Safety risk assessment of edible fungi

Open Access

Issue		BIO Web Conf. Volume 124, 2024 The 2^nd International Conference on Food Science and Bio-medicine (ICFSB 2024)


Article Number		01007
Number of page(s)		5
Section		Food Science and Biomolecular Engineering
DOI		https://doi.org/10.1051/bioconf/202412401007
Published online		23 August 2024

Zhang Y., Wang D., Chen Y., Liu T., Zhang S., Fan H., Liu H., and Li Y., (2021) Healthy function and high valued utilization of edible fungi, Food Sci. Hum. Well., 10(4): 408–420. https://doi.org/10.1016/j.fshw.2021.04.003 [CrossRef] [Google Scholar]
Singh M. P., Rai S. N., Dubey S. K., Pandey A. T., Tabassum N., Chaturvedi V. K., and Singh N. B., (2022) Biomolecules of mushroom: a recipe of human wellness, Crit. Rev. Biotechnol., 42(6): 913–930. https://doi.org/10.1080/07388551.2021.1964431 [CrossRef] [PubMed] [Google Scholar]
Rauf A., Joshi P. B., Ahmad Z., Hemeg H. A., Olatunde A., Naz S., Hafeez N., and Simal-Gandara, J., (2023) Edible mushrooms as potential functional foods in amelioration of hypertension, Phytother. Res., 37(6): 2644–2660. https://doi.org/10.1002/ptr.7865 [CrossRef] [PubMed] [Google Scholar]
Ou J., and Li Y., (2019) Vector-kernel convolutional neural networks, Neurocomputing, 330: 253–258. https://doi.org/10.1016/j.neucom.2018.11.028 [Google Scholar]
Zahan N., Hasan M. Z., Malek M. A., and Reya S. S., (2021) A deep learning-based approach for edible, inedible and poisonous mushroom classification, International conference on information and communication technology for sustainable development (ICICT4SD), IEEE, pp. 440–444. [Google Scholar]
Ketwongsa W., Boonlue S., and Kokaew U., (2022) A new deep learning model for the classification of poisonous and edible mushrooms based on improved alexnet convolutional neural network, Appl. Sci., 12(7): 3409. https://doi.org/10.3390/app12073409 [CrossRef] [Google Scholar]
Gupta A. P., (2022) Classification of mushrooms using artificial neural network, bioRxiv, 2022.08.31. 505980. https://doi.org/10.1101/2022.08.31.505980 [Google Scholar]
Zhang B., Zhao Y., and Li Z., (2022) Using deep convolutional neural networks to classify poisonous and edible mushrooms found in China, arXiv, 2210. 10351. https://doi.org/10.48550/arXiv.2210.10351 [Google Scholar]
Xu X., Zhang Y., Cao H., Yang D., Zhou L., and Yu H., (2023) Recognition of edible fungi fruit body diseases based on Improved ShuffleNetV2, Agronomy, 13(6): 1530. https://doi.org/10.3390/agronomy13061530 [CrossRef] [Google Scholar]
Esteves C. S. M., de Redrojo E. M. M., Luis García Manjón J., Moreno G., Antunes F. E., Montalvo G., and Ortega-Ojeda F. E., (2022) Combining FTIR-ATR and OPLS-DA methods for magic mushrooms discrimination, Forensic Chem., 29: 100421. https://doi.org/10.1016/j.forc.2022.100421 [CrossRef] [Google Scholar]
Chen J., Li J. Q., Li T., Liu H. G., and Wang Y. Z., (2021) Application of UV-Vis and infrared spectroscopy on wild edible bolete mushrooms discrimination and evaluation: a review, Crit. Rev. Anal. Chem., 53(4): 852–868. https://doi.org/10.1080/10408347.2021.1984870 [Google Scholar]
Chen J., Liu H., Li T., and Wang Y., (2023) Edibility and species discrimination of wild bolete mushrooms using FT-NIR spectroscopy combined with DD-SIMCA and RF models, LWT, 180: 114701. https://doi.org/10.1016/j.lwt.2023.114701 [CrossRef] [Google Scholar]
Landi N., Ragucci S., Culurciello R., Russo R., Valletta M., Pedone P. V., Pizzo E., and Di Maro, A., (2021) Ribotoxin-like proteins from Boletus edulis: structural properties, cytotoxicity and in vitro digestibility, Food Chem., 359: 129931. https://doi.org/10.1016/j.foodchem.2021.129931 [CrossRef] [Google Scholar]
Deakin G., Dobbs E., Bennett J. M., Jones I. M., Grogan H. M., and Burton K. S., (2017) Multiple viral infections in Agaricus bisporus-characterisation of 18 unique RNA viruses and 8 ORFans identified by deep sequencing, Sci. Rep., 7(1): 2469. https://doi.org/10.1038/s41598-017-01592-9 [CrossRef] [Google Scholar]
Gilbert K. B., Holcomb E. E., Allscheid R. L., and Carrington J. C., (2019) Hiding in plain sight: new virus genomes discovered via a systematic analysis of fungal public transcriptomes, PloS One, 14(7): e0219207. https://doi.org/10.1371/journal.pone.0219207 [CrossRef] [PubMed] [Google Scholar]
Li C., Zeng H., Zhang J., He W., Ling N., Chen M., Wu S., Lei T., Wu H., Ye Y., Ding Y., Wang J., Wei X., Zhang Y., and Wu Q., (2019) Prevalence, antibiotic susceptibility, and molecular characterization of Cronobacter spp. isolated from edible mushrooms in China, Front. Microbiol., 10: 283. https://doi.org/10.3389/fmicb.2019.00283 [CrossRef] [Google Scholar]
Meng B., Jang A. R., Song H., and Lee S. Y., (2024) Microbiological quality and safety of fresh mushroom products at retail level in Korea, Food Sci. Biotechnol., 33(5): 1261–1268. https://doi.org/10.1007/s10068-023-01385-z [Google Scholar]
Nnorom I. C., Eze S. O., and Ukaogo P. O., (2020) Mineral contents of three wild-grown edible mushrooms collected from forests of south eastern Nigeria: an evaluation of bioaccumulation potentials and dietary intake risks, Sci. Afr., 8: e00163. https://doi.org/10.1016/j.sciaf.2019.e00163 [Google Scholar]
Soceanu A., Matei N., Dobrinas S., Birghila S., Popescu V., and Crudu G., (2024) Metal content in caps and stalks of edible mushrooms: health benefits and risk evaluation, Biol. Trace Elem. Res., 202(5): 2347–2356. https://doi.org/10.1007/s12011-023-03800-2 [CrossRef] [PubMed] [Google Scholar]
Širić I., Kumar P., Adelodun B., Abou Fayssal S., Bachheti R. K., Bachheti A., Ajibade F. O., Kumar V., Taher M. A., and Eid E. M., (2022) Risk assessment of heavy metals occurrence in two wild edible oyster mushrooms (Pleurotus spp.) collected from Rajaji National Park, J. Fungi, 8(10): 1007. https://doi.org/10.3390/jof8101007 [CrossRef] [Google Scholar]
Wang L., Liu H., Li T., Li J., and Wang Y., (2022) Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS-DA, J. Sci. Food Agr., 102(4): 1531–1539. https://doi.org/10.1002/jsfa.11488 [CrossRef] [PubMed] [Google Scholar]

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