Synchronous front-face fluorescence;
Edible vegetable oil;
Used frying oil;
Adulteration;
Partial least squares regression;
VIRGIN OLIVE OIL;
EXTRA VIRGIN;
COOKING OIL;
FOOD SAFETY;
CHEMOMETRICS;
QUALITY;
CLASSIFICATION;
DETERIORATION;
OPTIMIZATION;
DEGRADATION;
D O I:
10.1016/j.foodchem.2016.08.053
中图分类号:
O69 [应用化学];
学科分类号:
081704 ;
摘要:
Synchronous front-face fluorescence spectroscopy has been developed for the discrimination of used frying oil (UFO) from edible vegetable oil (EVO), the estimation of the using time of UFO, and the determination of the adulteration of EVO with UFO. Both the heating time of laboratory prepared UFO and the adulteration of EVO with UFO could be determined by partial least squares regression (PLSR). To simulate the EVO adulteration with UFO, for each kind of oil, fifty adulterated samples at the adulterant amounts range of 1-50% were prepared. PLSR was then adopted to build the model and both full (leave-one-out) cross-validation and external validation were performed to evaluate the predictive ability. Under the optimum condition, the plots of observed versus predicted values exhibited high linearity (R-2 > 0.96). The root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were both lower than 3%. (C) 2016 Elsevier Ltd. All rights reserved.
机构:
Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R ChinaBeijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Liu, Huan
Ji, Zengtao
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机构:
Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R ChinaBeijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Ji, Zengtao
Liu, Xinliang
论文数: 0引用数: 0
h-index: 0
机构:
Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R ChinaBeijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Liu, Xinliang
Shi, Ce
论文数: 0引用数: 0
h-index: 0
机构:
Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R ChinaBeijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Shi, Ce
Yang, Xinting
论文数: 0引用数: 0
h-index: 0
机构:
Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R ChinaBeijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China