分析测试学报2017，Vol.36Issue(3)：372-376,5.DOI:10.3969/j.issn.1004-4957.2017.03.013
LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂
Classification of Edible Vegetable Oils and Discrimination of Catering Waste Oils by LF-NMR Combined with Chemometrics Method
摘要
Abstract
To establish an effective analysis method for evaluating the quality of edible oil is of great significance to ensure the safety of edible oil market.Principal component analysis(PCA) and cluster analysis (CA)were used to analyze the low-field nuclear magnetic resonance(LF-NMR) T2 relaxation characteristics of 9 kinds of normal edible oil and 100 catering waste oil samples.The results indicated that good classification of refined edible oil according to their vegetable types could be achieved by PCA,and the distributions of different vegetable oils on the PCA plot have clear boundaries.While for the discrimination of authentic vegetable oil and the catering waste oil,good identification results could be achieved by CA (Euclidean distance =5).After the introduction of 30 testing samples,the overall correct classification rate was still as high as 94.49％,and the misjudgment rate was only 5.51％.Therefore,LF-NMR combined with chemometrics method is feasible for rapid classification of edible vegetable oils and discrimination of catering waste oils.关键词
低场核磁共振(LF-NMR)/食用油/餐饮废油/主成分分析(PCA)/聚类分析(CA)Key words
low-field nuclear magnetic resonance (LF-NMR)/edible oil/catering waste oil/principal component analysis (PCA)/cluster analysis (CA)分类
数理科学引用本文复制引用
毛锐,王欣,史然..LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂[J].分析测试学报,2017,36(3):372-376,5.基金项目
国家自然科学基金项目(NSFC31201365) （NSFC31201365）
上海市科委重点攻关项目(11142200403) （11142200403）
上海市教委科研创新项目(11YZ109) （11YZ109）
