Visual Intrinsic Character

Table of Contents

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Perhaps the earliest indicator of the quality of the eating experience expected is how the food appears. Although appearances can be deceiving, under many eating circumstances, the visual appearance of the food is predictive of the eating experience perhaps through the Pavlovian effect. This chapter addresses the subjective sense of visual presentation in the realization that the importance of the visual presentation varies across cultures and the character desired by different cultures also varies. As for all intrinsic characteristics of red meat, this chapter presents a review of the scientific literature concerning the methodology of objectively quantifying a subjective sensory experience (vision), the science governing the nature of important visual characteristics of red meat, and the production system elements thought sensitive to these characteristics.


Visual quality characteristics include portion size, lean and fat color, firmness, and the amount of visible subcutaneous, intermuscular, and intramuscular fat. The amount, composition, and color of fat and lean may not only impact visual attractiveness but also eating quality and product healthfulness (as discussed in this text). Different markets may differ in visual characteristics considered attractive. For example, South and Central American markets are more lenient in accepting yellow fat than North American markets. The appearance of food is possibly more important in Asia, especially in Japan and in China (Busboom and Reeves (2013). In the U.S., visual quality characteristics associated with grass-fed beef are discriminated against. These visual quality characteristics are lean, that is two-toned or heat-ringed and fat that is not creamy white (Miller, 2001).

Among intrinsic meat characteristics, the two that are arguably the most important are price and visual appearance because these are the first two attributes for which information is available to the potential consumer before purchase and, therefore, play leading roles at the decision point at which a customer ascertains quality in his search for purchasing red meat (Henchion, McCarthy, and Resconi, 2017). In this text, price is considered a “bottom-line” trait as the culmination of efficiencies resulting from a cascade of production delivery decisions. This text will, therefore, consider price in the last section when the production system and the resulting product are considered holistically. Since visual traits are apparent to the potential consumer at the crucial decision point at the time of purchase, these intrinsic traits are considered only second in importance after the over-riding trait of safety. In fact, the only cues the customer has concerning safety at the decision time of purchase are visual cues. 

Portion Size

In the U.S., the 2005 U.S. National Beef Quality Audit reported that inconsistency in portion size is a concern to consumers (Smith et al., 2005b). Beef ribeye areas less than 71.0 cm2 or more significant than 103.2 cm2 were less desirable (Savell, 2007). In 2000, the NBQA reported that the percentage of carcasses with a ribeye area of less than 71.0 cm2 or more significant than 103.2 cm2 were 7.9 and 5.3%, respectively (Smith et al., 2000a). The 2005 NBQA indicated that these percentages had increased to7.6% for ribeye areas less than 71.0 cm2 and 7.8% for ribeye areas greater than 103.2 cm2 (Savell, 2007 and Bass et al., 2009). American beef industry pricing structure and prevailing industry conditions encourage heavier carcasses (Tatum et al., 2006) and larger muscle sizes. Sweeter et al., 2005, however, reported that consumers have a wide tolerance in the ribeye area, although most ribeye steaks are sold on a weight basis.

If the steak has a big surface area, it must be cut thin to meet the weight specification. These thin-cut steaks are likely to be over-cooked resulting in a reduction in tenderness and juiciness. Bass et al., 2009 reported low correlations between the ribeye area and the size of other cuts indicating that the focus given ribeye area in the USDA grading system may be unwarranted as an indicator of the size of different cuts.

Lean Color

Consumer Preferences

American consumers consider red meat to have an acceptable color when the quantitative measurements of color are within a specific threshold range of values. Consumers of lamb consider the color acceptable when L* value is greater than 34 (Hopkins, 1996; Khliji et al., 2010) and a* value is less than 19 (Hopkins, 1996) but is more significant than 9.5 (Khliji et al., 2010).

