支持向量回归在葡萄酒红外定量分析中的应用  

Application of SVR in Quantitative Analysis of Wines

作　　者：罗韬[1] 魏纪平[2] 赵玉平[3] 张军 

机构地区：[1]天津大学计算机科学与技术学院,天津市认知计算与应用重点实验室,天津300072 [2]天津大学化工学院,天津300072 [3]烟台大学生命科学学院,山东烟台264005 [4]中法合营王朝葡萄酿酒有限公司,天津300402

出　　处：《光谱学与光谱分析》2013年第11期3014-3018,共5页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61274021);教育部新教师基金项目(20120032120093);山东省自然科学基金项目(ZR2011CM026);天津市自然科学基金青年项目(13JCQNJC00600)资助

摘　　要：近年来,基于朗伯-比尔定律和化学计量学的红外光谱定量分析方法发展十分迅速。其中,选择合适的预处理方法和有效的校正模型是定量分析成功的关键。选取30个葡萄酒样品,运用红外光谱结合向量回归算法SVR,对葡萄酒乳酸、酒石酸、乙酸异戊酯、3-甲基-1-丁醇进行了红外含量预测。选用标准归一化、基线校正以及异常样本点剔除三种谱图预处理方法,结合支持向量回归算法。实验结果表明该方法行之有效,计算值与标准值间的相对误差可被控制在5%以内。该方法可应用于葡萄酒中代表性物质含量的定量分析检测。Fourier transform infrared spectroscopy has been widely used in some related fields, thus induces the rapid develop ment of quantitative analysis method based on Lambert-Beer＇s Law and chemometrics in recent years. The selection of appro priate pre-processing method and calibration model is extremely crucial to the quantitative analysis. The present paper selected 30 wine samples and used infrared spectroscopy combined with vector regression algorithm SVR quantitative analysis model with standard normal variate, baseline correction and outliers elimination to analyze four representative components of wine. Satisfac tory results were gained while the relative errors were limited to less than 5%. This method can be applied to the wine represent ative quantitative analysis for the material content.

关 键 词：傅里叶变换红外光谱法 定量分析 支持向量回归 

分 类 号：O433.4[机械工程—光学工程] TS262.6[理学—光学]