The subfamily Xerocomoideae (Boletaceae, Boletales) in China

Xerocomoideae is an ecologically and economically important Boletaceae subfamily (Boletales) comprising 10 genera. Although many studies have focused on Xerocomoideae in China, the diversity, taxonomy and molecular phylogeny still remained incompletely understood. In the present study, taxonomic and phylogenetic studies on Chinese species of Xerocomoideae were carried out by morphological examinations and molecular phylogenetic analyses. Eight genera in Xerocomoideae, viz. Aureoboletus, Boletellus, Heimioporus, Hemileccinum, Hourangia, Phylloporus, Pulchroboletus, and Xerocomus were confirmed to be distributed in China; 97 species of the subfamily were accepted as being distributed in China; one ambiguous taxon was tentatively named Bol. aff. putuoensis; two synonyms, viz. A. marroninus and P. dimorphus were defined. Among the Chinese accepted species, 13 were newly described, viz. A. albipes, A. conicus, A. ornatipes, Bol. erythrolepis, Bol. rubidus, Bol. sinochrysenteroides, Bol. subglobosus, Bol. zenghuoxingii, H. squamipes, P. hainanensis, Pul. erubescens, X. albotomentosus, and X. fuscatus, 36 known species were redescribed, and the other 48 species were reviewed. Keys to accepted species of Aureoboletus, Boletellus, Heimioporus, Hemileccinum, Hourangia, Phylloporus, and Xerocomus in China were also provided. Taxonomic novelties: New species: Aureoboletus albipes N.K. Zeng, Xu Zhang & Zhi Q. Liang, A. conicus N.K. Zeng, Xu Zhang & Zhi Q. Liang, A. ornatipes N.K. Zeng, Xu Zhang & Zhi Q. Liang, Boletellus erythrolepis N.K. Zeng, R. Xue, S. Jiang & Zhi Q. Liang, Bol. rubidus N.K. Zeng, R. Xue, Y.J. Hao & Zhi Q. Liang, Bol. sinochrysenteroides N.K. Zeng, R. Xue & Kuan Zhao, Bol. subglobosus N.K. Zeng, R. Xue, S. Jiang & Zhi Q. Liang, Bol. zenghuoxingii N.K. Zeng, R. Xue, S. Jiang & Zhi Q. Liang, Hemileccinum squamipes N.K. Zeng, Chang Xu & Zhi Q. Liang, Phylloporus hainanensis N.K. Zeng, L.L. Wu, & Zhi Q. Liang, Pulchroboletus erubescens N.K. Zeng, Chang Xu & Zhi Q. Liang, Xerocomus albotomentosus N.K. Zeng, H.J. Xie, Chang Xu & Zhi Q. Liang, and X. fuscatus N.K. Zeng, H.J. Xie, Chang Xu & Zhi Q. Liang. Citation: Xue R, Zhang X, Xu C, Xie HJ, Wu LL, Wang Y, Tang LP, Hao YJ, Zhao K, Jiang S, Li Y, Yang YY, Li Z, Liang ZQ, Zeng NK (2023). The subfamily Xerocomoideae (Boletaceae, Boletales) in China. Studies in Mycology 106: 95-197. doi: 10.3114/sim.2022.106.03.

Deep learning for species identification of bolete mushrooms with two-dimensional correlation spectral (2DCOS) images

Bolete is well-known and widely consumed mushroom in the world. However, its medicinal properties and nutritional are completely different from one species to another. Therefore, the consumers need a fast and effective detection method to discriminate their species. A new method using directly digital images of two-dimensional correlation spectroscopy (2DCOS) for the species discrimination with deep learning is proposed in this paper. In our study, a total of 2054 fruiting bodies of 21 wild-grown bolete species were collected in 52 regions from 2011 to 2014. Firstly, we intercepted 1750-400 cm^-1 fingerprint regions of each species from their mid-infrared (MIR) spectra, and converted them to 2DCOS spectra with matlab2017b. At the same time, we developed a specific method for the calculation of the 2DCOS spectra. Secondly, we established a deep residual convolutional neural network (Resnet) with 1848 (90%) 2DCOS spectral images. Therein, the discrimination of the bolete species using directly 2DCOS spectral images instead of data matric from the spectra was first to be reported. The results displayed that the respective identification accuracy of these samples was 100% in the training set and 99.76% in the test set. Then, 203 samples were accurately discriminated in 206 (10%) samples of external validation set. Thirdly, we employed t-SNE method to visualize and evaluate the spectral dataset. The result indicated that most samples can be clustered according to different species. Finally, a smartphone applications (APP) was developed based on the established 2DCOS spectral images strategy, which can make the discrimination of bolete mushrooms more easily in practice. In conclusion, deep learning method by using directly 2DCOS spectral image was considered to be an innovative and feasible way for the species discrimination of bolete mushrooms. Moreover, this method may be generalized to other edible mushrooms, food, herb and agricultural products in the further research.