红外光谱结合化学计量学快速预测铁皮石斛中总黄酮含量

doi:10.3964/j.issn.1000-0593(2018)06-1702-06

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摘要：评价药用植物质量的主要手段之一是有效成分含量检测，不同采收期对药用植物有效成分含量有明显影响。通过傅里叶变换红外光谱(FTIR)结合化学计量学建立快速预测不同采收期铁皮石斛中总黄酮含量的方法，以期为铁皮石斛质量快速预测评价提供研究基础。采收2014年1至12月的铁皮石斛样品干燥粉碎；以氯化铝显色法测定铁皮石斛中总黄酮含量，分析不同采收时间铁皮石斛总黄酮的累积规律；采集样品红外光谱信息，归属红外吸收峰，拟合红外光谱数据和总黄酮含量数据，结合一阶导数、二阶导数、多元散射校正、标准正态变量、正交信号校正等对数据进行预处理，建立偏最小二乘回归模型预测样品中总黄酮含量。结果显示：(1)样品和标准品芦丁均在270 nm附近有共有吸收峰，实验以270 nm为总黄酮定量波长，标准曲线为y=6.076 5x+0.055 8，相关系数r=0.996 6，线性关系良好；重现性、精密度和稳定性相对标准偏差分别为1.00%，0.37%和0.28%，该方法稳定可靠；(2)总黄酮含量随时间变化趋势为先升高后降低，6月—8月样品含量较高，平均含量大于64.10 mg·g^-1；(3)铁皮石斛红外光谱数据与总黄酮含量拟合后进行一阶导数、二阶导数、多元散射校正、标准正态变量、正交信号校正等组合处理，用PLSR模型预测铁皮石斛的总黄酮含量，结果最佳预处理方式为2D+SG5+SNV+OSC-PLSR，训练集和验证集r分别为0.979 0和0.882 4，验证均方根误差(RMSEE)和预测均方根误差(RMSEP)分别为2.438 2和4.169 9，铁皮石斛中总黄酮含量预测值与测量值较接近，表明PLS模型可用于总黄酮含量的快速预测。傅里叶变换红外光谱结合化学计量学能实现铁皮石斛中总黄酮含量准确预测，为铁皮石斛质量评价提供快速、有效的方法。

关键词：傅里叶变换红外光谱；铁皮石斛；总黄酮；含量预测

Abstract：The detection of active ingredient contents is to evaluate the quality of medicinal plants, and different harvesting time has a significant influence on effective component contents of medicinal plants. In this study, Fourier transform infrared (FTIR) spectroscopy combined with chemometrics was used to establish a rapid method for predicting the total flavonoids content in dendrobium officinale at different harvesting time, in order to provide a basis for the rapid quality evaluation of this species. From January to December in 2014, a total of 111 samples were collected, dried and crushed. In addition, the content of total flavonoids in D. officinale was determined by using traditional method and the accumulation regularity of total flavonoids in D. officinale was studied. Moreover, the infrared spectra of the samples were recorded and their bands were identified. The original spectra were pre-processed by combining the first derivative, second derivative, standard normal variable, multiple scattering correction and orthogonal signal correction. Processed FTIR spectra were set as variable X and the contents of total flavonoids were set as variable Y. The PLS model was established to predict the total flavonoids content. The results showed that: (1) Both the sample and the standard rutin had a common absorption peak near 270 nm. In this experiment, the total flavonoid was measured at 270 nm. The standard curve equation of total flavonoids was y=6.076 5x+0.055 8, r=0.996 6 and the precision, reproducibility and stability of relative standard deviation (RSD) were 0.37%, 1.00% and 0.28%, respectively. (2) The content of total flavonoids increased with time at first and then decreased, and the content of total flavonoids was higher than 64.10 mg·g^-1 from June to August. (3) The FTIR data was integrated with the total flavonoid content of D. officinale by partial least squares regression analysis (PLSR). The results showed that the optimal pretreatment method was 2D+SG5+SNV+OSC-PLSR, the r and RMSEE of the training set were 0.979 0 and 2.438 2, respectively while R² and RMSEP of validation set were 0.882 4 and 4.169 9, respectively. The predicted and the measured value of total flavonoid content was close to each other, indicating that PLS model can be used for rapid prediction of total flavonoid content. FTIR combined with chemometrics can accurately predict the total flavonoids content in D. officinale and provide a rapid and effective method for the quality evaluation of D. officinale.

Key words：Infrared spectroscopy; Dendrobium officinale; Total flavonoids; Content prediction

收稿日期: 2016-10-08 修订日期: 2017-05-22

中图分类号:

O657.3

基金资助: 国家自然科学基金项目(81660638)，云南省自然科学基金重大项目(2013FC006)资助

通讯作者: 和加卫，王元忠 E-mail: hejw4522@163.com; boletus@126.com

作者简介: 孙恒，1990年生，云南省农业科学院高山经济植物研究所研究实习员 e-mail: sunhengsci@163.com

引用本文:

孙恒，金航，胡强，康平德，陈骏飞，和加卫，王元忠. 红外光谱结合化学计量学快速预测铁皮石斛中总黄酮含量[J]. 光谱学与光谱分析, 2018, 38(06): 1702-1707.
SUN Heng, JIN Hang, HU Qiang, KANG Ping-de, CHEN Jun-fei, HE Jia-wei, WANG Yuan-zhong. Infrared Spectroscopy Combined with Chemometrics for Rapid Determination of Total Flavonoids in Dendrobium Officinale. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1702-1707.

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