A family of Chisini mean based Jensen-Shannon divergence kernels

Jensen-Shannon divergence is an effective method for measuring the distance between two probability distributions. When the difference between these two distributions is subtle, Jensen-Shannon divergence does not provide adequate separation to draw distinctions from subtly different distributions. We extend Jensen-Shannon divergence by reformulating it using alternate operators that provide different properties concerning robustness. Furthermore, we prove a number of important properties for this extension: the lower limits of its range, and its relationship to Shannon Entropy and Kullback-Leibler divergence. Finally, we propose a family of new kernels, based on Chisini mean Jensen-Shannon divergence, and demonstrate its utility in providing better SVM classification accuracy over RBF kernels for amino acid spectra. Because spectral methods capture phenomenon at subatomic levels, differences between complex compounds can often be subtle. While the impetus behind this work began with spectral data, the methods are generally applicable to domains where subtle differences are important

Investigating manifold neighborhood size for nonlinear analysis of LIBS amino acid spectra

Classification and identification of amino acids in aqueous solutions is important in the study of biomacromolecules. Laser Induced Breakdown Spectroscopy (LIBS) uses high energy laser-pulses for ablation of chemical compounds whose radiated spectra are captured and recorded to reveal molecular structure. Spectral peaks and noise from LIBS are impacted by experimental protocols. Current methods for LIBS spectral analysis achieves promising results using PCA, a linear method. It is well-known that the underlying physical processes behind LIBS are highly nonlinear. Our work set out to understand the impact of LIBS spectra on suitable neighborhood size over which to consider pattern phenomena, if nonlinear methods capture pattern phenomena with increased efficacy, and how they improve classification and identification of compounds. We analyzed four amino acids, polysaccharide, and a control group, water. We developed an information theoretic method for measurement of LIBS energy spectra, implemented manifold methods for nonlinear dimensionality reduction, and found while clustering results were not statistically significantly different, nonlinear methods lead to increased classification accuracy. Moreover, our approach uncovered the contribution of micro-wells (experimental protocol) in LIBS spectra. To the best of our knowledge, ours is the first application of Manifold methods to LIBS amino-acid analysis in the research literature

Age-specific discrimination of blood plasma samples of healthy and ovarian cancer prone mice using laser-induced breakdown spectroscopy

International audienceEpithelial ovarian cancer (EOC) mortality rates are strongly correlated with the stage at which it is diagnosed. Detection of EOC prior to its dissemination from the site of origin is known to significantly improve the patient outcome. However, there are currently no effective methods for early detection of the most common and lethal subtype of EOC. We sought to determine whether laser-induced breakdown spectroscopy (LIBS) and classification techniques such as linear discriminant analysis (LDA) and random forest (RF) could classify and differentiate blood plasma specimens from transgenic mice with ovarian carcinoma and wild type control mice. Herein we report results using this approach to distinguish blood plasma samples obtained from serially bled (at 8, 12, and 16 weeks) tumor-bearing TgMISIIR-TAg transgenic and wild type cancer-free littermate control mice. We have calculated the age-specific accuracy of classification using 18,000 laser-induced breakdown spectra of the blood plasma samples from tumor-bearing mice and wild type controls. When the analysis is performed in the spectral range 250 nm to 680 nm using LDA, these are 76.7 (± 2.6)%, 71.2 (± 1.3)%, and 73.1 (± 1.4)%, for the 8, 12 and 16 weeks. When the RF classifier is used, we obtain values of 78.5 (± 2.3)%, 76.9 (± 2.1)% and 75.4 (± 2.0)% in the spectral range of 250 nm to 680 nm, and 81.0 (± 1.8)%, 80.4 (± 2.1)% and 79.6 (± 3.5)% in 220 nm to 850 nm. In addition, we report, the positive and negative predictive values of the classification of the two classes of blood plasma samples. The approach used in this study is rapid, requires only 5 μL of blood plasma, and is based on the use of unsupervised and widely accepted multivariate analysis algorithms. These findings suggest that LIBS and multivariate analysis may be a novel approach for detecting EOC

Sample treatment and preparation for laser-induced breakdown spectroscopy

One of the most widely cited advantages of laser-induced breakdown spectroscopy (LIBS) is that it does not require sample preparation, but this may also be the biggest factor holding it back from becoming a mature analytical technique like LA-ICP-MS, ICP-OES, or XRF. While there are certain specimen types that have enjoyed excellent LIBS results without any sample treatment (mostly homogeneous solids such as metals, glass, and polymers), the possible applications of LIBS have been greatly expanded through the use of sample preparation techniques that have resulted in analytical performance (i.e., limits of detection, accuracy, and repeatability) on par with XRF, ICP-OES, and often ICP-MS. This review highlights 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. Strategies, not only for solids, but also liquids, gases, and aerosols are discussed, including newly developed nanoparticle enhancement and biological imaging and tagging techniques