基于氢核磁共振与偏最小二乘法对酸枣仁粉末及其掺伪品的鉴别

摘要/Abstract

摘要： 摘要：本研究采用氢核磁共振（1H nuclear magnetic resonance, 1H NMR）代谢组学技术对酸枣仁、理枣仁以及不同比例的掺伪粉末进行对比，结合多元统计分析建立一种快速鉴别酸枣仁粉末及其掺伪品的方法。主成分分析（principal component analysis，PCA）显示掺伪比例较低的样品与酸枣仁有部分的重叠。偏最小二乘-判别分析（partial least squares discriminant analysis，PLS-DA）显示，当掺伪比例≥40%时，酸枣仁粉和掺伪品沿t[1]轴完全分开。在此基础上，采用偏最小二乘分析（partial least squares，PLS），对酸枣仁及其不同掺伪比例粉末样品训练集数据进行了回归分析，并建立了掺伪判别模型。1H NMR与PLS相结合的模型预测结果准确性较高，可实现酸枣仁中理枣仁掺假的快速鉴别。该方法为酸枣仁及含酸枣仁的保健食品、复方制剂质量评价提供了一条新途径。

关键词: 酸枣仁, 理枣仁, 掺伪, 氢核磁共振, 代谢组学技术, 偏最小二乘

Abstract: Abstract: In the present study, the chemical compositions of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS) and adulterant samples were compared by 1H NMR-based metabolic fingerprint.Then the obtained spectral data were analyzed by multivariate analysis to develop a new method for rapid detection of ZSS adulteration. Principal component analysis (PCA) performed on 1H NMR data revealed that only a few samples at lower adulteration ratios were overlapped with ZSS. In addition, data was analyzed by partial least squares discriminant analysis (PLS-DA), and a clear separation can be seen between ZSS and 40% adulteration samples. Then the partial least squares (PLS) model based on 1H NMR data was applied to detect adulteration in ZSS falsified by intentional addition of different concentrations of ZMS, and a quantitative calibration model was established. The PLS regression approach provide a highly reliable and accurate prediction model. The results demonstrates 1H NMR spectroscopy coupled with PLS, a fast and effective technique, is helpful to check for adulteration in ZSS. Meanwhile, the method provided a potential standard for quality control of ZSS.

Key words: Ziziphi Spinosae Semen, Ziziphi Mauritianae Semen, adulterants, 1H NMR, metabonomics, PLS

中图分类号:

TS207.7

申晨曦杜晨晖李震宇李爱平闫艳秦雪梅. 基于氢核磁共振与偏最小二乘法对酸枣仁粉末及其掺伪品的鉴别[J]. 食品科学, 0, (): 0-0.

Zhen-Yu LI Ai-Ping LI. Ziziphi Spinosae Semen powder and adulterants differentiation by 1H NMR spectroscopy combined with PLS[J]. FOOD SCIENCE, 0, (): 0-0.

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