Methodology and applications of elemental mapping by laser induced breakdown spectroscopy.

In the last few years, LIBS has become an established technique for the assessment of elemental concentrations in various sample types. However, for many applications knowledge about the overall elemental composition is not sufficient. In addition, detailed information about the elemental distribution within a heterogeneous sample is needed. LIBS has become of great interest in elemental imaging studies, since this technique allows to associate the obtained elemental composition information with the spatial coordinates of the investigated sample. The possibility of simultaneous multi-elemental analysis of major, minor, and trace constituents in almost all types of solid materials with no or negligible sample preparation combined with a high speed of analysis are benefits which make LIBS especially attractive when compared to other elemental imaging techniques. The first part of this review is aimed at providing information about the instrumental requirements necessary for successful LIBS imaging measurements and points out and discusses state-of-the-art LIBS instrumentation and upcoming developments. The second part is dedicated to data processing and evaluation of LIBS imaging data. This chapter is focused on different approaches of multivariate data evaluation and chemometrics which can be used e.g. for classification but also for the quantification of obtained LIBS imaging data. In the final part, current literature of different LIBS imaging applications ranging from bioimaging, geoscientific and cultural heritage studies to the field of materials science is summarized and reviewed.

A novel substrate for multisensor hyperspectral imaging.

The quality of chemical imaging, especially multisensor hyperspectral imaging, strongly depends on sample preparation techniques and instrumental infrastructure but also on the choice of an appropriate imaging substrate. To optimize the combined imaging of Raman microspectroscopy, scanning-electron microscopy and energy-dispersive X-ray spectroscopy, a novel substrate was developed based on sputtering of highly purified aluminium onto classical microscope slides. The novel aluminium substrate overcomes several disadvantages of classical substrates like impurities of the substrate material and contamination of the surface as well as surface roughness and homogeneity. Therefore, it provides excellent conditions for various hyperspectral imaging techniques and enables high-quality multisensor hyperspectral chemical imaging at submicron lateral resolutions.

How salt lakes affect atmospheric new particle formation: A case study in Western Australia.

New particle formation was studied above salt lakes in-situ using a mobile aerosol chamber set up above the salt crust and organic-enriched layers of seven different salt lakes in Western Australia. This unique setup made it possible to explore the influence of salt lake emissions on atmospheric new particle formation, and to identify interactions of aqueous-phase and gas-phase chemistry. New particle formation was typically observed at enhanced air temperatures and enhanced solar irradiance. Volatile organic compounds were released from the salt lake surfaces, probably from a soil layer enriched in organic compounds from decomposed leaf litter, and accumulated in the chamber air. After oxidation of these organic precursor gases, the reaction products contributed to new particle formation with observed growth rates from 2.7 to 25.4nmh-1. The presence of ferrous and ferric iron and a drop of pH values in the salt lake water just before new particle formation events indicated that organic compounds were also oxidized in the aqueous phase, affecting the new particle formation process in the atmosphere. The contribution of aqueous-phase chemistry to new particle formation is assumed, as a mixture of hundreds of oxidized organic compounds was characterized with several analytical techniques. This chemically diverse composition of the organic aerosol fraction contained sulfur- and nitrogen-containing organic compounds, and halogenated organic compounds. Coarse mode particles were analyzed using electron microscopy, energy dispersive X-ray spectroscopy and Raman spectroscopy. Ultra-high resolution mass spectrometry was applied to analyze filter samples. A targeted mass spectral analysis revealed the formation of organosulfates from monoterpene precursors and two known tracers for secondary organic aerosol formation from atmospheric oxidation of 1,8-cineole, which indicates that a complex interplay of aqueous-phase and gas-phase oxidation of monoterpenes contributes to new particle formation in the investigated salt lake environment.

Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy.

Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets (<50). In this study the K-nearest neighbour algorithm (KNN) was used as a reference method for the classification of contaminated and blank corn samples. A Partial least squares (PLS) regression model was evaluated using full cross validation. Mid-Infrared spectra were collected using the attenuated total reflection (ATR) technique and the fingerprint range (800-1800 cm(-1)) of 21 maize samples that were contaminated with 300 - 2600 µg/kg deoxynivalenol (DON) was investigated. Separation efficiency after principal component analysis/cluster analysis (PCA/CA) classification was 100%. Cross validation of the PLS model revealed a correlation coefficient of r=0.9926 with a root mean square error of calibration (RMSEC) of 95.01. Validation results gave an r=0.8111 and a root mean square error of cross validation (RMSECV) of 494.5 was calculated. No outliers were reported.

Classification of maize contaminated withFusarium Graminearum Using Mid-infrared Spectroscopy and Chemometrics.

Advances in the development of a novel method for the detection ofFusarium graminearum employing mid-infrared spectroscopy are described. The dried and ground sample was sieved and the fraction with particle sizes between 250 and 100 µm was used for spectroscopy. The sample was pressed against a diamond ATR-crystal, mounted in the sample chamber of an FT-IR spectrometer and the mid-infrared absorption spectrum was recorded. The most prominent features in the spectrum were identified as protein, lipid and carbohydrate bands, which are changed by fungal infection. Multivariate data analysis was employed to detect the spectral changes: Principal component analysis (PCA) of mean centred data proved to be the most efficient method for the separation of contaminated maize from uninfected samples using the first two principal components. Ergosterol and the toxin deoxynivalenol (DON) served as reference parameters and were obtained from each sample using conventional analytical techniques. Samples with a toxin content of as low as 309 µg/kg could be separated from blank samples, which is well in the range of natural contamination. Investigated concentration ranges were 0.73-4.5 mg/kg for ergosterol and 0-2.6 mg/kg for DON. The percentage of correctly classified samples was between 75 and 100%.

Using mid-infrared Fourier-Transform-Spectroscopy with attenuated total reflection (FT-IR/ATR) as a tool for the determination ofFusarium graminearum on maize.

A novel method for the detection ofFusarium graminearum employing mid-infrared spectroscopy is described. In this study the fungus itself was determined on maize by pressing the ground sample against a diamond ATR-crystal, mounted horizontally in the sample chamber of an FT-IR spectrometer and recording the mid-infrared absorption spectrum. Multivariate data analysis was employed for the interpretation of spectra: Principal component analysis (PCA) was used to separate contaminated maize from uninfected samples. For concentrations higher than 8.23 mg/kg >80% of the samples were correctly classified with PCA. Spectra were correlated with HPLC results from reference measurements for ergosterol using principal component regression (PCR). A correlation coefficient of 0.9786 with an F-statistic of 107 was calculated (concentration range: 0.79-947 mg/kg ergosterol).