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Author: search.filters.author.Phetpan, KittisakSubject: search.filters.subject.Moisture content

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    The improvement of germination method for producing the germinated brown rice using a water spraying system with a revolved sieve
    (2024-11-01)
    Chungcharoen, Thatchapol
    ;
    Sansiribhan, Sansanee
    ;
    Munsin, Ronnachart
    ;
    Phetpan, Kittisak
    ;
    Fonghiransiri, Surasak
    Water soaking is an important method in germinated brown rice (GBR) production that causes fermentation, leading to an unpleasant smell of GBR. In this research, a water spraying system with a revolved sieve is applied to produce the GBR. The increased speed and time of spray break led to higher moisture content and water absorption. The spray break of 30 min and revolved speed of 15 rpm provided the shortest time to obtain the paddy with a moisture content of 30% (w.b.). The incubation pattern with a revolved sieve and water spray provided the shortest incubation time for 90% germination. When producing the GBR with a water spraying system with a revolved sieve (GBR-WSSRS), it had a lower number of microorganisms compared to the GBR with a water soaking (GBR-WS), leading to higher scores of overall acceptability. However, the GBR-WSSRS had a lower GABA content than the GBR-WS.
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    In-line near infrared spectroscopy for the prediction of moisture content in the tapioca starch drying process
    (2019-03-01)
    Phetpan, Kittisak
    ;
    Udompetaikul, Vasu
    ;
    Sirisomboon, Panmanas
    Moisture content is an important parameter measured in tapioca starch production as this parameter has been shown to correlate strongly with the quality of the finished product. However, there is currently no in-line sensor which can be used to directly measure the moisture content of the product in real time. The objective of the present work was to study the use of an in-line measurement which can be introduced at the end of the drying process for tapioca starch moisture content evaluation. Either in-line NIR data or at-line NIR data was used to develop the necessary calibration models for evaluating the moisture content. Furthermore, calibration models were also developed by pooling the in-line and at-line data. Its performance was then verified using additional in-line data. The NIR model developed using 100% of the at-line data and 50% of the in-line data was validated using the unused 50% of the inline data. This model was shown to provide better performance in moisture content prediction with an SEP of 0.61% and a bias of 0.001%. In addition, the results showed that the at-line spectrum can also be used for the calibration model development to predict the moisture content of the samples scanned by an in-line spectrometer. However, the in-line spectrometer installation on a pneumatic conveying circular tube where tapioca starch and air mixed was found to be complicated due to significant vibration. This caused additional variation in the data with time. Therefore, it is concluded that the most suitable place for installing a spectrometer would be at a position involving a low pressure, or where the stream flow of a product is steadier in order to avoid the dynamic mixing of the product within the drying tube affecting the uncertainty of NIR scattering during the measurement.
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    Evaluation of the moisture content of tapioca starch using near-infrared spectroscopy
    (2015-03-25)
    Phetpan, Kittisak
    ;
    Sirisomboon, Panmanas
    The purpose of this study was to develop a calibration model to evaluate the moisture content of tapioca starch using the near-infrared (NIR) spectral data in conjunction with partial least square (PLS) regression. The prediction ability was assessed using a separate prediction data set. Three groups of tapioca starch samples were used in this study: tapioca starch cake, dried tapioca starch and combined tapioca starch. The optimum model obtained from the baseline-offset spectra of dried tapioca starch samples at the outlet of the factory drying process provided a coefficient of determination (R<sup>2</sup>), standard error of prediction (SEP), bias and residual prediction deviation (RPD) of 0.974, 0.16%, -0.092% and 7.4, respectively. The NIR spectroscopy protocol developed in this study could be a rapid method for evaluation of the moisture content of the tapioca starch in factory laboratories. It indicated the possibility of real-time online monitoring and control of the tapioca starch cake feeder in the drying process. In addition, it was determined that there was a stronger infl uence of the NIR absorption of both water and starch on the prediction of moisture content of the model.