Sirisomboon, Panmanas
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Preferred name
Sirisomboon, Panmanas
Alternative Name
Sirisomboon, P.
Main Affiliation
Email
panmanas.si@kmitl.ac.th
5 results
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Item type:Publication, Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice(2013-09-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sensory quality evaluation of rice using visible and shortwave near-infrared spectroscopy(2015-05-04) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Eating quality of cooked rice determination using Fourier transform near infrared spectroscopy(2014-10-20); The goal of this research was to study the relationship between the eating quality of cooked rice and near infrared spectra measured by a Fourier Transform near infrared (FT-NIR) Spectrometer. Samples of milled: parboiled rice, white rice, new Jasmine rice (harvested in 2012) and aged Jasmine rice (harvested in 2006 or during the period 2007-2011) were used in this study. The eating quality of the cooked rice, i.e., adhesiveness, hardness, dryness, whiteness and aroma, were evaluated by trained sensory panelists. FT-NIR spectroscopy models for predicting the eating quality of cooked rice were established using the partial least squares regression. Among the eating quality, the stickiness model indicated its highest prediction ability (i.e., R <inf>val</inf><sup>2</sup> = 0:71; RMSEP = 0:65; Bias = 0:00; RPD = 1:87) and SEP/SD of 2. In addition, it was clear that the water content did not affect the eating quality of cooked rice, rather the main chemical component implicated was starch. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Shortwave near-infrared spectroscopy for rapid detection of aflatoxin B1 contamination in polished rice(2019-01-01); Sirisomboon, C. DachoupakanThe 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%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of Hom Mali rice with different degrees of milling based on physicochemical measurements by principal component analysis(2011-09-01); Areekul, VaripatThe effect of the degree of milling on the physicochemical properties of Hom Mali rice compared with low, intermediate and high amylose rice groups was investigated in order to differentiate Hom Mali rice from the other groups at different degrees of milling by principal component analysis (PCA). For all the rice groups, the apparent amylose content, alkali spreading value and pasting properties such as maximum viscosity, breakdown, final viscosity and setback viscosity increased with increases in the degree of milling except for the gel consistency which was reduced. Milled rice with a degree of milling of 15% showed the highest apparent amylose content, alkali spreading value and pasting properties compared with milled rice with degrees of milling of 10% and 5% and with brown rice. PCA could be applied to classify Thai rice varieties into four groups-Hom Mali, low, intermediate and high amylose rice groups-by two principal components (PCs). Rotated PC <inf>1</inf> and PC <inf>2</inf> using the Varimax method were better at explaining the variance of the parameters than the unrotated PCs. PCA clearly differentiated the classification of Rice Department 15 variety from the Pathum Thani 1 variety at the same degree of milling. Therefore, the stability of the degree of milling using PCA based on physicochemical measurements made it a preferable classification procedure for rice.