## 4 Discussion  
In this study, by combining fluorescence fingerprint data with a CNN, the origin and variety of dried kelp from Hokkaido were identified with high accuracy. The statistical validity of the model was evaluated using accuracy, precision, recall, and F1 score (Table 3). The test accuracy was 0.9886 (average of 10 trials), showing high prediction accuracy even for samples of unknown variety. Comparison of the unified and individual scale datasets (Figures 1 and 2) showed that scale standardization improved classification performance. These results confirm the robustness of the method in this study. This result was similar to or better than the accuracy of machine learning studies [8,9]. In addition, although the training loss was very low at 0.04684 (average of 10 trials), the validation loss (0.1235, average of 10 trials) was high, suggesting the possibility of overfitting and the need for improved preprocessing methods. Although elemental analysis by ICP-MS [4] and DNA analysis [6] have a certain degree of accuracy, ICP-MS requires expensive equipment and skilled techniques, and DNA analysis has the problem that the accuracy of identification decreases when the genetic similarity is high. The method proposed in this study was particularly effective in identifying Rausukombu and Rishiri-kombu. Although these varieties have similar fluorescence properties, CNN analysis enabled capturing subtle differences in the fluorescent patterns. Moreover, Hidaka-kombu had a prominent fluorescent peak derived from amino acids, which helped in achieving high identification accuracy. Therefore, the fluorescent fingerprints reflected the differences in chemical properties among the kombu varieties. Identification methods that utilize such differences in chemical properties are expected to be applied in the fields of food quality assurance and origin certification, as used for identifying the geographical origin of rice [12]. This study converts fluorescence fingerprint data from text data to image data using a CNN model. The results of this study indicate that a CNN is superior to conventional ma \begin{table}
\begin{tabular}{c c c} \hline
**Trial Number** & **Accuracy** & **Loss** \\ \hline
1 & 0.9912 & 0.5934 \\
2 & 0.9912 & 0.5709 \\
3 & 0.9737 & 0.5884 \\ \hline
4 & 0.9912 & 0.5728 \\
5 & 0.9912 & 0.5735 \\ \hline
6 & 0.9912 & 0.5758 \\
7 & 0.9912 & 0.5777 \\
8 & 0.9825 & 0.5755 \\
9 & 0.9912 & 0.5837 \\ \hline
10 & 0.9912 & 0.5742 \\ Average & 0.9886 & 0.5786 \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy and loss of test data by weighted average ensemble method based on the accuracy of each fold of 10 trials. \begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Hidaka & 0.970 & 1.000 & 0.990 \\ Rausu & 0.995 & 0.997 & 0.996 \\ Rishiri & 0.997 & 0.965 & 0.985 \\ \hline \end{tabular}
\end{table} 
Table 3: Classification performance evaluation index values for each variety (average of 10 trials). chine learning methods (such as KNN and SVM) in pattern recognition of image data. This result is consistent with that previously reported [21]. However, because the CNN model requires a large amount of data and computational resources, a hybrid approach using lightweight models (e.g., MobileNet and EfficientNet) or other algorithms (e.g., XGBoost and RF) can be considered for future applications. The fluorescence properties of dried kelp are significantly affected by components such as amino acids and polyphenols. In particular, fluorescence peaks derived from amino acids were prominent in Hidaka-kombu, and this characteristic may be the main factor in achieving high identification accuracy. However, Rausu-kombu and Rishiri-kombu had similar fluorescence characteristics at approximately 280-330 and 350-440 nm, and this similarity may have been a cause of misclassification. [PERSON] et al. [22] have identified fluorescent substances, such as chlorophyll and tocopherol, and distinguished olive oil from Malta and other origins using fluorescence spectroscopy.