Literature DB >> 22903624

Kinetics of mould growth in the stored barley ecosystem contaminated with Aspergillus westerdijkiae, Penicillium viridicatum and Fusarium poae at 23-30 °C.

Jolanta Wawrzyniak1, Antoni Ryniecki, Marzena Gawrysiak-Witulska.   

Abstract

BACKGROUND: Owing to the lack of a rapid method for determining fungi on cereals, the best way to enhance the safety and nutritive value of stored grain is to develop prognostic tools based on the relationship between easily measurable online parameters, e.g. water activity (a(w)) and temperature (t) of grain, and fungal growth. This study examined the effect of unfavourable temperature (23 and 30 °C) and humidity (0.80-0.94 a(w)) storage conditions on mould growth in the stored barley ecosystem with its adverse microbiological state provided by contamination with Aspergillus westerdijkiae, Penicillium viridicatum and Fusarium poae.
RESULTS: Among the applied storage parameters, a(w) turned out to be the main factor affecting mould development. The longest lag phase and period of fungal activation were observed for grain with 0.80 a(w), which was not threatened with fungal development for at least 30 days. However, in grain with 0.92 and 0.94 a(w), fungal activation occurred within 24-48 h.
CONCLUSION: The obtained data and the identification of critical points in mould growth may be used to develop a control system for the postharvest preservation of barley based on a(w) and temperature of grain, which are easy to measure in practice.
© 2012 Society of Chemical Industry.

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Year:  2012        PMID: 22903624     DOI: 10.1002/jsfa.5820

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  3 in total

1.  Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed.

Authors:  Krzysztof Przybył; Jolanta Wawrzyniak; Krzysztof Koszela; Franciszek Adamski; Marzena Gawrysiak-Witulska
Journal:  Sensors (Basel)       Date:  2020-12-19       Impact factor: 3.576

2.  Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression.

Authors:  Jolanta Wawrzyniak; Magdalena Rudzińska; Marzena Gawrysiak-Witulska; Krzysztof Przybył
Journal:  Molecules       Date:  2022-04-10       Impact factor: 4.927

3.  Predictive Modeling and Validation on Growth, Production of Asexual Spores and Ochratoxin A of Aspergillus Ochraceus Group under Abiotic Climatic Variables.

Authors:  Ahmed Abdel-Hadi; Bader Alshehri; Mohammed Waly; Mohammed Aboamer; Saeed Banawas; Mohammed Alaidarous; Manikandan Palanisamy; Mohamed Awad; Alaa Baazeem
Journal:  Microorganisms       Date:  2021-06-17
  3 in total

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