Wang Tao, Yang Yanjun, Lu Haoran, Cui Jiaheng, Chen Xianyan, Ma Ping, Zhong Wenxuan, Zhao Yiping
Department of Statistics, Franklin College of Arts and Sciences, University of Georgia, Athens, Georgia 30602, USA.
Department of Physics and Astronomy, Franklin College of Arts and Sciences, University of Georgia, Athens, Georgia 30602, USA.
Analyst. 2025 Jan 27;150(3):460-469. doi: 10.1039/d4an01177e.
Surface-enhanced Raman spectroscopy (SERS) holds remarkable potential for the rapid and portable detection of trace molecules. However, the analysis and comparison of SERS spectra are challenging due to the diverse range of instruments used for data acquisition. In this paper, a spectra instrument transformation framework based on the penalized functional regression model (SpectraFRM) is introduced for cross-instrument mapping with subsequent machine learning classification to compare transformed spectra with standard spectra. In particular, the nonparametric forms of the functional response, predictors, and coefficients employed in SepctraFRM allow for efficient modeling of the nonlinear relationship between target spectra and standard spectra. In the leave-one-out training and test of 20 analytes across four instruments, the results demonstrate that SpectraFRM can provide interpretable corrections to peaks and baseline spectra, leading to approximately 11% error reduction, compared with original spectra. With an additional feature extraction step, the transformed spectra outperform the original spectra by 10% in analytes identification tasks. Overall, the proposed method is shown to be flexible, robust, accurate, and interpretable despite varieties of analytes and instruments, making it a potentially powerful tool for the standardization of SERS spectra from various instruments.
表面增强拉曼光谱（SERS）在痕量分子的快速便携式检测方面具有巨大潜力。然而，由于用于数据采集的仪器种类繁多，SERS光谱的分析和比较具有挑战性。本文介绍了一种基于惩罚函数回归模型的光谱仪器转换框架（SpectraFRM），用于跨仪器映射，随后进行机器学习分类，以将转换后的光谱与标准光谱进行比较。特别是，SpectraFRM中使用的函数响应、预测变量和系数的非参数形式允许对目标光谱和标准光谱之间的非线性关系进行有效建模。在对四种仪器上的20种分析物进行留一法训练和测试时，结果表明，与原始光谱相比，SpectraFRM可以对峰和基线光谱提供可解释的校正，从而使误差降低约11%。通过额外的特征提取步骤，转换后的光谱在分析物识别任务中比原始光谱的性能高出10%。总体而言，尽管存在各种分析物和仪器，但所提出的方法被证明是灵活、稳健、准确且可解释的，使其成为用于标准化来自各种仪器的SERS光谱的潜在强大工具。
