Rapid identification of fish species by laser-induced breakdown spectroscopy and Raman spectroscopy coupled with machine learning methods

Abstract

There has been an increasing demand for the rapid verification of fish authenticity and the detection of adulteration. In this work, we combined LIBS and Raman spectroscopy for the fish species identification for the first time. Two machine learning methods of SVM and CNN are used to establish the classification models based on the LIBS and Raman data obtained from 13 types of fish species. Data fusion strategies including low-level, mid-level and high-level fusions are used for the combination of LIBS and Raman data. It shows that all these data fusion strategies offer a significant improvement in fish classification compared with the individual LIBS or Raman data, and the CNN model works more powerfully than the SVM model. The low-level fusion CNN model provides a best classification accuracy of 98.2%, while the mid-level fusion involved with feature selection improves the computing efficiency and gains the interpretability of CNN.

Keywords: Data fusion; Fish species identification; Machine learning; Raman spectroscopy; convolutional neural network (CNN); laser-induced breakdown spectroscopy (LIBS).