Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation of exact peak-picked or “labeled” chromatograms since the exact start and end points were often unclear in overlapping peaks in real chromatograms. We present a design of a fake chromatogram generator, along with a method for deep learning of peak-picking neural networks. Fake chromatograms were generated by generation of fake peaks, random sampling of peak positions from feature distributions, and merging with real blank sample chromatograms. Information on the exact start and end points, as labeled on the fake chromatograms, were effective for training and evaluating peak-picking neural networks. The peak-picking neural networks constructed herein outperformed conventional peak-picking software and showed comparable performance with that of experienced operators for processing the widely targeted metabolome data. Results of this study indicate that generation of fake chromatograms would be crucial for developing peak-picking neural networks and a key technology for further improvement of peak picking neural networks.

在色谱图中寻找峰并确定其起点和终点（峰选择）是基于色谱的生物技术的核心任务。但是，通过深度学习构建峰采集神经网络的过程因准备精确的峰采集或“标记”色谱图而受到阻碍，因为在实际色谱图中重叠的峰中，确切的起点和终点通常不清楚。我们提出了一种伪色谱发生器的设计，以及一种用于深度学习峰值神经网络的方法。伪色谱图是通过生成假峰，从特征分布中随机抽取峰位置并与真实空白样品色谱图合并而生成的。假色谱图上标出的有关确切起点和终点的信息可有效地训练和评估峰采集神经网络。本文构建的峰提取神经网络的性能优于常规的峰提取软件，并且在处理广泛靶向的代谢组数据方面表现出与经验丰富的操作员相当的性能。这项研究的结果表明，伪色谱图的生成对于开发峰选择神经网络至关重要，并且是进一步改善峰选择神经网络的关键技术。