*3.7. Analysis of Modeling Results Based on Fusion Mapping Feature Information*

To further verify whether the integration of the image and spectral data from shrimp samples could optimize the prediction model and improve the accuracy for moisture content, color (*L*\*, *a*\* and *b*\*), and texture (hardness and elasticity), the variables from

the optimal spectra and HSI color and texture information were integrated by featurelevel fusion using the normalization technique. Thus, fusion data comprising the optimal wavelength of each indicator and 13 color and texture features were used to establish new PLSR and LSSVM models. The prediction results of full bands, characteristic bands, and fusion information are given and compared in Figure 9. Regarding shrimp moisture content (Figure 9a), the fusion models achieved limited improvement. The LSSVM model using full-band spectral information exhibited the best performance for dried shrimp (*R*c = 0.959; *R*<sup>p</sup> = 0.938; RPD = 2.814). For *L*\*, *a*\*, and *b*\* (Figure 9b–d), the fusion-based PLSR and LSSVM models exhibited substantial improvement. The LSSVM model was superior to the PLSR model, with RPD values for *L*\*, *a*\*, and *b*\* of 3.292, 2.753, and 3.211, indicating an increase in the prediction performance of 0.866, 0.172, and 0.859 than the PLSR model, respectively. For hardness and elasticity (Figure 9e,f), the fusion-based LSSVM model also showed excellent results compared to the fusion-based PLSR model, with the RPD values increasing from 2.612 to 2.807 and from 2.717 to 2.842, respectively. Thus, combining the internal components and external attributes of shrimp can more fully explain the color and texture changes of shrimp during drying, leading to better prediction results.

**Table 5.** Results of PLSR and LSSVM models based on image information.


**Figure 9.** Comparison results of (**a**) moisture content, (**b**) *L*\*, (**c**) *a*\*, (**d**) *b*\*, (**e**) hardness, and (**f**) elasticity models.
