Understanding Colour Measurement in Food Quality Control

Colour is one of the first things we notice about food. Before we taste, smell, or touch a product, our eyes have already made a judgement. A bright red tomato signals ripeness. A dull, brownish piece of meat raises suspicion about freshness. This visual snap-judgement is not random – it is deeply tied to our instincts about food safety and quality. That is exactly why colour measurement has become a core part of food quality control. Food manufacturers, researchers, and quality assurance teams rely on precise colour data to ensure products meet consumer expectations consistently, batch after batch.

Why colour matters so much in food quality 🔗

Colour is not just about looks – it carries real information. In fruit and vegetable processing, colour helps determine the best harvest time and whether a product is ready for the next stage of processing. In the meat industry, colour is used to assess freshness, cooking endpoints, and to detect abnormalities. For dairy products, colour consistency directly affects consumer acceptance; the shade of milk, for instance, can indicate fat content and processing conditions.

Research consistently shows that consumers judge food quality primarily based on surface colour, which significantly shapes their purchasing decisions. A well-pigmented tomato rich in lycopene, for example, is preferred because its deep red colour is associated with freshness and nutritional value. Colour also serves as an indirect indicator of other quality parameters like flavour, pigment content, and chemical composition, often correlating well with these attributes.

Given this enormous influence, food scientists cannot afford to leave colour evaluation to chance. Standardised measurement systems provide the objectivity and repeatability that visual judgement alone cannot.

Visual colour assessment: the starting point 🔗

Before instruments became widespread, colour evaluation in food relied on the human eye. Visual assessment is still used today, but it comes with limitations. Factors like lighting conditions in the facility, the observer’s angle of view, background differences, and even individual variations in colour perception can all affect the result. Two trained panellists looking at the same sample under different lights might describe the colour differently.

To reduce this subjectivity, the food industry developed standardised visual tools. Colour atlases and dictionaries are collections of organised colour chips or swatches against which food samples can be compared. A quality inspector holds a sample next to the closest matching chip and records the corresponding colour code. While this approach is more structured than a freeform description, it still depends on the observer’s eyes and the ambient lighting. That said, colour atlases remain useful in field settings where instruments are impractical – for instance, USDA Munsell colour standards are widely used in agriculture to grade produce and evaluate soil colour.

Three attributes of colour: hue, value, and chroma 🔗

Before diving into specific measurement systems, it helps to understand three fundamental properties that define any colour. Every colour measurement system – whether visual or instrumental – describes colour using some version of these three dimensions.

Hue 🔗

Hue is what we typically think of as “colour” – red, yellow, green, blue, and so on. It is determined by the dominant wavelength of light reflected from an object. When you say a tomato is red or a banana is yellow, you are describing its hue.

Value (lightness) 🔗

Value, also called lightness, describes how light or dark a colour is. A pale pink and a deep maroon may share a similar hue (red family), but they differ greatly in value. In the L*a*b* colour system, value is represented by the L* axis, which runs from 0 (pure black) to 100 (pure white). In the baking industry, for instance, L* values are commonly used to check whether a bread crust has reached the desired level of browning.

Chroma (saturation) 🔗

Chroma, sometimes called saturation or colour intensity, indicates how vivid or dull a colour appears. A bright orange carrot has high chroma, while a greyish, washed-out carrot has low chroma. Chroma is measured as the distance from the neutral grey axis in a colour space – the farther from grey, the more saturated the colour.

The Munsell colour system 🔗

The Munsell colour system, developed by Albert H. Munsell in the early 1900s, was one of the first to organise colours into a systematic, three-dimensional framework based on human visual perception. It describes every colour using three attributes: hue (H), value (V), and chroma (C).

In the Munsell system, hues are arranged in a circular order with five principal hues – Red, Yellow, Green, Blue, and Purple – and five intermediate hues between them, making ten hue families in total. Value runs vertically on a scale from 0 (black) at the bottom to 10 (white) at the top. Chroma extends horizontally outward from a neutral grey centre, with lower numbers indicating dull, muted colours and higher numbers representing vivid, saturated ones.

A specific colour is written in the notation format H V/C. For example, 5R 4/14 describes a colour in the red hue family (5R), at a medium-dark value (4), with high saturation (14). This notation eliminates the ambiguity of descriptive colour names like “cherry red” or “sunset orange.”

The Munsell system is particularly important in agriculture and food science. The United States Department of Agriculture (USDA) adopted it as the official colour reference for soil research, and it continues to be used for grading produce, evaluating colour at various stages of food processing, and maintaining quality standards in packaging.

The CIE colour system 🔗

While the Munsell system relies on visual matching against physical colour chips, the Commission Internationale de l’Éclairage (CIE) developed a more mathematical approach. The CIE system, established in 1931, is the most widely used international standard for colour specification. It forms the foundation for nearly all modern colour measurement in food science.

CIE tristimulus values (X, Y, Z) 🔗

The CIE system begins with the concept of a standard observer and a standard illuminant. Using carefully controlled experiments, the CIE defined how an average human eye responds to different wavelengths of light. From this, they derived three values – X, Y, and Z – known as tristimulus values. These represent the amounts of red, green, and blue primaries needed to match any given colour. However, X, Y, and Z values on their own do not relate intuitively to how we see colour, so further colour spaces were developed.

CIELAB (L*a*b*) colour space 🔗

In 1976, the CIE introduced the CIELAB system (also written as CIE L*a*b*), which translates tristimulus values into a more visually meaningful three-dimensional space. It has three coordinates:

L* represents lightness, ranging from 0 (black) to 100 (white). a* represents the red-green axis – positive values indicate redness, and negative values indicate greenness. b* represents the yellow-blue axis – positive values indicate yellowness, and negative values indicate blueness.

