**5. Conclusions**

The possibility of using HSI techniques (400–1000 nm) as a tool for determining the muscle texture profile in scaled common carp was evaluated. The optimal wavelength selected based on the RC data downscaling method with ML methods (BP-ANN, PLSR, and LS-SVM) performed most efficiently in predicting the TPA of different muscle regions in common carp. The results showed excellent performance in predicting gumminess, cohesiveness, adhesiveness, and chewiness. The *r*<sup>P</sup> ranged from 0.8726 to 0.9847. Moreover, the visualization map of the distribution of TPA values was generated based on the optimal models, which provided further insight into the texture parameters in the common carp muscles. This study illustrated the tremendous potential of hyperspectral imaging technology as a robust and effective tool for the rapid and non-destructive measurement of TPA in different scaled common carp muscle regions. Despite the superior results of this study in predicting muscle texture parameters in common carp, it is still necessary to validate the developed models by applying numerous samples to ensure their reliability. In future studies, using hyperspectral imaging to acquire hyperspectral image data of other species of fish could be attempted for the rapid and non-destructive detection of meat quality.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/foods12173154/s1, Table S1: Eight textural parameters and HSI data of four muscle regions of 387 common carp, Table S2: Correlation analysis of eight textural parameters in four muscle regions, Table S3: Correlation analysis of the textural parameters in abdominal muscle of common carp (17 pairs/28), Table S4: Correlation analysis of the textural parameters in pectoral muscle of common carp (14 pairs), Table S5: Correlation analysis of the textural parameters in dorsal muscle of common carp (9 pairs), Table S6: Correlation analysis of the textural parameters in gluteal muscle of common carp (7 pairs), Table S7: Calibration and prediction results of TPA values for four muscle regions of common carp on the full spectral range, Table S8: Calibration and prediction results of TPA values for four muscle regions of common carp on the optimal spectral range, Figure S1: PCA plots clustering all common carp with eight textural indicators of dorsal muscles, Figure S2: PCA plots clustering all common carp with eight textural indicators of pectoral muscles, Figure S3: PCA plots clustering all common carp with eight textural indicators of abdominal muscles, Figure S4: PCA plots clustering all common carp with eight textural indicators of gluteal muscles, Figure S5: Box plot of cohesiveness in the four muscle regions of common carp, Figure S6: Scatter plot of adhesiveness values in the four muscle regions of common carp, Figure S7: Scatter plot of chewiness values in the four muscle regions of common carp, Figure S8: Scatter plot of gumminess values in the four muscle regions of common carp, Figure S9: Scatter plot of resilience values in the four muscle regions of common carp, Figure S10: Scatter plot of brittleness values in the four muscle regions of common carp, Figure S11: Scatter plot of springiness values in the four muscle regions of common carp.

**Author Contributions:** Conceptualization, J.-T.L.; methodology, Y.-M.C., Y.Z. and J.-T.L.; software, Y.-M.C.; validation, Y.-M.C. and J.-T.L.; formal analysis, Y.-M.C.; investigation, Y.-M.C., Y.Z., S.-T.Y., K.-K.W., Y.-J.C., Z.-M.X., Z.-Y.M., H.-L.C., Q.W., R.Z. and X.-Q.S.; resources, J.-T.L.; data curation, Y.-M.C. and S.-T.Y.; writing—original draft preparation, Y.-M.C.; writing—review and editing, J.-T.L.; visualization, Y.-M.C.; supervision, J.-T.L.; project administration, Y.Z. and J.-T.L.; funding acquisition, Y.Z. and J.-T.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program (grant number 2021YFD1200804), Beijing Municipal Natural Science Foundation (grant number 6212033), the Special Scientific Research Funds for Central Non-profit Institutes, Chinese Academy of Fishery Sciences (grant number 2020TD24), the fisheries innovation team of Beijing Agriculture Innovation Consortium (grant number BAIC07-2023-03), the Special Scientific Research Funds for Central Nonprofit Institutes, Chinese Academy of Fishery Sciences (grant number 2023A003 and 2023XT0102), and the National Freshwater Genetic Resource Centre (grant number FGRC: 18537). All authors have contributed significantly to the manuscript.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of China and approved by the Animal Care and Use Committee of the Chinese Academy of Fishery Sciences (protocol code ACUC-CAFS-20191202 and date of approval is 27 December 2018).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data presented in this study are available in Supplementary Materials.

**Conflicts of Interest:** The authors declare no conflict of interest.
