Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis

TL;DR

This paper presents a novel CNN-based approach for analyzing fish biochemical composition using Raman spectroscopy, effectively handling small datasets and outperforming existing models.

Contribution

It introduces a new CNN architecture tailored for small Raman spectroscopic datasets to predict fish biochemical components.

Findings

CNN outperforms traditional machine learning models

Effective data preparation mitigates small dataset challenges

First successful CNN application on small Raman datasets for fish analysis

Abstract

The rapid and accurate detection of biochemical compositions in fish is a crucial real-world task that facilitates optimal utilization and extraction of high-value products in the seafood industry. Raman spectroscopy provides a promising solution for quickly and non-destructively analyzing the biochemical composition of fish by associating Raman spectra with biochemical reference data using machine learning regression models. This paper investigates different regression models to address this task and proposes a new design of Convolutional Neural Networks (CNNs) for jointly predicting water, protein, and lipids yield. To the best of our knowledge, we are the first to conduct a successful study employing CNNs to analyze the biochemical composition of fish based on a very small Raman spectroscopic dataset. Our approach combines a tailored CNN architecture with the comprehensive data preparation procedure, effectively mitigating the challenges posed by extreme data scarcity. The results demonstrate that our CNN can significantly outperform two state-of-the-art CNN models and multiple traditional machine learning models, paving the way for accurate and automated analysis of fish biochemical composition.