## 4 Discussion  
Hyperspectral imaging technology has demonstrated significant advantages in the field of non-destructive testing of fruit quality, providing important support for the quality monitoring system of the fruit industry. In this study, the spectral data of three blueberry varieties were collected using this technology, and the exploration of a universal model for cross-variety detection was accomplished by combining the optimized preprocessing scheme, the multilevel screening of variables, and the model simplification strategy, which provides reliable technical support for the quality control of the blueberry industry. In this study, the combined pretreatment of S-G, MSC and SNV was applied to the fusion of multi-variety blueberry spectral data for the first time, which effectively solved the problem of spectral heterogeneity caused by variety differences (as shown in Figure 2). Figure 8: SPA processing results. (**a**) Number of SPA screening variables. (**b**) Distribution of the position of the selected variable in the spectrum of SPA. Figure 9: MLR model predictions after SPA screening variables. After PCA of the spectral data, the principal component distribution of different varieties was significantly concentrated after pretreatment (as shown in Figure 4). [PERSON] et al. [41] systematically evaluated the effects of using different preprocessing methods (including normalization, MSC, SNV, S-G, centering and autoscaling) on the kiwifruit SSC prediction model. In their study, a PLSR model was developed by combining diffuse transmission and diffuse reflection spectra, respectively, and the preprocessing methods that could most strongly improve the model fitting and prediction performance were identified through comparative analysis. The results show that the optimal preprocessing method for both spectral modes is a single method rather than a combination of methods. However, the combined preprocessing strategy adopted in this study is designed not only to improve the prediction accuracy of the model, but more importantly, to effectively eliminate the heterogeneity among different varieties through the optimization of spectral features, so as to make up for the shortcomings of the traditional single preprocessing method in cross-species modeling. In terms of the characteristic wavelength screening method, this study proposes a hierarchical wavelength screening strategy based on UVE-SPA, which makes the model more concise through two-stage optimization. This is very similar to the study by [PERSON] et al. [42]. In their study, the synergy interval partial least squares (SiPLS) was used to downscale the spectral data and combined with the competitive adaptive reweighted sampling (CARS), UVE and SPA methods for secondary screening of feature wavelengths to establish a deep belief network (DBN) and a PLSR model with optimal feature wavelengths, which realized efficient online detection of blueberry brix. This step-by-step screening of feature variables effectively reduces the interference of irrelevant information variables and significantly improves the effectiveness of the model. The PLSR model constructed after screening variables by the UVE method showed excellent performance on the prediction set (see Table 3), with its \(R_{P}^{2}\) value reaching 0.93, RMSEP remaining at a low value of 0.35%, and RPD exceeding 3.50 reaching 3.70. [PERSON] et al. [43] developed a universal prediction model for detecting the quality of apples from different origins, and its core concept was similar to the universal model for detecting the quality of multi-variety blueberries constructed in this study. [PERSON] et al. [43] used the CARS-PLS method to predict the SSC of apples and obtained good prediction results with \(R_{P}^{2}\) and RMSEP of 0.94 and 0.54%, respectively, but the UVE-PLSR model constructed in this study had higher prediction performance. [PERSON] et al.