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作 者:张凯萍 ZHANG Kaiping(School of Information Engineering,Xuchang Electrical Vocational College,Xuchang 461002,China)
机构地区:[1]许昌电气职业学院,信息工程学院,河南许昌461002
出 处:《光散射学报》2024年第4期410-417,共8页The Journal of Light Scattering
基 金:河南省重点研发与推广专项(232400410357);河南省高等教育学会高等教育研究项目(2021SXHLX190)。
摘 要:针对牡丹籽油市场中存在的制假贩假问题。采用原位拉曼光谱和化学计量学方法对牡丹籽油的真伪进行快速分类和鉴别。制备不同掺伪浓度的牡丹籽油混合油品并采集其拉曼光谱,采用小波算法对光谱数据进行去基线处理,基于主成分分析提取拉曼光谱的特征信息,利用多元线性回归(MLR)、主成分回归(PCR)和偏最小二乘回归(PLSR)3种化学计量学方法,建立牡丹籽油和菜籽油的掺假定量模型,其中MLR、PCR及PLSR的R^(2)分别为0.9675、0.9839以及0.9846,RMSE分别为0.057、0.041及0.040。结果表明PLSR算法预测效果最优。本文提出并实现了一种利用原位拉曼光谱和化学计量学方法快速检测牡丹籽油真伪和掺假的方法,这为牡丹籽油掺假的快速检测提供方法学的参考和指导。Aiming at the problem of making and selling fake peony seed oil in the market,in situ,Raman spectroscopy and chemometrics were used to rapidly classify and identify the authenticity of peony seed oil.Peony seed oil mixtures with different adulteration concentrations were prepared,and their Raman spectra were collected.The spectral data were de-baseline processed by a wavelet algorithm.The characteristic information of the Raman spectrum was extracted based on principal component analysis.The adulteration quantitative models of peony seed oil and rapeseed oil were established by using three chemometrics methods,including multiple linear regression(MLR),principal component regression(PCR),and partial least squares regression(PLSR).The R^(2) of MLR,PCR,and PLSR were 0.9675,0.9839,and 0.9846,respectively,and the RMSE were 0.057,0.041,and 0.040,respectively.The results show that the PLSR algorithm has the best prediction effect.In this paper,a rapid detection method for the authenticity and adulteration of peony seed oil using in-situ Raman spectroscopy and chemometrics was proposed and implemented,which provides methodological reference and guidance for the rapid detection of peony seed oil adulteration.
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