## 1 Introduction  
Meat has become an important resource for human health; it has a high nutritional value, flavor, and juiciness characteristics [1]. In recent years, consumers and buyers have become more conscious of meat quality and safety [2]. A United Nations Food and Agriculture Organization (FAO) report illustrates that, during the past decade, meat production and consumption has increased continuously, and will increase by 60% by 2050. To satisfy future meat demands, governments and organizations will likely be concerned about meat production, quality, and safety. A consumer's meat selection is usually based on color, marbling, texture, and juiciness parameters [3]. These parameters are influenced by several chemical attributes. The attributes of moisture, protein, and fat are the largest contributors to meat quality [4]. They play an important role in meat quality and are directly associated with the juiciness, tenderness, marbling, and nutritional characteristics of the meat [5]. Thus far,a variety of instrumental techniques has been employed to assess the chemical properties in meat samples. Conventional techniques for meat-quality assessment generally rely on visual evaluations and laboratory-based chemical analyses, e.g., Kjeldahl nitrogen, Soxhet extraction, and chromatography techniques [6]. Although these techniques provide very sensitive chemical identification, they are tedious, time-consuming, destructive, and often require expensive chemicals. Because of these weaknesses, conventional techniques are not applicable for large-scale meat industries, where speed is significant, for on-line product-quality measurements [7]. To overcome such issues, spectroscopic techniques, which are known as very powerful tools, are widely implemented in large-scale meat-processing industries [8]. Example techniques include hyperspectral imaging (HSI), infrared spectroscopy (IR), nuclear magnetic resonance (NMR) spectroscopy, and Raman spectroscopy [9; 10; 11]. In recent years, spectroscopic techniques have become more popular in large-scale meat-processing industries [12]. The spectroscopic techniques are quite expensive, due to their high-cost sensor and optic elements; nevertheless, the meat industry invests in a large budget to employ these techniques to assure the quality of their product. These spectroscopic tools provide the company with a non-destructive, chemical-free, and rapid quality assessment during the online meat-product measurement [13]. Several studies have confirmed the potential use of spectroscopic techniques for monitoring the chemical properties of meat samples with outstanding prediction accuracies [14; 15; 16; 17; 18; 19; 20]. Previous studies have already proven that spectroscopic devices have great potential for product-quality management and are easily applicable to large-scale meat-processing industries with high-throughput product screening. Apart from these applications, a range of studies has been particularly conducted for monitoring different quality parameters of various meat and meat products utilizing an HSI technique in different spectral ranges [21]. This technology combines the advantages of both spectroscopy and imaging techniques in one system to provide both spectral and spatial information for effective quality inspection. Although HSI is a promising technique that can provide rapid quality monitoring of various food commodities including meat, it still faces some inherent limitation in terms of application, complex data handling, and initial investment [22]. To achieve rapid detection of quality parameters and to increase the industrial operating efficiency of meat products, the multispectral imaging technique can be adopted for accurate quality prediction in meat products. For a multispectral imaging system, the concept is similar to the HSI, however, the main difference is in the spectral bands, which are discrete, non-contiguous, and irregular during image acquisition [23]. Thus, the complexity of multispectral imaging is comparably lower than that of the HSI technique.