The CIELAB system is based on opponent colour theory, which mirrors how the human brain processes colour. Our visual system interprets colours as contrasts between light and dark, red and green, yellow and blue. A colour cannot be simultaneously red and green, or yellow and blue – the system captures this principle mathematically.

In food applications, CIELAB is used extensively. L* values can indicate the lightness of baked products like bread crusts. The a* value helps measure the degree of redness in meat or tomato products. The b* value reflects yellowness in products like cheese, pasta, or cooking oils. From L*, a*, and b*, additional derived values like ΔE (total colour difference between two samples) can be calculated, making it straightforward to define quality tolerance limits.

The Hunter Lab colour system 🔗

The Hunter L, a, b system, developed by Richard S. Hunter in 1942, predates CIELAB and uses a similar three-coordinate structure. It also measures lightness (L), redness/greenness (a), and yellowness/blueness (b). The key difference lies in the mathematics: Hunter Lab uses the square root of the CIE X, Y, Z tristimulus values, while CIELAB uses the cube root.

This mathematical distinction leads to differences in sensitivity. Research from Cornell University found that the Hunter system is more sensitive to whiteness differences in milk-based beverages, while the CIE system is significantly more sensitive to yellowness differences. For green-red colour variations, both systems perform similarly.

The Hunter Lab system remains widely used in the food industry, especially for applications where whiteness is a critical parameter – such as dairy products, flour, and sugar. Many food testing laboratories continue to offer Hunter colour analysis alongside CIELAB measurements, and manufacturers must specify which system, illuminant, and measurement temperature they are using when setting product specifications.

Instruments used for colour measurement 🔗

Modern food quality control relies on specialised instruments that remove human subjectivity from colour evaluation. The two most common types are colorimeters and spectrophotometers.

Colorimeters 🔗

A colorimeter measures colour by filtering reflected light through red, green, and blue filters that mimic the human eye’s response. It directly outputs values in a chosen colour space such as L*a*b* or Hunter Lab. Colorimeters are relatively affordable, easy to use, and well-suited for routine quality control on the production floor. They are commonly used for measuring the colour of dairy products, sauces, bakery items, chocolate, and fresh produce.

Spectrophotometers 🔗

A spectrophotometer measures the full spectral reflectance or transmittance curve of a sample across the visible light range (380-780 nm). This comprehensive data can then be converted to any colour space. Spectrophotometers are more expensive than colorimeters, but they offer greater precision, the ability to perform colour matching, and the flexibility to work with different illuminants and observer angles. They are particularly valuable for measuring both opaque and transparent food products, including juices, oils, syrups, and processed meats.

Computer vision systems 🔗

In recent years, computer vision systems have gained significant traction in the food industry. These systems use digital cameras coupled with image processing software to capture and analyse colour across the entire surface of a food product – not just a single spot. This makes them especially useful for non-uniform products like cereals, snack foods, and meat cuts, where colour can vary across the surface. Studies have shown that camera-based measurements can be more representative of overall product colour than readings from a single-point colorimeter.

Factors that affect colour measurement accuracy 🔗

Getting reliable colour data is not just about having the right instrument. Several factors can influence results and must be carefully controlled.

Illuminant and observer angle 🔗

The type of light source (illuminant) and the angle at which the observer or instrument views the sample both affect colour readings. Standard illuminants like D65 (daylight) and illuminant C are commonly used. The CIE defines standard observer functions at 2° and 10° angles, with the 10° observer generally correlating better with visual assessments.

Sample temperature 🔗

Temperature can significantly affect colour measurement, particularly in liquid foods. Research on milk-based beverages showed that measurement temperature (4°C, 20°C, or 50°C) had a large effect on colour results in both the Hunter and CIE systems, with the impact being greatest for skim milk. This underscores the need to control product temperature during both sensory evaluation and instrumental analysis.

Sample preparation and presentation 🔗

How a sample is prepared and presented to the instrument matters. For solid foods, the surface must be even and free of air pockets. For liquids, transparency, turbidity, and the container used can all influence the reading. Standardised protocols for sample preparation are essential to ensure that measurements are repeatable and meaningful.

Practical applications across the food industry 🔗

Colour measurement is not confined to a laboratory. It plays a role at almost every stage of the food supply chain.

In raw material inspection, incoming ingredients are checked against colour specifications to ensure consistency. Spice manufacturers, for instance, verify the redness of paprika or the yellowness of turmeric. In process monitoring, colour readings track changes during cooking, drying, fermentation, or storage – all processes where colour shifts can signal quality deviation. In the flour milling industry, the colour of flour indicates the level of bran contamination, directly affecting product grade.

Shelf-life testing also relies heavily on colour. By measuring colour at regular intervals, food scientists can track pigment degradation, Maillard browning, or fat oxidation over time, all of which produce visible colour changes well before other quality attributes deteriorate. And in final product quality control, colour measurements ensure that every batch leaving the factory matches the approved standard – critical for brand recognition and consumer trust.

Choosing between measurement systems 🔗

With multiple colour systems available, selecting the right one depends on the specific application. CIELAB is the most widely recommended for general food colour measurement because it provides perceptually uniform colour differences and is an international standard. Hunter Lab remains preferred in applications where whiteness sensitivity is important, particularly in dairy, baking, and sugar industries. The Munsell system, while less common in instrumental analysis, is still valuable for visual grading and as a reference standard in agriculture.

Regardless of the system chosen, consistency is key. Product specifications should clearly state the colour measurement system, illuminant type, observer angle, and sample temperature to ensure that data is comparable across different locations, instruments, and time periods.

What do you think? How much does the colour of food influence your own purchasing decisions – and do you believe instrumental colour measurement could eventually replace trained human panels in food quality control entirely?

References

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