Advanced Search
HE Xinlong, WANG Jifen. The identification about the automotive bumper based on Newton interpolation polynomial-infrared derivative spectroscopy[J]. LASER TECHNOLOGY, 2020, 44(3): 333-337. DOI: 10.7510/jgjs.issn.1001-3806.2020.03.011
Citation: HE Xinlong, WANG Jifen. The identification about the automotive bumper based on Newton interpolation polynomial-infrared derivative spectroscopy[J]. LASER TECHNOLOGY, 2020, 44(3): 333-337. DOI: 10.7510/jgjs.issn.1001-3806.2020.03.011

The identification about the automotive bumper based on Newton interpolation polynomial-infrared derivative spectroscopy

More Information
  • Received Date: July 17, 2019
  • Revised Date: November 02, 2019
  • Published Date: May 24, 2020
  • In order to improve the efficiency of identification, reduce the cost of detection, and realize the rapid and non-destructive classification of the automotive bumper fragments, a rapid and accurate identification method about the automotive bumper was proposed based on infrared fingerprint spectroscopy, Newton interpolation polynomial, spectral derivation, and discriminant analysis. Infrared spectra of six kinds of brands of bumper samples including 40 different versions were acquired in this paper, and a discriminant model was established by taking Newton polynomial interpolation, spectral derivation, and other methods into account. The results show that the overall accuracy rate of the discriminant model based on the fingerprint zone (80.0%) is higher than that of the full-band model (77.5%). The accuracy rate of the discriminant based on fingerprint spectroscopy combined with 4th Newton interpolation polynomial processing can reach 85%. Selecting DF1 and DF2 as the discriminant axis to construct the discriminant classification model, the accuracy rate of the discriminant can be promoted to 100%. In summary, combining infrared fingerprint spectroscopy, 4th Newton interpolation polynomial, first derivative and discriminant analysis, the new method has higher accuracy in detecting the automotive bumper, and provides a new idea and reference for the identification of other physical evidence in the forensic science.
  • [1]
    MARTYNA A, MICHALSKA A, ZADORA G. Interpretation of FTIR spectra of polymers and Raman spectra of car paints by means of likelihood ratio approach supported by wavelet transform for reducing data dimensionality[J]. Analytical and Bioanalytical Chemistry, 2015, 407(12):3357-3376. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bd45ab8dab218eafb61a83e6afc6f5df
    [2]
    GRUNERT T, STEPHAN R, EHLINGSCHULZ M, et al. Fourier transform infrared spectroscopy enables rapid differentiation of fresh and frozen/thawed chicken[J]. Food Control, 2016, 60: 361-364. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b5bb3c2aba476e7336df3210be5e6191
    [3]
    BUITRAGO M F, SKIDMORE A K, GROEN T A, et al. Connecting infrared spectra with plant traits to identify species[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 139: 183-200. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cf94fc4add48f3d5544440c329b8e382
    [4]
    DAVOODI M M, SAPUAN S M, AHMAD D, et al. Mechanical properties of hybrid kenaf/glass reinforced epoxy composite for passenger car bumper beam[J]. Materials & Design, 2010, 31(10): 4927-4932. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0ba2084c82388686ccedcf453704ee16
    [5]
    AGUNSOYE J O, ODUMOSU A K, DADA O, et al. Novel epoxy-carbonized coconut shell nanoparticles composites for car bumper application[J]. The International Journal of Advanced Manufacturing Technology, 2019, 102: 893-899. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b13d0454c3818926d450b7a5ced88808
    [6]
    HE X L, WAN J F, L Q Sh, et al. Infrared spectral identification of vehicle bumper based on multilayer perceptron-fisher discriminant analysis[J]. China Test, 2019, 45(5):74-78 (in Chinese).
    [7]
    HE X L, MA Y, WANG J F, et al. Qualitative and quantitative rapid detection of mid-infrared spectroscopy for vehicle bumpers[J]. Engineering Plastics Application, 2019, 47(5): 122-126(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTotal-ACSN201905027.htm
    [8]
    KITAZATO K, MILLIKEN R E, IWATA T, et al. The surface composition of asteroid 162173 ryugu from hayabusa2 near-infrared spectroscopy[J]. Science, 2019, 364(6437):272-275. http://www.zhangqiaokeyan.com/academic-journal-foreign_other_thesis/0204112952111.html
    [9]
    de BRUYNE S, SPEECKAERT R, BOELENS J, et al. Infrared spectroscopy as a novel tool to diagnose onychomycosis[J]. British Journal of Dermatology, 2019, 180(3): 637-646. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1111/bjd.17199
    [10]
    HE X L, WANG J F, ZHANG Q, et al. Infrared spectral analysis ofmarker ink based on multi-classification model[J]. Chemistry, 2019, 82(2):169-174(in Chinese).
    [11]
    MANFREDI M, ROBOTTI E, QUASSO F, et al. Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics[J]. Spectrochimica Acta Part: Molecular and Biomolecular Spectroscopy, 2018, A189: 427-435. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d7102a20001ab2b44c60ed2c79b0cce0
    [12]
    CRAIG A P, BOTELHO B G, OLIVEIRA L S, et al. Mid infrared spectroscopy and chemometrics as tools for the classification of roasted coffees by cup quality[J]. Food Chemistry, 2018, 245: 1052-1061. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=38b30cad657ecb2b6dc1f39b34a6cb4b
    [13]
    OUYANG Y P, CHENG L, WU H Ch, et al. Study on general model of qualitative and quantitative analysis of alcohol gasoline[J]. Laser Technology, 2019, 43(3):363-368(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jgjs201903014
    [14]
    LIU Y D, XU H, SUN X D, et al. Non-destructive detection of tomato maturity by near-infrared diffuse transmission spectroscopy[J]. Laser Technology, 2019, 43(1):25-29(in Chinese). http://www.cqvip.com/main/zcps.aspx?c=1&id=7001048709
    [15]
    HE X L, WANG J F, LIU T F, et al. Fourier infrared spectroscopy combined with chemometrics method to distinguish and identify plastic steel windows[J]. Physical and Chemical Testing (Chemistry), 2018, 54(11):1318-1323(in Chinese).
    [16]
    SONG S Y, LEE Y K, KIM I, et al. Sugar and acid content of citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis[J]. Food Chemistry, 2016, 190: 1027-1032. https://www.sciencedirect.com/science/article/pii/S0308814615009607
    [17]
    HONG T, HAN D, KIM D H, et al. Simultaneous estimation of PD, T1, T2, T2*, and ΔB0 using magnetic resonance fingerprinting with background gradient compensation[J]. Magnetic Resonance in Medicine, 2019, 81(4): 2614-2623. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1002/mrm.27556
    [18]
    DING H, WANG X, WANG Y, et al. Ensemble classification of hyperspectral images by integrating spectral and texture features[J]. Journal of the Indian Society of Remote Sensing, 2019, 47(1): 113-123. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0dc39945fa30b22f28acf2d08f03be92
    [19]
    CHEN H, XU W, BRODERICK N G, et al. An adaptive and fully automated baseline correction method for raman spectroscopy based on morphological operations and mollification[J]. Applied Spectroscopy, 2019, 73(3): 284-293. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=eb84daaf27b4f960f00bfaacb07c5e32
    [20]
    AHMED M, AZAM M. Causal nexus between energy consumption and economic growth for high, middle and low income countries using frequency domain analysis[J]. Renewable & Sustainable Energy Reviews, 2016, 60: 653-678. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=f8bd63dac7fd9c1dae68d5e813af7f99
    [21]
    HE X L, WANG J F, WU F L, et al. Identification of rubber particles based on chemometrics by infrared spectroscopy[J].Journal of Analytical Science, 2019, 35(3):357-361(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1080/00032719.2019.1668947
  • Cited by

