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,
    Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice
    (2013-09-01)
    Dachoupakan Sirisomboon, C.
    ;
    Putthang, R.
    ;
    Sirisomboon, P.
    The objective of this research was to apply the near infrared spectroscopy (NIRS), with a wavelength range between 950 and 1650 nm, to determine the percentage of fungal infection found in rice samples. The total fungal infection and yellow-green Aspergillus infection, which is often indicative of aflatoxigenic fungal infection, are the focus of this research. Spectra were obtained on 106 rice samples, by reflection mode, including 90 naturally contaminated samples, and 16 artificially contaminated samples. Calibration models for the total fungal infection were developed using the original and pretreated absorbance spectra in conjunction with partial least square regression (PLSR). The statistical model developed from the untreated spectra provided the greatest accuracy in prediction, with a correlation coefficient (. r) of 0.668, a standard error of prediction (SEP) of 28.874%, and a bias of -0.101%. For yellow-green Aspergillus infection, the most accurate predictive statistical model was developed using a pretreated (maximum normalization) NIR spectra, with the following statistical characteristics (. r = 0.437, SEP = 18.723% and bias = 4.613%). Therefore, the result showed that the NIRS could be used to detect aflatoxigenic fungal contamination in rice with caution and the technique should be improved to get better prediction model. However, there is an evident from NIR spectra that the moisture and starch content in rice affects the overall extent of fungal infection. © 2013 Elsevier Ltd.
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    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%.