Cancer Diagnosis using LIBS and Machine Learning Tools: Progress and Challenges

Despite numerous research and development efforts that provide important tools to fight cancer, this disease still poses great challenges to diagnosis and treatment, and it remains one of the leading causes of death worldwide. Early diagnosis is crucial to increase the survival rate and quality of life of cancer patients. Thus, developing non-invasive screening methods would represent a key step towards point-of-care large scale screening and prevention of asymptomatic tumors such as Epithelian Ovarian Cancer (EOC) and others. Our group has developed two experimental strategies to pursue early cancer diagnosis through Laser-Induced Breakdown Spectroscopy (LIBS), a versatile atomic spectroscopy technique whose main advantages are: little or no sample preparation required; real-time multi-elemental response; virtually no limitation about the kind of sample that can be analyzed. The first is a LIBS-based immunoassay (Tag-LIBS), where a cancer biomarker is tagged with a suitably functionalized inorganic microparticles, which are in turn quantitatively and sensitively detected by LIBS. The second is based on the direct analysis of biological fluids through the combined use of LIBS and machine learning algorithms. By combining femtosecond LIBS with unsupervised classification techniques, we have shown that it is possible to discriminate blood samples extracted from healthy and diseased mice with an accuracy that approaches 80%. We will present our most recent results obtained with both approaches, and in particular we will report about the effects of various substrates used for LIBS measurements on the classification accuracy of blood samples extracted from cancerous and healthy mice

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of “dark signal” and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian

The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description

On the NASA 2020 rover mission to Jezero crater, the remote determination of the texture, mineralogy and chemistry of rocks is essential to quickly and thoroughly characterize an area and to optimize the selection of samples for return to Earth. As part of the Perseverance payload, SuperCam is a suite of five techniques that provide critical and complementary observations via Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), visible and near-infrared spectroscopy (VISIR), high-resolution color imaging (RMI), and acoustic recording (MIC). SuperCam operates at remote distances, primarily 2-7 m, while providing data at sub-mm to mm scales. We report on SuperCam's science objectives in the context of the Mars 2020 mission goals and ways the different techniques can address these questions. The instrument is made up of three separate subsystems: the Mast Unit is designed and built in France; the Body Unit is provided by the United States; the calibration target holder is contributed by Spain, and the targets themselves by the entire science team. This publication focuses on the design, development, and tests of the Mast Unit; companion papers describe the other units. The goal of this work is to provide an understanding of the technical choices made, the constraints that were imposed, and ultimately the validated performance of the flight model as it leaves Earth, and it will serve as the foundation for Mars operations and future processing of the data.In France was provided by the Centre National d'Etudes Spatiales (CNES). Human resources were provided in part by the Centre National de la Recherche Scientifique (CNRS) and universities. Funding was provided in the US by NASA's Mars Exploration Program. Some funding of data analyses at Los Alamos National Laboratory (LANL) was provided by laboratory-directed research and development funds

Diagnosis of disease using laser-induced breakdown spectroscopy and machine learning

Diagnosing a pathology with LIBS and biological fluids includes focusing light on to a sample on a substrate to cause ablation of the sample and formation of a plasma, collecting optical emission from the plasma, and providing the optical emission to a spectroscopic acquisition component that provides information on spectral data of the plasma. The spectral data is provided to a machine learning algorithm to diagnose a pathology in the sample, where the algorithm is trained on a training set that includes spectral features in a different spectrum derived from differences between a first LIBS optical emission spectrum collected from one or more sample that have the pathology or known progress of the pathology and a second LIBS optical emission spectrum collected from one or more samples of the predetermined biological fluids that do not have the pathology or the known progress of the pathology

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