姚玉梅,韩鲁佳,杨增玲,刘贤.基于显微图像处理的不同种属肉骨粉鉴别分析[J].农业机械学报,2016,47(s1):347-352.
Yao Yumei,Han Lujia,Yang Zengling,Liu Xian.Discriminant Analysis of Different Species of Meat and Bone Meal Based on Microscopic Image Processing[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(s1):347-352.
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基于显微图像处理的不同种属肉骨粉鉴别分析   [下载全文]
Discriminant Analysis of Different Species of Meat and Bone Meal Based on Microscopic Image Processing   [Download Pdf][in English]
投稿时间:2016-07-20  
DOI:10.6041/j.issn.1000-1298.2016.S0.053
中文关键词:  肉骨粉  种属鉴别  显微图像  组织特性
基金项目:“十二五”国家科技支撑计划项目(2014BAD08B11-2)和国家国际科技合作专项(2015DFG32170)
作者单位
姚玉梅 中国农业大学 
韩鲁佳 中国农业大学 
杨增玲 中国农业大学 
刘贤 中国农业大学 
中文摘要:以20个可靠来源的肉骨粉样品(猪源、禽源、反刍源)为研究对象,采用标准方法制备骨颗粒试样,并采用光学显微镜图像系统获取其显微图像。通过系列图像处理提取骨颗粒表面代表性腔隙的显微组织特性数据,计算所得腔隙的面积、周长、长短轴长度及比率。研究发现,猪、鸡、牛、羊肉骨粉骨颗粒腔隙的面积、周长和长短轴长度数据均为正态分布,且不同种属之间均有显著性差异。PLS-DA结果显示,基于骨颗粒腔隙的面积、周长特性数据可以鉴别哺乳动物源与鸡源肉骨粉,而基于长短轴长度数据的不同种属肉骨粉识别率均较低。独立验证集结果表明:基于面积、周长及其组合特性数据可以有效进行鸡源和哺乳动物源肉骨粉的鉴别分析,模型的判别正确率均达0.93。其中基于面积和周长组合特性数据的鉴别分析结果优于基于单一特性数据的结果。而基于骨颗粒显微图像处理,很难进一步对不同哺乳动物(反刍动物和猪)源性肉骨粉进行鉴别分析。
Yao Yumei  Han Lujia  Yang Zengling  Liu Xian
China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:meat and bone meal  species identification  microscopic image  texture characteristics
Abstract:Discriminant analysis of different species of meat and bone meal (MBM) can provide necessary technical support for feed safety. A total of 20 MBM samples, including five porcine, five poultry, five bovine and five ovine samples, were chosen. All the samples were of reliable sources. Bone fragment samples were prepared using standard methods and their microscopic images were obtained by biological microscope imaging system. Five lacunas’ characterization data were extracted from each image by Matlab software including the area, perimeter, major axis and minor axis of each lacuna. Results showed that all the specific characterization data of lacunas fit the normal distribution and significant difference was found between different species. Partial least squares discriminant analysis (PLS-DA) results showed that it is feasible to classify mammalian MBM and poultry MBM based on the area and perimeter values of lacuna, while it was hard to discriminant different species of MBM samples based on the major axis and minor axis. Results of separate validation proved that successful discrimination of mammalian and poultry MBM could be performed based on the separate area, perimeter and pair combination values of bone fragments; the correct discriminant rate of established model were all 0.93, while result based on the pair combination values was better than that of separate parameter values. However, it was difficult to further discriminant ruminant and porcine MBM samples by this technique.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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