利用Far-IR和THz-TDS光谱法测定大米中西维因

发布时间：2019-03-12 18:48

【摘要】：利用太赫兹时域光谱(THz-TDS)和傅里叶变换远红外光谱(FT-Far-IR)技术研究了大米中西维因在太赫兹频段的吸收光谱特征,并结合化学计量学方法对大米中西维因进行了测定。样品的制备采取待测农药西维因与大米粉末混合压片的方法模拟真实检测情景,无需样品的分离富集。分别将样品在1.8~6.3THz特征波段内的Far-IR吸收光谱数据和在0.5~1.5THz特征波段内的THz-TDS吸收光谱数据随机划分为训练集和验证集。采用偏最小二乘回归(PLSR)方法建立定量分析模型,将校正均方根误差(RMSECV)、预测均方根误差(RMSEP)、预测相关系数(R_v)作为模型性能评判的依据,RMSECV,RMSEP越小,R_v越大,则所建立的模型越好。两种检测技术均得到较好的结果。其中,运用Far-IR技术所得数据建立的定量分析模型预测相关系数(R_v)为0.99,校正均方根误差(RMSECV)为0.007 7,预测均方根误差(RMSEP)为0.008 6;运用THz-TDS技术所得数据建立的定量分析模型预测相关系数为0.98,校正均方根误差(RMSECV)为0.002 5、预测均方根误差(RMSEP)为0.004 4。该研究为定量检测粮食中的农残提供了一种新方法。
[Abstract]:Terahertz time domain spectroscopy (THz-TDS) and Fourier transform far infrared spectroscopy (FT-Far-IR) were used to study the absorption spectra of rice in the terahertz band. The method of chemometrics was used for the determination of carbamyl in rice. The sample was prepared by mixing the pesticide Sivaine and the rice powder to simulate the real detection situation without the need for separation and enrichment of the sample. The Far-IR absorption spectrum data in the 1.8~6.3THz characteristic band and the THz-TDS absorption spectrum data in the 0.5~1.5THz characteristic band are randomly divided into the training set and the verification set. The partial least squares regression (PLSR) method is used to establish the quantitative analysis model. The correlation coefficient of (RMSEP), prediction for root mean square error correction (RMSECV),) is taken as the basis for evaluating the performance of the model. The smaller the RMSECV,RMSEP is, the less the RMS error prediction (RMSEP), prediction correlation coefficient is. The larger the R _ v, the better the model is built. Good results were obtained by both methods. The prediction correlation coefficient (R _ (v), the corrected root mean square error (RMSECV) and the prediction root mean square error (RMSEP) of the quantitative analysis model based on the Far-IR technique are 0.999, 0.007 and 0.008 6, respectively. The prediction correlation coefficient is 0.98, the correction root mean square error (RMSECV) is 0.002, and the prediction root mean square error (RMSEP) is 0.004 4. The correlation coefficient of the quantitative analysis model is 0.98, the correction root mean square error (RMSECV) is 0.002. This study provides a new method for quantitative detection of agricultural residues in grain.
【作者单位】：首都师范大学;
【基金】：国家重大科学仪器设备专项(2012YQ140005) 国家自然科学基金项目(21275101)资助
【分类号】：O657.3;TS210.7

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