Encoding of Luminescent Ink Markers Using Low-Level Data Fusion and Chemometrics

The identification and analysis of documentary fraud is always a challenge for forensic science. Document analysis has proven to be an important branch of forensics in elucidating the authenticity of documents. The development and incorporation of luminescent inks in authentic documents have proved to be an excellent security feature. This paper purposes the use of a possible luminescent ink marker for anti-counterfeiting applications, aiming to create a document encoding process that is simple, robust, sensitive, and non-destructive. Since luminescent inks markers provide a visual, chemical, and spectral signature, and can be easily detected by using a UV lamp, the aid of unsupervised chemometric tools makes it possible to differentiate the luminescent markers inserted in the ink. Unsupervised models of principal component analysis (PCA) and K-mean were successful in correctly associating marked inks with their respective pure markers, while a supervised classification model based on partial least squares discriminant analysis (PLS-DA) correctly classified all samples from the prediction set and the blind test samples. For comparison, a soft independent modeling of class analogy (SIMCA) model was also built, which despite showing a misclassified sample it is also a strong candidate for future applications.文件欺诈的识别与分析始终是法医学领域的一大挑战。文件检验作为法医学的重要分支，已被证实可为阐明文件真伪提供关键支撑。发光油墨在正品文件中的研发与应用，已被证明是一项优异的防伪技术。本研究旨在开发一款可应用于防伪场景的发光油墨标记物，以期构建一套简便、稳健、灵敏且无损的文件编码流程。由于发光油墨标记物可提供可视化、化学及光谱特征，且可通过紫外灯轻松检测，借助无监督化学计量学工具，即可实现对油墨中掺入的发光标记物的有效区分。主成分分析（PCA）与K均值（K-means）的无监督模型可成功将标记油墨与其对应的纯标记物进行精准关联；基于偏最小二乘判别分析（PLS-DA）的监督分类模型，则可对预测集与盲测样本中的全部样品实现准确分类。为进行对比，本研究还构建了软独立分类类比（SIMCA）模型，尽管该模型存在1个误分类样本，但仍不失为未来应用的有力备选方案。