No abstract

No abstract

Optical spectroscopy is increasingly used for cancer diagnostics. Tumor detection feasibility in human kidney samples using mid- and near-infrared (NIR) spectroscopy, fluorescence spectroscopy, and Raman spectroscopy has been reported (Artyushenko et al., Spectral fiber sensors for cancer diagnostics in vitro. In Proceedings of the European Conference on Biomedical Optics, Munich, Germany, 21–25 June 2015). In the present work, a simplification of the NIR spectroscopic analysis for cancer diagnostics was studied. The conventional high-resolution NIR spectroscopic method of kidney tumor diagnostics was replaced by a compact optical sensing device constructively represented by a set of four light-emitting diodes (LEDs) at selected wavelengths and one detecting photodiode. Two sensor prototypes were tested using 14 in vitro clinical samples of 7 different patients. Statistical data evaluation using principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) confirmed the general applicability of the LED-based sensing approach to kidney tumor detection. An additional validation of the results was performed by means of sample permutation.

No abstract

The feasibility of direct (ie, without sample preparation) quantitative analysis of total hydrocarbons and water in oil-contaminated soils using mid-infrared spectroscopy and an attenuated total reflection (ATR) probe has been investigated. Spectral characteristics of unpolluted and oil-contaminated soils composed of sand, clay, dolomite, and humus have been studied over the full mid-infrared range (4000-400 cm). Spectra of 25 typical soil samples containing varying levels of oil and water have been analyzed using a chalcogenide infrared fiber-based probe with a ZrO 2 crystal as an ATR element. The spectral data were used to build calibration models for the analysis of hydrocarbon contamination as well as moisture content of soil samples. The low quality of ATR spectra of drier samples and variable spectral intensity inherent in the ATR measurement of solids has been overcome by suitable data processing. Further improvement of the model performance has been achieved using a variable selection based on the modified genetic algorithm. Our proposed method allows the determination of oil and moisture content in soils with accuracies of 1.1% and 0.6%, respectively, which is sufficient for a number of practical applications. The reported results may be used to develop portable devices for measuring petroleum and water content of soils.