Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

翻译：拉曼光谱作为一种无标记、非破坏性的化学表征手段，被广泛应用于多个科学领域。许多应用场景需要从混合分子物种的信号中进行解混，以识别存在的单个组分及其比例，但传统的化学计量学方法在处理实践中遇到的复杂混合场景时往往存在困难。本文基于自编码器神经网络开发了高光谱解混算法，并利用自建合成与实验基准数据集进行了系统性验证。结果表明，与标准解混方法相比，解混自编码器在准确性、鲁棒性和效率方面均有所提升。我们还通过单核细胞体积拉曼成像数据的化学表征改善，展示了自编码器在复杂生物场景中的适用性。