Hard to cook (HTC) phenomenon is developed by storing bean grains under the adverse conditions of high temperature (≥ 25 °C ) and high humidity (≥ 65 %). Bean grains that have undergone this HTC phenomenon are characterized by loss of color lightness, development of browning and darkening, and decrease of Water Absorption Capacity (WAC). The objective of this study was to develop a CFA (Color Filter Array) image processing system to measure Water Absorption Capacity (WAC) of bean grains with high precision in short time intervals (10 min). The relationships between the CFA image features, extracted from raw images captured by CCD (charge coupled device) camera, and the measured WAC were established. The calibration models using multiple linear regression (MLR) were developed to predict WAC. The MLR models for prediction samples resulted in correlation coefficient (R2) in the range of 0.811 to 0.947, standard error of prediction (SEP) in the range of 7.587 to 11.669, and Fisher variable value (F) in the range of 52.300 to 221.690. Results indicate that computer vision system (CVS) based on CFA image analysis technique can provide an accurate, reliable and nondestructive measurement method of WAC to evaluate the hard to cook defect in bean grains.DOI:http://dx.doi.org/10.11591/ijece.v3i2.214
