Understanding Color and Color Measuring With Spectrophotometer

7/21/2019 Understanding Color and Color Measuring With Spectrophotometer

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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

Understanding Color and Color Measuring With Spectrophotometer

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