针对传统稻种发芽率检测操作复杂,耗时长,稻种外壳污染物干扰光谱检测等问题,提出一种基于稻种糙米近红外光谱检测实现稻种发芽率快速检测的新方法.将192份糙米样品的光谱分为校正集144份和预测集48份,通过不同预处理方法和化学计量学...针对传统稻种发芽率检测操作复杂,耗时长,稻种外壳污染物干扰光谱检测等问题,提出一种基于稻种糙米近红外光谱检测实现稻种发芽率快速检测的新方法.将192份糙米样品的光谱分为校正集144份和预测集48份,通过不同预处理方法和化学计量学建模方法,分析不同老化时间糙米的光谱差异,建立糙米发芽率的预测模型.结果表明：在全波段570~1 848 nm采用二阶导数＋SNV and Detrend的预处理并结合偏最小二乘法（PLS）建立的模型最优,其校正集决定系数RC与标准偏差SEC分别为0.976和1.244,预测集相关系数RP与标准偏差SEP分别为0.951和1.935.采用近红外光谱分析技术对稻种糙米发芽率进行测定是可行的,所建模型在稻种糙米发芽率预测方面有较好的预测能力.展开更多
Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative i...Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.展开更多
文摘针对传统稻种发芽率检测操作复杂,耗时长,稻种外壳污染物干扰光谱检测等问题,提出一种基于稻种糙米近红外光谱检测实现稻种发芽率快速检测的新方法.将192份糙米样品的光谱分为校正集144份和预测集48份,通过不同预处理方法和化学计量学建模方法,分析不同老化时间糙米的光谱差异,建立糙米发芽率的预测模型.结果表明：在全波段570~1 848 nm采用二阶导数＋SNV and Detrend的预处理并结合偏最小二乘法（PLS）建立的模型最优,其校正集决定系数RC与标准偏差SEC分别为0.976和1.244,预测集相关系数RP与标准偏差SEP分别为0.951和1.935.采用近红外光谱分析技术对稻种糙米发芽率进行测定是可行的,所建模型在稻种糙米发芽率预测方面有较好的预测能力.
基金Project(61201381) supported by the National Natural Science Foundation of ChinaProject(YP12JJ202057) supported by the Future Development Foundation of Zhengzhou Information Science and Technology College,China
文摘Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.
