Analysis of Fusarium toxins in grain via dust: a promising field of application for rapid test systems

Abstract

On site, mycotoxin measurements shall enable rapid decisions on the acceptance or rejection of lots. Hence, results have to be available fast, easy to get and, first of all, reliable. An innovative approach using dust samples was tested for its fitness for on-site mycotoxin analyses of grain lots and compared to current practice in grain testing. To prove correlation between mycotoxin concentrations in dust and respective concentrations in grain, regression analyses were performed. To obtain data points, dust was sieved from grain and both samples were analysed. As the contamination of the overall sample and its dust particles correlated well (wheat: R2DON=0.85, R2ZEA=0.82; rye: R2DON=0.73), contaminations in the grain were predictable from concentrations determined in respective dust particles. For on-site analysis, common lateral flow devices (LFD) were evaluated for their suitability to detect deoxynivalenol (DON) in grain dusts. On site, grain and dust samples were taken during the unloading of trucks using a customised dust-sampler. In contrast to grain samples, no additional physical sample preparation or homogenisation step was needed for dust. Instead, the sample was directly extracted and analysed for DON using LFD. By means of the regression line DON concentrations in grain were predicted from dust results and compared to concentrations directly measured in grain samples. No false negative results were observed and a contaminated grain lot (<1000 ?g/ kg DON) could be clearly identified. Evidence for reduced measurement uncertainty compared to current practice at lower total measurement costs was given. In this way, the fitness for purpose of the new approach combining rapid analyses with dust sampling for on-site mycotoxin screening was shown. The innovative high-throughput technology has the potential to improve on-site mycotoxin measurements in terms of speed, sensitivity, manageability and reliability and thus is a promising tool for enhanced industrial self-control.