Establishment of Rice Quality Prediction Model for Intermittent Drying

Highlights This study explored the feasibility of developing an evaluation method for rice quality. A unified quality scale for different drying cycles facilitates evaluation of rice quality after drying. A head rice yield (HRY) prediction model was established that fit well with the actual HRY. The established HRY prediction model can be used as a performance index for optimization of rice drying. Abstract . Intelligent control of the drying process is important to achieve better rice quality. An effective quality evaluation method is the basis for intelligent control of rice drying. To study the effects of intermittent drying on the quality of paddy rice and explore the feasibility of establishing a quality evaluation method, intermittent drying experiments were conducted with variety Nanjing 9108 (Oryza sativa L.). The paddy samples were dried from an initial moisture content of 23.10% to 14% wet basis (w.b.). The paddy samples were initially dried at 60°C to various moisture contents without tempering. These pre-dried samples were then dried using different drying temperatures to obtain specific moisture content reductions, tempered, and then dried again at 60°C to the final moisture content of 14% w.b. without tempering. After drying, the quality parameters of the paddy samples were measured and analyzed. The R2 values of the head rice yield (HRY) prediction model, chalkiness prediction model, and protein prediction model established in this study were 0.75, 0.44, and 0.26, respectively. The HRY prediction model was shown to accurately predict HRY in the intermittent drying experiments. Within the range of the model parameters, the effectiveness of the HRY prediction model was explored by constant-temperature intermittent drying and variable-temperature intermittent drying. The results showed that if the summation of the predicted changes in HRY is large, then the measured HRY will be large. Therefore, the HRY prediction model can be used as a performance index for rolling optimization of the paddy drying process. Keywords: Head rice yield, Intermittent drying, Prediction model, Rice quality.