深度学习辅助的拉曼光谱技术用于在殖民地层面快速识别乳酸细菌
Yu Wang1, Lei Xu2, Lindong Shang1
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China; State Key Laboratory of Applied Optics, Changchun 130033, PR China; Key Laboratory of Advanced Manufacturing for Optical Systems, Chinese Academy of Sciences, Changchun 130033, PR China.
概括
我们开发了一种适应性殖民地拉曼获取方法 (ACRA-SNR) 和一个拉曼旋转变压器 (Ra-ST) 模型,用于快速细菌识别. 这种组合在分类和识别乳酸细菌菌株方面实现了高精度,提高了工业生产效率.
科学领域:
- 微生物学 微生物学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 准确有效地识别细菌殖民地对于工业应用至关重要.
- 传统方法面临的挑战是殖民地内的速度,准确性和空间异质性.
- 需要新的方法来改善细菌分类和选择过程.
研究的目的:
- 开发一种快速而准确的现场方法来识别细菌殖民地.
- 在工业环境中提高殖民地选择效率.
- 评估一种新的拉曼光谱采集技术与深度学习模型相结合的性能.
主要方法:
- 基于信号对噪声比选 (ACRA-SNR) 的适应性殖民地拉曼获取方法被提议用于现场光谱获取.
- 该ACRA-SNR方法与拉曼斯温变压器 (Ra-ST) 模型集成用于细菌分类.
- 用Ra-ST模型分析了14种乳酸细菌 (LAB) 菌株的光谱数据.
- 通过从不同来源识别LAB菌株来评估模型的概括性.
主要成果:
- 在Ra-ST模型中,LAB14种菌株的高分类准确率达到了98.2%.
- 该模型表现出良好的概括能力,对外部LAB菌株的识别准确度超过70%.
- 对比分析显示,Ra-ST模型在分类和预测任务中表现优于其他模型.
- ACRA-SNR技术有效地减轻了殖民地内部空间异质性的影响.
结论:
- ACRA-SNR和Ra-ST的组合为快速准确的细菌分类和识别提供了一个强大的工具.
- 预计这种方法将大大提高LAB和其他功能性细菌的工业生产效率和产量.
- 开发的方法表明,它有望在各个领域推进微生物识别技术.
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