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PREPARED BY;
FERNANDO D.A.M.R
JAYASINGHE J.A.L
UDUGAMPOLA S.A.B
UNDERSTANDING COLOR
COLOR MEASURING
WITH
SPECTROPHOTOMETER
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CONTENTS
1.
Understanding Color and Color Communication ................................................................................ 1
1.1
What is Color? ............................................................................................................................... 1
1.1.1
Light ....................................................................................................................................... 1
1.1.2 Object ..................................................................................................................................... 2
1.1.3 Spectral Data .......................................................................................................................... 2
1.1.4
Viewer .................................................................................................................................... 3
1.2
RGB and CMYK ........................................................................................................................... 3
1.3
Three Dimensions of Color ........................................................................................................... 4
1.3.1
Hue ......................................................................................................................................... 4
1.3.2 Chroma ................................................................................................................................... 4
1.3.3
Lightness (Value) ................................................................................................................... 5
1.3.4
Tints, Tones and Shades ......................................................................................................... 6
1.4
The Munsell Scale ......................................................................................................................... 6
1.5 Tristimulus Data ............................................................................................................................ 7
1.6 CIE standard observer ................................................................................................................... 8
1.6.1
Color Matching Functions ..................................................................................................... 8
1.7
The CIE Color Systems ................................................................................................................. 9
1.7.1 CIE XYZ and the Standard Observer..................................................................................... 9
1.7.2 CIE L*a*b* .......................................................................................................................... 11
1.7.3
CIE L*C*H ......................................................................................................................... 12
2. Various Light Sources in Visual Color Matching Applications ........................................................ 13
2.1
Light Source Descriptions ........................................................................................................... 14
3.
Color Sensing Methods ...................................................................................................................... 17
3.1 Ways to Measure Color ............................................................................................................... 18
3.1.1 Spherical ............................................................................................................................... 18
3.1.2
0/45 (or 45/0) ....................................................................................................................... 19
3.1.3
Multi-Angle .......................................................................................................................... 19
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3.1.4 Colorimeter .......................................................................................................................... 20
3.2 Differences between Tristimulus Method and Spectrophotometric Method .............................. 20
3.3 Metameric Colors ........................................................................................................................ 23
3.4
Metamerism ................................................................................................................................. 24
4.
Color Tolerancing .............................................................................................................................. 26
4.1 Color difference calculation ........................................................................................................ 26
4.2 CIELAB Tolerance ...................................................................................................................... 27
4.3
CIELCH Tolerance ...................................................................................................................... 28
4.4
CMC Tolerance ........................................................................................................................... 29
5.
Dye recipe creation using Spectrophotometer ................................................................................... 31
5.1
Specifying basic data ................................................................................................................... 31
5.2 Specifying Colorant Sets ............................................................................................................. 33
5.3 Specifying Combined Processes ................................................................................................. 33
5.4
Recipe Calculation (Matching).................................................................................................... 34
5.5
SmartMatch ................................................................................................................................. 35
6.
References .......................................................................................................................................... 36
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1. Understanding Color and Color Communication
1.1 What is Color?
Color results from an interaction between light, object, and the viewer.
It is lightthat has been modified by an object in such a manner that the viewersuch as thehuman visual systemperceives the modified light as a distinct color.
All three elements must be present for color as we know itto exist.
1.1.1 Light
Light is the visible part of the electromagnetic spectrum.
Light is often described as a waveform.
Wavelengths are measured in nanometers (nm). A nanometer is one-billionth of a meter.
The region of the electromagnetic spectrum visible to the human eye ranges from about 400
to 700 nanometers.
Figure 1- Visible Electromagnetic Spectrum When our visual system detects a wavelength around 700nm, we see red; when a
wavelength around 450-500nm is detected, we see blues; a 400nm wavelength gives us
violet; and so on. These responses are the basis for the billions of different colors that our
vision system detects every day.
However, we rarely see allwavelengths at once (pure white light), or just onewavelength at
once. Our world of color is more complex than that. When we see color, we are seeing light
that has been modified into a newcomposition of many wavelengths. We see a world full of
colorful objects becauseeach object sends to our eyes a unique composition of wavelengths.
E.g.:- when we see a red object, we are detecting light that contains mostly redwavelengths.
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1.1.2 Object
When light waves strike an object, the objects surface absorbssome of the spectrums
energy, while other parts of the spectrum are reflectedback from the object. The modified
light that is reflected from the object has an entirely new composition of wavelengths.
