表面增强拉曼光谱结合化学计量学用于生鲜山药中尿囊素含量的快速检测  

Rapid Detection of Allantoin in Raw Yam by Surface-Enhanced Raman Spectroscopy

作　　者：王威 李永玉[1] 彭彦昆[1] 马劭瑾 张悦湘 彭鲲 WANG Wei;LI Yong-Yu;PENG Yan-Kun;MA Shao-Jin;ZHANG Yue-Xiang;PENG Kun(College of Engineering,China Agricultural University,Beijing 100083,China)

机构地区：[1]中国农业大学工学院,北京100083

出　　处：《分析化学》2024年第11期1659-1668,共10页Chinese Journal of Analytical Chemistry

基　　金：国家重点研发计划项目(No.2021YFD1600101-06);中国农业大学2115人才工程项目资助。

摘　　要：尿囊素作为山药的功能性成分,在医学及化妆品领域有重要的作用。本研究基于实验室自行搭建的拉曼光谱检测系统,以生鲜铁棍山药为研究对象,分析了尿囊素标准品粉末的拉曼光谱以及生鲜山药的尿囊素提取液的表面增强拉曼光谱,确定了生鲜山药中尿囊素的表面增强拉曼特征峰为644、1027和1398 cm^(-1)。探究了生鲜山药中尿囊素和银溶胶吸附时间以及山药厚度对拉曼特征峰强度的影响,建立了直接获取生鲜山药中尿囊素表面增强拉曼特征信息的方法。基于本方法采集32个生鲜山药的表面增强拉曼光谱,建立了644、1027和1398 cm^(-1)处尿囊素拉曼特征峰一元线性回归(Unary linear regression,ULR)预测模型、多元线性回归(Multivariable linear regression,MLR)预测模型以及全波段光谱偏最小二乘回归(Partial least squares regression,PLSR)预测模型。结果表明,多元线性回归模型效果最佳,其验证集决定系数(R_(V)^(2))为0.93,均方根误差(RMSEV)为0.35 mg/g。但是,尿囊素特征峰易受溶液极性和基底变化的影响,其特征峰会发生一定偏移,影响检测精度,而利用全波段拉曼光谱建立PLSR定量预测模型可提升模型的鲁棒性。基于随机蛙跳(Random frog,RF)算法筛选特征变量建立了尿囊素的RF-PLSR定量预测模型,R_(V)^(2)提升为0.96,RMSEV降低至0.26 mg/g。采用未参与建模的10个生鲜山药样本对此模型进行外部验证,最大残差绝对值为0.74 mg/g。研究结果表明,本方法可以实现生鲜山药中尿囊素含量的快速定量检测。Allantoin,as a functional constituent of yam,has an extremely important role in the medical and cosmetic fields.In this study,based on the Raman spectroscopy detection system constructed in the laboratory,the Raman spectra of the powder of allantoin standard and the surface-enhanced Raman spectra of the allantoin extract of fresh yam were analyzed,and the surface-enhanced Raman characteristic displacements of allantoin in raw yam were determined to be 644,1027 and 1398 cm^(-1).The effects of the adsorption time of allantoin and silver sol and the thickness of the yam on the intensity of Raman feature displacement were investigated,and a method was established to directly obtain the surface-enhanced Raman feature information of allantoin in fresh yam.Based on this method,the surface-enhanced Raman spectra of 32 raw yams were collected,and the Raman feature displacements of allantoin at 644,1027 and 1398 cm^(-1) were established by unary linear regression(ULR),multivariable linear regression(MLR),and partial least squares regression(PLSR).The results showed that the MLR model was the most effective,with the validation set coefficient of determination(R_(V)^(2))of 0.93 and the root mean square error of validation(RMSEV)of 0.35 mg/g.However,the allantoin feature shift was susceptible to the changes of solution polarity and substrate,which led to a certain shift of the feature shift affecting the accuracy of the detection,and the quantitative prediction model of PLSR using the full-waveband Raman spectroscopy would improve the model′s Robustness.The random frog(RF)-PLSR quantitative prediction model of allantoin was established based on the RF algorithm to screen the feature variables,and the R_(V)^(2) was increased to 0.96,and the RMSEV was reduced to 0.26 mg/g.The model was externally validated using ten raw yam samples which were not involved in the modeling,and the absolute value of maximum residual was 0.74 mg/g.The method could realize the rapid quantitative detection of allantoin content in raw fresh yam,and

关 键 词：山药 尿囊素 表面增强拉曼光谱 预测模型 

分 类 号：O657.37[理学—分析化学] TS215[理学—化学]