## 1 Introduction  
Meat from beef, pork, lamb, and goat is an important dietary source of protein, vitamins, and minerals necessary for optimal human health [1]. In the past decade, the production and consumption of meat has continually increased due to customer demand [2]. As income levels increase, the interest in meat quality, meat safety, and animal welfare also increases [3]. When consumers purchase beef products, the primary factors driving the purchase decisions are price, color, and fat, which are the key points of beef grading [4]. The amount and type of fat content play a vital role during beef grading, which also has a great influence on the consumer choice and the price of beef [5]. Beef grading plays an important role not only in the beef industry, but also for the consumers. The beef quality and grading have a straightforward relationship, which is intensely critical to the business transactions, marketing, and buyer choice during selling or purchasing. The United States Department of Agriculture (USDA) established beef grading based on the intramuscular fat content and the maturity level of the beef [6]. Based on the USDA grading standard, the commercial beef grades are prime, choice, and select. Prime beef contains abundant marbling (intramuscular fat) and is produced from young and well-fed beef cattle. Choice beef is also high-quality beef but contains less marbling compared to prime beef. Another type of beef called Akaushi (AK) is produced from Akaushi beef cattle, which was created by the Japanese Association of Akaushi as a premium beef breed. Prime beef produced from Akaushi has great value due to its enhanced quality and plentiful amount of marbling. Beef palatability and marbling have a strong interconnection that has been established for decades [7; 8; 9; 10; 11]. Hence, marbling in beef is considered a major parameter for beef grading. In the standard method, meat fat is determined by the solvent extraction of the total lipids followed by the conversion of fatty acids to their methyl esters, and the final analysis is performed by gas chromatography [12]. This method requires time and chemicals and is a high-cost procedure, which are drawbacks for routine analysis. However, for beef grading, a subjective characteristic assessment is performed by highly skilled USDA meat graders and the meat attributes are measured using electronic instruments [13]. This process is subjective and requires expert-level knowledge. Therefore, the development of an accurate, objective, automated system that can give instantaneous results to grade beef, based on the marbling content, would revolutionize the industry. This is where HSI, in combination with machine learning, can be a potential option. Research, development, and application of machine vision technology in the food and meat industry has grown rapidly with increased public expectations for high-quality, palatable, and safe food products. Automated computer vision systems possess great potential for fast, objective, no-contact quality control and food safety inspection [14]. Hyperspectral imaging is a potential non-destructive computer vision technique that has been adopted for the product quality analysis of meat [15], fish [16], dairy foods [17], grain [18], lettuce [19], and green citrus [20]. [PERSON] et al. found that HSI was a powerful technique for predicting quality attributes commonly evaluated in red neats, including lean color, intramuscular pH, marbling, tenderness, chemical composition, water-holding capacity, and microbial spoilage [1]. Previously, [PERSON] et al. observed that HSI analyses performed in the spectral range of 400 to 1100 nm were able to predict 92% of the variation in beef marbling [21]. While these results are promising, there is limited research focused on using HSI to evaluate marbling levels for beef grading. Therefore, the objective of this research was to evaluate the potential of using HSI for beef grading based on marbling levels.