Color Measurement Of Specific Foods

An extensive review of the color measurement of various foods has been published (10,11). This discussion highlights treatments of color measurement that represent newer developments or deal with conditions that present real problems in color measurement in attempting to relate measured color to what the eye sees.

Orange juice is a food for which color is considered important enough to represent a large proportion of the grading system yet it is difficult to measure because of trans-lucency that may be compounded by the presence of particles. One approach to color measurement of this type of product has been the use of Kubelka-Munk equations developed for reflectance at a single wavelength (7), use with colorimeters is strictly an empirical adaptation. It has been observed that the effect of different rates of trans-mittance change relative to concentrations; <1.0 produced large changes in measured lightness, hue, and chroma (12). The falloff in lightness and chroma observed at higher concentrations resulted from smaller change in transmit-tance at concentrations >2.0. It was concluded that for strongly colored scattering materials in dilute suspension, instrumental measurement was inadequate because it did not measure what the human vision perceived as appearance (12). This is because the instrument measures intensity of back-scattered light over a limited angle whereas human vision is stimulated by internally scattered light emerging multidirectionally from the suspension in addition to that which is reflected directly. For purposes of grading, the color of orange juice is measured using the Citrus Colorimeter (3). This instrument has two scales: citrus redness (CR); and a subsidiary citrus yellow (CY), which are used to calculate a color-score equivalent to a visual color score.

The color of fresh meat is of interest because of the importance of the degree of oxygenation of the surface to consumer acceptability and also because of the incidence of dark cutting beef and pale, soft exudate pork. More traditional approaches have been to determine relative concentrations of the myoglobin, oxymyoglobin, and metmyoglo-bin pigments spectrophotometrically at appropriate wavelengths (13). The problem of measuring meat color is made difficult because of the variability in concentration of the heme pigment myoglobin, the condition of the cut surface that may have undergone a degree of desiccation, the chemical state of the myoglobin, and the light-scattering properties of the muscle pigments (12). The muscle of a freshly slaughtered animal is dark and translucent in appearance becoming lighter and more opaque as the pH falls and the glycogen is converted to lactic acid. Because of the degree of translucency inherent in fresh meat, the Kubelka-Munk equations have been explored as a means of measuring meat color (12,13). The scatter coefficient has been used to measure color as a condition of fresh meat; however, the range of scatter is too large to estimate pigment concentration accurately (12). Scattering data were supplemented with data for L, hue angle, and chroma to produce typical values for conditions of fresh meat. It has been pointed out that derived formulas and multiple regression equations for expressing meat color suffer in accuracy because most people tend to think of how the viewed samples differ from a mental image of ideal meat color (14).

A mathematical approach rather than a visual approach has been used for estimating physical color parameters L*, a*, and b* for red and tawny ports based on consumer data (15). Consumers ratio-scaled 15 blends of port for redness, brownness, and intensity of color and then provided ratings for their ideal port. Assessors were separated on the basis of those who preferred tawny and those who preferred ruby port. Deviations of the samples from the ideal were regressed against L*, a*, b*, hue angle, and chroma. Three simultaneous equations including port preference, hue angle, and chroma were established for relating sensory information with physical information. These data were further treated by the method of inverse simultaneous estimation to obtain estimates for color parameters for ideal ports. However, as with all ideal assessments, caution must be exercised because of the large degree of error. In this case the ideal fell outside the range of blends, a common occurrence with ideal data. Although the study does demonstrate that appropriate use of mathematics and data collection can increase efficiency in relating sensory and physical color data.

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