Literature DB >> 10880811

Volatiles as an indicator of fungal activity and differentiation between species, and the potential use of electronic nose technology for early detection of grain spoilage.

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Abstract

There is significant interest in methods for the early detection of quality changes in cereal grains. The development of electronic nose technology in recent years has stimulated interest in the use of characteristic volatiles and odours as a rapid, early indication of deterioration in grain quality. This review details the current status of this area of research. The range of volatiles produced by spoilage fungi in vitro and on grain are described, and the key volatile groups indicative of spoilage are identified. The relationship between current grain quality descriptors and the general classes of off-odours as defined in the literature, e.g. sour, musty, are not very accurate and the possible correlation between these for wheat, maize and other cereals, and volatiles are detailed. Examples of differentiation of spoilage moulds and between grain types using an electronic nose instrument are described. The potential for rapid and remote grain classification and future prospects for the use of such technology as a major descriptor of quality are discussed.

Entities:  

Year:  2000        PMID: 10880811     DOI: 10.1016/s0022-474x(99)00057-0

Source DB:  PubMed          Journal:  J Stored Prod Res        ISSN: 0022-474X            Impact factor:   2.643


  27 in total

1.  E-nose based rapid prediction of early mouldy grain using probabilistic neural networks.

Authors:  Xiaoguo Ying; Wei Liu; Guohua Hui; Jun Fu
Journal:  Bioengineered       Date:  2015-02-25       Impact factor: 3.269

2.  Implementation of the electronic nose for the identification of mycotoxins in durum wheat (Triticum durum).

Authors:  G Tognon; A Campagnoli; L Pinotti; V Dell'Orto; F Cheli
Journal:  Vet Res Commun       Date:  2005-08       Impact factor: 2.459

3.  Kiwi fruit (Actinidia chinensis) quality determination based on surface acoustic wave resonator combined with electronic nose.

Authors:  Liu Wei; Hui Guohua
Journal:  Bioengineered       Date:  2015-01-27       Impact factor: 3.269

4.  Behavioral responses of adult Sitophilus granarius to individual cereal volatiles.

Authors:  Giacinto S Germinara; Antonio De Cristofaro; Giuseppe Rotundo
Journal:  J Chem Ecol       Date:  2008-03-14       Impact factor: 2.626

5.  Early discrimination of fungal species responsible of ochratoxin A contamination of wine and other grape products using an electronic nose.

Authors:  Francisco Javier Cabañes; Natasha Sahgal; M Rosa Bragulat; Naresh Magan
Journal:  Mycotoxin Res       Date:  2009-10-06       Impact factor: 3.833

6.  Volatile organic compound patterns predict fungal trophic mode and lifestyle.

Authors:  Yuan Guo; Werner Jud; Fabian Weikl; Andrea Ghirardo; Robert R Junker; Andrea Polle; J Philipp Benz; Karin Pritsch; Jörg-Peter Schnitzler; Maaria Rosenkranz
Journal:  Commun Biol       Date:  2021-06-03

Review 7.  Diverse applications of electronic-nose technologies in agriculture and forestry.

Authors:  Alphus D Wilson
Journal:  Sensors (Basel)       Date:  2013-02-08       Impact factor: 3.576

8.  Differential detection of potentially hazardous Fusarium species in wheat grains by an electronic nose.

Authors:  Jakob Eifler; Eugenio Martinelli; Marco Santonico; Rosamaria Capuano; Detlev Schild; Corrado Di Natale
Journal:  PLoS One       Date:  2011-06-09       Impact factor: 3.240

9.  Evaluation of three electronic noses for detecting incipient wood decay.

Authors:  Manuela Baietto; Alphus D Wilson; Daniele Bassi; Francesco Ferrini
Journal:  Sensors (Basel)       Date:  2010-01-29       Impact factor: 3.576

10.  Can volatile organic metabolites be used to simultaneously assess microbial and mite contamination level in cereal grains and coffee beans?

Authors:  Angelo C Salvador; Inês Baptista; António S Barros; Newton C M Gomes; Angela Cunha; Adelaide Almeida; Silvia M Rocha
Journal:  PLoS One       Date:  2013-04-16       Impact factor: 3.240

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