基于可见-近红外光谱的咖啡品牌鉴别研究

利用可见-近红外光谱技术对市场上三种不同品牌咖啡品种进行鉴别.分别采用主成分分析法与BP神经网络结合和小波变换与BP神经网络结合两种组合模型进行分析预测.利用主成分分析法与小波变换的数据压缩功能和BP神经网络的学习预测能力实现对不同品牌咖啡的鉴别.实验采用3个品种共60个样本建立模型,30个样本进行品种鉴别,结果表明,两种鉴别模型的咖啡品种鉴别率均为100%.同时也表明,小波变换用于数据压缩无论是在压缩时间上还是在压缩能力上都优于主成分分析法.说明通过小波变换和BP神经网络相结合建立模型进行不同品牌咖啡鉴别具有分析速度快,鉴别能力强的特点,为快速鉴别纯品咖啡提供了新的方法,同时也为确定不同品牌咖啡选用咖啡豆品种奠定了基础.

Discrimination among Different Brands of Coffee by Using Vis-Near Infrared Spectra

Near infrared spectroscopy technology was used to distinguish three different brands of coffee bought from the supermarket. Two models, PCA-BP and WT-BP, were employed for the analysis and prediction of the samples. The discrimination among the different brands of coffee was based on the combination of the function of data compression in the PCA and WT technology and the ability of learning and prediction of the artificial neural network. In the experiment, 60 samples were used for model calibration and 30 for brand prediction. The result showed that both the PCA-BP and WT-BP models achieved 100% discrimination rate, and the wavelet transforms technology is superior to the principal component analysis both in time-consuming and the capability of data compression. It is indicated that the model set up by the combination of WT technology and BP neural network in the present study is rapid in analysis and precise in pattern discrimination. It can be concluded that a new approach to distinguishing pure coffee was of fered and the result of this experiment established the foundation for the determination of the raw material (coffee bean) of different brands of coffee in the market.