用小样本拉曼光谱数据，通过自研CNN模型精准预测鱼肉成分。
Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis
- 设计专用CNN架构，结合数据增强应对极小样本挑战。
- 在小样本下超越两种先进CNN和多种传统模型，预测精度显著提升。
- 适合海洋生物成分分析、智能水产加工等场景的科研与应用者。
快速准确检测鱼类生化成分是海产行业高效利用高价值产品的重要任务。拉曼光谱结合机器学习回归模型可实现快速无损分析，将拉曼光谱与生化参考数据关联。本文研究多种回归模型，提出一种新的卷积神经网络（CNN）结构，用于同时预测鱼肉中的水分、蛋白质和脂质含量。据我们所知，这是首个成功利用极小拉曼光谱数据集进行鱼类生化成分分析的CNN研究。该方法结合定制化CNN架构与全面的数据预处理流程，有效缓解数据极度稀缺带来的挑战。实验表明，该CNN显著优于两种前沿CNN模型及多种传统机器学习模型，为鱼类生化成分的精准自动化分析提供了新路径。
原文摘要 · Abstract (English)
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.
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