The Identification of Edible Boletus Based on Heterogeneous Multi-Spectral Information Fusion
LI Xiu-ping1, 2, LI Jie-qing1, LI Tao3, LIU Hong-gao1*, WANG Yuan-zhong2*
1. College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China
2. Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
3. College of Resources and Environment, Yuxi Normal University, Yuxi 653100, China
Abstract：Boletus is rich in nutrition, which is favored by consumers all over the world. Due to the differences of species and environmental factors, the quality of boletus of different species and origin vaires. At present, the shoddy, which undermines the sales of genuine boletus and the mushroom market, not only poses a health risks to consumers, but also restricts the international trade of boletus. In this study, the data fusion strategy was used to identify the species and origin of boletus, in order to provide a rapid and effective solution for tracing the source of edible fungi and correctly evaluating their quality. The test samples Boletus griseus, B. umbriniporus, B. edulis, Leccinum rugosicepes and B. tomentipes of five species of boletus fungi fruiting bodies collected from Baoshan, Kunming, Yuxi and Honghe Prefecture of Yunnan province. The chemical information was collected with Fourier transform infrared spectroscopy (FT-IR) and UV-Visible spectrophotometer (UV-Vis). The Kennard-Stone algorithm was used to divide the raw data of samples into calibration sets and validation sets. The calibration set established partial least squares discriminant analysis (PLS-DA) models based on FT-IR, UV-Vis, low-level, mid-level and high-level data fusion. The determination coefficients R2cal, predictive ability Q2, root mean square error of estimation (RMSEE) and root mean square error of estimation (RMSECV) were used to evaluate the robustness of the model. The results showed that: (1) The peak position, peak shape and number of peaks of FT-IR and UV-Vis absorption peaks of different species and origin were similar, and there were differences in absorption intensity. This showed that the chemical compositions of boletus were similar, but the content was different. (2) Two-dimensional scatter plots of PLS-DA model. It can be seen that mid-level fusion is better than low-level fusion to identify sample species and origin. (3) In each model, the mid-level fusion model has a larger Q2 and a minimum RMSECV, it showed that the model has the strongest robustness. (4) The test sets used to verify the model generalization ability, the correct rate of FT-IR, UV-Vis, low-level, mid-level and high-level data fusion model of samples kind identification were 92.86%, 35.71%, 97.62%, 100%, 95.23%, respectively; the correct rate of origin identification were 71.43%, 61.90%, 61.90%, 97.62%, 76.19%. The results showed that the data fusion is better than the independent model to some extent. Among them, the correct rate of mid-level data fusion is 100% in species identification, and the accuracy in origin identification is 97.62%. Mid-level data fusion model has better identification effect and generalization ability. FT-IR and UV-Vis combined with mid-level data fusion strategy can achieve the rapid and accurate identification of the boletus species, the fast and effective identification of origin. It can be used as a new method for traceability and quality evaluation of edible fungi.
Key words：Boletus mushroom; FT-IR; UV-Vis; Heterogeneous multi-source data fusion; Species and geographic origin identification
李秀萍，李杰庆，李 涛，刘鸿高，王元忠. 多源异构光谱信息融合的食用牛肝菌鉴别方法[J]. 光谱学与光谱分析, 2018, 38(12): 3897-3904.
LI Xiu-ping, LI Jie-qing, LI Tao, LIU Hong-gao, WANG Yuan-zhong. The Identification of Edible Boletus Based on Heterogeneous Multi-Spectral Information Fusion. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(12): 3897-3904.
[1] Wang X M, Zhang J, Wu L H, et al. Food Chemistry, 2014, 151: 279.
[2] Zhang A, Liu Y, Xiao N, et al. Food Chemistry, 2014, 146: 334.
[3] GU Ke-fei, ZHOU Chang-yan, SHAO Yi, et al(顾可飞, 周昌艳, 邵 毅, 等). Food Research and Develop(食品研究与开发), 2017, 17: 31.
[4] Hatoi H, Singdevsachan S K. African Journal of Biotechnology, 2014, 13(4): 523.
[5] Vauzour D. Oleagineux Corps Gras Lipides, 2017, 24(2): A202.