Different surfaces containing various pigments, dyes, and inks generate different, unique
wavelength compositions.
Light can be modified by striking a reflectiveobject such as paper; or by passing through a
transmissiveobject such as film or a transparency. The light sources themselves - emissive
objects such as artificial lighting or a computer monitor - also have their own unique
wavelength composition
Reflected, transmitted, or emitted light is the color of the object
1.1.3 Spectral Data
There are as many different colors as there are different object surfaceseach object affectslight in its own unique way. The pattern of wavelengths that leaves an object is the objects
spectral data, which is often called the colorsfingerprint.
Spectral data results from a close examinationor measurementof each wavelength.
This examination determines the percentage of the wavelength that is reflected back to
the viewerits reflectance intensity
Spectral data can be plotted as a spectral curve, providing a visual representation of a
colorsfingerprint. Lights wavelengths and reflectance intensity provide two absolute
points of reference for plotting a curve: the 300 nanometers of different wavelengths
comprise the horizontal axis, and the level of reflectance intensity comprises the vertical
axis.
To compute spectral data, spectrophotometers examine a number of points along the
wavelength axis, then determine the amount of reflectance intensity at each wavelength.
Figure 2 - Spectral Curve from a Measured Sample
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1.1.4 Viewer
For our visual palette of colors to exist, all three elements of colorlight, object, and viewer
must be present. Without lightthere would be no wavelengths; without objectsthere would be
only white, unmodified light; and without the viewerthere would be no sensory response that
would recognize or register the wavelengths as a unique color.
The basis for human vision is the network of light sensors in our eyes. These sensors respond todifferent wavelengths by sending unique patterns of electrical signals to the brain. In the brain,
these signals are processed into the sensation of sightof light and of color. As our memory
system recognizes distinct colors, we then associate a name with the color.
It breaks the visible spectrum down into its most dominant regions of red,green, and blue, then
concentrates on these colors to calculate color information.
Figure 3- Most Dominant Regions of the Spectrum Perceived by Human Eye
1.2 RGB and CMYK
By mixing these dominant colors (Red, Green & Blue - RGB)called the additive primaries
in different combinations at varying levels of intensity, the full range of colors in nature can be
very closely simulated. If the reflected light contains a mix of pure red, green, and blue light, the
eye perceives white; if no light is present, black is perceived. Combining two pure additive
primaries produces a subtractive primary. The subtractive primaries of cyan, magenta, and
yellow are the opposing colors to red, green, and blue.
Figure 4 - Primary Additive and Subtractive Colors
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1.3 Three Dimensions of Color
Each color can also be described by its own distinct appearance, based on three elements: Hue,
Chroma and Value (Lightness). By describing a color using these three attributes, you can
accurately identify a particular color and distinguish it from any other.
1.3.1 Hue
When asked to identify the color of an object, the first element that is considered is its hue. Hue
is how the color of an objectred, orange, green, blue, etc. is perceived. The color wheel in
the figure below shows the continuum of color from one hue to the next. As the wheel illustrates,
if you were to mix blue and green paints, you would get blue-green. Add yellow to green for
yellow-green, and so on.
Figure 5 - Color Wheel Showing the Continuum of color from one Hue to the Next
1.3.2 Chroma
Chroma describes the vividness or dullness of a colorin other words, how close the color is
to either grey or the pure hue. Figure below shows how Chroma changes as we move from center
to the perimeter. Colors in the center are grey (dull) and become more saturated (vivid) as they
move toward the perimeter. Chroma also is known as saturation.
Figure 6 - Color Wheel Showing the Change of Chroma from Centre to Perimeter
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1.3.3 Lightness (Value)
The luminous intensity of a colori.e., its degree of lightnessis called its Lightness (value).
Colors can be classified as light or dark when comparing their value. In the following figure, the
value, or lightness, characteristic is represented on the vertical axis.
Figure 7 - Vertical Axis showing the Variation of Lightness
Figure 8 - Spectral Curve Variation for Hue, Chroma & Lightness Variation
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1.3.4 Tints, Tones and Shades
These terms are often used inappropriately but they describe fairly simple colorconcepts. The
important thing to remember is how the color varies from its original hue. If white is added to a
color, the lighter version is called a "tint". If the color is made darker by adding black, the
result is called a "shade". And if gray is added, each gradation gives you a different "tone."
