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Color Measurement in Foods

Since visual color judgments can be affected by a wide variety of factors, from plant lighting conditions and angle of observation to individual differences in color perception, instrumentation to measure color provides a subjective and consistent method of color quality control. Color measurement systems are used to measure a broad range of food products. These include fresh and processed fruits and vegetables, formulated foods, dairy products, meat products, spices and flavors, cereals and grains, oils, syrups, sugar, and beverages. Visible light is found between 380 and 780 nm in the electromagnetic spectrum. It is bordered by ultraviolet light on the low end and infrared light on the upper end. When light strikes an object, it is reflected, absorbed, or transmitted. Because reflected light determines the color of a material, the appearance can change depending on amount of light, the light source, the observer's angle of view, size, and background differences.

Classification of Foods: Depending on how light acts, food products can be classified as opaque, translucent, or transparent. Opaque foods, such as fruit, crackers, corn flakes, cheese puffs, flour, tomato juice, cheese, and meat, are seen wholly by reflected light. The most difficult aspect of color sampling of opaque foods is the presentation of the sample to the light path. Often readings are taken at different angles, then averaged. Translucent foods, such as fruit juices, jams, and custards, are seen partially by reflected light and partially by transmitted light. Translucent foods are typically in liquid or semi-solid form, and sampling requires special handling. The color of a translucent sample will change when the light path length through it is changed. Thus, the path length must be fixed with a fixed white background. Transparent foods are typically liquids or semisolids, such as clear juices, wines, jellies, gelatins, vegetable oil, and soft drinks, are seen wholly by transmitted light. Products such as clear juice, soft drink, vegetable oil, and brewed tea are poured into a transmission cell for sampling. The sample cell is then placed against the sphere port in the transmission compartment of an instrument having the common light-sampling sphere geometry of d/8.

Color Scales: A variety of color scales or schemes are used to describe color. Those most often used in the food industry include the Commission International de l'Eclariage (CIE) system, the Hunter L,a,b system, and the Munsell color solid. The CIE system is the most influential system for the description of color. It is based on using a standard source of illumination and a standard observer. The system obtains CIE standard-observer curves for the visible spectrum for the tristimulus values which are converted to the unreal primaries X, Y, and Z. The HunterLab L*,a*,b* and the modified CIE system called CIELAB color scales are opponent-type systems commonly used in the food industry. The systems measure the degree of lightness (L), the degree of redness or greenness (+/-a), and the degree of yellowness or blueness (+/-b). The Munsell color-order system is a way of specifying colors and showing the relationships among them. Every color has three qualities or attributes: hue, value, and chroma. Munsell established numeric scales with visually uniform steps for each of these attributes. The color of any surface can be identified by comparing it to the chips under proper illumination and viewing conditions. The color is then identified by its hue, value, and chroma.

Colorimeters: Tristimulus filter colorimeters are designed to reproduce the "psycho-physical" sensation of the human eye's view of color. These colorimeters use sensors that simulate the way the human eye sees color and quantify color differences between a standard and a production sample. While colorimeters have sensitivities corresponding to the human eye, they always take measurements using the same light source and illumination method. As a result, the measurement conditions will be the same, regardless of whether it's day or night, indoors or outdoors.

Colorimeters usually consist of two main parts: the optical sensor, which contains the light source and the micro-processor, which automatically converts the colors to numeric equations.

Visual Colorimeters allows for color matching to a standard.

The potato chart uses visual comparison to classify the degree of "sugar-end" in potatoes.

Spectrophotometers: Spectrophotometers measure a ratio of the light reflected or transmitted from a food product to that from a known reference standard. Spectrophotometers are more accurate and more expensive than colorimeters. Spectrophotomers work best for liquids and for transmission. The choice of which instrument to use will depend on the food material and type of application. Colorimeters can be used as a rapid QA technique to monitor product quality during each processing step.

Preparation Techniques: Instrumental color measurements correspond to visual assessments of food color. It is important that sample preparation and presentation procedures are followed to obtain high repeatability of measurement. Grinding, mixing, milling, and blending are all preparation techniques that produce uniform samples, but also affect the light scattering properties.

Appearance Measurement: Along with color measurement, it may also be useful to measure the surface appearance of foods. The appearance of a food surface is a property that can be detected by human vision. It can be classified as (1) diffuse reflection (shiny), (2) specular reflection (glossy, mirror-like), (3) diffuse transmission (cloudy, opaque), or (4) specular transmission (translucent).

Machine Vision: During IFT's 2003 Annual Meeting + Food Expo in Chicago, Ill., in July, A.Z. Odabasi and M.O. Balaban of the University of Florida, Gainesville, presented a paper on the correlation of color measurement between a color machine vision system and a hand-held colorimeter. According to the authors, color quantification using machine vision is becoming more accessible, with recent advances in digital imaging. Color machine vision (CMV) has advantages over the instruments of colorimetry and spectrophotometry. It allows a larger area, even the entire sample surface, to be analyzed. Samples with nonuniform color and/or surface that are misrepresented in conventional instruments can be evaluated objectively in this high spatial-resolution method. Since it is a noncontact, nondestructive method, temporal color changes in a given sample can also be quantified. The CMV system consisted of a light box and a color camera connected to a computer with a frame grabber. Using previously developed software, the researchers grabbed images of samples to obtain color information. As soon as the sample was removed from the light box, a Minolta CR-200b colorimeter was used to obtain the L*a*b* values. The samples were marked to minimize the probability of reading different locations during each measurement. Tiles and peppers of four different colors were used as samples. The study found a high correlation among the L*a*b* measurements of the two methods when standard color tiles were used. When the peppers were sampled, with a colorimeter, discrepancies were found in the two sets of data. This was expected since the CMV system used a larger area on the nonuniform pepper samples that increased variability of the available sampled areas.