Sulfur Detection in Soil by Laser Induced Breakdown Spectroscopy Assisted by Multivariate Analysis.

Laser-induced breakdown spectroscopy (LIBS) is used for the detection and determination of sulfur content in some organic soil samples. The most suitable sulfur spectral lines for such tasks were found to occur in the vacuum ultraviolet (VUV) spectral region and they were used for the construction of calibration curves. For the analysis, both univariate and multivariate statistical models were employed. The results obtained by the different analysis techniques are evaluated and compared. The present study demonstrates both the applicability and efficiency of LIBS for fast sulfur detection in soil matrices when aided by multivariate analysis methods improving the accuracy and extending the potential use of LIBS in such applications.

Study of Controlled Atmosphere Flexible Microtube Plasma Soft Ionization Mass Spectrometry for Detection of Volatile Organic Compounds as Potential Biomarkers in Saliva for Cancer.

A new soft ionization device for mass spectrometry is presented using the flexible microtube plasma under controlled atmospheric conditions. The controlled atmosphere flexible microtube plasma consists of the plasma source itself connected to a gas chromatograph and a mass spectrometer using a borosilicate glass cross piece. Controlled atmosphere, for example, nitrogen and/or an oxygen mixture, is introduced to the system to create a clean ionization environment. Reproducibility issues are discussed, and solutions are presented manipulating the gas flow in the cross piece. A proof of concept is shown using a ketone mixture introduced to the mass spectrometer to optimize atmospheric conditions. Furthermore, application of the presented device for the sensitive and nonfragmenting ionization of volatile organic biomarkers relevant for cancer is carried out. Sample treatment for human saliva is described, and relevant candidate biomarkers are measured in the saliva matrix, showing a very good ionization efficiency and neglectable matrix effects with limits of detection below 80 ppt.

Laser-based classification of olive oils assisted by machine learning.

Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidity and its authentication in terms of PDO (Protected Designation of Origin) and PGI (Protected Geographical Indications) characterizations are nowadays necessary and of great importance for the market of olive oil and the related economic activities. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used assisted by machine learning algorithms for retrieving of the information contained in the LIBS spectra to provide a simple, reliable, and ultrafast methodology for olive oils classification in terms of the degree of acidity and geographical origin. The combination of LIBS technique with machine learning statistical analysis approaches constitute a very powerful tool for the fast, in-situ and remote quality control of olive oil.