Sirisomboon, Panmanas

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Sirisomboon, Panmanas
Alternative Name
Sirisomboon, P.
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Email
panmanas.si@kmitl.ac.th

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    Item type:Publication,
    Shortwave near-infrared spectroscopy for rapid detection of aflatoxin B1 contamination in polished rice
    (2019-01-01)
    Putthang, R.
    ;
    Sirisomboon, P.
    ;
    Sirisomboon, C. Dachoupakan
    The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B<inf>1</inf> (AFB<inf>1</inf>) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB<inf>1</inf> and 15 samples artificially contaminated with AFB<inf>1</inf>. Quantitative calibration models to detect AFB<inf>1</inf> were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 µg/kg, and a bias of-0.778 µg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 µg/kg, bias = 0.015 µg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 µg/kg, bias = 0.008 µg/kg). A classification-based partial least squares discriminant analysis model of AFB<inf>1</inf> contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB<inf>1</inf> contamination. HIGHLIGHTS • Shortwave near-infrared spectroscopy allowed rapid detection of AFB<inf>1</inf> in polished rice. • The partial least squares model provided the best accuracy for prediction (r = 0.967). • The partial least squares discriminant analysis model had a classification accuracy of 90%.