Department of Aerospace Engineering, Seoul National University, Republic of Korea.
Appl Spectrosc. 2018 Jul;72(7):1047-1056. doi: 10.1177/0003702818765183. Epub 2018 Mar 23.
A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.
一种新的技术被报道用于使用化学计量学方法分离重叠的潜在指纹，该方法结合了激光诱导击穿光谱（LIBS）和多元分析。LIBS 技术提供了实时分析和高频扫描的能力，以及有关重叠潜在指纹化学成分的数据。这些光谱通过实施适当的统计多元分析，为重叠潜在指纹的分类和重建提供了有价值的信息。本研究采用主成分分析和偏最小二乘法对来自 LIBS 光谱的潜在指纹进行分类。通过使用软独立建模分类类比（SIMCA）和偏最小二乘判别分析（PLS-DA）等分类方法对四个不同的潜在指纹进行分类研究，成功地验证了该技术。该新方法的准确率超过 85%，并且被证明足够稳健。此外，通过在 125 µm 的空间间隔进行激光扫描分析，重叠的指纹被重建为单独的二维形式。