Tints (addingWHITE to a pure hue)
Shades (addingBLACK to a pure
hue)
Tones (addingGRAY to a pure hue)
1.4 The Munsell Scale
In 1905, artist Albert H. Munsell originated a color ordering systemor color scalewhich
is still used today. The Munsell System of Color Notation is significant from a historical
perspective because its based on human perception. Moreover, it was devised beforeinstrumentation was available for measuring and specifying color. The Munsell System assigns
numerical values to the three properties of color: hue, value and Chroma. Adjacent color
samples represent equal intervals of visual perception. The following figure depicts the
Munsell Color Tree.
Figure 9 - Muncell Color Tree
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1.5 Tristimulus Data
A color space can be used to describe the range of visible or reproducible colorsorgamut
of a viewer or device.
This three-dimensional format is also a very convenient way to compare the relationship
between two or more colors.
Three-dimensional color models and three valued systems such as RGB, CMY, and HSL
are known as tristimulus data.
Locating a specific color in a tristimulus color space such as RGB or HSL is similar to
navigating around a city using a map. For example, on the HSL color space map, you first
locate the intersection where the Hue anglemeets the Saturation distance. Then, the Lightness
value tells you what floor the color is located on: from deep below ground (black) to street
level (neutral) to a high-rise suite (white).
Figure 10 - Locating a Specific Color in a Tristimulus Color Space
In many applications, the intuitiveness of tristimulus color descriptions makes them a
convenient measurement alternative to complex (yet more complete and precise) spectral
data. For example, instruments called colorimetersmeasure color by imitating the eye to
calculate amounts of red, green, and blue light. These RGB values are converted into a
more intuitive three-dimensional system where relationships between several color
measurements can be easily compared.
However, any system of measurement requires a repeatableset of standard scales. For
colorimetric measurement, the RGB color model cannot be used as a standard because it
is not repeatablethere are as many different RGB color spaces as there are human
viewers, monitors, scanners, and so on. These models are device dependent.
For a set of standard colorimetric measurement scales, it is possible to use the renownedwork of the CIEthe Commission Internationale dEclairage.
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1.6 CIE standard observer
Due to the distribution of cones in the eye, the tristimulus values depend on the observer's field
of view.
To eliminate this variable, the CIE defined a color-mapping function called the standard
(colorimetric) observer, to represent an average human's chromatic response within a 2 arc
inside the fovea, a part of the eye, located in the center of the retina. This angle was chosen
owing to the belief that the color-sensitive cones resided within a 2 arc of the fovea. Thus the
CIE 1931 Standard Observer function is also known as the CIE 1931 2 Standard Observer.
A more modern alternative is the CIE 1964 10 Standard Observer.
1.6.1 Color Matching Functions
Figure 11 - Color Matching Functions The CIE's color matching functions , and are the numerical description of
the chromatic response of the observer. They can be thought of as the spectral sensitivity
curves of three linear light detectors yielding the CIE tristimulus values X, Y and Z.
Collectively, these three functions are known as the CIE standard observer.
The tristimulus values for a color with a spectral power distribution are given in terms ofthe standard observer by:
where is the wavelength of the equivalent monochromatic light (measured in
nanometers)
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1.7 The CIE Color Systems
In 1931 the CIE standardized color order systems by specifying the light source (or
illuminants), the observer and the methodology used to derive values for describing color.
The CIEestablished standards for a series of color spaces that represent the visible spectrum.
Using these systems, we can compare the varying color spaces of different viewers and devices
against repeatablestandards.
The CIE color systems are similar to the other three-value models discussed earlier in that they
utilize three coordinates to locate a color in a color space. However, the CIE spaceswhich
include CIE XYZ, CIE L*a*b*, and CIE L*u*v*are device-independent, meaning the range of
colors that can be found in these color spaces is not limited to the rendering capabilities of a
particular device, or the visual skills of a specific observer.
1.7.1 CIE XYZ and the Standard Observer
The basic CIE color space is CIE XYZ. It is based on the visual capabilities of a Standard Observer, a hypothetical viewer derived from
the CIEs extensive research of human vision.
The CIE conducted color-matching experiments on a number of subjects, then used the
collective results to create color-matching functions and a universal color space that
represents the average humans range of visible colors.
The color matching functions are the values of each light primaryred, green, and bluethat
must be present in order for the average human visual system to perceive all the colors of the
visible spectrum. The coordinatesX, Y, andZwere assigned to the three primaries.
