摘要
白酒是一个复杂的混合物体系,它含有大量的微量成分,这些微量成分直接决定了白酒的品质、口感和香型。为实现对白酒香型的快速鉴别,可采集不同香型白酒的红外光谱图,并将其作为模式分类方法的输入模式,建立白酒香型鉴别模型。首次全面系统地介绍了白酒香型模式识别算法,这些算法包括统计分类器(线性判别函数、二次判别函数、正则判别分析、K近邻算法)、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,基于红外光谱的白酒香型检测模式识别算法达到了很高的分类准确率、识别率和拒绝率,显示出了很好的性能。
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 statis-tical 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 accura cy, recognition rate and rejection rate.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2010年第4期920-923,共4页
Spectroscopy and Spectral Analysis
基金
国家科技支撑计划重点项目(2006BAK07B04)资助
关键词
红外光谱
白酒香型检测
模式分类
高斯分类器
学习矢量量化
Infrared spectroscopy
Liquor flavor discrimination
Pattern classification
Gaussian classification
Learning vector quantization