Real-Time Objective Measurement

The meat production industry can objectively quantify red meat color in real-time using a colorimeter to compute CIE, L*, a*, b* color parameters (Tapp, Yancey, and Apple, 2011). In this color space, L* quantifies the dark to light continuum (lightness: 0 for black to 100 for white); a* characterizes the red-green continuum (redness), and b* is indicative of the yellow-blue continuum (yellowness. The range of both a* and b* is between −128 and +128 (CIE, 1978). In this technique, colorimeters are employed to measure color by scanning the surface at several discrete random loci to compute the average of the sample. There are problems with these quantitative measurement techniques.  Although color measurement using these instruments is rapid and simple, because of limitations in time and the amount of data that must be collected, these techniques do not represent the entire surface.

Since the meat surface is not homogeneous in color and texture and discoloration is variable across the surface with some discoloration occurring near the meat’s edge and not easily scanned with colorimeters, these instruments have limited repeatability and accuracy (Larraín, Schaefer, and Reed, 2008; Tapp et al., 2011; and Trinderup and Kim, 2015). Because of these problems and because the entire surface must be inspected to gain spatial information, calorimeters have limited capacity in the context of online measurement in fast-paced industrial environments (Leon et al., 2006). Any discoloration on any part of the cut is viewed as making the cut flawed in the eyes of the customer. Therefore, the industry requires real-time pixel-based color evaluation systems.

Hyperspectral imaging meets these requirements thereby having efficacy for online monitoring of color because of its ability to identify meat physical and chemical characteristics making it a popular method for performing rapid, nondestructive analyses for red meat (Menesatti et al., 2009; and Taghizadeh, Gowen, and  O’Donnell, 2009), pork (Barbin et al., 2012; ElMasry, Sun and  Allen, 2012; Kamruzzaman et al., 2011; Kamruzzaman, Makino, and  Oshita, 2016a,b;  and Wold, O’Farrell, Høy, and  Tschudi, 2011), as well as chicken (Feng and  Sun, 2013), ham (Gou et al., 2013) and seafood (Wu, Sun, and  He, 2012). The limitation of hyperspectral imaging technology for online monitoring is the considerable time required for processing the large volume of data generated. However, a simplified technique can be used initially to create the information necessary for a subsequent dedicated multispectral online system (Pu, Kamruzzaman, and Sun, 2015).

To build a multispectral vision system, optimal spectral bands must be identified from hyperspectral data analysis (ElMasry, Sun and Allen, 2012; and Kamruzzaman, Makino, and Oshita, 2015a; and Kamruzzaman, Nakauchi, and ElMasry, 2015b). After these bands are identified, a cost-effective and straightforward multispectral system can be computed to satisfy the needs of each production unit. These bands have been identified for different types of meat: for pork L*: 947, 1024, 1124, 1208, 1268, and 1654 nm (Barbin et al., 2012); for beef L*: 947, 1078, 1151, 1215, 1376, and 1645 nm (ElMasry, Sun and  Allen, 2012); and for lamb L*: 940, 980, 1037, 1104, 1151, 1258, 1365, and 1418 nm  (Kamruzzaman et al., 2011), and for the combination of beef, pork, and lamb L*: 450, 460, 600, 620, 820, and 980 nm (Kamruzzaman, Makino and Oshita, 2016). These bands can be considered as the initial “place to start” in developing systems configured for the meats specific to each production unit.


Association with Texture and Water Content

Meat color is the frontier criterion consumers use to assess beef quality (Conforth, 1994). Beef color is primarily a function of myoglobin content and meat texture (Giddings, 1977a,b; and Renerre, 1986). Meat texture is directly related to the ultimate pH and, therefore, to the amount of glycogen in the muscle at harvest. The high-water content of muscle is vital to the structural arrangement of the lattice and acts as a plasticizer of proteins influencing the meat structure (Hughes et al., 2014). Consequentially, loss of water from the structure affects the properties (incredibly lean color, juiciness, and tenderness) of the meat. Water loss from the muscle is impacted by various structural elements of the muscle but primarily the myofibrillar lattice spacing, membrane permeability, extracellular space, and drip channel formation (Hughes et al., 2014).

