Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions

Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples

The key to understanding the sulfur history on Mars is to identify and determine sulfate and sulfide compositions and then to draw from them geologic clues about their environments of formation. To lay a foundation for use of remote LIBS to sulfur analysis in planetary exploration, we have undertaken a focused study of sulfur LIBS in geological samples in a simulated Mars atmosphere, with experimental parameters replicating the ChemCam LIBS instrument. A suite of twelve samples was selected, including rocks rich in minerals representative of sulfates and sulfides that might be encountered on Mars. Univariate analysis of sulfur emission lines did not provide quantitative information. Partial least squares (PLS) analysis was successful at modeling sulfur concentrations for a subset of samples with similar matrices. Sulfide minerals were identified on the basis of other siderophile or chalcophile peaks, such as those arising from Zn and Cu. Because the S lines are very weak compared to those of other elements, optimal PLS results were obtained by restricting the wavelength range to channels close to the most intense sulfur lines ~ 540-570 nm. Principal components analysis was attempted on the dataset, but did not differentiate the samples into meaningful groups because the sulfur lines are not strong enough. However, areas of the relatively weak S, H, and O peaks may be used to correctly classify all samples. Based on these outcomes, a flowchart that outlines a possible decision tree for identification and quantification of sulfur in remote LIBS analysis was constructed. Results suggest that LIBS data acquired under Mars conditions can meet the science requirements for the ChemCam instrument.     

Characterization and forensic analysis of soil samples using laser-induced breakdown spectroscopy (LIBS)

A method for the quantitative elemental analysis of surface soil samples using laser-induced breakdown spectroscopy (LIBS) was developed and applied to the analysis of bulk soil samples for discrimination between specimens. The use of a 266 nm laser for LIBS analysis is reported for the first time in forensic soil analysis. Optimization of the LIBS method is discussed, and the results compared favorably to a laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method previously developed. Precision for both methods was <10% for most elements. LIBS limits of detection were <33 ppm and bias <40% for most elements. In a proof of principle study, the LIBS method successfully discriminated samples from two different sites in Dade County, FL. Analysis of variance, Tukey’s post hoc test and Student’s t test resulted in 100% discrimination with no type I or type II errors. Principal components analysis (PCA) resulted in clear groupings of the two sites. A correct classification rate of 99.4% was obtained with linear discriminant analysis using leave-one-out validation. Similar results were obtained when the same samples were analyzed by LA-ICP-MS, showing that LIBS can provide similar information to LA-ICP-MS. In a forensic sampling/spatial heterogeneity study, the variation between sites, between sub-plots, between samples and within samples was examined on three similar Dade sites. The closer the sampling locations, the closer the grouping on a PCA plot and the higher the misclassification rate. These results underscore the importance of careful sampling for geographic site characterization.     

Onboard calibration igneous targets for the Mars Science Laboratory Curiosity rover and the Chemistry Camera laser induced breakdown spectroscopy instrument

Accurate characterization of the Chemistry Camera (ChemCam) laser-induced breakdown spectroscopy (LIBS) on-board composition targets is of prime importance for the ChemCam instrument. The Mars Science Laboratory (MSL) science and operations teams expect ChemCam to provide the first compositional results at remote distances (1.5–7 m) during the in situ analyses of the Martian surface starting in 2012. Thus, establishing LIBS reference spectra from appropriate calibration standards must be undertaken diligently. Considering the global mineralogy of the Martian surface, and the possible landing sites, three specific compositions of igneous targets have been determined. Picritic, noritic, and shergottic glasses have been produced, along with a Macusanite natural glass. A sample of each target will fly on the MSL Curiosity rover deck, 1.56 m from the ChemCam instrument, and duplicates are available on the ground. Duplicates are considered to be identical, as the relative standard deviation (RSD) of the composition dispersion is around 8%. Electronic microprobe and laser ablation inductively coupled plasma mass spectrometry (LA ICP-MS) analyses give evidence that the chemical composition of the four silicate targets is very homogeneous at microscopic scales larger than the instrument spot size, with RSD < 5% for concentration variations > 0.1 wt.% using electronic microprobe, and < 10% for concentration variations > 0.01 wt.% using LA ICP-MS. The LIBS campaign on the igneous targets performed under flight-like Mars conditions establishes reference spectra for the entire mission. The LIBS spectra between 240 and 900 nm are extremely rich, hundreds of lines with high signal-to-noise, and a dynamical range sufficient to identify unambiguously major, minor and trace elements. For instance, a first LIBS calibration curve has been established for strontium from [Sr] = 284 ppm to [Sr] = 1480 ppm, showing the potential for the future calibrations for other major or minor elements.     

