文献类型： 外文期刊

作者： Hu Yi-ran ¹ ; Li Jie-qing ¹ ; Liu Hong-gao ² ; Fan Mao-pan ¹ ; Wang Yuan-zhong ³ ;

作者机构： 1.Yunnan Agr Univ, Coll Resources & Environm, Kunming 650201, Yunnan, Peoples R China

2.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China

3.Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China

关键词： Boletus edulis; Geographic origin identification; Data fusion; Fourier transform mid-infrared spectrum; Fourier transform near infrared spectrum

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2020 年 40 卷 4 期

页码：

收录情况： SCI

摘要： In recent years, food safety problems happened frequently, and consumers pay more and more attention to the environmental safety of food origin, which leads to an increase in demand for geographical indication products. As a healthy food, the quality of Boletus edulis is greatly affected by the environment of its origin. In order to protect consumers' health and prevent fake and inferior products from entering the market, it is urgent to develop an efficient and low-cost identification technology of the origin of delicious Boletus edulis. Data fusion strategy and partial least squares discrimination (PLS-DA) model were used to identify the origin of Boletus edulis. In this paper, Fourier transform near infrared and Fourier transform middle infrared spectra of 141 samples from 8 Origin (Kunming, Chuxiong, Yuxi, Diqing, Dali, Baoshan, Wenshan and Qujing) were scanned. Kennard-stone algorithm was used to divide all samples into 2/3 training set and 1/3 prediction set. Three fusion strategies (lowlevel, mid-level, high-level) were used to analyze four single spectral matrices spectra ; near-infrared average spectra of stipes (N-b) , near-infrared average spectra of caps (N-g) , mid-infrared average spectra of stipes (M-b) , mid-infrared average spectra of caps (M-g) and to establish a partial least squares discriminant (PLS-DA) model. In which root mean square error of cross validation (RMSECV) and the root mean square prediction error (RMSEP) are used to evaluate model stability. The purpose of the non-error ratio (NER) training set classification accuracy and forecast set classification accuracy evaluation model classification performance. It contributes to find out the best way to geographic origin identification of Boletus edulis. The results showed that ; (1) near infrared and middle infrared spectra can identify the origin of Boletus edulis; (2) the model established by middle infrared spectrum is better than that in near infrared spectrum; (3) all the three fusion strategies can improve the identification effect of origin of Boletus edulis, and the identification results of producing area from good to bad are in order of mid fusion, high fusion, low fusion and single spectral model. By using PLS-DA intermediate fusion strategy to fuse in near infrared and Mid-infrared spectrum, different origin Boletus edulis identification models are established, with the least number of variables (49), the highest accuracy of training set in producing area (100%), the highest accuracy of prediction set of origin (100%), the lowest RMSEP (0. 133). As a reliable method, it can identify the geographical origin of Boletus edulis fast and accurately.