    Periodical cited type(6)

    1. 史蒲英,陈林,刘向宏,庞智聪,何卫锋,李芹芹,李应红. 表面激光冲击对双态组织Ti55531钛合金疲劳裂纹扩展速率的影响. 稀有金属材料与工程. 2024(10): 2823-2830 .
    2. 覃恩伟,刘丽霞,刘成威,陆海峰,吴树辉. 316L不锈钢的低能量激光冲击强化工艺. 金属热处理. 2022(09): 92-97 .
    3. 王一刚. 强脉冲激光照射TC11合金组织和抗氧化性能分析. 激光技术. 2020(05): 639-642 . 本站查看
    4. 李翔,何卫锋,聂祥樊,罗思海,杨竹芳. 新型光斑搭接对平顶激光冲击钛合金力学性能的影响. 中国表面工程. 2019(01): 38-47 .
    5. 姜银方,季彬,赵勇,华程,孟李林,彭涛涛. 应力水平对激光冲击强化圆角疲劳寿命的影响. 激光技术. 2018(03): 369-373 . 本站查看
    6. 宁成义,张文武,徐子法,张正. 激光冲击强化5083铝合金力学性能的实验研究. 应用激光. 2017(06): 819-824 .

    Other cited types(7)

Catalog

    Article views (6) PDF downloads (5) Cited by(13)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return