Figure 12 - Color Matching Functions
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Figure 13 - xy Chromaticity Diagram
The tristimulus values XYZ are useful for defining a color, but the results are not easily
visualized. Because of this, the CIE also defined a color space for graphing color in two
dimensions independent of lightness; this is the Yxy color space, in which Y is the lightness
(and is identical to tristimulus value Y) and x and y are the chromaticity coordinates calculated
from the tristimulus values XYZ.
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1.7.2 CIE L*a*b*
The CIE Lab color space (also referred to as CIELAB) is presently one of the mostpopular color space for measuring object color and is widely used in virtually all fields.
It is one of the uniform color spaces defined by CIE in 1976 in order to reduce one of the
major problems of the original Yxy color space: that equal distances on the x, y chromaticity
diagram did not correspond to equal perceived color differences. In this color space, Lindicates lightness and aand bare the chromaticity coordinates.
The following figure shows the a, bchromaticity diagram. In this diagram, the aand bindicate color directions: +ais the red direction, -ais the green direction, +bis the yellowdirection, and -bis the blue direction. The center is achromatic; as the aand bvaluesincrease and the point moves out from the center, the saturation of the color increases. L axis
(Lightness) is perpendicular to the a*b plane and runs through the center of the plane.
Figure 14 - CIE L*A*B* Space
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1.7.3 CIE L*C*H
The L*a*b* color model uses rectangular coordinates based on the perpendicular yellow-blue
and green-red axes.
The CIE L*C*Hcolor model uses the same XYZ derived color space as L*a*b*, but instead
uses cylindrical coordinates ofLightness, Chroma, andHueangle. These dimensions are
similar to the Hue, Saturation (Chroma), and Lightness.
Both L*a*b* and L*C*H attributes can be derived from a measured colorsspectral data
via direct conversion from XYZ values, or directly from colorimetric XYZ values.
Figure 15 - CIE L*C*H Color Space
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2.Various Light Sources in Visual Color Matching Applications
Choosing the light sources will be an integral part of establishing a color matching procedure. Typically,
the evaluation needs to be performed with a predominantly blue source, a reddish yellow source, and a
greenish source. This allows for the efficient visual detection of metamerism.
Basic Definitions for Illuminants
CIE Rating- Based on CIE Publication 51, it is a very strict rating of a light sources ability to reproduce
daylight, in both the visible and ultraviolet spectrums. The first letter provides the rating for the visible
spectrum and the second letter the rating for the UV spectrum. An AA rating is the highest and EE
the lowest. A rating of BC or better isacceptable for color matching applications.
Color Rendering Index (CRI)- A rating of a light sources ability to reproduce a daylight source. Based
on a scale of 0 to 100, a rating of 92 or higher is required for critical color evaluation applications.
Color Temperature (Correlated Color Temperature) - The color temperature of a light source isthe temperatureof an idealblack-body radiatorthat radiates light of comparable hueto that of the light
source.It is based on the Kelvin scale in which 0 degrees is at Absolute Zero (-273 C) where all motion
in a molecule is deemed to stop. The lower the color temperature of the light source, the redder the
source will be. Inversely, the higher the color temperature of the source, the bluer it will be.
Some common color temperatures, common names associated with them and their associated colors are:
Color Temperature Common Names Associated Colors
7500K (D75) North Sky Daylight Moderate to Deep Blue
6500K (D65) Average Daylight Moderate Blue
5000K (D50) Equal Energy Daylight White
4100K Various fluorescent sources Greenish
3000K Various fluorescent sources Orangish
2000K Tungsten A Red/Yellow
2865K Illuminant A Yellowish Red
2300K Horizon Reddish
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2.1 Light Source Descriptions
Standard Illuminants
Standard Illuminant D65: Average daylight (including ultraviolet wavelength region) with acorrelated color temperature of 6504K; should be used for measuring specimens which will be
illuminated by daylight including ultraviolet radiation.Standard Illuminant C: Average daylight (not including ultraviolet wavelength region) with acorrelated color temperature of 6774K; should be used for measuring specimens which will be
illuminated by daylight in the visible wavelength range but not including ultraviolet radiation.
Standard Illuminant A: Incandescent light with a correlated color temperature of 2856K; should beused for measuring specimens which will be illuminated by incandescent lamps.
