No abstract

In recent years, new technologies are emerging in the field of judicial expertise, followed by more arduous challenges. In this study, ink marks are used as an example. Meanwhile, machine learning and laser-induced breakdown spectroscopy (LIBS) are used to analyze the ink marks. This is a new idea in the field of handwriting identification. First, the spectrum is obtained by LIBS. The characteristic spectral lines of C, N, O, Si, Mg, Al, and Ca are observed in the spectrum. Second, a detailed spectrum of the ink mark is provided in this article; in addition, different kinds of inks are used for analogy observation to analyze the influence of different components on ink marks. Finally, the K-nearest neighbor algorithm based on the principal component analysis is used to build the ink recognition model and then analyze the differences in different inks and build a database. The identification results become more intuitive and accurate combining machine learning based on big data, which provide reliable evidence for judicial expertise.

Precious and half-precious metals are widely used in various fields, which makes it of great significance to recycle them, and copper was taken as an example for the investigation in this paper. A system based on laser-induced breakdown spectroscopy combined with machine learning algorithms was developed and employed in the lab to identify and classify several metal devices that contain copper element. According to the obtained emission spectra, 36 characteristic spectral lines of copper element are observed in the spectrogram of high-purity copper, as well as some metallic elements including Zn, Ca, Mg, and Na that also appeared. Moreover, eight types of similar metal devices containing copper element which are common in life (electrode, copper plug, copper tape, carbon brush, wire, circuit board, gasket, and coil) were selected to perform spectral analysis. Rough classification can be achieved by observing the spectra of eight metal devices. The effective classification process of metal devices was implemented by conducting principal component analysis, which built a model to reduce the dimension of spectral data for classification. Several samples are distributed at different positions in the principal component space, which is established based on the three principal components as the coordinate axis. K-nearest neighbors were employed to verify the classification effectiveness, acquiring the final classification accuracy of 99%. The results show that the development system has a broad development prospect for identifying metal copper and classifying metal devices that contain copper element.