Abstract: Objective To investigate the feasibility of surface-enhanced Raman spectroscopy (SERS) combined with chemometric methods for the nondestructive and rapid identification of black pen inks. Methods By optimizing silver nanoparticle substrates and spectral acquisition parameters, SERS spectra of 30 black pen inks were collected. Classification models were constructed using K-means clustering algorithm (K-Means), K-nearest neighbor classification algorithm (KNN), and random forest (RF) algorithm, with their performance evaluated based on accuracy and F1-score. Results SERS effectively suppressed fluorescence background and enhanced Raman signals, yielding distinct characteristic peaks for all tested samples. The carbon material D band and G band located at 1 338~ 1 392 cm-1 and 1 583~1 619 cm-1 were also identified. The KNN demonstrated optimal classification performance, as its results were fully consistent with those obtained from direct SERS discrimination. Conclusion The combination of SERS technology and chemometric methods can be applied to the accurate classification of black pen inks, providing a nondestructive, rapid and reliable approach for forensic questioned document examination.

Key words: surface-enhanced Raman spectroscopy (SERS), black pen ink, chemometrics, species identification

摘要： 目的 探讨表面增强拉曼光谱结合化学计量学方法，在实现黑色笔墨无损、快速种类鉴别方面的可行性。方法 通过优化银纳米颗粒基底与光谱采集参数，获取30种黑色笔墨的表面增强拉曼光谱数据，并采用K-均值聚类算法、K-近邻分类算法和随机森林算法进行分类建模，以准确率与F1值评价分类性能。结果 表面增强拉曼光谱有效抑制了荧光背景并增强了拉曼信号，可识别出分别位于1 338~1 392 cm⁻¹和1 583~1 619 cm⁻¹的碳材料D带与G带，所有样本均呈现清晰的特征峰。其中，K-近邻分类算法效果最优，其分类结果与表面增强拉曼光谱直接判别完全一致。结论 表面增强拉曼光谱技术结合化学计量学方法可用于黑色笔墨的准确鉴别，为文书鉴定提供了一种无损、快速、可靠的分析手段。

关键词: 表面增强拉曼光谱, 黑色笔墨, 化学计量学, 种类鉴别