基于三维生物散斑技术的牛肉质构特性预测

发布时间：2018-05-14 14:33

本文选题：生物散斑 + 三维成像　；参考：《食品科学》2017年03期

【摘要】：三维成像技术能够获得样品的空间信息,具有快速、方便、可实时的特点。为了缩短生物散斑技术的检测时间,减小其应用的局限性,将三维成像技术引入到传统生物散斑技术中,以期得到更好的预测效果。分别从两个不同角度拍摄同一样品的图像信息,利用广义差分法对图像进行预处理,并运用灰度模板匹配法、小波变换法和对比度调制融合法对两个角度的图像进行匹配融合,以时间序列散斑图灰度共生矩阵的参数对比度表示图像的散斑活性,建立其对牛肉质构特性的预测模型。通过传统相机标定法,获得相机的内外参数,并利用相似三角形原理提取图像的深度信息,对因物体摆放位置不同引起的误差进行校正,使获得的结果更加准确。结果表明,相似三角形原理可对样品的深度信息进行校正。三维生物散斑技术能更好地对牛肉的硬度和咀嚼性进行预测,对3种图像融合方法进行比较可知,小波变换法的预测效果最好,对硬度和咀嚼性的预测相关系数分别可达到0.944 4和0.928 8。
[Abstract]:Three-dimensional imaging technology can obtain spatial information of samples, which is fast, convenient and real-time. In order to shorten the detection time of biological speckle technology and reduce the limitation of its application, the 3D imaging technology is introduced into the traditional biological speckle technology in order to obtain better prediction effect. The image information of the same sample was taken from two different angles, the image was preprocessed by the generalized difference method, and the image from the two angles was matched and fused by gray template matching method, wavelet transform method and contrast modulation fusion method. The speckle activity of time series speckle pattern is represented by the contrast of the parameter of gray level co-occurrence matrix of time series speckle pattern, and the prediction model of beef texture property is established. Through the traditional camera calibration method, the internal and external parameters of the camera are obtained, and the depth information of the image is extracted by using the principle of similar triangle, and the error caused by the different position of the object is corrected, which makes the result more accurate. The results show that the similar triangle principle can correct the depth information of the sample. Three-dimensional biospeckle technique can better predict the hardness and chewiness of beef. The comparison of three image fusion methods shows that wavelet transform is the best method to predict beef hardness and chewiness. The predicted correlation coefficients for hardness and mastication were 0.944 4 and 0.928 8 respectively.
【作者单位】：上海理工大学医疗器械与食品学院;
【基金】：“十二五”国家科技支撑计划项目(2015BAK36B04) 国家自然科学基金面上项目(31271896) 上海市科委2015年长三角科技联合攻关领域项目(15395810900)
【分类号】：TS251.52;TP391.41

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