LIBS for landmine detection and discrimination

The concept of utilizing laser-induced breakdown spectroscopy (LIBS) technology for landmine detection and discrimination has been evaluated using both laboratory LIBS and a prototype man-portable LIBS systems. LIBS spectra were collected for a suite of landmine casings, non-mine plastic materials, and ‘clutter-type’ objects likely to be present in the soil of a conflict area or a former conflict area. Landmine casings examined included a broad selection of anti-personnel and anti-tank mines from different countries of manufacture. Other materials analyzed included rocks and soil, metal objects, cellulose materials, and different types of plastics. Two ‘blind’ laboratory tests were conducted in which 100 broadband LIBS spectra were obtained for a mixed suite of landmine casings and clutter objects and compared with a previously-assembled spectral reference library. Using a linear correlation approach, ‘mine/no mine’ determinations were correctly made for more than 90% of the samples in both tests. A similar test using a prototype man-portable LIBS system yielded an analogous result, validating the concept of using LIBS for landmine detection and discrimination.     

Principal coordinate maps of molecular potential energy surfaces

Obtaining useful representations of molecular conformation spaces and visualizing the associated potential energy surfaces is a complex task, mainly due to the high dimensionality of these spaces. Principal component analysis (PCA), which projects multidimensional data on low-dimensional subspaces, is thus becoming a common technique for studying such spaces. Three issues, relating to the use of principal component techniques for mapping molecular potential energy surfaces, are discussed in this study: the effectiveness of the projection; its accuracy; and the mapping procedure. The effectiveness of PCA is demonstrated through detailed analyses of principal component projections of several peptides. In these cases PCA projected conformation space into a subspace smaller even than that defined by the peptide's backbone dihedral angles. The average accuracy as well as the distribution of errors in the projection (i.e., the errors in reproducing individual distances) are studied as a function of the dimensionality of the projection. The wide variation in accuracy between different systems suggests that it is imperative to indicate the accuracy of the projection whenever PCA projections are used. Furthermore, when projecting potential energy surfaces on the principal two-dimensional (2D) plane, the projection errors result in artificial roughening of the surface. A new mapping procedure, the “minimal energy envelope” procedure, is introduced to overcome this problem. This procedure yields relatively smooth “energy landscapes,” which highlight the basin structure of the real multidimensional energy surface. It is demonstrated that the projected potential energy maps can be used for charting conformational transitions or dynamic trajectories in the system. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1255–1267, 1998     

Study on the quantitative structureactivity relation-ship of C-10 substituted artemisinin (QHS)’s derivatives using rough set theory

The structure-activity relationship study of C-10 substituted artemisinin (QHS) derivatives that are used as antimalarial was performed with the RS (rough sets) method. An RS process is a concise nonlinear process, and it has broad application foreground in the data mining of nonlinear life courses. In this work, initially the parameters of C-10 substituted QHS’s derivatives were computed with the quantum chemistry method, and the information table was constructed from the parameters (condition attributes) and biological activity (decision attributes). Based on the analysis of rough set theory, the core and reduction of attributes sets were obtained. Then the decision rules were extracted and the struc-ture-activity relationship was analyzed. As a nonlinear system, RS theory can extract the special rela-tion in the database. It has the advantage of being nonlinear over multiple linear regression (MLR), principal component analysis (PCA), partial least square (PLS), etc., and the advantage of obtaining results with unambiguous physical meanings over artificial neuron networks (ANNs), etc. The result obtained in this study is instructive to the study of pharmacodynamics, resistance mechanism of QHS and development of QHS’s derivatives.     

