基于稻谷X射线荧光光谱测定快速识别糙米和精米中的镉含量

Rapid identification of cadmium content in brown and polished rice based on X-ray fluorescence spectroscopy detection of paddy rice

中文关键词:  稻谷  糙米  精米  镉  回归模型  定量识别  X射线荧光光谱

英文关键词:paddy rice  brown rice  polished rice  cadmium  regression model  quantitative identification  X-ray fluorescence spectroscopy

中文摘要:

      目的 基于稻谷镉的X射线荧光光谱测定, 建立糙米、精米镉的快速定量识别模型, 简化入仓稻谷重金属检测的砻谷、碾米等预处理步骤。方法 采用X射线荧光光谱法一一对应分析26组稻谷-糙米-精米样品中镉含量。根据线性、对数、逆、二次、三次、幂、指数等函数关系分别拟合稻谷-糙米、稻谷-精米、糙米-精米镉含量之间的回归模型。采用另外4组样品对决定系数(r2)大于0.95的模型准确性进行验证, 根据决定系数、误差值等筛选最优拟合模型。结果 稻谷-糙米-精米镉含量之间存在较强的相关性, 在此基础上建立的3个可食用米镉含量快速识别模型分别为: 稻谷-糙米三次函数回归模型Y=0.0131+0.7178X+0.5722X2-0.3492X3 (r2=0.9859); 稻谷-精米三次函数回归模型Y=0.0284+0.3779X+1.5500X2-1.2046X3 (r2=0.9855); 糙米-精米幂函数回归模型Y=0.9412×X1.0233 (r2=0.9902), 3个模型预测结果的绝对误差分别为8.91%、8.57%和10.24%。结论 本研究建立的回归模型具有良好的稻米镉含量相互预测性能, 该法有望简化稻谷镉检测前的砻谷、碾米等预处理流程, 提高检测效率。

英文摘要:

      Objective To establish a rapid quantitative identification model of cadmium in brown rice and polished rice based on the determination of cadmium in paddy-rice by X-ray fluorescence spectrometry to simplify the pretreatment steps such as hulling and milling of stored rice for heavy metal detection. Methods X-ray fluorescence spectrometry was used to analyze the cadmium content in 26 groups of paddy-brown-polished rice samples in one-to-one correspondence. The regression models of cadmium content in paddy-brown rice, paddy-polished rice, and brown-polished rice were fitted respectively according to the linear, logarithmic, inverse, quadratic, cubic, power, exponential functional relationships. Another 4 groups of samples were used to verify the accuracy of the model whose coefficient of determination (r2) was greater than 0.95, and the best fit models were screened according to the coefficient of determination and error value of the verification experiment. Results There were strong correlations among paddy-brown-polished rice in cadmium content, based on this, the rapid identification models of cadmium content in edible rice were shown as follow: Paddy-brown rice cubic function regression model Y=0.0131+0.7178X+0.5722X2-0.3492X3 (r2=0.9859); paddy-polished rice cubic function regression model Y=0.0284+0.3779X+1.5500X2-1.2046X3 (r2=0.9855); brown-polished rice power function regression model Y=0.9412×X1.0233 (r2=0.9902), the absolute errors of the prediction results of the 3 models were 8.91%, 8.57% and 10.24%, respectively. Conclusion The regression models established in this study have good mutual prediction performance of cadmium content in rice, which is expected to simplify the pretreatment process of rice hulling and milling before cadmium detection and improve detection efficiency.