ABSTRACT 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), R2Y(cum), and Q2 (cum). 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.

中文翻译：

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灵芝科菌种判别分析新方法

摘要 通过使用基于衰减全反射傅里叶变换红外 ATR-FTIR 和紫外-可见 (UV-vis) 光谱的数据融合策略，并应用化学​​计量学工具，开发了一种用于灵芝科蘑菇物种鉴别的新分析方法。描述了紫外-可见光谱测定的优化。使用的多元判别方法是 t 分布随机邻域嵌入 (t-SNE)、偏最小二乘判别分析 (PLS-DA) 和随机森林 (RF)。所使用的数据融合级别为中低数据融合。模型的性能通过几个参数进行评估，如估计的均方根误差 (RMSEE)、交叉验证的均方根误差 (RMSECV)、R2Y(cum) 和 Q2 (cum)。区分结果通过测试集的准确性进行评估，测试集由来源不明的样本组成。新提出的方法花费的时间更短，成本更低，结果显示了对各种物种的良好区分能力。基于中级融合数据的 PLS-DA 和 RF 模型能够根据真正的来源对蘑菇进行分类，证实了数据融合和化学计量学在蘑菇物种识别中的潜力。