基于光谱技术的褐壳血斑蛋鉴别方法研究  

Detection of Blood Spots in Brown Eggs Based on Spectroscopic Techniques

作　　者：徐惠荣[1] 徐文豪[1] 陈华瑞[1] 姚洋[1] 张安红[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院,杭州310058

出　　处：《农业机械学报》2014年第2期194-198,共5页Transactions of the Chinese Society for Agricultural Machinery

基　　金：'十二五'国家科技支撑计划资助项目(2011BAD20B12);高等学校基本科研业务费专项资金资助项目(2012FZA6006);杭州市农业科研攻关专项资助项目(20120232B55)

摘　　要：血斑蛋是一种带有血丝的异常蛋。通过自制的鸡蛋内部品质光谱检测系统,采集40个正常蛋和60个人工注射血样的血斑蛋的可见/近红外光谱,研究比较了3种不同的血斑蛋判别方法:传统的血值判别法、偏最小二乘判别法(DPLS)以及融合光谱信息与蛋壳颜色信息的最小二乘支持向量机(LS-SVM)判别法,结果表明基于颜色信息融合的最小二乘支持向量机的判别结果明显优于传统的血值判别法,正常蛋的判别正确率为90%,血斑蛋的判别正确率为91.7%,证明了此方法可用于褐色蛋的血斑检测。The abnormal egg, blood-egg, has the internal optically active anomalies blood spots. A detection system based on Vis/NIR spectroscopy was designed for eggs＇ internal quality inspection. Then, spectra data of 40 brown normal eggs and 60 artificial abnormal eggs obtained by injecting a small amount of blood ingredient were collected. Three analytical methods： the traditional discrimination method using blood value, discriminant partial least squares analysis （DPLS） and least squares support vector machine （ LS - SVM） combined with spectral information and eggshell color information were compared for discrimination analysis. The final results showed that the discrimination rate based on LS - SVM method was 90.0% for normal eggs and that was 91.7% for blood-spot eggs, better than the results using traditional discrimination analysis methods. The results suggested that LS - SVM was an effective analytical tool in detecting blood-spot eggs with brown eggshell.

关 键 词：褐壳蛋 血斑 可见 近红外光谱 最小二乘支持向量机 判别分析 

分 类 号：S123[农业科学—农业基础科学] TP391.4[自动化与计算机技术—计算机应用技术]