In the production of fermented feed, each crop can be contaminated with a variety of microorganisms that may produce natural pollutants. Biogenic amines, mycotoxins, and undesirable organic acids can decrease health feed safety. The aim of this study was to compare the counts of microorganisms, levels of biogenic amines, and the mycotoxins in forage legumes, and also to compare the occurrence of microorganisms and levels of mycotoxins in green fodder and subsequently produced silage and the influence of additives on the content of natural harmful substances in silage. The experimental plot was located in Troubsko and Vatín, in the Czech Republic. Two varieties of Medicago sativa and one variety of Trifolium pratense were compared. Green fodder and subsequently produced silage reaching up to 23% of dry matter were evaluated and prepared using a bio-enzymatic additive and a chemical additive. Green fodder of Medicago sativa was more contaminated by Enterococci than Trifolium pratense fodder. The obvious difference was determined by the quality of silage leachate. The silage prepared from Medicago sativa fodder was more contaminated with butyric acid. Fungi were present in higher counts in the anaerobic environment of green fodder and contaminated it with zearalenone and deoxynivalenol. Lower counts of fungi were found in silage, although the zearalenone content did not change. Lower content of deoxynivalenol was detected in silage, compared with green fodder. Silages treated with a chemical additive were found not to contain butyric acid. Lower ethanol content was determined, and the tendency to reduce the risk of biogenic amines occurrence was evident. The additives proved to have no influence on the content of mycotoxins.

• Keywords
• biological additives, butyric acid, cadaverine, chemical additives, deoxynivalenol, enterococci, fungi, green matter, putrescine, silage, spermine, tyramine, zearalenone,
• MeSH
• Biogenic Amines chemistry   MeSH
• Fermentation MeSH
• Food Contamination prevention & control   MeSH
• Animal Feed microbiology   MeSH
• Medicago sativa chemistry   microbiology   MeSH
• Mycotoxins chemistry   MeSH
• Food Additives pharmacology   MeSH
• Silage analysis   microbiology   MeSH
• Trifolium chemistry   microbiology   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic MeSH
• Names of Substances
• Biogenic Amines MeSH
• Mycotoxins MeSH
• Food Additives MeSH

BACKGROUND: Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level=5 wavelet transform. CONCLUSIONS/SIGNIFICANCE: On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue.

• MeSH
• Electrochemistry methods   MeSH
• Data Interpretation, Statistical MeSH
• Rabbits MeSH
• Rats MeSH
• Metallothionein chemistry   MeSH
• Rats, Wistar MeSH
• Proteomics methods   MeSH
• Decision Trees MeSH
• Models, Statistical MeSH
• Models, Theoretical MeSH
• Tissue Distribution * MeSH
• Computational Biology methods   MeSH
• Research Design MeSH
• Animals MeSH
• Check Tag
• Rabbits MeSH
• Rats MeSH
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Metallothionein MeSH

The issue of moulds and, thus, contamination with mycotoxins is very topical, particularly in connexion with forages from grass stands used at the end of the growing season. Deoxynivalenol (DON), zearalenone (ZEA), fumonisins (FUM) and aflatoxins (AFL) are among the most common mycotoxins. The aim of the paper was to determine concentrations of mycotoxins in selected grasses (Lolium perenne, Festulolium pabulare, Festulolium braunii) and their mixtures with Festuca rubra an/or Poa pratensis during the growing season as a marker of grass safety, which was assessed according to content of the aforementioned mycotoxins. During the growing season grass forage was contaminated with mycotoxins, most of all by DON and ZEA. The contents of AFL and FUM were zero or below the limit of quantification. Moreover, the level of the occurrence of mould was quantified as ergosterol content, which was higher at the specific date of cut. All results were statistically processed and significant changes were discussed.

• Keywords
• aflatoxins, contamination, deoxynivalenol, forage, fumonisins, grass, mycotoxins, zearalenone,
• MeSH
• Aflatoxins analysis   MeSH
• Enzyme-Linked Immunosorbent Assay MeSH
• Ergosterol analysis   MeSH
• Festuca chemistry   microbiology   MeSH
• Fumonisins analysis   MeSH
• Fungi isolation & purification   MeSH
• Poisons analysis   MeSH
• Lolium chemistry   microbiology   MeSH
• Food Contamination analysis   MeSH
• Animal Feed analysis   microbiology   toxicity   MeSH
• Poaceae chemistry   microbiology   MeSH
• Mycotoxins analysis   MeSH
• Poa chemistry   microbiology   MeSH
• Food Microbiology MeSH
• Seasons MeSH
• Trichothecenes analysis   MeSH
• Zearalenone analysis   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Czech Republic MeSH
• Names of Substances
• Aflatoxins MeSH
• deoxynivalenol MeSH Browser
• Ergosterol MeSH
• Fumonisins MeSH
• Poisons MeSH
• Mycotoxins MeSH
• Trichothecenes MeSH
• Zearalenone MeSH