摘要
为了实现对不同剂量辐照处理后米粉的快速鉴别,提出了一种基于可见-近红外光谱技术的快速、无损检测方法。试验先利用不同剂量的60Coγ-射线对米粉进行辐照处理,得到了4种样品共200个样本。再应用ASD可见-近红外光谱仪获取所有样本的反射光谱数据,并采用主成分分析方法对数据进行聚类分析,将提取的前6个主成分作为BP神经网络的输入值,建立不同米粉样品的鉴别模型。结果表明,在设定偏差标准为±0.1的情况下,利用该模型对预测集样本进行鉴别,识别率达到100%。该文提出的方法具有很好的分类和鉴别作用,为快速鉴别米粉类产品是否经辐照灭菌及处理剂量等提供了新的技术方法。
In order to discriminate the rice flour processed by different doses of irradiation,a fast and nondestructive method was proposed based on the visible-near infrared spectroscopy.Four groups of rice flour were irradiated using different doses of 60Coγ-rays,and 200 test samples were obtained.The reflection spectrum data of all samples were collected by using ASD visible-near infrared spectrometer,and the data were analyzed by principal component analysis（PCA）method.Taking the first 6 principal components（PCs）as the inputs of the back-propagation artificial neural network（BP-ANN）,an identification model was established.The results showed that the identification accuracy of the model for predicting samples could reach up to 100%in the setting of standard deviation of±0.1.The proposed method has good classification and identification effects,which can provide a new technical method for fast identification of irradiation rice flour products.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2012年第7期271-274,共4页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金资助项目(30600371)
农业部农业公益性行业科研专项经费项目资助(200803034)
教育部重点项目(109090)
关键词
近红外光谱
辐照
主成分分析
BP神经网络
米粉
near infrared spectroscopy
irradiation
principal component analysis
BP neural networks
rice flour
