Establishing standardized methods for a consistent analysis of spectral data remains a largely underexplored aspect in surface-enhanced Raman spectroscopy (SERS), particularly applied to biological and biomedical research. Here we propose an effective machine learning classification of protein species with closely resembled spectral profiles by a mixed data processing based on principal component analysis (PCA) applied to multipeak fitting on SERS spectra. This strategy simultaneously assures a successful discrimination of proteins and a thorough characterization of the chemostructural differences among them, ultimately opening up new routes for SERS evolution toward sensing applications and diagnostics of interest in life sciences.

中文翻译：

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基于化学结构决定簇的机器学习分类的无标记SERS蛋白质检测

建立用于光谱数据一致性分析的标准化方法，在表面增强拉曼光谱（SERS）中仍然是一个尚未得到充分研究的方面，特别是在生物学和生物医学研究中。在这里，我们提出了一种有效的机器学习分类方法，通过基于主成分分析（PCA）的混合数据处理对SERS光谱进行多峰拟合，对具有近似相似光谱特征的蛋白质物种进行分类。这种策略同时确保了蛋白质的成功区分和它们之间化学结构差异的全面表征，最终为SERS进化开辟了新途径，朝着生命科学中的传感应用和诊断学发展。