Fluorescent Illuminants (recommended by CIE for measurements)
F2: Cool white
F7: Daylight
F11: Three narrow band cool white
Figure 16 Spectral Distribution of CIE Illuminants
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Figure 17 CIE xy 1931 chromaticity diagram including the Planckian Locus1
Following is an example of what happens if a specimen (apple) is measured using a spectrophotometer
under Standard Illuminant D65 (example 1) and Standard Illuminant A (example 2). In example 1, is
the graph of the spectral power distribution of Standard Illuminant D65 andis a graph of the spectral
reflectance of the apple. is the spectral power distribution of the light reflected from the specimen
(apple) and is the product of and . In example 2, is the spectral power distribution of Standard
Illuminant A and is the spectral reflectance of the specimen (apple), which is the same as in example
1.is the spectral power distribution of the light reflected from the specimen (apple) and is the product
of , and . If we compare and , we notice that the light in the red region is much stronger in
, meaning that the apple would appear much redder under Standard Illuminant A. This shows that the
color of a subject changes according to the light under which it is viewed. A spectrophotometer actually
measures the spectral reflectance of the specimen; the instrument can then calculate numerical color
values in various color spaces using the spectral power distribution data for the selected illuminant and
data for the color-matching functions of the Standard Observer.
1The Planckian locus is the path that a black bodycolor will take through the diagram as the black body temperature changes.Lines crossing the locus indicate lines of constant correlated color temperature. Monochromatic wavelengths are shown inblue in units of nanometers. Latest version (16 April 2005) uses 1931 CIE standard observer, since this is the most commonlyused standard observer.
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3.
Color Sensing Methods
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3.1 Ways to Measure Color
Today, the most commonly used instruments for measuring color are spectrophotometers. Spectro
technology measures reflected or transmitted light at many points on the visual spectrum, which results
in a curve. Since the curve of each color is as unique as a signature or fingerprint, the curve is an excellent
tool for identifying, specifying and matching color. The following information can help to understand
which type of instrument is the best choice for specific applications.
3.1.1 Spherical
Spherically based instruments have played a major role in formulation systems for nearly 50 years.
Most are capable of including the specular component (gloss) while measuring. By opening a small
trap door in the sphere, the specular component is excluded from the measurement. In most cases,
databases for color formulation are more accurate when this component is a part of the measurement.
Spheres are also the instrument of choice when the sample is textured, rough, or irregular or approaches
the brilliance of a first surface mirror. Textile manufacturers, makers of roofing tiles or acoustic ceiling
materials would all likely select spheres as the right tool for the job.
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3.1.2 0/45 (or 45/0)
No instrument sees color more like the human eye than the 0/45. This simply is because a viewer does
everything in his or her power to exclude the specular component (gloss) when judging color. When
we look at pictures in a glossy magazine, we arrange ourselves so that the gloss does not reflect back to
the eye. A 0/45 instrument, more effectively than any other, will remove gloss from the measurement
and measure the appearance of the sample exactly as the human eye would see it.
3.1.3 Multi-Angle
In the past 10 or so years, car makers have experimented with special effect colors. They use special
additives such as mica, pearlescent materials, ground up seashells, microscopically coated colored
pigments and interference pigments to produce different colors at different angles of view.
Large and expensive goniometers were traditionally used to measure these colors until recent past.
Companies have now introduced a battery-powered, hand-held, multi-angle instrument.
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3.1.4 Colorimeter
Colorimeters are not spectrophotometers. Colorimeters are tristimulus (three-filtered) devices that make
use of red, green, and blue filters that emulate the response of the human eye to light and color. In some
quality control applications, these tools represent the lowest cost answer. Colorimeters cannot
compensate for metamerism (a shift in the appearance of a sample due to the light used to illuminate the
surface). As colorimeters use a single type of light (such as incandescent or pulsed xenon) and becausethey do not record the spectral reflectance of the media, they cannot predict this shift.
Spectrophotometers can compensate for this shift, making spectrophotometers a superior choice for
accurate, repeatable color measurement.
3.2 Differences between Tristimulus Method and Spectrophotometric Method
As shown inFigure 19(b), the tristimulus method measures the light reflected from the object using
three sensors filtered to have the same sensitivity (), ), and () as the human eye and thus directlymeasures the tristimulus values X, Y, and Z. On the other hand, the spectrophotometric method showninFigure 19(c) utilizes multiple sensors (40 in the CM-2600d) to measure the spectral reflectance of the
object at each wavelength or in each narrow wavelength range. The instruments microcomputer then
calculates the tristimulus values from the spectral reflectance data by performing integration. For the
apple used in the example, the tristimulus values are X=21.21, Y=13.37, and Z=9.32; these tristimulus
values can then be used to calculate values in other color spaces such as Yxy or Lab.Figure 19showshow the tristimulus values X, Y, and Z are determined. Light with spectral distribution reflected by
the specimen is incident on sensors with spectral sensitivity , whose filters divide the light into
wavelength regions corresponding to the three primary colors and the sensors output the tristimulusvalues (X, Y, and Z) . Thus, =x. The results in the three wavelength regions of are also
shown: -1: x(), -2: y(), and -3: z(). The tristimulus values are equal to the integrations of the
shaded area in the three graphs.
