食品科学 ›› 2018, Vol. 39 ›› Issue (8): 212-217.doi: 10.7506/spkx1002-6630-201808033

• 安全检测 • 上一篇    下一篇

多光谱数据融合技术对绒柄牛肝菌产地的鉴别

姚森1,2,李涛3,刘鸿高1,李杰庆1,*,王元忠2,4,*   

  1. (1.云南农业大学农学与生物技术学院,云南?昆明 650201;2.云南省农业科学院药用植物研究所,云南?昆明 650200;3.玉溪师范学院资源环境学院,云南?玉溪 653100;4.云南省省级中药原料质量检测技术服务中心,云南?昆明 650200)
  • 出版日期:2018-04-25 发布日期:2018-04-17
  • 基金资助:
    国家自然科学基金地区科学基金项目(31660591;21667031);云南省教育厅科学研究基金项目(2016ZZX106);云南省高校食用菌资源开发与利用重点实验室建设项目

Identification of Geographical Origin of Boletus tomentipes by Multi-Spectral Data Fusion

YAO Sen1,2, LI Tao3, LIU Honggao1, LI Jieqing1,*, WANG Yuanzhong2,4,*   

  1. (1. College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; 2. Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; 3. College of Resources and Environment, Yuxi Normal University, Yuxi 653100, China; 4. Yunnan Technical Center for Quality of Chinese Materia Medica, Kunming 650200, China)
  • Online:2018-04-25 Published:2018-04-17

摘要: 采集5?个产地96?份绒柄牛肝菌样品的红外光谱和紫外光谱,结合多光谱信息融合策略,建立快速、有效鉴别绒柄牛肝菌产地的方法。运用多元散射校正、标准正态变量、二阶导数等预处理方法对原始光谱数据进行优化处理,减少噪音干扰。选取具有指纹特性的光谱信息进行初级数据融合;通过偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)筛选光谱数据中变量在投影方向重要程度大于1的波段,进行中级数据融合。利用优化处理后的单一光谱数据及多光谱融合数据建立PLS-DA模型和支持向量机(support vector machine,SVM)判别模型,比较两种判别模型对绒柄牛肝菌产地的鉴别效果。结果显示,通过红外光谱、紫外光谱、初级融合和中级融合数据分别建立PLS-DA模型,对绒柄牛肝菌产地的预测正确率为56.25%、56.25%、62.50%和81.25%;建立SVM判别模型,产地预测正确率分别为90.63%、65.63%、87.50%和96.88%,表明中级融合技术对绒柄牛肝菌产地鉴别效果显著,优于其他技术;并且SVM判别模型对牛肝菌产地区分效果优于PLS-DA模型。采用中级融合技术建立SVM判别模型,能够快速、有效鉴别不同产地绒柄牛肝菌,同时为食品质量监控提供有效方法和理论基础。

关键词: 数据融合, 绒柄牛肝菌, 产地鉴别, 紫外光谱, 红外光谱

Abstract: In this study, a rapid method using Fourier transform infrared and ultraviolet absorption spectroscopies coupled with data fusion was established for the identification of the geographical origin of Boletus tomentipes. The original spectra of 96 samples collected from different growing regions were preprocessed by multiplicative signal correction (MSC), standard normal variate (SNV) and second derivative (2D) to decrease the noise interference. The spectral information about the fingerprint characteristics was chosen for low-level data fusion, and the spectral information of variables important in projection greater than 1 was selected by partial least squares-discriminant analysis (PLS-DA) for mid-level data fusion. Then the single and fused spectral data were analyzed by PLS-DA and support vector machine (SVM). The prediction performance of PLS-DA and SVM was compared. The results showed that based on FTIR, UV, low-level data fusion and mid-level data fusion data matrixes, the prediction accuracies of PLS-DA were 56.25%, 56.25%, 62.50% and 81.25%, respectively, and the prediction accuracies of SVM were 90.63%, 65.63%, 87.50% and 96.88%, respectively, suggesting that mid-level data fusion was better than the other data sources and SVM was better than PLS-DA. In conclusion, SVM based on mid-level data fusion can be a rapid and effective method for the identification of the geographical origin of B. tomentipes that facilitates food quality monitoring.

Key words: data fusion, Boletus tomentipes, geographical identification, ultraviolet absorption spectroscopy, infrared spectroscopy

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