Yuri Markushin

TL;DR: In this paper, the authors highlight the work of many LIBS researchers who have developed, adapted, and improved upon sample preparation techniques for various specimen types in order to improve the quality of the analytical data that LIBS can produce in a large number of research domains.

TL;DR: It is shown that elemental encoded particle assay coupled with femtosecond laser-induced breakdown spectroscopy for simultaneous multi-elemental analysis can significantly improve biomarker detectability and lead to sensitive detection of ovarian cancer biomarker CA125 in human blood plasma.

TL;DR: LIBS and multivariate analysis may be a novel approach for detecting EOC and the results suggest that it is possible to distinguish blood plasma samples obtained from serially bled tumor-bearing TgMISIIR-TAg transgenic and wild type cancer-free littermate control mice.

TL;DR: The proposed approach demonstrates that highly accurate automatic classification of complex protein samples from laser-induced breakdown spectroscopy data can be successfully achieved using principal component analysis with a sufficiently large number of extracted features, followed by a wrapper technique to determine the optimal number of principal components.

TL;DR: Experiments performed on real life data suggest that both classification methods are quite efficient in distinguishing among four types of proteins and they have a fairly robust detection performance for a range of the numbers of extracted features as well as the algorithms' parameters.