Origin and Variety Identification of Dried Kelp Based on Fluorescence Fingerprinting and Machine Learning Approaches

Accurate labeling of the origin of food ingredients is essential to ensure quality and safety; however, establishing a reliable identification method remains an urgent task. The origin and variety of dried kelp are generally identified based on their morphological characteristics; however, they are difficult to distinguish unless experts are involved. In addition, genetically close varieties have almost no differences in their base sequences; therefore, the accuracy of conventional identification methods using genetic analysis is limited. This study aimed to develop a system for identifying the origin and variety of dried kelp using fluorescence fingerprint data obtained by fluorescence spectroscopy and a convolutional neural network (CNN). The fluorescence characteristics of dried kelp were measured in the range between 250 and 550 nm. The obtained fluorescence fingerprint data were converted into image data and analyzed using a CNN model implemented in Python, TensorFlow, and Keras. Unlike conventional methods that rely on morphological characteristics and genetic analyses, by combining fluorescence spectroscopy and CNN, a high identification accuracy of 98.86% was achieved even for genetically close varieties. These results highlight the excellent potential of fluorescent fingerprints in identifying the origin and variety of food and are believed to contribute to preventing food fraud and quality control.