## 1 Introduction  
In recent years, the consumption of poultry meat has increased continually and reached high levels worldwide [1]. Meanwhile, high-quality and safe chicken meat and meat products are expected. Water-holding capacity (WHC), which refers to the ability of muscle to retain water, is the result of early postmortem biochemical and biophysical changes occurring in muscle tissues [2]. The WHC of fresh chicken meat is important because it influences the retention of vitamins, mineral salts, and water [3]. From an economic point of view, as the meat is sold by weight, poor WHC will lead to high drip loss and a reduction in meat weight [3;4]. Furthermore, poor WHC will cause a large amount of dripping water that appears as a red liquid and remains in the package, leading to unsatisfactory consumer acceptability [5]. There are many techniques that have been used to predict WHC, such as drip loss, filter paper tests, cooking loss, and centrifuge force [6; 7; 8; 9; 10]. Unfortunately, most of these methods tend to be time-consuming, laborious, and destructive, making them unsuitable for industry online inspection [11]. Therefore, new, fast, non-destructive, accurate, and efficient analytical methods are needed. Hyperspectral imaging (HSI), first introduced in the 1980s and originating from remote sensing applications, contains aspects of imaging and spectroscopy [12]. HSI has found vast applications in recent years, since it can provide both external characteristics (e.g., shape, color, size, and texture) and internal properties (e.g., chemical compositions and component distribution) of the sample [13]. In the application of food quality and safety assessment, HSI has been successfully used for chilling injury detection of citrus fruit [14], distribution measurements of texture profile analysis and color in fish [15; 16], melamine detection in milk powders [17], tenderness and WHC prediction in beef [5; 18], fecal material inspection in poultry carcass [19], etc. However, most of these applications have only used spectral information, without considering the performance of spatial information, which is also important in food quality evaluation [20; 21]. As compared to common spectroscopic techniques and machine vision imaging, one of the advantages of HSI is that it can provide both spectral and spatial information at the same time. To fully seek the advantages of HSI, some researchers [21; 22; 23; 24] have investigated the fusion of spectral and textural data to improve the accuracy of model prediction and classification. For example, in order to improve the detection accuracy of soluble solids content of apples, spectral and textural features extracted from hyperspectral reflectance images were integrated to build combined partial least square (CPLS) regression models [21]. The prediction results showed that the CPLS model based on the integration of spectra and correlational features, compared to spectral data used alone, obtained improved results. In the study to predict pH values of salted pork [22], spectral, textural, and integrated data were used to develop partial least squares regression (PLSR) models, separately. The coefficient of determination (R\({}^{2}\)p) of 0.794 for the model based on data fusion was superior to the results for models based on spectral data (R\({}^{2}\)p = 0.783) or textural data (R\({}^{2}\)p = 0.593) alone. These results indicated that integration of spectral and textural data could be an alternative way to improve the accuracy of calibration models. Therefore, the goal of this research was to investigate the potential of HSI for differentiation fresh chicken breast fillets with high WHC or low-WHC.