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

Li Wang,Yuanzhong Wang,Yunmei Wang,Qinqin Wang,Ying Hu

doi:10.1155/2021/5818999

Abstract

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.

Highlights

Macrohyporia cocos is a wood-decay fungus in the Polyporaceae family
(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) e mid-level Boruta partial least squares discriminant analysis (PLS-DA) model took full advantage of information synergy and showed the best performance. is 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
Fourier transform infrared (FTIR) spectra of M. cocos (Figure 2) presented the structural information of mixture, including the bands of C O, C CH2, C-O, C-OH, O-H, C-C, and C-H. e variables in the bands of 2670–1750 cm−1 and 4000–3700 cm−1 were excluded after spectral pretreatment. e specific reasons were as follows: firstly, there was no absorption in these regions

Summary

Introduction

Macrohyporia cocos is a wood-decay fungus in the Polyporaceae family. It transforms the wood of pine trees into medicinal products, which could treat various edemas, invigorate the spleen function, and calm the mind. E sclerotium of M. cocos, called Poria, is one of the most widely used raw materials of Chinese herbal compound preparations. E Chinese Pharmacopoeia (version 2015) records over one hundred types of prescriptions including Poria. The National Health Commission of the People’s Republic of China has approved that this fungus could be used for food. Plenty of Poria-based skin cosmetics like facial masks have been produced and used. Poria has shown high economic value and medicinal value

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