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.
Laboratory of Quality Control and Safety Food - LACQSA/LANAGRO-MG/MAPA, Av. Raja Gabaglia, 245, Cidade Jardim, CEP 30380-090 - Belo Horizonte, MG, Brazil.
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Carvalho
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Gonçalves
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Lopes
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Magalhães
D016607:Aflatoxin M1 / Q000032:analysis*; D000818:Animals; D001938:Brazil; D002138:Calibration; D002846:Chromatography, Affinity / Q000379:methods; D002855:Chromatography, Thin Layer / Q000379:methods*; D003720:Densitometry / Q000379:methods; D005506:Food Contamination / Q000032:analysis* / Q000706:statistics & numerical data; D016018:Least-Squares Analysis; D008892:Milk / Q000737:chemistry*; D015233:Models, Statistical; D012044:Regression Analysis; D035501:Uncertainty
D016607:Aflatoxin M1