Introduction
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Beef is a major source of essential amino acids needed in the human diet, and it attracts a premium price. Aside from being a source of protein, it is also a major source of other valuable nutrients such as vitamin, fat, and micronutrients, all of which are responsible for good human health. In recent years, meat quality has become a relevant topic for consumers concerning health and for meat industry stakeholders because it affects their profitability (Hocquette and Chatellier, 2011). Meat quality is usually defined by physical, chemical and biological attributes. With the current growing need for low production cost and high efficiency, the meat processing industry is facing several challenges, including maintenance of high-quality standards and assurance of food safety while avoiding liability issues. Meeting these challenges has become crucial in regards to grading beef for different markets. Traditionally, quality assessment of beef involves human visual inspection, in addition to chemical or biological determination experiments which are tedious, time-consuming, destructive and sometimes environmentally unfriendly. Meat processing companies and suppliers need accurate, fast, real-time, low-cost and non-chemical detection technologies to optimize quality assurance of meat to enable them to satisfy different market’s needs, thereby raising their competitiveness and expanding their market share. Imaging methods have been recently applied successfully to visually assess the quality or to classify meat or food products on the processing line based on color, shape, size, surface texture features (Chmiel et al., 2011; Girolami et al., 2013; Iqbal et al., 2010; Jackman et al., 2011). Computer vision (CV) is one such method. It is a nondestructive, fast, cost-efficient, consistent and objective method for the inspection and assessment of food quality and safety in the processing line (Gümüs et al., 2011). It is an RGB color vision method that has achieved good results in the assessment of the external features of foodstuffs (Tan, 2004). CV has such advantages as being online, non-invasive and thus nonhazardous (Chmiel et al., 2016). As a rapid and non-destructive technique, imaging technique has received huge attention in recent times for measuring quality attributes of agricultural products including meat and meat products. Part of the reasons why CV has gained popularity in times includes the fact that it can obtain reliable and reproducible results (Yagiz et al., 2009). It can potentially replace human vision and perception of images in meat quality assessment and safety assurance. Furthermore, machine vision is capable of providing reliable descriptive data with human intervention, which speed up the overall evaluation or measurement processes. Finally, it is proved to be objective, effective, reliable, non-destructive and capable of constant recording of food samples being examined and the effects of processing regimes which is suitable and important for further or subsequent analysis (Brosnan and Sun, 2004). The superiority of the imaging technique compared to traditional analysis methods is that they allow the display and overlay the distribution of the analyzed properties (Turgut et al., 2014). CV system has been used for color measurement in meat by Fatih et al. (2016). Researchers used CV technology for assessing water holding capacity in meat (ElMasry et al., 2011; Monroy et al., 2010; Qiao et al., 2007). Analysis of images obtained from a digital camera is presently being used for assessing the external qualities of meat. However, these assumptions may be possible because one previous research reported that frozen breast meat with low water-holding capacity had more flat in shape during extended storage time (Lee et al., 2008). Direct measurements are inconvenient and time-consuming when used in the continuous processing of meat. Thus, image analysis with a digital camera may provide an alternative method for evaluating or predicting the quality attributes by determining the conformation parameters and surface appearance such as color, texture, bitonality etc. A number of high-performance techniques have been applied successfully for determining or predicting the quality characteristics of various meat and meat products, such as the hyperspectral imaging technique (Iqbal et al., 2013; Qiao et al., 2007), near-infrared (NIR) imaging (ElMasry et al., 2011), and nuclear magnetic resonance (NMR) (Bertram et al., 2001) have been used. However, these techniques require costly equipment, whereas image analysis using a digital camera is less expensive. Although the machine vision has been originated during the dates back to the 1960s, it has not been introduced commercially in the food or processing industries until the 1990s. Machine vision has a distinct drawback such as its application during analysis of digital images, it is restricted to the identification and extraction of external image features or quality factors like color, size, and surface structure (Chmiel et al., 2011; Chmiel et al., 2012; Penman, 2001; Zhang et al., 2015). In consequence, it can not be used in chemometrics modeling in which chemical composition and internal quality characteristics of meat or samples under consideration (Peng and Dhakal, 2015).
Though the hypothesis of CV technology is related to conformation parameter of foodstuffs, an effort has been taken to find out the correlation between image value and chemical composition of beef through this experiment.
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