文献类型： 外文期刊
第一作者： Li, Xiu-Ping
作者： Li, Xiu-Ping;Li, Jieqing;Liu, Honggao;Li, Xiu-Ping;Wang, Yuan-Zhong
作者机构：
关键词： Ganodermataceae mushrooms; ATR-FTIR spectroscopy; UV-vis spectroscopy; Data fusion; Discrimination analysis
期刊名称：INTERNATIONAL JOURNAL OF FOOD PROPERTIES （ 影响因子：2.727； 五年影响因子：2.938 ）
ISSN： 1094-2912
年卷期： 2020 年 23 卷 1 期
页码：
收录情况： SCI
摘要： A new analytical approach for the species discrimination of Ganodermataceae mushroom was developed by using data fusion strategy based on attenuated total reflectance Fourier transform infrared ATR-FTIR and ultraviolet-visible (UV-vis) spectroscopy, and applying the chemometric tools. The optimization for determination of UV-vis spectra was described. The multivariate discrimination ways used were t-distributed Stochastic Neighbor Embedding (t-SNE), Partial Least Squares-Discriminant Analysis (PLS-DA), and Random Forest (RF). The data fusion levels used were low- and mid-data fusion. The performance of the model was assessed by several parameters as root mean square error of estimation (RMSEE), root mean square error of cross validation (RMSECV), (RY(cum))-Y-2, and Q((cum))(2). The discrimination results were evaluated by accuracy from the test set, which was composed of samples with unknown origin. The new proposed method took shorter time and lower cost, and the results showed good discrimination power among various species. PLS-DA and RF models based on mid-level fusion data were able to classify mushrooms according to real origin, confirming the potential of data fusion and chemometrics in mushrooms species discrimination.
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