widely adopted formula until its limitations were realized was ΔEab (CIE 1976). Further development led to ΔE94 and ΔE00 (CIE 2000) – both which provide improved correlation with visual assessment, and ΔECMC which is better for textiles than its predecessors.