## 8 Conclusions  
This study presented important and innovative findings using a combination of laser reflectance data and visual data for fish freshness classification. Laser reflectance measurements supported the analysis of physical changes on the surfaces of fish through optical reflectance properties. The experiments showed that laser reflectance data have limited predictive capacity, and their predictive performance demonstrated their role in providing complementary information. On the other hand, the stronger representational capability of the image data resulted in high accuracy rates, especially in classifications  
Figure 6: Confusion matrices for ResNet50 + MLP model in (**a**) the worst-performing fold and (**b**) the best-performing fold of the five-fold cross-validation.  
with models such as ConvNeXt and ResNet50. These results suggest that visual attributes are an important source of data for tasks such as fish freshness prediction.  
The combination of laser data and visual data further improved the prediction performance, and it was observed that these two data types provide complementary information. While the combination of ResNet50 features and the MLP provided the highest accuracy, the integration of laser and visual data was found to be a promising method for more complex and high-accuracy tasks. However, the low accuracy rates observed in some classes, especially in the Day 2 class, suggest that there is still work to be done for more effective feature selection and model refinement.  
In future studies, the generalization performance of the models can be improved by expanding the dataset with visual and laser reflection data obtained from different fish species. The current system has only been tested on mackerel; however, the proposed methodology can be easily adapted to different fish species with appropriate retraining and calibration processes. Due to differences in characteristics such as the skin structure, mucus density, and surface texture between species, retraining with species-based datasets is important. Additionally, integrating biological factors such as the fish's age, size, and gender into the model can contribute to the more accurate and biologically consistent modeling of spoilage tendencies. In this study, only a laser sensor operating at a wavelength of 940 nm was used. However, future experiments using different wavelengths could provide a more detailed understanding of the tissue and optical responses. Furthermore, the integration of attention mechanisms could enable models to focus on the most meaningful regions within image and reflection data, thereby further improving their classification accuracy.  
Finally, the sensor infrastructure used in this study is based on low-cost and portable components. This will enable real-time fish freshness monitoring to be performed in the future through smartphones or handheld compact devices. These applications have widespread and effective potential for use in fish markets, supermarkets, logistics chains, and end consumer points, both in terms of quality control and food safety.  
Conceptualization, C.B., N.O. and M.C.; data curation, C.B. and N.O.; formal analysis, C.B., N.O. and M.C.; investigation, C.B. and N.O.; methodology, C.B. and N.O.; resources, C.B. and N.O.; software, C.B. and N.O.; supervision, C.B. and N.O.; validation, N.O.; visualization, N.O. and M.C.; writing--original draft, N.O.; writing--review and editing, C.B. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.