拉曼光谱结合变分自动编码器和深度学习对病原菌进行分类,Journal of Biophotonics

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拉曼光谱结合变分自动编码器和深度学习对病原菌进行分类
Journal of Biophotonics ( IF 2.0 ) Pub Date : 2022-12-15 , DOI: 10.1002/jbio.202200270
Bo Liu _{1,

2} , Kunxiang Liu _{1,

2} , Jide Sun ₃ , Lindong Shang _{1,

2} , Qingxiang Yang ₄ , Xueping Chen ₅ , Bei Li _{1,

2}

Affiliation

病原体的快速和早期鉴定对于指导抗生素治疗至关重要。拉曼光谱作为一种无创诊断技术，可以快速准确地检测病原体。单细胞的拉曼光谱作为细胞的“指纹”，揭示其代谢特征。通过结合拉曼光谱和深度学习，可以实现病原体的快速识别。传统的分类技术通常需要大量数据进行训练，这会花费大量时间来收集拉曼光谱。对于难以培养的微量样品和菌株，很难提供准确的分类模型。为了减少采集的样本数量并提高分类模型的准确性，一种新的结合拉曼光谱的病原体检测方法，本文提出了变分自动编码器 (VAE) 和长短期记忆网络 (LSTM)。我们收集病原体的拉曼信号并将其输入到 VAE 进行训练。VAE会产生大量与真实光谱无法区分的拉曼光谱数据，信噪比高于真实光谱。这些光谱与真实光谱一起输入LSTM进行训练，得到良好的分类模型。实验结果表明，该方法不仅将病原体分类的平均准确率提高到96.9%，而且将采集的拉曼光谱数量从1000个减少到200个。采用该技术，采集的拉曼光谱数量可以大大减少，以便快速鉴定难以培养或追踪的菌株。本文提出了长短期记忆网络（LSTM）。我们收集病原体的拉曼信号并将其输入到 VAE 进行训练。VAE会产生大量与真实光谱无法区分的拉曼光谱数据，信噪比高于真实光谱。这些光谱与真实光谱一起输入LSTM进行训练，得到良好的分类模型。实验结果表明，该方法不仅将病原体分类的平均准确率提高到96.9%，而且将采集的拉曼光谱数量从1000个减少到200个。采用该技术，采集的拉曼光谱数量可以大大减少，以便快速鉴定难以培养或追踪的菌株。本文提出了长短期记忆网络（LSTM）。我们收集病原体的拉曼信号并将其输入到 VAE 进行训练。VAE会产生大量与真实光谱无法区分的拉曼光谱数据，信噪比高于真实光谱。这些光谱与真实光谱一起输入LSTM进行训练，得到良好的分类模型。实验结果表明，该方法不仅将病原体分类的平均准确率提高到96.9%，而且将采集的拉曼光谱数量从1000个减少到200个。采用该技术，采集的拉曼光谱数量可以大大减少，以便快速鉴定难以培养或追踪的菌株。

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Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the “fingerprint” of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.

更新日期：2022-12-15

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