光谱学与光谱分析 |
|
|
|
|
|
Study on Identification of PBDEs and Characteristic Information Extraction of Biological Toxicity Based on Infrared Spectrum Partition |
JIANG Long1, 2, LI Yu1* |
1. Environmental Research Academy, North China Electric Power University, Beijing 102206, China 2. North China Electric Power Research Institute Co. Ltd., Beijing 100045, China |
|
|
Abstract In this paper, 9 most abundant PBDE congeners in environment and 14 congeners with high/medium biological toxicity were selected as target compounds. Firstly, IR spectra of 23 compounds were divided into 6 spectrum regions based on known vibrational assignment, and their identification abilities for PBDEs were evaluated by using similarity analysis. Then, the spectrum regions with high identification ability relatively have been analyzed to search identification method on the basis of each spectrum characteristic. At last, the principal component analysis and discriminant analysis method were used to extract characteristic spectrum information of congeners with high/medium biological toxicity and establish biological toxicity discriminant model. The triangle breathing vibration region (961~1 153 cm-1) and C—O stretching vibration region (1 153~1 346 cm-1) has been filtered as characteristic IR spectrum regions because of smaller similarity among PBDEs relatively. For the former, the information of vibration peaks distribution can be taken as tool to identification, with the smallest frequency difference of 3.19 cm-1 among 23 congeners and bad ability to distinguish PBDEs from dioxin-similar compounds. For the later, the frequency and shape of main peak is as mark and the smallest frequency difference among PBDEs and between PBDEs with dioxin-similar compounds are 0.74 and 0.67 cm-1 separately, both larger than the common minimum resolution of IR spectrum 0.5 cm-1, depicting good ability for both inner and outer identification of PBDEs. The established biological toxicity discriminant model via discriminant analysis also have shown well ability to biological toxicity prediction, with accuracy of 100% and 88.9% for high/medium and low biological toxicity relatively, representing the extracted IR spectrum information from congeners with high/medium toxicity can realize the accurate classification of biological toxicity for PBDEs.
|
Received: 2015-01-20
Accepted: 2015-05-19
|
|
Corresponding Authors:
LI Yu
E-mail: liyuxx@jlu.edu.cn
|
|
[1] Erickson P R, Grandbois M, Arnold W A, et al. Environ. Sci. Technol., 2012, 46(15): 8174. [2] JIANG Long, CHENG Bing-chuan, LI Yu(姜 龙,程冰川,李 鱼). Acta Chim. Sinaca(化学学报), 2014, 72(5): 743. [3] Na S, Kim M, Paek O, et al. Chemosphere, 2013, 90(5): 1736. [4] JIANG Long, ZENG Ya-ling, CAI Xiao-yu, et al(姜 龙,曾娅玲,蔡啸宇,等). Chinese Journal of Luminescence(发光学报), 2014, 35(5): 628. [5] Ikonomou M G, Rayne S, Addison R F. Environ. Sci. Technol., 2002, 36(9): 1886. [6] Nelson C, Drouillard K, Cheng K, et al. Chemosphere, 2015, 118(1): 322. [7] Hites R A. Environ. Sci. Technol., 2004, 38(4): 945. [8] Chen D, Hale R C. Environ. Int., 2010, 36(7): 800. [9] Yu M, Luo X J, Wu J P, et al. Environ. Int., 2009, 35(7): 1090. [10] Qiu S S, Tan X H, Wu K, et al. Spectrochim. Acta A, 2010, 76(5): 429. [11] ZHANG Xiu-lan, LUO Xiao-jun, CHEN She-jun, et al(张秀蓝,罗孝俊,陈社军,等). Chinese Journal of Analytical(分析化学), 2009, 37(11): 1577. [12] Jiang L, Wen J Y, Zeng Y L, et al. Asian Journal of Chemistry,2015, 27(2): 575. [13] Xu H Y, Zou J W, Yu Q S, et al. Chemosphere, 2007, 66(10): 1998. [14] JIANG Long, WEN Jing-ya, LI Yu(姜 龙,温静雅,李 鱼). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(5): 1211. [15] CHENG Yi-yu, CHEN Min-jun, WU Yong-jiang(程翼宇,陈闽军,吴永江). Acta Chim. Sinica(化学学报), 2002, 60(11): 2017. |
[1] |
CHENG Jia-wei1, 2,LIU Xin-xing1, 2*,ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
[2] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[3] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
[4] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[5] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[6] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[7] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[8] |
LI Xiao-dian1, TANG Nian1, ZHANG Man-jun1, SUN Dong-wei1, HE Shu-kai2, WANG Xian-zhong2, 3, ZENG Xiao-zhe2*, WANG Xing-hui2, LIU Xi-ya2. Infrared Spectral Characteristics and Mixing Ratio Detection Method of a New Environmentally Friendly Insulating Gas C5-PFK[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3794-3801. |
[9] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[10] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[11] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[12] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[13] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
[14] |
SUN Wei-ji1, LIU Lang1, 2*, HOU Dong-zhuang3, QIU Hua-fu1, 2, TU Bing-bing4, XIN Jie1. Experimental Study on Physicochemical Properties and Hydration Activity of Modified Magnesium Slag[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3877-3884. |
[15] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
|
|
|
|