利用电子舌技术鉴别超临界CO₂萃取的籽油。
Discrimination of seed oils extracted by supercritical CO using electronic tongue technology.
作者信息
Duan Di, Huang Yong, Zou Ying, He Bingju, Tang Ruihui, Yang Liuxia, Zhang Zecao, Su Shucai, Wang Guoping, Zhang Deyi, Zhou Chunhui, Li Jing, Deng Maocheng
机构信息
Center of Guangdong Higher Education for Engineering and Technological Development of Specialty Condiments, Department of Food and Biological Engineering, Guangdong Industry Technical College, Guangzhou, 510300 China.
Guangdong Fanlong Agricultural Technology Development Co., Ltd, Jieyang, 522000 China.
出版信息
Food Sci Biotechnol. 2021 Aug 29;30(10):1303-1312. doi: 10.1007/s10068-021-00973-1. eCollection 2021 Oct.
UNLABELLED
Analytical method which combines electronic tongue technique and chemometrics analysis is developed to discriminate oil types and predict oil quality. All the studied oil samples from pressing, -hexane extraction and supercritical CO extraction (SCCE), were successfully identified by principal component analysis (PCA) and hierarchical cluster analysis (HCA). Furthermore, multi factor linear regression model (MLRM) was established to predict oil quality, which are indicated by acid value (AV) and peroxide value (POV). The practical potential of e-tongue for the discrimination and assessment of oils has shown promising application in the characterization of oils in the oil quality evaluation.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s10068-021-00973-1.
未标注
开发了一种结合电子舌技术和化学计量学分析的分析方法，用于区分油的类型并预测油的质量。通过主成分分析（PCA）和层次聚类分析（HCA）成功鉴定了所有来自压榨、正己烷萃取和超临界CO₂萃取（SCCE）的研究油样。此外，建立了多因素线性回归模型（MLRM）来预测以酸值（AV）和过氧化值（POV）表示的油的质量。电子舌在油的鉴别和评估方面的实际潜力在油质量评估中油的表征方面显示出有前景的应用。
补充信息
在线版本包含可在10.1007/s10068-021-00973-1获取的补充材料。
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