The available literature revealed a gap in reporting the rough rice drying kinetics parameters under isothermal conditions, particularly for Arkansas medium- and long-grain varieties. Therefore, medium-grain (RO170112...The available literature revealed a gap in reporting the rough rice drying kinetics parameters under isothermal conditions, particularly for Arkansas medium- and long-grain varieties. Therefore, medium-grain (RO170112 and Titan) and the long-grain (Diamond and Wells) rough rice varieties were dried under isothermal conditions. The drying process occurred under 40°C, 50°C, 60°C, 70°C, 80°C, 90°C, and 100°C in a system emulating the thermogravimetric analyzer. Drying kinetics models were studied for four well-known models: Page, Newton, Logarithmic, and Henderson & Pabis. The drying kinetics constants were determined for the four studied models. The initial moisture content of rough rice was 28.2% db. Profound moisture reduction was observed during the first three hours of drying, followed by less moisture content reduction. The results showed that at the drying temperature of 100°C and after 6 hours of the drying process, the lowest moisture content reached 13.9% (db) for Titan rough rice. The drying rate of rough rice ranged between 7.41 and 2.01%/h during the first hour of drying under the studied temperature range of 40°C to 100°C. The drying rate was higher with the higher temperature levels during the first three hours. Among all the studied models, the Page, Newton, and Logarithmic models best fit 25%, 25%, and 50% of the twenty-eight studied cases. The challenge that arose from these results led to evolving a mathematical solution by joining the three models in one equation. The combined model showed the best fit for all the studied cases, with R<sup>2</sup> ranging between 0.9999 and 0.9954 for the medium- and long-grain rice varieties. Increasing the drying temperature increased the effective moisture diffusivity values. The highest effective moisture diffusivity of 18.104 × 10<sup>-9</sup> m<sup>2</sup>/s was obtained at the drying temperature of 100°C for medium-grain rice, Titan. The activation energy values ranged between 17.77 and 24.48 kJ/mol for the four rough rice varieties.展开更多
碎米作为大米加工过程的常见产物,常会对产品的口感、味道产生影响,因此针对整米中碎米的有效筛分尤为重要。针对上述问题,该文建立基于大津法(maximal variance between clusters,OTSU)图像分割算法的逻辑回归模型用以检测整米中的碎...碎米作为大米加工过程的常见产物,常会对产品的口感、味道产生影响,因此针对整米中碎米的有效筛分尤为重要。针对上述问题,该文建立基于大津法(maximal variance between clusters,OTSU)图像分割算法的逻辑回归模型用以检测整米中的碎米。将检测结果与国标法进行对比,结果表明逻辑回归模型的曲线线下面积(area under the curve,AUC)值为0.987,柯尔莫可洛夫-斯米洛夫(Kolmogorov-Smirnov,KS)值为0.909,0.5为最佳阈值;而国标法的AUC值为0.922,KS值为0.669,21为最佳阈值。该文所建立的逻辑回归模型的准确率、精确率、召回率及F1分数均高于国标法。此外,逻辑回归模型的AUC值比国标法的AUC值更接近于1,KS值也更高,表明逻辑回归模型能够更好地区分碎米与整米。长轴(x_(1))、面积(x_(2))、短轴(x_(3))与长短轴比(x_(4))4个特征参数都是模型中具有显著影响的因素,对应的线性关系为z=-139.97-5.35x_(1)+10.93x_(2)+2.86x_(3)+34.59x_(4)。展开更多
文摘The available literature revealed a gap in reporting the rough rice drying kinetics parameters under isothermal conditions, particularly for Arkansas medium- and long-grain varieties. Therefore, medium-grain (RO170112 and Titan) and the long-grain (Diamond and Wells) rough rice varieties were dried under isothermal conditions. The drying process occurred under 40°C, 50°C, 60°C, 70°C, 80°C, 90°C, and 100°C in a system emulating the thermogravimetric analyzer. Drying kinetics models were studied for four well-known models: Page, Newton, Logarithmic, and Henderson & Pabis. The drying kinetics constants were determined for the four studied models. The initial moisture content of rough rice was 28.2% db. Profound moisture reduction was observed during the first three hours of drying, followed by less moisture content reduction. The results showed that at the drying temperature of 100°C and after 6 hours of the drying process, the lowest moisture content reached 13.9% (db) for Titan rough rice. The drying rate of rough rice ranged between 7.41 and 2.01%/h during the first hour of drying under the studied temperature range of 40°C to 100°C. The drying rate was higher with the higher temperature levels during the first three hours. Among all the studied models, the Page, Newton, and Logarithmic models best fit 25%, 25%, and 50% of the twenty-eight studied cases. The challenge that arose from these results led to evolving a mathematical solution by joining the three models in one equation. The combined model showed the best fit for all the studied cases, with R<sup>2</sup> ranging between 0.9999 and 0.9954 for the medium- and long-grain rice varieties. Increasing the drying temperature increased the effective moisture diffusivity values. The highest effective moisture diffusivity of 18.104 × 10<sup>-9</sup> m<sup>2</sup>/s was obtained at the drying temperature of 100°C for medium-grain rice, Titan. The activation energy values ranged between 17.77 and 24.48 kJ/mol for the four rough rice varieties.
文摘碎米作为大米加工过程的常见产物,常会对产品的口感、味道产生影响,因此针对整米中碎米的有效筛分尤为重要。针对上述问题,该文建立基于大津法(maximal variance between clusters,OTSU)图像分割算法的逻辑回归模型用以检测整米中的碎米。将检测结果与国标法进行对比,结果表明逻辑回归模型的曲线线下面积(area under the curve,AUC)值为0.987,柯尔莫可洛夫-斯米洛夫(Kolmogorov-Smirnov,KS)值为0.909,0.5为最佳阈值;而国标法的AUC值为0.922,KS值为0.669,21为最佳阈值。该文所建立的逻辑回归模型的准确率、精确率、召回率及F1分数均高于国标法。此外,逻辑回归模型的AUC值比国标法的AUC值更接近于1,KS值也更高,表明逻辑回归模型能够更好地区分碎米与整米。长轴(x_(1))、面积(x_(2))、短轴(x_(3))与长短轴比(x_(4))4个特征参数都是模型中具有显著影响的因素,对应的线性关系为z=-139.97-5.35x_(1)+10.93x_(2)+2.86x_(3)+34.59x_(4)。
