Choomjaihan, Prasan

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Choomjaihan, Prasan
Alternative Name
Choomjaihan, P.
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Email
prasan.ch@kmitl.ac.th

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    Feasibility study on estimation of rice weevil quantity in rice stock using near-infrared spectroscopy technique
    (2014-01-01)
    Jarruwat, Puttinun
    ;
    Choomjaihan, Prasan
    Thai rice is favored by large numbers of consumers of all continents because of its excellent taste, fragrant aroma and fine texture. Among all Thai rice varieties, Thai Hommali rice is the most preferred. Classification of rice as premium quality requires that almost all grain kernels of the rice be perfectly whole with only a small quantity of foreign particles. Of all the foreign particles found in rice, rice weevils can wreck severest havoc on the quality and quantity of rice such that premium grade rice is transformed into low grade rice. It is widely known that rice millers adopt the «overdose» fumigation practice to control the birth and propagation of rice weevils, the practice of which inevitably gives rise to pesticide residues on rice which end up in the body of consumers. However, if population concentration of rice weevils could be approximated, right amounts of chemicals for fumigation would be applied and thereby no overdose is required. The objective of this study is thus to estimate the quantity of rice weevils in both milled rice and brown rice of Thai Hommali rice variety using the near infrared spectroscopy (NIRS) technique. Fourier transforms near infrared (FT-NIR) spectrometer was used in this research and the near-infrared wavelength range was 780-2500 nm. A total of 20 levels of rice weevil infestation with an increment of 10 from 10 to 200 mature rice weevils were applied to 1680 rice samples. The spectral data and quantity of weevils are analyzed by partial least square regression (PLSR) to establish the model for prediction. The results show that the model is able to estimate the quantity of weevils in milled Hommali rice and brown Hommali rice with high R2val of 0.96 and 0.90, high RPD of 6.07 and 3.26 and small bias of 2.93 and 2.94, respectively. © 2014 The Authors.
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    Applicability of near infrared spectroscopy for detecting post-fumigated weevils in packaged rice
    (2017-01-01)
    Jarruwat, Puttinun
    ;
    Choomjaihan, Prasan
    This research proposes the utilisation of Fourier transform near infrared spectroscopy to estimate post-fumigation rice weevils in packaged rice. Different mixtures of dead rice weevils in rice samples were scanned via Fourier transform near infrared through the package, and then the data were statistically analysed. The rice samples were of milled hom mali rice and brown hom mali rice, while the packaging materials were polyethylene plastic bags, polypropylene woven plastic sacks and hemp sacks. The results revealed that Fourier transform near infrared is most applicable for detecting dead rice weevils in milled hom mali rice in polyethylene bags as indicated respectively by validation r<sup>2</sup> and RPD values of 0.93 and 3.92, followed by the milled hom mali rice-polypropylene case with r<sup>2</sup> and RPD values of 0.87 and 3.05. The r<sup>2</sup> and RPD values were 0.86 and 3.00 for brown hom mali rice-polyethylene and 0.75 and 2.04 for brown hom mali rice-polypropylene. Due to poor penetration of NIR light through the hemp sacks, experiments were not carried out for this packaging material type.
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    Applying singular value decomposition technique for quantifying the insects in commercial Thai Hommali Rice from NIR Spectrum
    (2017-03-01)
    Jarruwat, Puttinun
    ;
    Choomjaihan, Prasan
    Insect infestation in rice stock is a significant issue in rice exporting business, resulting in the loss of product quality, nutrient as well as the economic losses. However, detecting the insect contamination with the traditional sorting techniques were destructive, inaccurate, time consuming and unable to detect the internal insect infestation. This study used near infrared (NIR) spectroscopy for obtaining the absorbent spectra from the insect contamination in two kinds of rice samples, Milled Hommali rice (MHR) and Brown Hommali rice (BHR). The mathematical methods of partial least squares (PLSs) regression and singular value decomposition (SVD) were employed to construct the predicting model. The statistical analysis results, R2, RMSEP, RPD and bias, concluded that the predictive models from PLS for MHR and BHR were 0.95 and 0.90, 0.014 and 0.019, 4.79 and 3.11, as well as -0.007 and -0.008, respectively; while the statistical analysis results from SVD for MHR and BHR were 0.97 and 0.96, 0.012 and 0.013, 5.71 and 5.39, as well as -0.003 and 0.002, respectively. It showed that SVD technique performed better than PLS technique which shows that using the advantage of SVD technique required less amounts of wave numbers for predicting and was possible to construct the low cost handheld equipment for detecting the insects in rice samples.