摘要
采用CARS波长变量挑选方法优化建模,对食用油中4种主要脂肪酸(棕榈酸、硬脂酸、油酸和亚油酸)进行近红外定量分析。应用预测浓度残差法剔除奇异样本后,对样品集光谱进行标准化预处理,通过CARS优选出的波长变量分别建立4种脂肪酸的偏最小二乘法(PLS)模型。与采用OPUS软件自动优化建模相比,CARS法所建模型的决定系数(R2)、交叉校验均方根误差(RMSECV)和预测均方根误差(RMSEP)都优于后者所建模型。CARS法有效地简化了模型,且所挑选出的特征波长较少。
Competitive adaptive reweighted sampling（CARS） method was employed to improve the prediction accuracy of the NIR quantitative model of four kinds of fatty acid（palmitic acid,stearic acid,oleic acid and linoleic acid） in edible oil.Predict concentration residual method was employed to detect the outlier before preprocessing the spectroscopy by normalization.The key variables were selected by CARS method.The partial least squares（PLS） calibration models of four kinds of fatty acid were established respectively in the optimal conditions,and compared with the results using OPUS software.Determination coefficient（R2）,root mean square error of cross validation（RMSECV）and root mean square error of prediction（RMSEP）were used to evaluate the quality of the modes.The results showed that better prediction was obtained by CARS.The result showed that using CARS could effectively simplify the model and the less number of wavelength variables selected could be reference for developing filter spectrometer of edible oil.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2011年第10期162-166,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
北京市优秀人才资助项目(20081D0500300130)
关键词
食用油
脂肪酸
近红外光谱
CARS变量筛选
偏最小二乘法
Edible oil
Fatty acid
Near-infrared spectroscopy
Competitive adaptive reweighted sampling
Partial least squares
