Johan Olsson

Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose.

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 microg kg(-1) (range 0-80), while samples with off-odour had average OA contents of 76 microg kg(-1) (range 0-934) and DON contents of 69 microg 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 microg 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 microg kg(-1) had higher concentration of aldehydes (nonanal, 2-hexenal) and alcohols (1-penten-3-ol, 1-octanol). Samples with OA levels above 5 microg 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 microg 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 microg kg(-1), respectively.

Microbial Community Growth and Utilization of Carbon Constituents During Thermophilic Composting at Different Oxygen Levels

Composting is characterized by dramatic changes in microbial community structure, to a high extent driven by changes in temperature and in the composition of the organic substrate. This study focuses on the interrelationships between decomposition of major classes in the organic material and dynamics in microbial populations during thermophilic composting of source-separated organic household waste.Experiments were performed in a 200-L laboratory reactor at 16, 2.5, and 1% O2 in the compost atmosphere. Major classes of carbon constituents were analyzed by chemical methods, and the microbial biomass and community structure determined by fatty acid analyses with phospholipid fatty acids (PLFA) and total ester-linked fatty acids (EL) methods. At all three O2 levels, the process was characterized by a rapid increase in microbial activity and biomass in the early thermophilic phase, although this period was delayed at the lower O2 concentrations. Starch and fat were the main substrates utilized at all three O2 levels during this period. The depletion of the starch fraction coincided with the beginning of a microbial biomass decrease, suggesting thatstarch is an important carbon substrate for the growth of thermophilic microorganisms during composting. Growth yields in the microbial community based on consumption of major carbon constituent classes in the high-activity period fell between 22 and 28%. Multivariate statistical analysis of changes in fatty acid composition revealed small, but statistically significant differences in the microbial community succession. At 16% O2, 10Me fatty acids from Actinomycetes and cyclopropyl fatty acids (from Gram-negative bacteria) became more important with time, whereas 18:1ω7t was characteristic at 2.5 and 1% O2, indicating a more stressed bacterial community at the lower O2 concentrations.Although adequate composting was achieved at O2 levels as low as 2.5 and 1%, it is not recommended to compost at such low levels in large-scale systems, because the heterogeneous gas transport through the material in these systems might lead to anaerobic conditions and inefficient composting.

Morphological characteristics of sporangiospores of the tempe fungus Rhizopus oligosporus differentiate it from other taxa of the R-microsporus group

The fungus Rhizopus oligosporus (R. microsporus var. oligosporus) is traditionally used to make tempe, a fermented food based on soybeans. Interest in the fungus has steadily increased, as it can also ferment other substrates, produce enzymes, and treat waste material. R. oligosporus belongs to the R. microsporus group consisting of morphologically similar taxa, which are associated with food fermentation, pathogenesis, or unwanted metabolite production (rhizonins and rhizoxins). The ornamentation pattern, shape, and size of sporangiospores of 26 R. microsporus group strains and two R. oryzae strains were studied using low-temperature SEM (LT-SEM) and LM. This study has shown that: (1) LT-SEM generates images from well-conserved sporangiophores, sporangia, and spores. (2) Robust spore ornamentation patterns can be linked to all different taxa of the R. microsporus group, some previously incorrectly characterized as smooth. Ornamentation included valleys and ridges running in parallel, granular plateaus, or smooth polar areas. Distribution of ornamentation patterns was related to spore shape, which either was regular, ranging from globose to ellipsoidal, or irregular. Specific differences in spore shape, size, and ornamentation were observed between Rhizopus taxa, and sometimes between strains. (3) R. oligosporus has a defect in the spore formation process, which may be related to the domesticated nature of this taxon. It had a high proportion, 10-31%, of large and irregular spores, and was significantly differentiated from other, natural Rhizopus taxa as evaluated with partial least squares discriminant analysis. It is remarkable that the vehicle of distribution, the sporangiospore, is affected in the strains that are distributed by human activity. This provides information about the specificity and speed of changes that occur in fungal strains because of their use in (food) industry.

Image analysis for monitoring the barley tempeh fermentation process

Aims:  To develop a fast, accurate, objective and nondestructive method for monitoring barley tempeh fermentation.

