摘要
以从企业采集的50个油茶籽油样品为试验材料,通过扫描获取红外光谱并筛选特征波段,利用偏最小二乘法(PLS)建立油茶籽油中甾醇、维生素E和类胡萝卜素含量的预测模型,并通过系列参数对模型进行评价。结果表明:在400~1 850 cm^(-1)波数范围内,甾醇、维生素E和类胡萝卜素校正集相关系数(R_C)分别为0.978 9、0.980 1和0.949 9,交叉验证均方根误差(RMSECV)分别为42.38、25.64、0.84 mg/kg,经对模型进行验证,上述3种成分预测集相关系数(R_P)分别为0.993 4、0.997 4和0.959 0,预测均方根误差(RMSEP)分别为13.31、6.24、0.18 mg/kg,RPD分别为7.742、12.696和2.889。可见,模型的预测效果较好,说明红外光谱法可应用于油茶籽油中甾醇、维生素E和类胡萝卜素等功能活性成分含量的快速检测。
50 Oil-tea camellia seed oil samples collected from enterprise were analyzed by infrared spectroscopy,and the characteristic bands were screened to establish a quantitative model for the contents of sterols,vitamin E and carotenoids in oil-tea camellia seed oil using partial least squares method( PLS),and then the model was evaluated by series parameters.The results showed that the correlation coefficients of the calibration set( RC) of sterol,vitamin E and carotenoid were 0.978 9,0.980 1 and0.949 9 respectively in the characteristic bands with wavenumber 400-1 850 cm-1,and the root mean square errors of cross validation( RMSECV) were 42.38,25.64 mg/kg and 0.84 mg/kg,respectively.The models were validated,and the correlation coefficients of the prediction set( RP) of the three components were 0.993 4,0.997 4 and 0.959 0 respectively,the root mean square errors of the prediction set( RMSEP) were 13.31,6.24 mg/kg and 0.18 mg/kg,respectively,and the relative analysis errors( RPD) were 7.742,12.696 and 2.889,respectively.So the model had better prediction effect and could be used as a fast and accurate method for the determination of sterols,vitamin E and carotenoid in oil-tea camellia seed oil.
作者
吴雪辉
王泽富
WU Xuehui;WANG Zefu(College of Food Science,South China Agricultural University,Guangzhou 510642,China;Guangdong Engineering Research Center for Oil-Tea Camellia,Guangzhou 510642,China)
出处
《中国油脂》
CAS
CSCD
北大核心
2018年第10期140-143,共4页
China Oils and Fats
基金
林业公益性行业科研专项经费项目(201504703)
广东省林业科技创新项目(2017KJCX005)
关键词
油茶籽油
红外光谱
活性成分
偏最小二乘法
模型
评价
oil - tea camellia seed oil
infraredspectroscopy
active ingredient
squares method
model
evaluationpartial least