Browsing by Author "Weightman, R. M."
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Item Open Access Geostatistical analysis of the spatial distribution of mycotoxin concentration in bulk cereals(Taylor & Francis, 2009-06-30T00:00:00Z) Rivas Casado, Monica; Parsons, David J.; Weightman, R. M.; Magan, Naresh; Origgi, S.Deoxynivalenol (DON) and ochratoxin A (OTA) in agricultural commodities present hazards to human and animal health. Bulk lots are routinely sampled for their presence, but it is widely acknowledged that designing sampling plans is particularly problematical because of the heterogeneous distribution of the mycotoxins. Previous studies have not take samples from bulk. Sampling plans are therefore designed on the assumption of random distributions. The objective of this study was to analyse the spatial distribution of DON and OTA in bulk commodities with geostatistics. This study was the first application of geostatistical analysis to data on mycotoxins contamination of bulk commodities. Data sets for DON and OTA in bulk storage were collected from the literature and personal communications, of which only one contained data suitable for geostatistical analysis. This data set represented a 26-tonne truck of wheat with a total of 100 sampled points. The mean concentrations of DON and OTA were 1342 and 0.59 mu g kg(-1), respectively. The results showed that DON presented spatial structure, whilst OTA was randomly distributed in space. This difference between DON and OTA probably reflected the fact that DON is produced in the field, whereas OTA is produced in storage. The presence of spatial structure for DON implies that sampling plans need to consider the location of sample points in addition to the number of points sampled in order to obtain reliable estimates of quantities such as the mean contamination.Item Open Access Modelling a two-dimensional spatial distribution of mycotoxin concentration in bulk commodities to design effective and efficient sample selection strategies(Taylor & Francis, 2009-12-31T00:00:00Z) Rivas Casado, Monica; Parsons, David J.; Magan, Naresh; Weightman, R. M.; Origgi, S.Mycotoxins in agricultural commodities are a hazard to human and animal health. Their heterogeneous spatial distribution in bulk storage or transport makes it particularly difficult to design effective and efficient sampling plans. There has been considerable emphasis on identifying the different sources of uncertainty associated with mycotoxin concentration estimations, but much less on identifying the effect of the spatial location of the sampling points. This study used a two-dimensional statistical modelling approach to produce detailed information on appropriate sampling strategies for surveillance of mycotoxins in raw food commodities. The emphasis was on deoxynivalenol (DON) and ochratoxin A (OTA) in large lots of grain in storage or bulk transport. The aim was to simulate a range of plausible distributions of mycotoxins in grain from a set of parameters characterising the distributions. For this purpose, a model was developed to generate data sets which were repeatedly sampled to investigate the effect that sampling strategy and the number of incremental samples has on determining the statistical properties of mycotoxin concentration. Results showed that, for most sample sizes, a regular grid proved to be more consistent and accurate in the estimation of the mean concentration of DON, which suggests that regular sampling strategies should be preferred to random sampling, where possible. For both strategies, the accuracy of the estimation of the mean concentration increased significantly up to sample sizes of 40-60 (depending on the simulation). The effect of sample size was small when it exceeded 60 points, which suggests that the maximum sample size required is of this order. Similar conclusions about the sample size apply to OTA, although the difference between regular and random sampling was small and probably negligible for most sample sizes.Item Open Access A short geostatistical study of the three-dimensional spatial structure of fumonisins in stored maize(Wageningen Academic Publishers, 2010-02-09T00:00:00Z) Rivas Casado, Monica; Parsons, David J.; Magan, Naresh; Weightman, R. M.; Battilani, Paola; Pietri, A.The heterogeneous three-dimensional spatial distribution of mycotoxins has proven to be one of the main limitations for the design of effective sampling protocols. Current sample collection protocols for mycotoxins have been designed to estimate the mean concentration and fail to characterise the spatial distribution of the mycotoxin concentration due to the aggregation of the incremental samples. Geostatistical techniques have been successfully applied to overcome similar problems in many research areas. However, little work has been developed on the use of geostatistics for the design of sampling protocols for mycotoxins. This paper focuses on the analysis of the two and three-dimensional spatial structure of fumonisins B1 (FB1) and B2 (FB2) in maize in a bulk store using a geostatistical approach and on how results help determine the number and location of incremental samples to be collected. The spatial correlation between FB1 and FB2, as well as between the number of kernels infected and the level of contamination was investigated. For this purpose, a bed of maize was sampled at different depths to generate a unique three-dimensional data set of FB1 and FB2. The analysis found no clear evidence of spatial structure in either the two- dimensional or three-dimensional analyses. The number of Fusarium infected kernels was not a good indicator for the prediction of fumonisin concentration and there was no spatial correlation between the concentrations of the two fumonisins.