基于超声成像技术的火腿肠质构分析与等级判别  

Texture analysis and grade discriminant of sausages based on ultrasound imaging

作　　者：邹小波[1] 赵号 石吉勇[1] 王圣[1] 翟晓东[1] 胡雪桃 Zou Xiaobo;Zhao Hao;Shi Jiyong;Wang Sheng;Zhai Xiaodong;Hu Xuetao(School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China)

机构地区：[1]江苏大学食品与生物工程学院,镇江212013

出　　处：《农业工程学报》2017年第23期284-290,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然基金(31671844);国家科技支撑项目(2015BAD17B04);"十三五"国家重点研发计划(2016YFD0401104);国家自然科学基金(31601543);江苏省自然科学基金(BK20160506)

摘　　要：为了研究超声成像技术在火腿肠质构分析与等级判别方面应用的可行性。通过对火腿肠蛋白质、淀粉等理化指标的测定将其分为特级、优级、普通级,并采集2个品牌3个等级的火腿肠共240份超声图像信息,在Matlab 7.0环境下提取图像角二阶矩、平均值等纹理特征值,最后利用线性判别式分析(linear discriminant analysis,LDA)和支持向量机(support vector machine,SVM)建立火腿肠的等级判别模型。结果表明:同品牌不同等级火腿肠超声图像、纹理特征值均具有较大差异,而同等级不同品牌火腿肠差异较小。建立的识别模型中:SVM优于LDA模型,当主成分为3时,SVM模型对应的校正集、预测集识别率均为100%,模型效果最佳。因此,超声成像技术可实现火腿肠内部质构的分析和等级的快速准确识别,研究结果可为超声成像技术在火腿肠内部质构分析和等级判别方面的应用提供参考。Sausage is an emulsification-type,popular meat product,because of its unique flavor,high in nutrition and easy to store procedures.According to national standards of China(GB/T20712-2006),sausage can be divided into three types of grades(general,excellent and premium).Traditional sausage grade detection methods are laborious and time consuming.So,it is imperative to develop a rapid and simple detection method.In this research,ultrasound imaging system was evaluated as rapid and precise detection method to differentiate between different grades of sausage.And,the texture of the sausage was also analyzed simultaneously.A total of120sausage samples from2different manufacturers were collected from local supermarkets of Zhenjiang,Jiangsu,China.From each sausage,small sample(2.5cm×1.5cm)were obtained for ultrasound imaging,moisture,starch and protein measurement.These measurements were utilized to divide sausages into general,excellent and premium quality grades.Ultrasound imaging system worked with the UTEX320equipment in pulse echo mode.The parameters of ultrasound imaging system were as follow:the pulse voltage;300V,the pulse repetition frequency;800Hz,the gain;35dB,and the scanning speed was5mm/s.Total of240ultrasound images(2brands3grades,each had40samples)were collected by ultrasound imaging system.Images generated from different grades had obvious difference,however,different brands’images with the same grade were similar.Grey level co-occurrence matrix(GLCM)was generated in0,45,90and135°directions,respectively.The commonly used angular second moment(ASM),contrast(CON),correlation(COR)and homomorphity(HOM)were extracted in all directions,and a total of16texture feature variables were generated.Combined with the average average image(AVG),variance(VAR)of the image,18texture feature variables were finally obtained.Furthermore,the textural features of different grades had significant difference(P<0.05).All the texture feature values were randomly divided into calibration set(162samples data)and prediction set(7

关 键 词：质构 图像处理 模型 超声成像 火腿肠 纹理特征 支持向量机 

分 类 号：R445.1[医药卫生—影像医学与核医学]