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中国精品科技期刊2020
赵昕,刘鑫,王韵彭,等. 基于近红外光谱的酸枣仁不同伪品掺假检测[J]. 食品工业科技,2022,43(21):294−301. doi: 10.13386/j.issn1002-0306.2022010028.
引用本文: 赵昕,刘鑫,王韵彭,等. 基于近红外光谱的酸枣仁不同伪品掺假检测[J]. 食品工业科技,2022,43(21):294−301. doi: 10.13386/j.issn1002-0306.2022010028.
ZHAO Xin, LIU Xin, WANG Yunpeng, et al. Detection for Different Adulterants of Ziziphi Spinosae Semen Based on Near Infrared Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(21): 294−301. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022010028.
Citation: ZHAO Xin, LIU Xin, WANG Yunpeng, et al. Detection for Different Adulterants of Ziziphi Spinosae Semen Based on Near Infrared Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(21): 294−301. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022010028.

基于近红外光谱的酸枣仁不同伪品掺假检测

Detection for Different Adulterants of Ziziphi Spinosae Semen Based on Near Infrared Spectroscopy

  • 摘要: 本文采用近红外光谱技术对酸枣仁及其三种常见伪品理枣仁、枳椇子和兵豆进行定性定量检测研究。分别制备不同伪品掺杂质量分数为1%~90%的单种掺杂物实验样品,以及多种伪品同时掺杂的样品,采集800~2500 nm范围的近红外光谱数据。首先利用主成分分析(principal component analysis,PCA)对酸枣仁及三种伪品进行初步定性鉴别。对于单一掺假物样品,采用五种不同预处理方法对光谱数据进行去噪。利用偏最小二乘回归(partial least squares regression,PLS)方法,建立PLS1模型定量预测掺假物含量,并采用连续投影算法(successive projection algorithm,SPA)挑选最优波长,优化定量模型。结果表明,理枣仁掺假建立的3波长检测模型的预测集决定系数R2p为0.9659,均方根误差(root mean square error,RMSEP)为6.1910%。枳椇子掺假建立的8波长检测模型的预测集决定系数R2p为0.9491,均方根误差(RMSEP)为7.6232%。兵豆掺假建立的5波长检测模型的预测集决定系数R2p为0.9666,均方根误差(RMSEP)为6.1437%。对于多掺杂物样品,建立了PLS2模型同时对不同成分进行定量预测,酸枣仁效果最好,R2p≥0.7115,枳椇子预测效果最差,R2p≥0.2007。研究表明,利用近红外光谱技术可以实现酸枣仁不同伪品掺假的快速无损检测。所建方法为后续酸枣仁及其他种子类中药材便携式无损检测仪器的开发提供了理论基础与参考依据,对保证中药材质量安全具有重要社会意义。

     

    Abstract: In this paper, near infrared spectroscopy technology was used to study the qualitative and quantitative detection of Ziziphi Spinosae Semen and its three kinds of counterfeits, Ziziphus mauritiana lam, Hovenia dulcis Thunb. and Lens culinaris. Different single-adulterant samples were prepared with adulterant concentration in range of 1%~90%. Multiple-adulterants samples were also prepared by adding the three kinds of counterfeits simultaneously. Near-infrared spectroscopy data in the range of 800~2500 nm were acquired. Principal component analysis (PCA) was used firstly to qualitatively identify Ziziphi Spinosae Semen and three kinds of counterfeits. For single-adulterant samples, five different pretreatment methods were applied to denoising. Partial least squares regression was used to establish PLS1 models to quantitatively predict concentrations of the adulterants. Successive projection algorithm (SPA) was used to select the optimal wavelength to optimize the PLS1 models. Three-wavelength prediction model for the adulteration detection of Ziziphus mauritiana Lam was established, with determination coefficient R2p for prediction set of 0.9659 and root mean square error (RMSEP) of 6.1910%. Eight-wavelength prediction model for the adulteration detection of Hovenia dulcis Thunb. was established, with R2p of 0.9491 and RMSEP of 7.6232%. Five-wavelength prediction model for the Lens culinaris adulteration detection was established, with R2p of 0.9666 and RMSEP of 6.1437%. For the multiple-adulterants samples, PLS2 models were established to determine concentrations of the different counterfeits simultaneously. The prediction results for Ziziphi Spinosae Semen were the best with R2p≥0.7115, while the results for Hovenia dulcis Thunb. were the worst with R2p≥0.2007. The results showed that the near infrared spectroscopy could be used to inspect different counterfeits adulterated in Ziziphi Spinosae Semen. The established method provided a theoretical basis for the subsequent development of portable detection equipment for authenticity of Ziziphi Spinosae Semen. It was also as a reference for other studies on quality inspection for seed Chinese medicinal materials. The method was of an important social significance for ensuring quality and safety of Chinese medicinal materials.

     

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