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- Olsson, J., et al.
(författare)
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Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose
- 2002
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Ingår i: International Journal of Food Microbiology. - : Elsevier. - 0168-1605 .- 1879-3460. ; 72:3, s. 203-214
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Tidskriftsartikel (refereegranskat)abstract
- Mycotoxin contamination of cereal grains can be detected and quantified using complex extraction procedures and analytical techniques. Normally, the grain odour, i.e. the presence of non-grain volatile metabolites, is used for quality classification of grain. We have investigated the possibility of using fungal volatile metabolites as indicators of mycotoxins in grain. Ten barley samples with normal odour, and 30 with some kind of off-odour were selected from Swedish granaries. The samples were evaluated with regard to moisture content, fungal contamination, ergosterol content, and levels of ochratoxin A (OA) and deoxynivalenol (DON). Volatile compounds were also analysed using both an electronic nose and gas chromatography combined with mass spectrometry (GC-MS). Samples with normal odour had no detectable ochratoxin A and average DON contents of 16 mug kg(-1) (range 0-80), while samples with off-odour had average OA contents of 76 mug kg(-1) (range 0-934) and DON contents of 69 mug kg(-1) (range 0-857). Data were evaluated by multivariate data analysis using projection methods such as principal component analysis (PCA) and partial least squares (PLS). The results show that it was possible to classify the OA level as below or above the maximum limit of 5 mug kg(-1) cereal grain established by the Swedish National Food Administration, and that the DON level could be estimated using PLS. Samples with OA levels below 5 mug kg(-1) had higher concentration of aldehydes (nonanal, 2-hexenal) and alcohols (1-penten-3-ol, 1-octanol). Samples with OA levels above 5 mug kg(-1) had higher concentrations of ketones (2-hexanone, 3-octanone). The GC-MS system predicted OA concentrations with a higher accuracy than the electronic nose, since the GC-MS misclassified only 3 of 37 samples and the electronic nose 7 of 37 samples. No correlation was found between odour and OA level, as samples with pronounced or strong off-odours had OA levels both below and above 5 mug kg(-1). We were able to predict DON levels in the naturally contaminated barley samples using the volatile compounds detected and quantified by either GC-MS or the electronic nose. Pentane, methylpyrazine, 3-pentanone, 3-octene-2-ol and isooctylacetate showed a positive correlation with DON, while ethylhexanol, pentadecane, toluene, 1-octanol, 1-nonanol, and 1-heptanol showed a negative correlation with DON. The root mean square error of estimation values for prediction of DON based on GC-MS and electronic nose data were 16 and 25 mug kg(-1) respectively.
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| 2. |
- Olsson, J., et al.
(författare)
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Volatiles for mycological quality grading of barley grains : determinations using gas chromatography-mass spectrometry and electronic nose
- 2000
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Ingår i: International Journal of Food Microbiology. - : Elsevier. - 0168-1605 .- 1879-3460. ; 59:3, s. 167-178
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Tidskriftsartikel (refereegranskat)abstract
- The possibility of using an electronic nose or gas chromatography combined with mass spectrometry (GC-MS) to quantify ergosterol and colony forming units (CFU) of naturally contaminated barley samples was investigated. Each sample was split into three parts for (i) ergosterol and CFU analysis, (ii) measurements with the electronic nose and (iii) identification of volatiles collected on an adsorbent with a GC-MS system. Forty samples were selected after sensory analysis to obtain 10 samples with normal odour and 30 with some degree of off-odour. The data set of volatile compounds and the data collected from the electronic nose were evaluated by multivariate analyse techniques. SIMCA classification (soft independent modelling of class analogy) was used for objective evaluation of the usefulness of the data from the GC-MS or electronic nose measurements for classification of grain samples as normal or with off-odour. The main volatile compounds of grain with normal odour were 2-hexenal, benzaldehyde and nonanal, while 3-octanone, methylheptanone and trimethylbenzene were the main volatile compounds of grain with off-odours. Using data from the electronic nose three samples of 40 were misclassified, while data analysis of the volatile compounds detected with the GC-MS, led to six misclassified samples. Regression models (partial least-squares, PLS) were built to predict ergosterol- and CFU-levels with data from the GC-MS or electronic nose measurements. PLS models based on both GC-MS and electronic nose data could be used to predict the ergosterol levels with high accuracy and with low root mean square error of prediction (RMSEP). CFU values from naturally infected grain could not be predicted with the same degree of confidence.
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