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，已被证实可用于阐明文件的真伪。发光油墨的研发及其在正品文件中的应用，已被证实是一项优异的防伪安全特性。本文提出将一种潜在的发光油墨标记物应用于防伪场景，旨在构建一套简便、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视觉、化学与光谱特征，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精准区分。主成分分析（PCA）与K均值的无监督模型，可成功将标记油墨与其对应的纯标记物进行精准关联；而基于偏最小二乘判别分析（PLS-DA）的监督分类模型，则可对预测集样本与盲测样本实现全部正确分类。为进行对比验证，本文同时构建了软独立建模分类法（SIMCA）模型，尽管该模型存在1个误分类样本，但仍可作为未来应用的有力备选方案。