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基于近红外透射光谱的乳制品蛋白质、脂肪含量检测
引用本文:郭中华,王磊,金灵,郑彩英.基于近红外透射光谱的乳制品蛋白质、脂肪含量检测[J].光电子.激光,2013(6):1163-1168.
作者姓名:郭中华  王磊  金灵  郑彩英
作者单位:宁夏大学 物理电气信息学院,宁夏 银川 750021;宁夏大学 物理电气信息学院,宁夏 银川 750021;宁夏大学 物理电气信息学院,宁夏 银川 750021;宁夏大学 物理电气信息学院,宁夏 银川 750021
基金项目:宁夏回族自治区自然科学基金(NZ1103)资助项目 (宁夏大学 物理电气信息学院,宁夏 银川 750021)
摘    要:应用近红外透射光谱(NITS)法对乳制品中蛋白质和 脂肪含量进行快速检测。首先分别对光谱进行二阶导数 加S-G平滑(SD+S-G)和一阶导数加多元散射校正加S-G平滑(FD+MSC+S-G)预处理;然 后对处理后 的光谱进行小波基为db3、分解尺度为6的小波压缩;最后以压缩后光谱数据作为输入变量, 采用径向基 函数人工神经网络(RBF-ANN)建立4种乳制品的蛋白质和脂肪定量分析模型。经过反复实 验得出最佳扩散 常数spread值,其中,蛋白质模型在spread值为135时预测精度最高, 其相关系数(R)和预测集均方差(RMSEP)分别为 0.9999和0.0301,脂肪模型在spread值为105时 预测精度最高,其R和RHSEP分别为0.999和0.096。结果表明,基于RBF-A NN和小波压缩建模更稳定、精度更高,可以实现乳制品品质快速无损检测。

关 键 词:近红外透射光谱(NITS)    乳制品    小波压缩    径向基函数人工神经网络(RBF-ANN)
收稿时间:2012/12/15 0:00:00

Determination of the contents of protein and fat in dairy product based on n ear infrared transmittance spectroscopy
Affiliation:School of Physics and Electronic Information Engineering, Ning Xia University, Yinchuan 750021,China;School of Physics and Electronic Information Engineering, Ning Xia University, Yinchuan 750021,China;School of Physics and Electronic Information Engineering, Ning Xia University, Yinchuan 750021,China;School of Physics and Electronic Information Engineering, Ning Xia University, Yinchuan 750021,China
Abstract:The contents of protein and fat in dairy products are determined quickl y by the near infrared transmittance spectroscopy (NITS).The spectroscopies are preprocessed by the t wo mixed methods:the second derivative adding S-G smoothing (SD+S-G) and the first derivative adding multi plicative scatter correction adding S-G smoothing (FD+MSC+S-G),respectively,and thenthe processed spectroscopic d ata are compressed by the wavelet with function db3and compression level 6.The quantitative analysis models of pr otein and fat in the four dairy products are established by radial basis function artificial neural network (RB F-ANN) using the compressed spectroscopy data as the input variables.The best spread value is obtained b y repeated experiment.When the spread is 135,the prediction accuracy of protein is the hig hest and the correlation coefficient and mean square error are 0.9999and 0.0301,respectively.In the same way,when the spread is 105,the prediction accuracy of fat is the highest and the correlation coefficient and mean square error are 0.9997and 0.0968,respectively.The re sults show that the model based on RBF-ANN combined with wavelet is more stable and with a higher accuracy.It could be used to test the q ualities of dairy products quickly and destructively.
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