## 1 Introduction  
Blueberries, also known as \"bilberries\", are popular for their unique flavor and remarkable health benefits. The fruit is rich in natural antioxidants such as ascorbic acid, anthocyanins and phenolic compounds, and has outstanding nutritional value [1, 2]. As a popular high-value fruit, blueberries are mainly grown in the core production areas of Canada, the United States and China. With the deepening development of global agricultural trade, China has become the largest blueberry producer and exporter in Asia [3]. Under the trend of global consumption upgrading, the requirements for blueberry quality in the international market have been improving, and the demand for high-quality blueberries has shown rapid growth [4]. Among the internal quality parameters of fruits, soluble solids content (SSC), pH and moisture content (MC) are important indicators for evaluating the internal quality of blueberries [5]. The pH directly affects the flavor of blueberries by regulating the acidity intensity, and MC can effectively reflect the freshness of the fruit, which is closely related to the taste [6]. While the SSC measured in this study is rich in a variety of substances, in which the balance between sugars and organic acids determines the overall flavor of blueberries, the vitamins and antioxidants contained in SSC have an important impact on the nutritional value and health benefits of blueberries [7; 8]. However, traditional SSC detection methods are mostly destructive experiments with cumbersome processes, which are unable to realize rapid and non-destructive quality detection, making it difficult to meet modern agricultural production and market demand. Hyperspectral imaging (HSI) technology has become an important technical means for fruit internal quality assessment due to its high efficiency and non-destructive advantages [9]. Currently, hyperspectral imaging technology has achieved remarkable results in the internal quality detection of a variety of fruits, such as apples [10; 11; 12], blueberries [13], kiwifruit [14], etc. [PERSON] et al. [15] established a non-destructive detection model of apple brix by using hyperspectral imaging and combining it with spectral super-resolution technology, which provided a low-cost and high-efficiency way of acquiring hyperspectral images, realizing convenient and fast detection of apple brix. [PERSON] et al. [16] used a hyperspectral imaging system to simultaneously detect and analyze blueberry hardness and soluble solids content. The results showed that the prediction accuracy of hardness was significantly higher than that of soluble solids content in the prediction model constructed by the partial least squares method, and the feasibility of hyperspectral imaging technology in blueberry hardness and SSC sorting was proved. [PERSON] et al. [17] presented a combined partial least squares regression method combining spectral and texture features, proving the significant potential of combining spectral and texture features in the non-destructive detection of SSC in apples. A study by [PERSON] et al. [18] also utilized hyperspectral imaging combined with the partial least squares regression (PLSR) method to establish a monitoring model for textural and structural characteristics of pears under different freezing and thawing conditions, which provided a reliable method for non-destructive evaluation of the quality of frozen agricultural products. In addition to the above applications, hyperspectral imaging technology also shows significant advantages in the field of fruit pest detection and defect recognition. [PERSON] et al. [19] used hyperspectral imaging technology in combination with chemometrics to realize the early nondestructive detection of Staphylococcus priseus-infected kiwifruit and the prediction of quality attributes. There are many other studies like these, but all of these studies have used a single variety of fruit as the object to establish quality detection models.