In this study, we propose a single‐layer multiple‐kernel‐based convolutional neural network (SLMK‐CNN) as an analysis tool for biological Raman spectra. We investigated the characteristics of SLMK‐CNN and then analyzed and classified the biological Raman spectra by optimizing the structure of SLMK‐CNN. We have found that the kernel size used in SLMMK‐CNN plays an important role in changing the characteristics of Raman spectra such as intensity and peak position. As a result, the kernel size affects the classification performance and histological interpretation of biological Raman spectra. We also evaluated the classification performance of SLMK‐CNN using Raman spectra obtained from the porcine skin samples irradiated by an ultraviolet (UV) source for different time. For three sample groups according to UV irradiation time (0, 10, and 24 hr), SLMK‐CNN showed the classification accuracy of 96.4% and 92.5% for the preprocessed and raw Raman spectra, respectively. This is superior to other classification methods such as single‐layer single‐kernel‐based CNN and principal component‐linear discriminant analysis in this study.

中文翻译：

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基于单层多核的卷积神经网络用于生物拉曼光谱分析

在这项研究中，我们提出了一种基于单层多核的卷积神经网络（SLMK-CNN）作为生物拉曼光谱的分析工具。我们研究了SLMK-CNN的特征，然后通过优化SLMK-CNN的结构对生物学拉曼光谱进行了分析和分类。我们发现，SLMMK-CNN中使用的核尺寸在改变拉曼光谱的特征（例如强度和峰位置）方面起着重要作用。结果，籽粒大小影响了生物拉曼光谱的分类性能和组织学解释。我们还使用拉曼光谱评估了SLMK-CNN的分类性能，拉曼光谱是从不同时间的紫外线（UV）源照射的猪皮肤样品中获得的。对于三个样品组，根据紫外线照射时间（0、10和24小时），SLMK-CNN对预处理和原始拉曼光谱的分类准确度分别为96.4％和92.5％。这优于本研究中的其他分类方法，例如基于单层单核的CNN和主成分线性判别分析。