中文摘要：

为了实现葡萄酒品种的快速无损鉴别,选用五种干红葡萄酒,进行可见和近红外光谱实验,提出了一种用可见和近红外光谱技术快速鉴别葡萄酒品种的新方法。采用独立主成分分析进行模式特征分析,经过选用不同的独立主成分数进行建模和预测,确定最佳独立主成分数为20。将这20个主成分作为神经网络的输入变量,建立三层BP神经网络,实现类别预测的同时也完成了数学建模与优化分析工作。5个品种的葡萄酒样本数均为35,共计175个样本。在神经网络学习中,将其分成训练集样本150个和预测集样本25个。对25个未知样本进行预测,准确率为100%。该研究在独立主成分分析的基础之上,根据干红葡萄酒各独立主成分的混合矩阵向量载荷图,选取了两个波段（400～430nm与512～532nm）作为葡萄酒的独立主成分分析的特征波段。说明该文提出的基于光谱技术和模式识别的方法不仅对葡萄酒具有很好的分类和鉴别能力,并且可以提取出葡萄酒的指纹特征,可用于葡萄酒的检测与技术开发。

英文摘要：

In order to achieve the rapid discrimination of the varieties of red wines, the authors selected 5 kinds of dry red wine for study with Vis/NIR spectroscopy. Firstly, Characteristics of the pattern were analyzed by＇independent component analysis （ICA）. Through comparing the results of modeling performance by different number of independent components, 20 principal components presenting important information of spectra were confirmed as the best number of principal components. The 20 in- dependent components （ICs）extracted by ICA were employed as the inputs of the BP neural networks, and then a three layers of BP neural network was built, category analysis was performed, and the work of building mathematics model and optimizing the algorithm was completed. Five samples from each variety and a total of 25 samples were selected randomly as the prediction sets. The remaining 150 samples were used as the training sets to build the training model, which was validated by the samples of the prediction sets. The recognition rate was 100%. In addition, based on the independent component analysis, the authors selected two characteristic wave bands in reference to vector loading map of mixed matrix. So the pattern recognition methods developed in this paper not only played a good role in the classification and discrimination, but also had the capability to extract the finger feature of red wine, and offered a new way for detecting and developing red wines.