The proteins involved in these structural elements are both pH and temperature-dependent and influence the extent of structural changes that occur postmortem (Hughes et al., 2014).  The structure of the muscle impacts the color perception and light scattering properties of the lean. Water loss induced by cooking or during aging could also reduce the myofibrillar lattice spacing and fiber diameter and impact the osmolarity of the medium, which in turn contributes to increases in the lightness of the color of the meat surface. Thus, the structure of the muscle could be a determinant of meat color (Hughes et al., 2014).

Myoglobin Oxidation

Myoglobin content is intrinsic to the muscle but varies with bovine species, age at slaughter, feeding system, pre-slaughter conditions, slaughter procedure, and the degree of oxygenation and oxidation during aging (Hughes et al., 2014). These either directly affect the myoglobin content or indirectly alter the ultimate pH of the meat (Honikel, 1997; and Renerre and Labas, 1987). The physical-chemical state of myoglobin (i.e., purple reduced myoglobin, red oxymyoglobin, and brown metmyoglobin) is a primary determinant of the color of fresh meat (Strange, et al., 1974). Postmortem glycolysis decreases muscle ultimate pH making it brighter in color and increasing its water holding capacity (Swatland, 1989). If the ultimate pH is high, structural proteins exist above their iso-electric points allowing them to associate with more water, and muscle fibers are tightly packed. A carcass having this character is classified as a “dark cutter,” and the meat is classified as dark, firm, and dry (DFD).

It is dark because its surface does not scatter light to the same extent as the more open surface of red meat having a more acid ultimate pH (Seideman, et al., 1984). Therefore, the ultimate pH is a primary determinant of the meat’s pigment status. The pigment status is also influenced by the degree of oxidation and other factors such as blooming time (Jakobsen and Bertelsen, 2000). These differences can be detected spectrophotometrically (Swatland, 1989; Renerre and Mazuel, 1985; Harrison et al., 1980; Franke and Solberg, 1971; and Strange et al., 1974). Out of all the indexes evaluated in these experiments, those that best explain color differentials are the L*(lightness), C*(chroma), and h*(hue) co-ordinates. Several authors have attempted to classify meat color into different groups as a function of pH (Korkeala et al., 1986; Negueruela et al., 1992; and Abril et al., 2001). These authors concluded that ultimate pH < 6.1 results in an acceptable beef color.

Variation among Muscles

The myoglobin content and the rate of myoglobin oxidation are muscle-specific. Muscles that have a high proportion of red muscle fibers have a higher myoglobin content, and thus, meat that is redder (Kim et al., 2010b). Increasing the proportion of type I fibers decreases color stability with a possible shift to a brownish metmyoglobin color (Renerre, 1990). Hypertrophy of fast-twitch oxido-glycolytic fibers (IIA) is detrimental to water holding capacity (Larzul et al., 1997; Maltin et al., 1998). Red oxidative muscles generally contain more intramuscular fat than white glycolytic muscle (Hwang et al., 2010).  McKenna et al. (2005) classified beef muscles on color stability, categorizing the semimembranosus (SM) and biceps femoris (BF) as being “moderate” and “low” color stability muscles, respectively, when aged for three days and subjected to 5 days of retail display.

Production System Elements (Critical Control Points)

Injection site blemishes and blood splash are considered unacceptable sources of discoloration in the U.S. Dark red lean is also discriminated against in the American and Japanese markets even though “dark cutters” that are young can be more tender than those having bright red color. As severity of dark cutting increases, longissimus lumborum was increasingly tender and juicy but more likely to have fat-like, rancid, heated oil, chemical, and musty/earthy/hummus flavors while being less likely to have metallic, sour, and salty flavors (Grayson et al., 2016). The advantage for dark cutters in tenderness appeared to be due to shorter sarcomeres whereas non-dark cutters had greater desmin proteolysis (Grayson et al., 2016). Dark-colored beef is generally associated with either young cattle that were excited when harvested or with old cow and bull meat.