A random version of principal component analysis in data clustering

Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance/correlation matrix of the analyzed data. However, to properly work with high-dimensional data sets, PCA poses severe mathematical constraints on the minimum number of different replicates, or samples, that must be included in the analysis. Generally, improper sampling is due to a small number of data respect to the number of the degrees of freedom that characterize the ensemble. In the field of life sciences it is often important to have an algorithm that can accept poorly dimensioned data sets, including degenerated ones. Here a new random projection algorithm is proposed, in which a random symmetric matrix surrogates the covariance/correlation matrix of PCA, while maintaining the data clustering capacity. We demonstrate that what is important for clustering efficiency of PCA is not the exact form of the covariance/correlation matrix, but simply its symmetry.     

The analysis of seasonal air pollution pattern with application of neural networks

Air pollution monitoring includes measuring the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, some polycyclic aromatic hydrocarbons(PAHs), suspended particulate matter (PM) and tar substances. The purpose of this study was to determine the possibility of using artificial neural networks for identification of any patterns occurring during heating and nonheating seasons. The samples included in the study were collected over a period of 5 years (1997–2001) in the area of the city of Gdansk and the levels of pollutants measured in the samples collected were used as inputs to two different types of neural networks: multilayer perceptron (MLP) and self-organizing map (SOM). The MLP was used as a tool to predict in what heating season a certain sample was collected, and the SOM was applied for mapping all samples to recognize any similarities between them. This study also presents the comparison between two projection methods—linear (principal component analysis, PCA) and nonlinear (SOM)—in extracting valuable information from multidimensional environmental data. In the research the MLP model with 13-12-1 topology was developed and successfully trained for classification of air samples from different seasons. The sensitivity analysis on the inputs to the MLP indicated benz[α]anthracene, benzo[α]pyrene, PM₁, SO₂, tar substances and PM₁₀ as the most distinctive variables, while PCA pointed to PAHs and PM₁.     

Laser induced breakdown spectroscopy library for the Martian environment

The NASA Mars Science Laboratory rover will carry the first Laser Induced Breakdown Spectroscopy experiment in space: ChemCam. We have developed a laboratory model which mimics ChemCam's main characteristics. We used a set of target samples relevant to Mars geochemistry, and we recorded individual spectra. We propose a data reduction scheme for Laser Induced Breakdown Spectroscopy data incorporating de-noising, continuum removal, and peak fitting. Known effects of the Martian atmosphere are confirmed with our experiment: better Signal-to-Noise Ratio on Mars compared to Earth, narrower peak width, and essentially no self-absorption. The wavelength shift of emission lines from air to Mars pressure is discussed. The National Institute of Standards and Technology vacuum database is used for wavelength calibration and to identify the elemental lines. Our Martian database contains 1336 lines for 32 elements: H, Li, Be, B, C, N, O, F, Na, Mg, Al, Si, P, S, Cl, K, Ar, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Rb, Sr, Cs, Ba, and Pb. It is a subset of the National Institute of Standards and Technology database to be used for Martian geochemistry. Finally, synthetic spectra can be built from the Martian database. Correlation calculations help to distinguish between elements in case of uncertainty. This work is used to create tools and support data for the interpretation of ChemCam results.     

Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)

This article describes the applicability of multivariate projection techniques, such as principal-component analysis (PCA) and partial least-squares (PLS) projections to latent structures, to the large-volume high-density data structures obtained within genomics, proteomics, and metabonomics. PCA and PLS, and their extensions, derive their usefulness from their ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. Three examples are used as illustrations: the first example is a genomics data set and involves modeling of microarray data of cell cycle-regulated genes in the microorganism Saccharomyces cerevisiae. The second example contains NMR-metabonomics data, measured on urine samples of male rats treated with either of the drugs chloroquine or amiodarone. The third and last data set describes sequence-function classification studies in a set of G-protein-coupled receptors using hierarchical PCA.     

Synergistic effect of the simultaneous chemometric analysis of H NMR spectroscopic and stable isotope (SNIF-NMR,O, C) data: Application to wine analysis

It is known that ¹H NMR spectroscopy represents a good tool for predicting the grape variety, the geographical origin, and the year of vintage of wine. In the present study we have shown that classification models can be improved when ¹H NMR profiles are fused with stable isotope (SNIF-NMR, ¹⁸O, ¹³C) data. Variable selection based on clustering of latent variables was performed on ¹H NMR data. Afterwards, the combined data of 718 wine samples from Germany were analyzed using linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), factorial discriminant analysis (FDA) and independent components analysis (ICA). Moreover, several specialized multiblock methods (common components and specific weights analysis (ComDim), consensus PCA and consensus PLS-DA) were applied to the data.     

