Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914960136732672 |
|---|---|
| author | Zhou, Yun Chen, Gang Xue, Bing Zhang, Mengjie Rooney, Jeremy S. Lagutin, Kirill MacKenzie, Andrew Gordon, Keith C. Killeen, Daniel P. |
| author_facet | Zhou, Yun Chen, Gang Xue, Bing Zhang, Mengjie Rooney, Jeremy S. Lagutin, Kirill MacKenzie, Andrew Gordon, Keith C. Killeen, Daniel P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19688 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis Zhou, Yun Chen, Gang Xue, Bing Zhang, Mengjie Rooney, Jeremy S. Lagutin, Kirill MacKenzie, Andrew Gordon, Keith C. Killeen, Daniel P. Machine Learning Artificial Intelligence Signal Processing 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. |
| title | Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2409.19688 |