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作 者:张辉[1] 吴迪[1] 李想[1] 石品艳[1] 王思寒[1] 冯凤琴[1] 何勇[1]
机构地区:[1]浙江大学生物系统工程与食品科学学院,杭州310029
出 处:《农业工程学报》2012年第7期266-270,共5页Transactions of the Chinese Society of Agricultural Engineering
基 金:国家科技支撑计划(No.2006BAD05A02);国家高技术研究发展计划("863"计划)项目(2007AA10Z210)
摘 要:为了建立食用油必需脂肪酸快速检测的方法,该研究提出了基于近红外光谱技术检测食用油中α-亚麻酸和亚油酸含量的快速测定方法。对光谱信息分别采用偏最小二乘回归方法(PLS)和最小二乘支持向量机(LS-SVM)建立模型。比较了多种光谱预处理方法对模型预测能力的影响。结果表明对于亚油酸含量的预测,采用Savitzky-Golay平滑法结合多元散射校正(MSC)的光谱预处理所建立的LS-SVM模型最优。预测集的决定系数(R2)、预测均方根误差(RMSEP)和剩余预测偏差(RPD)分别达到了0.989,0.0161和9.4783。对于α-亚麻酸含量的预测,采用Savitzky-Golay平滑法结合标准正态变换(SNV)的光谱预处理所建立的LS-SVM模型最优。α-亚麻酸含量预测结果的R2、RMSEP和RPD为0.972,0.0036和6.0561,据此表明,应用近红外光谱技术能够检测食用油中α-亚麻酸和亚油酸的含量,为快速检测食用油的必需脂肪酸提供了参考。In order to find out a fast quantitative determination method for the essential fatty acids in edible oils,near infrared spectroscopy was applied in determination of the contents ofα-linolenic acid and linoleic acid.The chemometrics models between near infrared spectra and the contents ofα-linolenic acid and linoleic acid were established by partial least squares regression(PLS)and least-squares support vector machine(LS-SVM).Several common used spectral pretreatment methods were used to establish different PLS and LS-SVM models.For linoleic acid prediction,best predictive performance was obtained using LS-SVM model and spectral pretreatment of Savitzky-Golay smoothing and multiplicative scatter correction(MSC).Coefficient of determination(Rp 2),root mean square error for prediction(RMSEP)and residual predictive deviation(RPD)were 0.0161,0.989 and 9.4783,respectively.For α-linolenic acid prediction,best predictive performance was obtained using LS-SVM model and the spectral pretreatment of Savitzky-Golay smoothing and standard normal variation(SNV).Rp 2,RMSEP and RPD were 0.0036,0.972 and 6.0561,respectively.The results indicate that it is feasible to use near infrared spectroscopy for fast determination ofα-linolenic acid and linoleic acid contents in edible oils.
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