Jiang An, Peng Jiang-Tao, Peng Si-Long, Wei Ji-Ping, Li Chang-Wen
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Apr;30(4):920-3.
Chinese liquor is a complex mixture and contains a large amount of microconstituents, which affects the quality and flavor of liquor. In order to discriminate liquor flavors rapidly, the spectra of liquors were obtained by FTIR and employed as the input patterns of pattern classification algorithms, then liquor flavor discrimination models were built. This paper introduces liquor flavor pattern recognition algorithms comprehensively and systematically for the first time, and the algorithms contain statistical classifications (linear discriminant function, quadratic discriminant function, regularized discriminant analysis, and K nearest neighbor), prototype learning algorithm (learning vector quantization), support vector machine and adaboost algorithm. Experimental results show that the liquor flavor classification algorithms demonstrate good performance and achieve high accuracy, recognition rate and rejection rate.
白酒是一种复杂的混合物，含有大量的微量成分，这些成分会影响白酒的品质和风味。为了快速鉴别白酒风味，通过傅里叶变换红外光谱仪（FTIR）获取白酒的光谱，并将其作为模式分类算法的输入模式，进而建立白酒风味鉴别模型。本文首次全面系统地介绍了白酒风味模式识别算法，这些算法包括统计分类法（线性判别函数、二次判别函数、正则化判别分析和K近邻法）、原型学习算法（学习矢量量化）、支持向量机和adaboost算法。实验结果表明，白酒风味分类算法具有良好的性能，能够实现较高的准确率、识别率和拒识率。