[6] Emieszek M K, Ribeiro M, Alves H G, et al. Food & Function, 2016, 7(7): 3163.
[7] Wen H, Kang S, Song Y, et al. Phytochemical Analysis, 2010, 21(1): 73.
[8] Chen J, Sun S, Ma F, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2014, 128: 629.
[9] SUN Li-ping, CHANG Wei-dan, BAO Zhang-jun, et al(孙丽平, 常惟丹, 鲍长俊, 等). Modern Food Science & Technology(现代食品科技), 2016，(12): 279.
[10] Falandysz J, Zhang J, Wiejak A, et al. Ecotoxicology and Environmental Safety, 2017, 142: 497.
[11] Pontes A G O, Silva K L, da Cruz Fonseca S G, et al. Carbohydrate Polymers, 2016, 149: 391.
[12] Martins A R, Talhavini M, Vieira M L, et al. Food Chemistry, 2017, 229: 142.
[13] Wu Z, Zhao Y, Zhang J, et al. Molecules, 2017, 22(7): 1238.
[14] Tripathy S, Middha A, Swain S R. Imperial Journal of Interdisciplinary Research, 2016, 2(10): 32.
[15] Hirri A, Bassbasi M, Platikanov S, et al. Food Analytical Methods, 2016, 9(4): 974.
[16] D’Archivio A A, Maggi M A. Food Chemistry, 2017, 219: 408.
[17] El Darra N, Rajha H N, Saleh F, et al. Food Control, 2017, 78: 132.
[18] HUANG Man-guo, FAN Shang-chun, ZHENG De-zhi, et al(黄漫国, 樊尚春, 郑德智). Transducer and Microsystem Technologies(传感器与微系统), 2010，(3): 5.
[19] Chandra G R, Katyayani A, Sandhya N. Journal of Theoretical & Applied Information Technology, 2017, 95(12): 2626.
[20] Liggins I I, Martin David Hall, James Llinas, et al. Handbook of Multisensor Data Fusion: Theory and Practice. CRC Press, 2017.
[21] Spiteri M, Dubin E, Cotton J, et al. Analytical and Bioanalytical Chemistry, 2016, 408(16): 4389.
[22] Casale M, Bagnasco L, Zotti M, et al. Talanta, 2016, 160: 729.
[23] Kennard R W, Stone L A. Technometrics, 1969, 11(1): 137.
[24] Pizarro C, Rodríguez-Tecedor S, Pérez-del-Notario N, et al. Food Chemistry, 2013, 138(2): 915.
[25] Borràs E, Ferré J, Boqué R, et al. Analytica Chimica Acta, 2015, 891: 1.
[26] Di Anibal C V, Callao M P, Ruisánchez I. Talanta, 2011, 84(3): 829.
[27] Noda I. Chinese Chemical Letters, 2015, 26(2): 167.
[28] Mecozzi M, Sturchio E. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015, 137: 90.
[29] YANG Tian-wei, ZHANG Ji, SHI Yun-dong, et al(杨天伟, 张 霁, 史云东, 等). Food Science (食品科学), 2015, 36(24): 116.
[30] Adib A M, Jamaludin F, Kiong L S, et al. Journal of Pharmaceutical and Biomedical Analysis, 2014, 96: 104.
[31] Mickiewicz B, Heard B J, Chau J K, et al. Journal of Orthopaedic Research, 2015, 33(1): 71
[32] Lenhardt L, Bro R, Zekovic′ I, et al. Food Chemistry, 2015, 175: 284.
[33] Pande R, Mishra H N. Food Chemistry, 2015, 172: 880.
[34] Zadeh L A. Information and Control, 1965, 8(3): 338.
[35] Rodríguez R M, Martínez L, Torra V, et al. International Journal of Intelligent Systems, 2014, 29(6): 495.
[36] Westad F, Marini F. Analytica Chimica Acta, 2015, 893: 14.
[37] Márquez C, López M I, Ruisánchez I, et al. Talanta, 2016, 161: 80.
[38] Biancolillo A, Bucci R, Magrì A L, et al. Analytica Chimica Acta, 2014, 820: 23.