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Figure 18 - Determination of the tristimulus values in color measurements
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Figure 19 - The human eye and instrument
(a)
(b)
(c)
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3.3 Metameric Colors
Figure 20 Color SpectrumEach of this spectral colors represent a single pure wavelength. Each of these colors can be matched
using combinations of red, green and blue light. Consider the yellow light which has a wave length
about 580 nm (Figure 23). One can make the same yellow light using green and red light combination.
As for an example yellow color can be matched using equal amount of red and green light (Figure 24).
Yet it is impossible for an eye to detect the difference
between the pure spectral yellow and the yellow
produced by red and green light combination. But a
spectrophotometer will be able to identify this
difference. If pure spectral yellow direct through a
prism it color would remain the same. But if the
combination shine through a prism it would separate to
its component colors (Figure 25). Yet our brains sees
each of this colors as the same yellow. Colors looks the same but have different spectral compositions
are called metameric colors.
(a) (b)
Figure 22 Color matching functions
Figure 21 Color spectrum
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3.4 Metamerism
A phenomenon, in which two colors appear the same under one light source but different under another,
is called metamerism. For metameric objects, the spectral reflectance characteristics of the colors of the
two objects are different, but the resulting tristimulus values are the same under one light source and
different from each other under another. This problem is often due to the use of different pigments ormaterials. Consider theFigure 25,if we look at the spectral reflectance curves for the two specimens,
we can immediately see that they are different. However, the Labvalues for measurements underStandard Illuminant D65 are the same for both specimens, but the values for measurements under
Standard Illuminant A are different from each other. This shows that even though the two specimens
have different spectral reflectance characteristics, they would appear to be the same color under daylight
(Standard Illuminant D65).
To evaluate metamerism, it is necessary to measure the specimens under two or more illuminants with
very different spectral power distributions, such as Standard illuminant D65 and Standard Illuminant A.
Although both tristimulus colorimeters and spectrophotometers use a single light source, they cancalculate measurement results based on illuminant data in memory to provide data for measurements
under various illuminants. Tristimulus colorimeters can generally take measurements under only
Standard Illuminant C and Standard Illuminant D65, both of which represent daylight and which have
very similar spectral power distributions; because of this, tristimulus colorimeters cannot be used to
measure metamerism. The spectrophotometer, on the other hand, is equipped with the spectral power
distributions of a wide range of illuminants and thus can determine metamerism. Moreover, with the
spectrophotometers capability to display spectral reflectance graphs, one can see exactly how the
spectral reflectance of the two colors are different (Figure 25).
Figure 23 Pure spectral yellow vs. combined yellow shines through a prism
Figure 24 Metamerism
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Figure 25 Spectral reflectance graph under different illuminants explains metamerism
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4.Color Tolerancing
4.1 Color difference calculation
Color difference may calculated as a numerical value using L*, a* and b* values. Mainly when
a representation of the difference of two colors is required the total difference calculation can be used.
Assume two colors have their own L*, a*, b* values. By subtracting the corresponding
parameters of two colors we get L*, a*, b*, values where denotes the difference. Then the
total difference (E*ab) can be calculated as follows.
= [ + () + ]
An example can be given as follows.
L* = +11.10, a* = 6.10, b* = 5.25
= [ +11.1
+6.1
+ 5.25
]
= 13.71
Furthermore tolerancing is required in color matching because of the mismatches between numerical
color data and the actual human sense of color. Each person accepts or rejects color matches based on
their own color perception skills. In any industry this can lead to confusion and frustration between
customers, suppliers, vendors, production, and management. Therefore it is required to introduce a
standard way of tolerancing to avoid such confusions.
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4.2 CIELAB Tolerance
CIELAB method uses L* (lightness), a*(red/green value) and b* (yellow/blue value) to represent a color
mathematically. Therefore a tolerance limit must be defined by giving acceptable differences to the
above parameters.
