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草业学报 ›› 2026, Vol. 35 ›› Issue (1): 140-153.DOI: 10.11686/cyxb2025054

• 研究论文 • 上一篇    

基于近红外光谱分析技术的蕨麻产地判别及定量检测模型评价

吕博文1,2,3(), 温馨4, 李军乔1,2,3(), 王聪1   

  1. 1.青海民族大学生态环境与资源学院,青海 西宁 810007
    2.青海省特色经济植物高值化利用重点实验室,青海 西宁 810007
    3.青海民族大学青藏高原蕨麻产业研究院,青海 西宁 810007
    4.青海卫生职业技术学院,青海 西宁 810000
  • 收稿日期:2025-02-25 修回日期:2025-04-28 出版日期:2026-01-20 发布日期:2025-11-13
  • 通讯作者: 李军乔
  • 作者简介:E-mail: ljqlily2002@126.com
    吕博文(1999-),男,河北邢台人,硕士。E-mail: 17731812526@163.com
  • 基金资助:
    国家自然科学基金项目(U23A20152)

Development of a model based on near-infrared spectral data to evaluate the origin and quality of Potentilla anserina materials

Bo-wen LYU1,2,3(), Xin WEN4, Jun-qiao LI1,2,3(), Cong WANG1   

  1. 1.College of Ecological Environment and Resources,Qinghai Nationalities University,Xining 810007,China
    2.Key Laboratory of High Value Utilization of Characteristic Economic Plants of Qinghai Province,Xining 810007,China
    3.Tibetan Plateau Potentilla anserina L. Industry Research Institute,Qinghai Nationalities University,Xining 810007,China
    4.Qinghai College of Health Professions and Technology,Xining 810000,China
  • Received:2025-02-25 Revised:2025-04-28 Online:2026-01-20 Published:2025-11-13
  • Contact: Jun-qiao LI

摘要:

本试验旨在利用近红外光谱技术,建立蕨麻产地判别模型和营养成分快速检测的近红外预测模型。以32个采样点蕨麻块根为研究对象,参考国家标准和行业标准分别测定蕨麻中淀粉、蛋白质、多糖、鞣质和总皂苷5个关键质量属性的含量,并采集其全反射红外光谱(ATR)和近红外光谱(NIR)数据。利用ATR和NIR光谱数据结合建模方法、校正方法、图谱类型为因素进行3因素3水平的正交试验,选取蕨麻样品分为校正集430个,预测集215个构建并验证产地判别模型的优劣。经比对分析,ATR模型中,以建模方法为扩散模型(DM),校正方法为标准正态变量校正(SNV),谱图类型为原谱图的组合为最优建模条件,优化后,其识别率为99.07%,预测率为97.21%,判别效果较好。在此基础上,通过优化预处理方法、建模波段等建模条件,建立了蕨麻5种有效营养成分的定量检测模型。其中,鞣质最优模型为PCR+MSC+D1+Norris平滑(5,5),建模波段为6148~5379 cm-1,其预测相关系数(Rp)为0.9393,外部验证相对分析误差(RPD)为2.86,>2.00;多糖的预测模型效果次之,其最优模型为PCR+MSC+spectrum+Norris平滑(5,5),建模波段为7000~4173 cm-1,其Rp为0.8470,RPD为1.68,>1.40。近红外光谱技术结合化学计量学可实现蕨麻产地判别及多种营养成分综合质量的快速准确检测,为蕨麻快速综合质量评价模型的建立奠定了基础。

关键词: 蕨麻, 红外光谱, 产地判别模型, 定量检测模型

Abstract:

In this study, we established a model based on near-infrared data to predict the origin of Potentilla anserina tubers and rapidly detect their nutrient status. Tubers of P. anserina from 32 sampling sites were analyzed to quantify five key quality attributes namely amylum farina, protein, polysaccharide, ellagitannin, and total saponin contents. These analyses were conducted according to the national standards and industry standards, and attenuated total reflection (ATR) and near infrared spectroscopy (NIR) infrared spectroscopic data were also collected for each material. The ATR and NIR spectral data combined with a modeling method, optical range type, and map type was used to conduct a three-factor, three-level orthogonal test. Selected P. anserina samples were divided into 430 correction sets and 215 prediction sets to construct and validate the origin discrimination model, and then the strengths and weaknesses of the model were evaluated. Comparative analyses were conducted to establish the optimal conditions for the ATR model, which were as follows: modeling method, diffusion model, light range type, standard normal variate, spectrogram type, original spectrogram. After optimization, the recognition rate of the ATR model was 99.07% and its prediction rate was 97.21%, indicating that it had a better discriminatory effect. On this basis, we established models for the quantitative detection of five compounds in P. anserina tubers. These models were established by optimizing the pre-processing method, modeling band, and other modeling conditions. The optimal model, which was for tannins, had the following conditions: PCR+MSC+D1+Norris smoothing (5, 5); modeling band, 6148-5379 cm-1. The predictive correlation coefficient (Rp) of the tannins model was 0.9393, and the ratio of standard deviation of the validation set to standard error of prediction (RPD) was 2.86 (>2.00). The prediction model for polysaccharides was the second most effective. The optimal conditions for the polysaccharide model were as follows: PCR+MSC+spectrum+Norris smoothing (5, 5); modeling band, 7000-4173 cm-1. The Rp was 0.8470 and the RPD was 1.68 (>1.40). Our study documents the development and optimization of models incorporating NIR spectroscopy data and chemometric data for the detection of the origin and quality of P. anserina materials. These results laid a foundation for the establishment of a rapid and reliable method for evaluating P. anserina based on NIR spectral data.

Key words: Potentilla anserina, infrared spectroscopy, origin discrimination model, quantitative detection model