| 基于生物散斑图像和惯性矩谱分析的牛肉掺腐检测 |
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| 引用本文： | 贾桂锋,陈伟,冯耀泽.基于生物散斑图像和惯性矩谱分析的牛肉掺腐检测[J].农业工程学报,2018,34(16):281-286. |
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| 作者姓名： | 贾桂锋 陈伟 冯耀泽 |
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| 作者单位： | 华中农业大学工学院;农业部长江中下游农业装备重点实验室 |
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| 基金项目： | 湖北省自然科学基金重点项目（2015CFA106） |
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| 摘 要： | 牛肉掺假严重危害消费者的健康与经济利益,因此对牛肉掺假进行无损检测具有重要意义。该文基于生物散斑技术对牛肉掺假进行定量检测。试验将新鲜牛肉和非新鲜牛肉按不同比例(0、1%、3%、5%~60%(5%梯度)和100%)混合制备掺假样本,并采集样本的生物散斑图像。针对单列惯性矩(inertia moment,IM)表征样本生物活性存在稳定性差的问题,首次提出惯性矩谱(IM谱)分析的方法并用于建立基于支持向量回归机(support vector regression machine,SVR)的牛肉掺假检测模型。结果表明基于IM谱建立的SVR模型能较为准确预测牛肉中掺假物含量,校正集和测试集的决定系数分别为0.85和0.81,均方根误差分别为0.12和0.11。该研究证明了利用生物散斑技术和惯性矩谱分析方法对新鲜牛肉中掺杂腐败牛肉进行定量检测是可行的。
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| 关 键 词： | 无损检测 激光器 图像处理 激光散斑 牛肉 掺假 惯性矩谱 支持向量机 |
| 收稿时间： | 2018/4/17 0:00:00 |
| 修稿时间： | 2018/7/5 0:00:00 |
Quantification detection of beef adulteration with spoiled beef based on biospeckle imaging and inertia moment spectrum |
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| Jia Guifeng,Chen Wei and Feng Yaoze.Quantification detection of beef adulteration with spoiled beef based on biospeckle imaging and inertia moment spectrum[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(16):281-286. |
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| Authors: | Jia Guifeng Chen Wei and Feng Yaoze |
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| Institution: | 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture, Wuhan, 430070, China,1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; and 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture, Wuhan, 430070, China |
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| Abstract: | Abstract: Adulteration of beef with spoiled beef seriously endangers the health and economic interest of consumers. Therefore, it is of great significance to implement nondestructive detection of beef adulteration. Traditional methods for detecting meat adulteration are destructive and laborious. Biological speckle (bio-speckle) technique is a non-invasive and rapid detection method. Therefore, in this paper, bio-speckle technique was used for quantitative detection of beef adulteration by detecting bioactivity variance among samples. In the experiment, a total of 62 adulterated beef samples with different adulteration concentrations of 0, 1%, 3%, 5%-60% (5% increment) and 100% (w/w) were prepared by mixing fresh and spoiled beef at different ratios, and the samples were divided into calibration set and test set by sample set partitioning based on joint x-y distance (SPXY). The samples were illuminated by a 10 mw laser at 632 nm with 60° incident angle. Five hundred biological speckle images (640×480 pixels) of each sample were collected by a CCD (charge-coupled device) camera at 20 fps. To solve the problem of poor stability of traditional single row/column inertia moment (IM) method in characterizing bio-speckle activities of samples, a new method named inertia moment spectrum (IM spectrum) was developed. Specifically, the temporal history speckle patterns (THSPs) for individual columns of the bio-speckle images were generated, based on which IM was calculated for each column. By splicing IM of each column, a spatially continuous spectrum, i.e., IM spectrum is established. IM spectrum represents the spatial biological activity information of the sample and thus has strong anti-interference ability. By comparing the IM spectrum of different adulteration samples, it was found that IM spectrum offered a good alternative for visualizing sample variance due to adulteration levels. The width of a broad peak corresponding to the illumination center of laser decreased with the increase of adulteration levels, which may be contributed to physical and chemical component difference that resulted in scatter coefficient variations of different samples. Moreover, the IM spectrum was normalized and utilized in the development of support vector regression machine (SVR) model for beef adulteration detection. Model parameters including penalty parameter and kernel function parameter were optimized by particle swarm algorithm. The results showed that the SVR model based on IM spectrum was feasible to predict the levels of adulteration in beef, in which the coefficients of determination and the root mean squared errors were 0.85 and 0.12 as well as 0.81 and 0.11 for calibration set and test set, respectively. The penalty parameter and kernel function parameter of the model were 1.96 and 0.01, respectively. The coefficients of determination of the model were greater than 0.8, and the root mean squared errors for calibration and test were close, indicating that the model has high stability and good precision. Besides, from the scatter plot of the predicted and true values of the model, it was found that the main errors of the model originated from 100% adulteration samples and one unadulterated sample. However, since all spoiled beef samples were predicted as high adulteration levels, it was therefore considered that such SVR model was still of practical use. This study demonstrates that it is feasible to use bio-speckle imaging and IM spectrum analysis for detecting beef adulteration with spoiled beef. Nevertheless, more studies are needed to further improve model performance and explore the usefulness of IM spectrum in bio-speckle analysis. |
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| Keywords: | nondestructive determination laser image processing laser speckle beef adulteration inertia moment spectrum support vector machine |
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