Bacteria identification in an optical system with optimized diffraction pattern registration condition supported by enhanced statistical analysis.

It is possible to identify bacteria species basing on their diffraction patterns followed by statistical analysis. The new approach exploits two steps: optimization of the recording conditions and introduction of new interpretable features for the identification. First, optimal diffraction registration plane, was determined. Next, results were verified by the analysis workflow based on ANOVA and Fisher divergence for feature selection, QDA and SVM models for classification and identification and CV with stratified sampling, sensitivity and specificity for performance assessment of the identification process. The proposed approach resulted in high sensitivity 0.9759 and specificity 0.9903 with very small identification error 1.34%.

Biodeterioration of optical glass induced by lubricants used in optical instruments technology.

The process of biodeterioration of optical glass was studied after being induced by an auxiliary material (lubricant 4CKP) used in the production of optical instruments. It was determined that the lubricant can initiate growth of conidia of Aspergillus niger fungus. Acid spawn metabolites cause deterioration of the glass surface. Measurements of laser light beam transmittance through the glass plate and the AAS chemical analysis method of the post-culture fluid allowed to determine that glass with a high SiO2 content is most resistant to corrosion caused by the growth of A. niger fungi spawn.