Electronic nose for microbial quality classification of grains

Jönsson, Anders; Winquist, Fredrik; Schnürer, Johan; Sundgren, H.; Lundström, Ingemar

doi:10.1016/s0168-1605(96)01218-4

Cited by 123 publications

(51 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These and several other studies [46][47][48][49][50][51] prove that an electronic nose reacts on volatile products of microbial metabolism, and that it can detect the presence of microorganisms in different kinds of samples.…”

Section: Electronic Nosesupporting

confidence: 52%

“…Jonsson et al [48] used an electronic nose, consisting of different types of sensors, to analyse samples of oats, rye, barley and wheat with different odours, different levels of ergosterol and colony forming units (CFUs). A pattern recognition technique based on an artificial neural network (ANN) could predict the odour classes of good, mouldy, weakly and strongly musty oats with a high degree of accuracy.…”

Section: Electronic Nose For Fungal Detectionmentioning

confidence: 99%

See 1 more Smart Citation

05/01826 Microbial volatile organic compounds as indicators of fungi. Can an electronic nose detect fungi in indoor environments?

2005

Fuel and Energy Abstracts

View full text Add to dashboard Cite

Abstract:The paper presents a review of several studies on the detection of microbial volatile organic compounds (MVOCs) considered as indicators of fungal contamination. As fungi produce specific profiles, or fingerprints of volatile compounds, the electronic nose technology is a very promising opportunity for rapid and non costly detection of fungi in buildings. E-noses are able to distinguish between mouldy and non-mouldy samples, and also to recognise certain species of fungi. However, two limiting factors may appear decisive for employment of electronic noses in indoor fungi detection: low concentrations of MVOCs and presence of interfering substances in indoor environments.

show abstract

Section: Electronic Nosesupporting

confidence: 52%

Section: Electronic Nose For Fungal Detectionmentioning

confidence: 99%

05/01826 Microbial volatile organic compounds as indicators of fungi. Can an electronic nose detect fungi in indoor environments?

2005

Fuel and Energy Abstracts

View full text Add to dashboard Cite

show abstract

“…In fact, electronic noses seem to be particularly useful in cereal grains qualification, where the information on absence or presence of fungi and mycotoxins is required immediately. Several recent studies showed that sensor arrays and different techniques of statistical analysis should allow detecting and classifying selected fungal species growing on cereals [24,[31][32][33][34] . Electronic noses used in these studies were able to differentiate between mycotoxigenic and non-mycotoxigenic strains [33,24] , and to classify grains according to the degree of contamination [31,24] .…”

Section: Electronic Nose For Fungal Detectionmentioning

confidence: 99%

Fuzzy k-NN applied to moulds detection

Kuske

Rubio

Romain

et al. 2005

Sensors and Actuators B: Chemical

View full text Add to dashboard Cite

The possibility to detect Aspergillus versicolor growing on different building materials by a metal oxide sensor array is studied. Results show that an accurate classification rate of 89 ± 3% can be obtained combining an extended linear discriminant analysis plus a fuzzy k-NN classifier. The classification ability of the classifier is assessed within the dataset by cross validation and also in a second dataset collected 5 months later. There is a slight decrease in the classification performance for all the algorithms, being the most sensitive the most accurate one.

show abstract

“…Investigated the possibility of detecting fungal volatile metabolites as indicators of mycotoxins in cereal grain, using both an electronic nose and gas chromatography combined with mass spectrometry (GC-MS). Other studies [24][25][26][27] showed that sensor arrays and different techniques of statistical analysis allow detecting and classifying selected fungal species growing on cereals. These techniques can be used for quality classification of grain.…”

Section: Introductionmentioning

confidence: 99%