【摘要】 应用近红外光谱分析技术,采用偏最小二乘法,对141份花椒粉末样品近红外光谱建立挥发油含量定量模型,交叉验证测定系数R2为0.936,交叉验证误差均方根RMSECV为0.421,经直接正交信号校正(DOSC)预处理后,相应的交叉验证测定系数R2提高到0.95,RMSECV减小为0.374。使用105份样品近红外光谱所建立的模型对36份样品的预测集进行预测,光谱采用DOSC预处理前后,预测测定系数R2由0.923提高到0.969,RMSEP由0.452减小到0.289,RSD由11.65%减小到7.44%,RPD由3.622增加到5.573。研究结果表明,使用DOSC预处理后的花椒挥发油含量近红外光谱定量模型的预测效果有较大的提高,且具有较好的稳定性和较强的预测能力,可用于实际的花椒挥发油检测。更多还原

【Abstract】 Based on near infrared spectroscopy technique and partial least squares(PLS),calibration model of volatile oil content of 141 powder samples with particle size of 40 meshes was established to classify Zanthoxylum bungeagum Maxim more rapidly.After spectra were preprocessed by direct orthogonal signal correction(DOSC),the determination coefficient(R2)increased from 0.936 to 0.95 and the root mean square error of cross validation(RMSECV)decreased from 0.421 to 0.374.Applying the model established by 105 samples to the test set with 36 samples,R2 increased from 0.923 to 0.969,the root mean square error of prediction(RMSEP)decreased from 0.452 to 0.289,RPD increased from 3.622 to 5.573 and RSD decreased from 11.65% to 7.44% after spectra were preprocessed by DOSC.The research indicated that the model preprocessed by DOSC is better,and the precision of model got higher.The result showed that rapid detection of volatile oil content in Zanthoxylum bungeagum Maxim by NIR and DOSC is feasible and efficient.更多还原