Gao Chi, Fan Qi, Zhao Peng, Sun Chao, Dang Ruochen, Feng Yutao, Hu Bingliang, Wang Quan
Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 May 5;312:124036. doi: 10.1016/j.saa.2024.124036. Epub 2024 Feb 12.
Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single-component and multi-component mixture datasets, remarkably improving regression precision while without needing user-selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real-world scenarios.
拉曼光谱已成为一种强大的分析工具，由于其具有非侵入性、操作简单、快速且易于使用等特点，在微生物样本分析、食品质量控制、环境科学和药物分析等众多应用领域中备受需求。其中，利用拉曼光谱进行定量研究是光谱分析的一个关键应用领域。然而，定量建模的整个过程在很大程度上依赖于有效光谱特征的提取，特别是对于复杂样本的测量或在光谱信号质量较差的环境中。在本文中，我们提出了一种利用光谱编码器提取有效光谱特征的方法，该方法可以显著提高定量分析的可靠性和精度。我们构建了一个潜在编码特征回归模型；在利用自动编码器重建光谱仪输出的过程中，提取从中间瓶颈层获得的潜在特征。然后，将这些潜在特征输入到深度回归模型中进行成分浓度预测。通过详细的消融实验和对比实验，我们提出的模型在单组分和多组分混合数据集上表现出优于常用方法的性能，在无需用户选择参数的情况下显著提高了回归精度，并消除了无关和冗余信息的干扰。此外，深入分析表明，潜在编码特征具有强大的非线性特征表示能力、低计算成本、广泛的适应性以及对噪声干扰的鲁棒性。这突出了其在光谱回归任务中的有效性，并表明了其在其他应用领域的潜力。充分的实验结果表明，我们提出的方法为光谱分析提供了一种新颖有效的特征提取方法，该方法简单，适用于各种方法，能够满足不同实际场景的测量需求。
