Comparison of Geographical Traceability of Wild and Cultivated Macrohyporia cocos with Different Data Fusion Approaches

Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos, this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques, like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid-, and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method—Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis—were considered in data fusion. The results indicate the following: (1) The difference between wild and cultivated samples did exist in terms of the content analysis of vital chemical components and fingerprint analysis. (2) Wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The mid-level Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification.

Comparison of Geographical Traceability of Wild and Cultivated Macrohyporia Cocos With Different Data Fusion Approaches

Background Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos, the study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Methods Supervised pattern recognition techniques like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid- and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method—Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis were considered in data fusion. Results (1) the difference of wild and cultivated samples did exist in terms of the content analysis of vital chemical component and fingerprint analysis. (2) the cultivated samples from different origins could be easily identified by Fourier transform infrared spectroscopy or liquid chromatography, while the wild required data fusion. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) Mid-level-Boruta preceded Mid-level-PCA, low-level and high-level data fusion and individual techniques. The Mid-level-Boruta PLS-DA model took full advantage of information synergy and showed the best performance. Conclusions This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification.