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Author: search.filters.author.Sirisomboon, P.Subject: search.filters.subject.Near infrared spectroscopy

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    Potential of near infrared spectroscopy as a rapid method to detect aflatoxins in brown rice
    (2019-06-01)
    Dachoupakan Sirisomboon, C.
    ;
    Wongthip, P.
    ;
    Sirisomboon, P.
    Brown rice is a main popular health food with high nutritional value and health benefits. As a result of poor post-harvest drying and inappropriate storage conditions, rice grains are often damaged through fungal spoilage as well as mycotoxin production. The objective of this research was to evaluate the possibility of using the near infrared spectroscopy, with a wavenumber range between 12500 and 4000 cm<sup>−1</sup> (800–2500 nm), as a rapid method for detection of aflatoxins in brown rice. Firstly, storage trials were carried out to generate representative of samples contaminated and non-contaminated with aflatoxins. These data were used to create a partial least squares regression model using 120 brown rice samples with the required near infrared spectral data and aflatoxin concentration levels that were determined using a standard enzyme-linked immunosorbent assays method. The accuracy of developed models was externally validated using the test set. The statistical model developed from the treated spectra (vector normalization; SNV) provided the best accuracy in prediction with a coefficient of determination of prediction (r<sup>2</sup>) of 0.95, a root mean square error of prediction of 415.00 µg kg<sup>−1</sup> and a bias −54.00 µg kg<sup>−1</sup>. The model developed showed good predictive performance which suggests that it could have practical applications as a rapid method to detect aflatoxins in brown rice.
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    Determination of the gamma-aminobutyric acid content of germinated brown rice by near infrared spectroscopy
    (2014-01-01)
    Kaewsorn, K.
    ;
    Sirisomboon, P.
    Rice rich in gamma-aminobutyric acid (GABA) has been increasing in popularity worldwide, particularly within the commercially important health food market. The aim of this research was to develop a near infrared (NIR) spectroscopic method to evaluate the GABA content of germinated brown rice. Germinated brown rice (GBR) from two groups was used in this study. The first group contained GABA adjusted rice samples produced from germinated rough rice, which had been soaked for 24 h and 48 h and then incubated for 0 h, 6 h, 12 h, 18 h, 24 h, 30 h and 36 h (GBR samples). The second group was the GBR purchased from local markets (MGBR samples). The GABA content of each sample was subsequently determined by high performance liquid chromatography (HPLC). A prediction model for the GABA content was subsequently established using the NIR spectral data in conjunction with partial least square regression (PLSR), which was validated using test set validation. There were three optimal models obtained from GBR samples, MGBR-Khao Dawk Mali 105 samples and MGBR-various varieties. The first model was established using spectral data pre-treated with first derivative + multiple scatter correction and the second and third models were from first derivative + vector normalisation pre-treated spectra. The coefficient of determination (r2), root mean squared error of prediction (RMSEP) and a bias of 0.97-0.98 mg, 0.21-0.52 mg per 100 g dry matter and -0.285-0.067 mg per 100 g dry matter, respectively, were obtained. This is the first report on the application of NIR spectroscopy to evaluate the GABA content of the germinated brown rice, which could prove useful in an industrial applications and consumers. © IM Publications LLP 2014.
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    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.