• Journal of Biosystems Engineering
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• v.11 no.2
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• pp.31-40
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• 1986

Cylindrical and conical types of grain-straw separation equipment which has a stationary crimped sieve drum with rotating inner rotor were constructed. The developed equipments were tested to investigate the characteristics of separating performance under various mechanical conditions and crop conditions. As increase of the inclination of equipment and decrease of pitch of cover vane, the grain recovery was increased while straw rejection was decreased. The grain recovery and overall efficiency were decreased as the rotor speed and feeding velocity were increased for both varieties of rice, moisture contents, and test equipments. Conical prototype equipment performed higher straw rejection, lower grain recovery, and lower power requirement. However, separation performance of conical type equipment was more widely varied with various test conditions compared to cylindrical one. The performance of both equipments showed relatively insensitive to crop feedrate and crop properties, such as variety, moisture content, and grain-to-straw ratio.

• Food Engineering Progress
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• v.15 no.1
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• pp.48-55
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• 2011

In this research, the near-infrared absorption from 1,100-2,300 nm was used to measure the content of capsaicinoids in the red-pepper powder by using the Acousto-optic tunable filters (AOTF) spectrometer with sample plate and sample rotating unit. Non-spicy red-pepper samples from one location (Younggwang-gun. Korea) were mixed with spicy one (var. Chungyang) to make samples separated by particle size (below 0.425 mm, 0.425-0.71 mm, and 0.71- 1.4 mm). The Partial Least Squares Regression (PLSR) model to predict the capsaicinoid content on particle sizes was developed with measured spectra by AOTF spectrometer and used to analyze the amount of capsaicinoids by HPLC. The PLSR Model of red-pepper powder of below 0.425 mm, 0.425-0.71 mm, and 0.71-1.4 mm with cross validation had ${R_V}^2$ = 0.948-0.979 and Standard Error of Prediction (SEP) = 6.56-7.94 mg%. The prediction error of smaller particle size of red-pepper powder was low. The best PLSR model was found in pretreatment of Range Normalization, Standard Normal Variate, and 1st Derivatives of red-pepper powder of below 1.4 mm with cross validation, having ${R_V}^2$ = 0.959 and SEP = 8.82 mg%.