Many production factors have been related to dark cutting including the age of the animal (Grayson et al., 2016), sex of the animal (Lorenzen et al., 1993), animal temperament (Voisenet et al., 1997a,b), pre-slaughter management (Lacourt and Tarrant, 1985; and Mach et al., 2008), and time of year/season of slaughter (Knee et al., 2004; and Kreikemeier, Unruh, and Eck, 1998). Anabolic growth promotents can increase the incidence of dark cutters (Morgan, 1997; Reiling and Johnson, 2003; and Schneider et al., 2007), although Reiling and Johnson (2003) found no effect of trenbolene acetate-estradiol and Vitamin D on lean color. The frequency of dark cutting is higher for heifer than for steer carcasses (Lorenzen et al., 1993), possibly because of their temperament (Voisenet et al., 1997) as associated with estrus activity (Kenny and Tarrant, 1988) or the resultant reduced carcass weight (Murray, 1989).

Empirical relationships between dark cutting and animal and carcass phenotypes have been reported (McGilchrist et al., 2012) in that increased carcass weight and associated increased fat depth, rib eye area, and growth rate have been related to reduced incidence of dark cutting (McGilchrist et al., 2012; Hawrysh, Gifford, and Price, 1985; Park, Lee, and Hwang, 2007; Młynek and Guliński, 2007; and Vestergaard, Oksbjerg, and Henckel, 2000; and Mahmood et al., 2016a,b). The relationship between dark cutting and marbling score is not understood. McGilchrist et al. (2012) reported that the marbling score was not related to dark color, while Park et al. (2007) reported higher levels of marbling reduced dark cutting frequency. Similarly, the relationship between dark cutting and muscle fiber type may also be an essential determinant for dark cutting red meats (Zerouala and Stickland, 1991; and Hunt and Hedrick, 1977a,b).

Arnett et al. (2012) compared Jerseys fed 12% roughage to those fed 24% to USDA select grade and found no differences among treatments for lightness (L*); however, Jerseys fed 12% roughage were less red (a*) and yellow (b*) than Jerseys fed high concentrate. Crouse, Cross, and Sideman (1984) reported that cattle finished on high concentrate diets had lean that was firmer, finer textured, and less dark. Zembayashi, Lunt and Smith (1999) demonstrated that feeding green tea to cattle reduces iron (and myoglobin) content of beef and impacts beef color. They also found that different production regimes affect beef iron and myoglobin content and beef color. Forage-finished cattle have darker lean than grain-finished in the U. S. (Crouse et al., 1984; Bennett et al., 1995; and Duckett et al., 2007, 2013), Uruguay (Realini et al., 2004), and Ireland (Dunne et al., 2006). Bidner et al. (1986) reported a darker lean color in forage-finished beef without any changes in muscle pH values. Marti et al. (2013) reported that beef from castrated Holsteins had more lightness, redness, and yellowness than beef from bulls.

This may result from the fact that bulls may be more easily stressed (Field, 1971; and Katz, 2007) and perform a more mounting activity (Katz, 2007; Mach et al., 2009) than steers. These two factors may explain the greater beef pH and darkness observed in bulls compared with that from steers (Jago et al., 1996; Price et al., 2003; and Katz, 2007). Koshered meat has been reported to undergo rapid color change (to brown) accompanied by the development of objectionable odors during refrigeration (Holzer et al, 2004; Hayes et al, 2015). This rapid color deterioration can be reduced by applying hydrodynamic pressure (Holzer et al., 2004). Hayes et al. (2015) evaluated the relationship of pre-harvest stress measurements and carcass characteristics between kosher and not-qualified-as kosher cattle. Cattle with shorter gate to exsanguination times and lower vocalization scores were more likely to qualify as kosher. On each day of simulated retail display, kosher steaks had lower L*, a*, and b* values.


From the original domestication of cattle before the industrial revolution, which brought on the revolutionary animals, production of meat has changed considerably. Food processing has moved to an emphasis on product quality, protection, and environmental sustainability from the provision of food security, to the conservation of maximum livestock numbers, and to maximum productivity and performance. Consumer disapproval of meat production culminated in the establishment of quality guidelines, codes of conduct, and certification schemes aimed at ensuring that products from animal sources are safe and high quality but are also focused on ethically appropriate and important production processes. These innovations provided the catalyst for the global meat industry to change customer expectations of meat production and quality as well as to ensure that manufacturing processes are economically and environmentally viable.