CAJ | 학술논문

将二维相关近红外谱参数化方法与BP神经网络结合，建立掺杂牛奶与纯牛奶的判别模型。分别配制含有尿素牛奶（1～20g·L -1）和三聚氰胺牛奶（0.01～3g·L -1）样品各40个。研究了纯牛奶、掺杂牛奶的二维相关近红外谱特性，在此基础上，分别提取了各样品二维相关同步谱的5个特征参数。将这5个特征参数作为BP神经网络的输入，分别建立掺杂尿素、掺杂三聚氰胺、两种掺杂牛奶与纯牛奶的判别模型，采用这些模型对未知样品进行预测，其预测正确率分别为95％，100％和96.7％。研究结果表明：该方法有效地提取了牛奶中掺杂目标物的特征光谱信息，同时又减少了BP神经网络输入变量的维数，实现了掺杂牛奶与纯牛奶的鉴别。
장이유상관근홍외보삼수화방법여BP신경망락결합，건립참잡우내여순우내적판별모형。분별배제함유뇨소우내（1～20g·L -1）화삼취청알우내（0.01～3g·L -1）양품각40개。연구료순우내、참잡우내적이유상관근홍외보특성，재차기출상，분별제취료각양품이유상관동보보적5개특정삼수。장저5개특정삼수작위BP신경망락적수입，분별건립참잡뇨소、참잡삼취청알、량충참잡우내여순우내적판별모형，채용저사모형대미지양품진행예측，기예측정학솔분별위95％，100％화96.7％。연구결과표명：해방법유효지제취료우내중참잡목표물적특정광보신식，동시우감소료BP신경망락수입변량적유수，실현료참잡우내여순우내적감별。
Discriminant models of adulterated milk and pure milk were established using BP neural network combined with two-dimensional (2D) correlation near-infrared spectra parameterization .Forty pure milk samples ,40 adulterated milk samples with urea (1~20 g · L -1 ) and 40 adulterated milk samples with melamine (0.01~3 g · L -1 ) were prepared respectively .Based on the characteristics of 2D correlation near-infrared spectra of pure milk and adulterated milk ,5 apparent statistic parameters were calculated based on the parameterization theory .Using 5 characteristic parameters ,discriminant models of urea adulterated milk , melamine adulterated milk and two types of adulterated milk were built by BP neural network .The prediction rate of unknown samples were 95% ,100% and 96.7% ,respectively .The results show that this method can extract effectively feature informa-tion of adulterant ,reduce the input dimensions of BP neural network ,and better realize qualitative analysis of adulterant in milk .