Signal Extraction and Separation in In Vivo Animal PET Studies with Masked Volumewise Principal-Component Analysis

The standardized uptake value is commonly used as a tool to supplement visual interpretation and to quantify the images acquired from static in vivo animal PET. The preferred approach for analyzing PET data is either to sum the images and calculate the standardized uptake value or to use kinetic modeling. The aim of this study was to investigate the performance of masked volumewise principal-component analysis (MVW-PCA) used in dynamic in vivo animal PET studies to extract and separate signals with different kinetic behaviors. Methods: PET data were acquired with a small-animal PET scanner and a fluorine tracer in a study of rats and mice. After acquisition, the data were reconstructed by use of 4 time protocols with different frame lengths. Data were analyzed by use of MVW-PCA with applied noise prenormalization and a new masking technique developed in this study. Results: The resulting principal-component images showed a clear separation of the activity in the spine into the first MVW-PCA component and the activity in the kidneys into the second MVW-PCA component. In addition, the different time protocols were shown to have little or no impact on the results obtained with MVW-PCA. Conclusion: MVW-PCA can efficiently separate different kinetic behaviors into different principal-component images. Moreover, MVW-PCA is a stable technique in the sense that the time protocol chosen has only a small impact on the resulting principal-component images.

Characterization and Reduction of Noise in Dynamic PET Data Using Masked Volumewise Principal Component Analysis

Masked volumewise principal component (PC) analysis (PCA) is used in PET to distinguish structures that display different kinetic behaviors after administration of a tracer. When masked volumewise PCA was introduced, one article proposed noise prenormalization because of temporal and spatial variations of the noise between slices. However, the noise prenormalization proposed in that article was applicable only to datasets reconstructed using filtered backprojection (FBP). The study presented in this article aimed at developing a new noise prenormalization that is applicable to datasets regardless of whether they were reconstructed with FBP or an iterative reconstruction algorithm, such as ordered-subset expectation maximization (OSEM). Methods: A phantom study was performed to investigate differences in the expectation values and SDs of datasets reconstructed with FBP and OSEM. A novel method, higher-order PC noise prenormalization, was suggested and evaluated against other prenormalization methods on clinical datasets. Results: Masked volumewise PCA of data reconstructed with FBP was much more dependent on an appropriate prenormalization than was analysis of data reconstructed with OSEM. Higher-order PC noise prenormalization showed an overall good performance with both FBP and OSEM reconstructions, whereas the other prenormalization methods performed well with only 1 of the 2 methods. Conclusion: Higher-order PC noise prenormalization has potential for improving the results from masked volumewise PCA on dynamic PET datasets independent of the type of reconstruction algorithm.

Performance of Principal Component Analysis and Independent Component Analysis with Respect to Signal Extraction from Noisy Positron Emission Tomography Data - a Study on Computer Simulated Images

Multivariate image analysis tools are used for analyzing dynamic or multidimensional Positron Emission Tomography, PET data with the aim of noise reduction, dimension reduction and signal separation. Principal Component Analysis is one of the most commonly used multivariate image analysis tools, applied on dynamic PET data. Independent Component Analysis is another multivariate image analysis tool used to extract and separate signals. Because of the presence of high and variable noise levels and correlation in the different PET images which may confound the multivariate analysis, it is essential to explore and investigate different types of pre-normalization (transformation) methods that need to be applied, prior to application of these tools. In this study, we explored the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) to extract signals and reduce noise, thereby increasing the Signal to Noise Ratio (SNR) in a dynamic sequence of PET images, where the features of the noise are different compared with some other medical imaging techniques. Applications on computer simulated PET images were explored and compared. Application of PCA generated relatively similar results, with some minor differences, on the images with different noise characteristics. However, clear differences were seen with respect to the type of pre-normalization. ICA on images normalized using two types of normalization methods also seemed to perform relatively well but did not reach the improvement in SNR as PCA. Furthermore ICA seems to have a tendency under some conditions to shift over information from IC1 to other independent components and to be more sensitive to the level of noise. PCA is a more stable technique than ICA and creates better results both qualitatively and quantitatively in the simulated PET images. PCA can extract the signals from the noise rather well and is not sensitive to type of noise, magnitude and correlation, when the input data are correctly handled by a proper pre-normalization. It is important to note that PCA as inherently a method to separate signal information into different components could still generate PC1 images with improved SNR as compared to mean images.