Evaluation of a compact spectrograph for in-situ and stand-off Laser-Induced Breakdown Spectroscopy analyses of geological samples on Mars missions

Laser-induced Breakdown Spectroscopy (LIBS) is actively under development for future use on surface probes to Mars. The analytical method can be deployed for in-situ and/or stand-off analysis with the latter embodiment providing the greatest advantages compared to previous and current elemental analysis methods used for planetary surface analysis. For this application, LIBS must be thoroughly investigated in terms of analytical capabilities and flight-rated instruments must be developed. Because of the low pressure of the predominantly CO₂ atmosphere on Mars, studies are needed to understand analytical requirements and to determine performance under these conditions. Stand-off analysis demands the most stringent requirements on instrumentation. Therefore, it must be determined if the high performance components that are normally used in a typical LIBS laboratory setup, which are generally not optimized for small size and weight, are essential to obtain the maximum scientific return from a mission. A key component of a LIBS apparatus is the detection system consisting of a spectrograph and a detector. Here we present an evaluation of one design of a compact spectrograph (Ocean Optics HR2000) for in-situ and stand-off LIBS analyses of geological samples under Mars atmospheric conditions.     

Comparative analysis of linear and nonlinear models for ion transport problem in chronoamperometry

Ion transport problem related to controlled potential experiments in electrochemistry is studied. The problem is assumed to be superposition of diffusion and migration under the influence of an electric field. The comparative analysis are presented for three well-known models—pure diffusive (Cottrell’s), linear diffusion-migration, and nonlinear diffusion-migration (Cohn’s) models. The nonlinear model is derived by the identification problem for a nonlinear parabolic equation with nonlocal additional condition. This problem reduced to an initial-boundary value problem for nonlinear parabolic equation. The nonlinear finite difference approximation of this problem, with an appropriate iteration algorithm is derived. The comparative numerical analysis for all three models shows an influence of the nonlinear migration term, the valences of oxidized and reduced oxidized species, also diffusivity to the value of the total charge. The obtained results permits one to estimate bounds of linear and nonlinear ion transport models.     

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz ¹H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Comparative study of different methodologies for quantitative rock analysis by Laser-Induced Breakdown Spectroscopy in a simulated Martian atmosphere

Laser-Induced Breakdown Spectroscopy was selected by NASA as part of the ChemCam instrument package for the Mars Science Laboratory rover to be launched in 2009. ChemCam's Laser-Induced Breakdown Spectroscopy instrument will ablate surface coatings from materials and measure the elemental composition of underlying rocks and soils at distances from 1 up to 10 m. The purpose of our studies is to develop an analytical methodology enabling identification and quantitative analysis of these geological materials in the context of the ChemCam's Laser-Induced Breakdown Spectroscopy instrument performance. The study presented here focuses on several terrestrial rock samples which were analyzed by Laser-Induced Breakdown Spectroscopy at an intermediate stand-off distance (3 m) and in an atmosphere similar to the Martian one (9 mbar CO₂). The experimental results highlight the matrix effects and the measurement inaccuracies due to the noise accumulated when low signals are collected with a detector system such as an Echelle spectrometer equipped with an Intensified Charge-Coupled Device camera. Three different methods are evaluated to correct the matrix effects and to obtain quantitative results: by using an external reference sample and normalizing to the sum of all elemental concentrations, by using the internal standardization by oxygen, a major element common to all studied matrices, and by applying the Calibration Free Laser-Induced Breakdown Spectroscopy method. The three tested methods clearly demonstrate that the matrix effects can be corrected merely by taking into account the difference in the amount of vaporized atoms between the rocks, no significant variation in plasma excitation temperatures being observed. The encouraging results obtained by the three methods indicate the possibility of meeting ChemCam project objectives for stand-off quantitative analysis on Mars.