Reflectance-based determination of age and species of blowfly puparia

文献类型： 外文期刊

第一作者： Voss, Sasha C.

作者： Voss, Sasha C.;Magni, Paola;Dadour, Ian;Nansen, Christian;Nansen, Christian

作者机构：

关键词： Forensic entomology;Post-mortem interval;Pupae;Hyperspectral imaging;Age estimation

期刊名称：INTERNATIONAL JOURNAL OF LEGAL MEDICINE （ 影响因子：2.686； 五年影响因子：2.545 ）

ISSN： 0937-9827

年卷期： 2017 年 131 卷 1 期

页码：

收录情况： SCI

摘要： Forensic entomology is primarily concerned with the estimation of time since death and involves determination of the age of immature insects colonising decomposing remains. Accurate age determination of puparia is usually accomplished by dissection, which means destructive sampling of evidence. As part of improving abilities to correctly identify species and developmental age, it is highly desirable to have available non-destructive methods. In this study, we acquired external hyperspectral imaging (HSI) data (77 spectral bands, 389-892 nm) from the dorsal and ventral sides of individual puparia of two species of blowfly (Diptera: Calliphoridae), Calliphora dubia Macquart 1855 and Chrysomya rufifacies Macquart 1842. Puparia were dissected to determine the presence/absence of eight internal morphological development characteristics (legs, wings, labella, abdominal segments, antennae, thoracic bristles, orbital/facial bristles and eye colour and arista). Based on linear discriminant analysis and independent validation of HSI data, reflectance features from puparia could be used to successfully (1) distinguish the two species (classification accuracy = 92.5 %), (2) differentiate dorsal and ventral sides of puparia (classification accuracy C. dubia = 81.5 %; Ch. rufifacies = 89.2 %) and (3) predict the presence of these morphological characteristics and therefore the developmental stage of puparia (average classification accuracy using dorsal imaging: C. dubia = 90.3 %; Ch. rufifacies = 94.0 %). The analytical approach presented here provides proof of concept for a direct puparial age relationship (i.e. days since the onset of pupation) between external puparial reflectance features and internal morphological development. Furthermore, this approach establishes the potential for further refinement by using a non-invasive technique to determine the age and developmental stage of blowflies of forensic importance.

分类号：

  • 相关文献

[1]Identification of Forensically Important Blow Flies (Diptera: Calliphoridae) in China Based on COI. Meng, Fanming,Ren, Lipin,Wang, Ziyue,Guo, Yadong,Finkelbergs, Dmitrijs,Cai, Jifeng,Deng, Jianqiang,Chen, Chao.

[2]Proteome Analysis of Hemolymph Changes during the Larval to Pupal Development Stages of Honeybee Workers (Apis mellifera ligustica). Woltedji, Dereje,Fang, Yu,Han, Bin,Feng, Mao,Li, Rongli,Lu, Xiaoshan,Li, Jianke. 2013

[3]Hemolymph proteome changes during worker brood development match the biological divergences between western honey bees (Apis mellifera) and eastern honey bees (Apis cerana). Feng, Mao,Ramadan, Haitham,Han, Bin,Fang, Yu,Li, Jianke. 2014

[4]Identification of seedling cabbages and weeds using hyperspectral imaging. Wei, Deng,Zhao Chunjiang,Xiu, Wang,Huang, Yanbo,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang. 2015

[5]Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging. Huang Wen-qian,Li Jiang-bo,Chen Li-ping,Guo Zhi-ming. 2013

[6]Comparative analysis of models for robust and accurate evaluation of soluble solids content in 'Pinggu' peaches by hyperspectral imaging. Chen, Liping. 2017

[7]Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging. Zhang, Dongyan,Zhang, Lifu,Zhang, Dongyan,Wang, Xiu,Zhang, Dongyan,Wang, Xiu,Lin, Fenfang,Huang, Yanbo. 2016

[8]Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis. Zhao, Xin,Wang, Wei,Chu, Xuan,Kimuli, Daniel,Li, Chunyang. 2017

[9]Rapid determination of biogenic amines in cooked beef using hyperspectral imaging with sparse representation algorithm. Yang, Dong,Lu, Anxiang,Wang, Jihua,Yang, Dong,Wang, Jihua,Lu, Anxiang,Ren, Dong,Wang, Jihua,Lu, Anxiang,Wang, Jihua. 2017

[10]Multi-Temporal Hyperspectral and Radar Remote Sensing for Estimating Winter Wheat Biomass in the North China Plain. Hennig, Simon D.,Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Chen, Xinping,Li, Fei,Miao, Yuxin,Jia, Liangliang. 2012

[11]Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Zhang, Baohua,Huang, Wenqian,Li, Jiangbo,Zhao, Chunjiang,Fan, Shuxiang,Wu, Jitao,Zhang, Baohua,Zhao, Chunjiang,Liu, Chengliang. 2014

[12]Measuring the Moisture Content in Maize Kernel Based on Hyperspctral Image of Embryo Region. Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua. 2016

[13]Principles and Applications of Hyperspectral Imaging Technique in Quality and Safety Inspection of Fruits and Vegetables. Zhang Bao-hua,Zhang Bao-hua,Li Jiang-bo,Fan Shu-xiang,Huang Wen-qian,Zhang Chi,Wang Qing-yan,Xiao Guang-dong. 2014

[14]Identification of Wheat Cultivars Based on the Hyperspectral Image of Single Seed. Zhu, Dazhou,Wang, Cheng,Wu, Qiong,Zhao, Chunjiang,Pang, Binshuang,Shan, Fuhua. 2012

[15]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[16]Detection of early bruises on peaches (Amygdalus persica L.) using hyperspectral imaging coupled with improved watershed segmentation algorithm. Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian. 2018

[17]Study on the optimal algorithm prediction of corn leaf component information based on hyperspectral imaging. Wu, Qiong,Xu, Tongyu,Wu, Qiong,Wang, Jihua,Wang, Cheng. 2016

[18]Vertical features of yellow rust infestation on winter wheat using hyperspectral imaging measurements. Zhao, Jinling,Zhang, Dongyan,Huang, Linsheng,Zhang, Qing,Liu, Wenjing,Yang, Hao. 2016

[19]Combination of spectral and textural information of hyperspectral imaging for the prediction of the moisture content and storage time of cooked beef. Yang, Dong,Lu, Anxiang,Wang, Jihua,Yang, Dong,Wang, Jihua,He, Dandan,Lu, Anxiang,Ren, Dong,Wang, Jihua,Lu, Anxiang,Wang, Jihua. 2017

[20]Measurement of Light Penetration Depth through Milk Powder Layer in Raman Hyperspectral Imaging System. Liu Chen,Chen Li-ping,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin. 2017

作者其他论文 更多>>

微信小程序