近红外光谱结合化学计量学快速测定阿里红饮片中齿孔酸含量  

A New Method for Determination of Eburicoic Acid in Fomes Officinalis Ames by NIR Combined With PLS

作　　者：谢玉玉[1,2,3] 陈志慧 侯雪玲[1,3] 刘永强[1,3] XIE Yu-yu;CHEN Zhi-hui;HOU Xue-ling;LIU Yong-qiang(Key Laboratory of Plant Resources and Chemistry of Arid Zone,Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Urumqi 830011,China;Analysis Center of Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Urumqi 830011,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院新疆理化技术研究所干旱区植物资源与化学重点实验室,新疆乌鲁木齐830011 [2]中国科学院新疆理化技术研究所分析测试中心,新疆乌鲁木齐830011 [3]中国科学院大学,北京100049

出　　处：《光谱学与光谱分析》2024年第10期2981-2987,共7页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金联合基金项目(U1703235)资助。

摘　　要：传统的阿里红中齿孔酸含量测定采用高效液相色谱法(HPLC),但该方法前处理复杂,操作繁琐。为了实现对中药阿里红饮片中齿孔酸含量的快速无损监测,尝试建立基于近红外光谱(NIR)的偏最小二乘(PLS)回归模型。用来预测阿里红中齿孔酸的含量,采用传统的HPLC方法对阿里红中的齿孔酸含量进行测定,其结果作为指标值。采集近红外数据后使用五种光谱变换方法对光谱数据进行预处理,即多元散射校正(MSC)、标准正态变化(SNV)、Savitzky-Golay平滑(7点)、一阶导数变换(FD)和二阶导数变换(SD)。通过竞争自适应重加权法(CARS)进行波长选择并对PLS模型进行优化,大大减少了光谱变量的数量,并显著提高了PLS模型的性能,尤其是SNV-CARS-PLS模型,仅占总光谱波长的5.53%,预测集的R2值为0.9823,预测均方根误差(RMSEP)值为0.1037%,残差预测偏差(RPD)值为5.34。通过t检验表明:该最优模型与传统HPLC法在预测阿里红中齿孔酸含量上没有显著差异。研究结果表明:近红外光谱结合竞争性自适应重加权算法对波长筛选后建立偏最小二乘回归模型对阿里红饮片中齿孔酸含量的无损检测可行。The traditional method for determining the content of Eburicoic acid is HPLC,which is inefficient and cumbersome to operate.To achieve rapid and non-destructive monitoring of Eburicoic acid,this paper attempted to establish a partial least squares(PLS)regression model based on near-infrared spectroscopy(NIR)to predict the Eburicoic acid content in Fomes officinalis Ames decoction pieces.Firstly,the traditional HPLC method was used to test the content of Eburicoic acid in Fomes officinalis Ames,and the test results were used as indicator values.Secondly,near-infrared data was collected,and five spectral transformation methods were used to preprocess spectral data,namely Multiplicative Scattering Correction(MSC),Normalized Normal Variation(SNV),Savitzky Golay Smoothing(7 points),First Derivative Transformation(FD),and Second Derivative Transformation(SD).Finally,wavelength selection was performed through competitive adaptive reweighted sampling(CARS)and the PLS model was optimized,greatly reducing the number of spectral variables and significantly improving the performance of the PLS model,especially the SNV-CARS-PLS model,which only accounted for 5.53%of the total spectral wavelength.The R2 value for prediction sets 0.9823.The root mean square error(RMSEP)value for prediction is 0.1037%,and the residual prediction deviation(RPD)value is 5.34.The t-tests indicated no significant difference in precision and accuracy between the results of the optimal model and that of the traditional HPLC method.The research results indicate that it is feasible to establish PLS models based on near-infrared spectroscopy combined with a competitive adaptive reweighting algorithm for non-destructive detection of Eburicoic acid content in Fomes officinalis Ames decoction pieces.

关 键 词：阿里红 齿孔酸 近红外 化学计量学 偏最小二乘法 

分 类 号：TS255[轻工技术与工程—农产品加工及贮藏工程]