Article by Ken Butts

Accurate digital color communication starts with accurate color measurement.

Even the most advanced spectrophotometer cannot compensate for inconsistent measurement techniques. If samples are measured differently across operators, laboratories or suppliers, the resulting color data becomes less reliable and more difficult to use for quality control, color approval, formulation and supplier alignment.

Before any standard, production sample or color specification is stored in a database or shared throughout a supply chain, organizations should establish a repeatable sample measurement process and ensure it is followed consistently.

A standardized measurement technique improves:

• Measurement repeatability
• Measurement reproducibility
• Color matching accuracy
• Supplier communication
• Color approval efficiency
• Confidence in digital color data

Why Repeatable Sample Measurement Matters

Digital color management depends on reliable data.

If a sample produces different results every time it is measured, that variation becomes embedded in the color workflow and can affect:

• Color standards
• Color tolerances
• Pass/fail decisions
• Supplier evaluations
• Color matching predictions

A repeatable measurement process helps ensure that differences in color data reflect actual color differences, not measurement errors.

Learn more in keys to reliable digital color communication and digital color communication and color measuring instruments.

Use the Largest Aperture Possible

As a general rule, samples should be measured using the largest viewing area available on the spectrophotometer whenever the sample size allows.

Larger apertures capture more of the sample surface and reduce the influence of:

• Unlevel dyeings
• Surface variation
• Fabric texture
• Localized color differences

Why Larger Apertures Improve Accuracy

Benefits of larger apertures include:

• Improved repeatability
• Lower measurement variability
• Better representation of the sample
• Greater confidence in stored color data

Smaller apertures should only be used when sample size limitations make larger apertures impractical.

When measuring with small apertures, additional measurements are often required to achieve acceptable repeatability.

For guidance on instrument selection, see what are the different types of color measurement instruments? and using a spectrophotometer for color measurement.

Sample Thickness Matters More Than Many Realize

Before measuring any material, it is important to ensure the sample is sufficiently opaque.

If light passes through the sample, it can reflect from the backing material or sample holder and influence the measurement.

This can produce misleading reflectance data and inaccurate color values.

Recommended Practice

For most woven and knitted textiles:

• Two layers may be sufficient.
• Four layers often provide greater confidence.
• Lightweight materials may require additional evaluation.

Many organizations choose to fold samples to four layers as a standard procedure to eliminate potential opacity issues.

Why Opacity Matters

Insufficient opacity can lead to:

• Inaccurate color measurements
• Poor repeatability
• Inconsistent approvals
• Supplier disagreements

Materials particularly susceptible to opacity effects include:

• Lightweight fabrics
• Translucent textiles
• Mesh constructions
• Open-knit materials

Table 1. Color difference for non-opaque samples.

Related reading: best practices for textile sample measurement and sample conditioning for digital color measurement.

Measuring Lightweight or Translucent Materials

Some materials require so many layers to achieve opacity that the sample no longer sits naturally against the instrument port.

This can create additional measurement errors.

For these materials, a practical alternative is to measure a limited number of layers against a standardized white ceramic backing tile similar to the instrument’s calibration standard.

When both the standard and production sample are measured using the same backing, the influence of the backing material is effectively controlled.

This approach is commonly used for:

• Lightweight textiles
• Sheer materials
• Mesh fabrics
• Translucent products

Sample Positioning Can Significantly Affect Results

Sample positioning is one of the most overlooked sources of measurement variability.

Many operators simply rotate a sample while leaving it in place on the instrument.

Although this method is quick, it does not adequately account for:

• Fabric construction
• Yarn directionality
• Texture variation
• Unlevel dyeings

Recommended Practice

For each measurement:

1. Remove the sample from the instrument.
2. Reposition or refold the sample.
3. Rotate the sample approximately 90 degrees.
4. Measure again.

This process produces a more representative average of the material and improves measurement repeatability.

Avoid Measuring Areas With:

• Fingerprints
• Creases
• Dirt
• Stains
• Dye blotches
• Contamination

Learn more in best practices for measuring color samples and how sample rotation improves color measurement accuracy.

How Many Measurements Should Be Taken?

The correct number of measurements depends on:

• Material construction
• Sample uniformity
• Aperture size
• Required accuracy level

A common mistake is assuming that one or two measurements are sufficient.

Research consistently shows that additional measurements dramatically improve repeatability.

Recommended Evaluation Method

To determine the optimal number of measurements:

1. Measure the sample eight times.
2. Rotate and reposition the sample between each measurement.
3. Save the average as the reference value.
4. Repeat the process using fewer measurements.
5. Compare the resulting color differences.

The objective is to identify the minimum number of measurements required to maintain acceptable repeatability.

Establish a Repeatability Target

An effective measurement procedure should allow a sample to be measured, removed, repositioned and remeasured while maintaining a variation of less than:

0.15 DE CMC (2:1)

When repeatability exceeds this level, confidence in the stored digital color data begins to decline.

Poor repeatability can lead to:

• Inaccurate color matching predictions
• Unreliable standards
• Inconsistent supplier communication
• Reduced confidence in quality control decisions

Why Four Measurements Often Outperform Two

Measurement studies consistently demonstrate the benefits of multiple readings.

Results comparing four-read and two-read measurement techniques showed:

Table 2. Measurement variability for four-read and two-read techniques.

The maximum variability observed using two measurements was significantly higher than with four measurements.

The conclusion is clear: two measurements are often insufficient for reliable digital color communication, even when using a large aperture.

For many textile applications, four measurements provide a practical balance between accuracy and efficiency.

Fabric Construction Influences Measurement Requirements

Not all materials behave the same.

Smooth woven fabrics typically require fewer measurements than highly textured or directional materials.

Materials That Typically Measure Well

• Poplin
• Twill
• Canvas
• Crepe
• Taffeta

Materials That Often Require Additional Measurements

• Corduroy
• Fleece
• Chenille
• Pointelle
• Rib constructions
• Textured knits

These materials introduce additional variability because surface structure affects how light interacts with the sample.

As texture increases, repeatability often decreases.

This makes standardized measurement procedures even more important.

Table 3. Measurement variability for various fabric types.

Measurement Technique Should Be Documented and Shared

A repeatable measurement process should never exist only in the mind of a single operator.

Every color management program should document:

Sample Preparation

• Number of layers
• Conditioning requirements
• Backing materials

Measurement Procedure

• Aperture size
• Instrument settings
• Number of measurements
• Rotation requirements
• Repositioning requirements

Communication Standards

• Internal laboratory procedures
• Supplier instructions
• Approval workflows

Consistency across the supply chain is critical for maintaining reliable digital color communication.

Learn more in how to manage color when working remotely and what you need to know about vendor empowerment.

Small Measurement Errors Create Large Color Management Problems

Failure to establish a repeatable measurement technique introduces uncertainty into every stage of the color workflow.

That uncertainty can affect:

• Color development
• Formulation accuracy
• Color approvals
• Supplier performance
• Quality control
• Production consistency

The investment required to develop a standardized measurement process is small compared with the costs of rework, delays, rejected materials and inconsistent color data.

Organizations that prioritize measurement repeatability create a stronger foundation for digital color communication, supplier alignment and long-term color quality control.

For additional guidance, explore best practices for textile sample measurement, using a spectrophotometer for color measurement and 5 steps for an effective color quality control program.

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