应用主成分和判别分析的红外光谱法快速鉴别酸败植物油  

Rapid authentication of rancid edible oil based on fourier transform infrared spectroscopy of principal component and discrimination analysis

作　　者：周俊[1,2] 张军[1,2] 谢梦圆[1,2] 陈哲[1,2] 汪勇[3] 关贺元[1,2] 

机构地区：[1]光电信息与传感技术广东普通高校重点实验室(暨南大学),广东广州510632 [2]暨南大学光电工程系,广东广州510632 [3]暨南大学食品科学与工程系,广东广州510632

出　　处：《食品工业科技》2015年第12期53-56,共4页Science and Technology of Food Industry

基　　金：国家自然科学基金项目(61177075;61275046;61475066;61405075);广东省战略性新兴产业核心技术攻关项目(2012A032300016;2012A080302004);广东省学科建设专项资金项目(2013CXZDA005);广东省自然科学基金项目(2014A030313377);中央高校基本科研业务费专项资金项目(21614313;21615307)

摘　　要：通过收集并分析40个合格植物油和44个酸败植物油的傅里叶变换红外光谱,选取25个合格植物油和39个酸败植物油组成训练集,利用主成分分析获得累积可信度95%的三个主成分及对应的1743~1710cm^-1、1172~1130cm^-1、2945~2844cm^-1、1728~1689cm^-1、2987~2840cm^-1和1731~1660cm^-1对植物油酸败最为敏感的光谱波数范围。在主成分分析的基础上,选取对植物油酸败敏感的波段,利用训练集建立鉴别植物油酸败判别分析模型。采用验证集20个样品验证判别分析模型,判别正确率达100%。主成分结合判别分析的红外光谱法能快速、准确、无损地区分合格植物油和酸败植物油。Abstract：40 qualified edible oils and 44 rancid ones were collected and analyzed. 25 qualified edible oils and 39 rancid ones were selected to compose training set. Principal component analysis（PCA） was used to compress thousands of spectral data into several variables and describe the body of spectra,the analysis suggested that the accumulate reliabilities of PC1 ,PC2 and PC3（the first three principle components） were more than 95% and corresponding 1743 - 1710cm^-1,1172 - 1130cm^-1, 2945 - 2844cm^-1,1728-1689cm^-1, 2987 - 2840cm^-1 and 1731-1660cm^-1 were the most sensitive bands for edible oil rancidity. The training set was used to build discrimination analysis （DA） model,and then the most sensitive bands were applied as DA model inputs. The model was validated by other 20 samples as validation set with the correct recognition rate of 100% ,which showed this method could be used to distinguish the rancid edible oil rapidly,accurately and soundly.

关 键 词：植物油酸败鉴别 主成分分析 判别分析 傅里叶变换红外光谱 

分 类 号：TS207.3[轻工技术与工程—食品科学]