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Development and Optimization of an Automatic System to Determine the Moisture Content in Single-Kernel Rough Rice Using Visible/NIR Spectroscopy
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. 62(6): 1651-1661. (doi: 10.13031/trans.13470) @2019
Authors: Adcha Heman, Ching-Lu Hsieh
Keywords: Automation, Moisture content, NIR spectroscopy, Rough rice, Single kernel.
The moisture content of rough rice directly affects the rice quality and market value. It is important to automatically measure the moisture content in single rice kernels to determine the product quality during processing and storage. This study developed an automatic measurement system to determine the moisture content in single-kernel rough rice using a spectrometer and optimized the performance of the system using a response surface method (RSM) experiment. The system consisted of a vibrator, a rotating plate, a spectrometer, and a PC. A Box-Behnken design was used for the RSM experiment to determine three performance indices: successful ratio, successful scan, and error ratio. The vibrator was tested at 352, 423, and 495 Hz. The rotation speed of the plate was 4, 8, and 16 s per cycle, and three scan tests were performed using one, two, or three replications per kernel. The five tested moisture contents were 33.3%, 24.4%, 19.5%, 13.5%, and 12.2% wet basis (w.b.). The RSM results showed that of the 15 response surfaces, eleven surfaces were saddles, three were hills, and one was a valley. The corresponding maxima and minima were determined. The parameter set of 495 Hz, 16 s per cycle, and one scan resulted in a capacity of 60 kernels per minute and an error ratio of 0.0001.
An automatic system was developed to determine the moisture content in single-kernel rough rice.
The optimal operation condition was found by response surface method.
With the optimal operation condition, the system processed 60 kernels per minute and had an error ratio of 0.0001.(Download PDF) (Export to EndNotes)