As seen on the above picture a rectangular tolerance box is drawn in such a way that the standard color
is at the middle of the cube. But, this cube conflicts with the nature of the human eye. The eye does not
detect differences in hue (red, yellow, green, blue, etc.), Chroma (saturation) or lightness equally. In
fact, the average observer will see hue differences first, Chroma differences second and lightness
differences last. Therefore visually acceptable color space is actually an ellipsoid.
Figure 27 - Visuallyl Acceptable Ellipsoidal Color Space
Figure 26- Rectangular Tolerance Box
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Therefore the tolerance box has some drawbacks when it is used to represent the color acceptability. As
a solution , the box can me made small enough to be inserted in the ellipsoid or the ellipsoid can be
fitted in the box, but still, some problems arise. When the box is larger, box-shaped tolerance around
the ellipsoid can give good numbers for unacceptable color. If the tolerance box is made small enough
to fit within the ellipsoid, it is possible to get bad numbers for visually acceptable color.
Figure 28 - Ellipsoid and the Tolerance Box
4.3 CIELCH Tolerance
CIELCH users must choose a difference limit for L* (lightness), C* (Chroma) and H* (hue).This
creates a wedge-shaped box around the standard. Although in the previous method it was a cube, the
Hue angle concept makes this particular tolerance space, a wedge shaped one. When this tolerance is
compared with the ellipsoid, we can see that it more closely matches human perception. This reduces
the amount of disagreement between the observer and the instrumental values.
Figure 29 - CIELCH tolerance method
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4.4 CMC Tolerance
This is not based on a color space but specially developed for tolerancing. The basic idea is to improve
the wedge shaped tolerance space achieved by CIELCH Tolerance into a more visually acceptable one.
This method uses the actual color ellipsoids to represent the tolerance limit by their volumes. The CMC
calculation mathematically defines an ellipsoid around the standard color with semi-axis corresponding
to hue, Chroma and lightness. It automatically varies in size and shape depending on the position of the
color in color space. The ellipsoids in the orange area of color space are longer and narrower than the
broader and rounder ones in the green area.
Figure 30 - CMC tolerancing using ellipsoids
The size and shape of the ellipsoids also change as the color varies.
Figure 31 -Tolerance ellipsoids in color space
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By varying the commercial factor (Total error - ), the ellipsoid can be made as large or small as
necessary to match visual assessment. The CF value is the tolerance, which means that if cf=1.0, then
E CMC less than 1.0 would pass, but more than 1.0 would fail.
Figure 32 - Commercial factor (cf) of tolerances
Since the eye will generally accept larger differences in lightness (l) than in Chroma (c), a default ratio
for (L: C) is 2:1. A 2:1 ratio will allow twice as much difference in lightness as in Chroma. This achieved
by assigning the ratios of the ellipsoid according to those values. (2:1)
Figure 33 - CMC tolerance ellipsoid
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5.Dye recipe creation using Spectrophotometer
5.1 Specifying basic data
Basic data together with the colorant set definitions are prerequisites for the recipe calculation. The
basic data is managed using property sheets. Basic data include:
a) Quality/Style: Data related to the substrates.
b) Product: Data related to the dyestuff and auxiliary.
c) Customer: Data related to the customer.
d) Color Type: Measured dye sample
e) Parameters: Definition of parameters with value ranges for the dyestuff properties.
a) Quality/Style is a summary of all data in relation to the substrate and contains:
Fiber Definition of all single fibers to be dyed
Fiber group Definition of all fibers used for a quality/style. A fiber group
can be a single fiber or a combination of different fibers, e.g.,
PES, PES/CO
Affinity (quality/style subgroup) Definition of a link to a fiber group and the part of each fiber
in %, e.g., PES = 60%, CO = 40%. Can be used for the
relationship to the colorant set.
Customer A customer can be assigned to each quality/style.
Substrate - blank dyeing Reflectance measurement of the substrate and quality/style
effect factor.
Special composition. All related colorant sets are assigned per default. The list can
be displayed using the Search Colorant Set button. In the
list, colorant sets can be selected and excluded using the
Excludebutton
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b) A Product is either a dyestuff or an auxiliary
Product supplier Supplier-specific data, e.g., name, address, phone number.
Supplier dye name Dye name of the supplier, e.g., Remazol, Terasil, etc.
Stock solution Definition of different dilutions used for optimizing the accuracy of
manual dyestuff pipetting and to prevent that the maximum of thedye solution is to be exceeded.
Dyestuff type Type of the delivered dyestuff, e.g., conc, gran., supra.
Dye class Classification of dyes according to the chemical composition and
reaction, e.g., disperse, reactive.
