Modelling uncertainty estimation for the determination of aflatoxin M(1) in milk by visual and densitometric thin-layer chromatography with immunoaffinity column clean-up.

The uncertainty of aflatoxin M(1) concentration in milk, determined by thin-layer chromatography (TLC) with visual and densitometric quantification of the fluorescence intensities of the spots, was estimated using the cause-and-effect approach proposed by ISO GUM (Guide to the expression of uncertainty in measurement) following its main four steps. The sources of uncertainties due to volume measurements, visual and densitometric TLC calibration curve, allowed range for recovery variation and intermediary precision to be taken into account in the uncertainty budget. For volume measurements the sources of uncertainties due to calibration, resolution, laboratory temperature variation and repeatability were considered. For the quantification by visual readings of the intensity of the aflatoxin M(1) in the TLC the uncertainty arising from resolution calibration curves was modelled based on the intervals of concentrations between pairs of the calibration standard solutions. The uncertainty of the densitometric TLC quantification arising from the calibration curve was obtained by weighted least square (WLS) regression. Finally, the repeatability uncertainty of the densitometric peak areas or of the visual readings for the test sample solutions was considered. For the test samples with aflatoxin M(1) concentration between 0.02 and 0.5 µg l(-1), the relative expanded uncertainties, with approximately 95% of coverage probability, obtained for visual TLC readings were between 60% and 130% of the values predicted by the Horwitz model. For the densitometric TLC determination they were about 20% lower. The main sources of uncertainties in both visual and densitometric TLC quantification were the intermediary precision, calibration curve and recovery. The main source of uncertainty in the calibration curve in the visual TLC analysis was due to the resolution of the visual readings, whereas in the densitometric analysis it was due to the peak areas of test sample solutions followed by the intercept and slope uncertainties of the calibration line.

Co-occurrence of aflatoxins B1, B2, G1 and G2, ochratoxin A, zearalenone, deoxynivalenol, and citreoviridin in rice in Brazil.

A total of 230 samples of processed rice and its sub-products or derived products were analysed to establish the co-occurrence of several mycotoxins. Samples were analysed in the period 2007-2009 due to the outbreak of beriberi associated with the consumption of rice stored in inappropriate conditions in Brazil. According to data from the Ministry of Health, 323 cases of disease were registered in 2006, of which at least 47 cases resulted in death. The occurrence of total aflatoxin (AFT) (aflatoxin B(1) + B(2) + G(1) + G(2)), ochratoxin A (OTA), zearalenone (ZON), deoxynivalenol (DON), and citreoviridin (CTV) was 58.7%, 40.0%, 45.2%, 8.3% and 22.5%, respectively. From 166 rice samples analysed, 55% had levels <0.11 µg kg(-1) for AFT. For OTA and ZON, of 165 rice samples analysed, 28% and 29% were contaminated with levels from 0.20 to 0.24 µg kg(-1) and from 3.6 to 290.0 µg kg(-1), respectively. One sample (0.6%) was contaminated with 4872.0 µg kg(-1) of ZON. A total of 91% of rice samples (n = 165) did not contain detectable DON (<30.00 µg kg(-1)), although the highest level of contamination was found to be 244 µg kg(-1). From the total of 65 samples analysed, 94% had no detectable CTV (<0.9 µg kg(-1)), with a range from 0.9 to 31.1 µg kg(-1) in 6% of the samples. The highest levels of contamination were found in rice sub-products or derived products from the husk and rice bran. Co-occurrence was observed for AFT and ZON in 17.0%, AFT and OTA in 24.2%, AFT and CTV in 6.2%, OTA and CTV in 4.6%, and ZON and CTV in 3.1%. These fractions were also the major contributors for the co-occurrence. The results found show the necessity of monitoring rice production.