Identifying yeats belonging to the Brettanomyces/Dekkera genera through the use of selective-differential media

The purpose of this work was to compare and optimise different selective and differential media to aid in isolating spoilage yeasts belonging to the Brettanomyces/Dekkera genera. Growth media containing selective and differential factors were employed. These were inoculated with strains of yeast representing Spanish oenological microbiota. Lastly, some of these isolation media were successfully applied in 24 types of wine with a high ethylphenol content, all of which were from the Haro Oenological Station (La Rioja, Spain). p-coumaric acid was determined using High performance liquid chromatography-photodiode-array detection-electrospray ionization mass spectrometry (HPLC-DAD-ESI/MS); 4-ethylphenol by using Solid phase micro extraction-gas chromatography-mass spectrometry (SPME-GC-MS); and the rest of the analysis was carried out using official OIV methodology. Actidione is the most effective selective factor for isolating Brettanomyces/Dekkera yeast genera. Other secondary selective factors (selective carbon sources, sorbic acid and ethanol as a microbicide agent) may be used successfully to eliminate potential false positivities; however, they slow growth and delay the time to obtain results.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Plasma dynamics studies by mass spectrometry techniques

We present temporal information obtained by mass spectrometry techniques about the evolution of plasmas generated by laser filamentation in air. The experimental setup used in this work allowed us to study not only the dynamics of the filament core but also of the energy reservoir that surrounds it. Furthermore, valuable insights about the chemistry of such systems like the photofragmentation and/or formation of molecules were obtained. The interpretation of the experimental results are supported by PIC simulations.

Multiresidue Analysis of Insecticides and Other Selected Environmental Contaminants in Poultry Manure by Gas Chromatography/Mass Spectrometry

An analytical method was developed for the simultaneous determination in poultry manure of 41 organic contaminants belonging to different chemical classes: pesticides, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and polybrominated diphenyl ethers. Poultry manure was extracted with a modified QuEChERS method, and the extracts were analyzed by isotope dilution GC/MS. Recovery of these contaminants from samples spiked at levels ranging from 25 to 100 ng/g was satisfactory for all the compounds. The developed procedure provided LODs from 0.8 to 9.6 ng/g. The analysis of poultry manure samples collected on different farms confirmed the presence of some of the studied contaminants. Pyrethroids and polycyclic aromatic hydrocarbons were the main contaminants detected.