Dye description Additional description of the dye, e.g., brilliant, dark.
Dyestuff color Color names, e.g., red, green, blue.Formula setting Settings for recipe calculation used for production: e.g. default unit.
c) The customer data contains name, identification, tolerance details, and status.
d) Measured color pattern. A color type is substrate-independent. A color type is a standard
and can be linked to a recipe.
e) The parameter values (e.g. fastness) are defined in a colorant set for each dye, and used
to set limits for the recipe calculation.
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5.2 Specifying Colorant Sets
A colorant set is a set of color information about the substrate and dyes the system uses to produce
match and correction recipes. It contains,
Information about the overall colorant set, e.g., the substrate and process that will be used with
the dyes. Product information about each dye, e.g., strength, minimum and maximum concentrate.
Color information about each dye.
5.3 Specifying Combined Processes
The user has to define combined processes and operations
Combined process
A combined process is used to describe the entire dyeing process either for laboratory or production. A
treatment is generated for each calibration dye process type (e.g., Exhaust, Continuous,) linked to the
combined process.
Treatment
A treatment consists of one or more operations describing the dyeing process for laboratory and/or
production.
Operation
The operation specifies the sequence of actions to be done during the dyeing. Actions may be parameters(e.g. temperature, volume,) or products (e.g. chemicals, etc.).
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5.4 Recipe Calculation (Matching)
Selection:
Quality/style (data of the substrate)
Combined process
Substrate delivery (only for deliveries with data different to the blank dyeing substrate) Dyed substrate (over-dyeing only)
Dyestuff group with dyes pre-selected from the assigned colorant set. The dyestuff group is used
to optimize the recipe calculation.
Selection criteria:
Dyes from the list
Parameter values, e.g., fastness information
Concentration values, e.g., min., max., conc.
Settings (parameters for calculation control)
Standard: Color to be matched.
Match: The recipes are calculated according to the selections and the results are displayed.
Review: The recipes can be reviewed according to the different criteria (various color difference
values, coordinates, price, etc.).
Further use: The recipes can be saved, printed and/or sent to a dispenser.
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5.5 SmartMatch
The SmartMatch facility is used to improve first-time matching and correction. Standard color
prediction uses the Kubelka-Munk theory, which assumes that dyes behave in the same way when used
together or stand-alone. However, this is not the case: dyes interact with one another. The SmartMatch
facility overcomes this problem by taking into account the performance of previous predictions, e.g.,
learning by experience. SmartMatch stores information about the concentrations used to dye a sample
and the results of dyeing, and uses this data to correct the first attempt made by Kubelka-Munk
calculations in future matching.
It stores information about previous predictions as SmartMatch points. Once you set your system to
SmartMatch, it runs automatically. However, you can also examine the SmartMatch points the system
is using and alter them to refine Smart-Match performance. For example, if you suspect that one of the
SmartMatch points being used is based on a bad dyeing, you can remove this point. This way, it is no
more used in the calculations.
The number of similar points is reduced by grouping them. In addition to the automatic SmartMatch
housekeeping a powerful graphical tool supports to check the SmartMatch population for SmartMatch
points to be deleted or grouped. All recipes calculated using the Match option will use SmartMatch
when SmartMatch is turned on and if relevant populations are available. The number of SmartMatch
points used in a recipe calculation are shown at the bottom of the dye concentration column in the recipe
table.
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6.References
http://www.xrite.com/top_support.aspx?action=downloads
http://www.konicaminolta.com/instruments/download/index.html
http://industrial.datacolor.com/portfolio-view/datacolor-650/
https://www.youtube.com/watch?v=iDsrzKDB_tA
http://www.xrite.com/top_support.aspx?action=downloadshttp://www.xrite.com/top_support.aspx?action=downloadshttp://www.konicaminolta.com/instruments/download/index.htmlhttp://www.konicaminolta.com/instruments/download/index.htmlhttp://industrial.datacolor.com/portfolio-view/datacolor-650/http://industrial.datacolor.com/portfolio-view/datacolor-650/https://www.youtube.com/watch?v=iDsrzKDB_tAhttps://www.youtube.com/watch?v=iDsrzKDB_tAhttps://www.youtube.com/watch?v=iDsrzKDB_tAhttp://industrial.datacolor.com/portfolio-view/datacolor-650/http://www.konicaminolta.com/instruments/download/index.htmlhttp://www.xrite.com/top_support.aspx?action=downloads