化学计量学
傅里叶变换红外光谱
偏最小二乘回归
主成分分析
化学
分析化学(期刊)
橄榄油
基质(化学分析)
光谱学
荧光光谱法
红外线的
傅里叶变换
线性判别分析
植物油
近红外光谱
傅里叶变换光谱学
红外光谱学
色谱法
荧光
生物系统
人工智能
数学
食品科学
计算机科学
光学
有机化学
数学分析
物理
统计
量子力学
生物
作者
Xiangru Meng,Chunling Yin,Libo Yuan,Yan Zhang,Ying Ju,Kehui Xin,Wenbo Chen,Kaidi Lv,Leqian Hu
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-03-01
卷期号:405: 134828-134828
被引量:33
标识
DOI:10.1016/j.foodchem.2022.134828
摘要
Several spectroscopic techniques have been used to detect olive oil adulteration. To evaluate the performance of these spectral techniques on this issue, this work performed a comparative study on identifying adulterated olive oil with different concentrations of soybean oil based on Fourier-transform infrared (FTIR), visible-near-infrared (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics. Principal component analysis (PCA)/ multi-way-PCA analysis showed the feasibility of the three spectral methods for the identification of olive oil adulteration. The accuracy of FTIR and Vis-NIR based on partial least squares discriminant analysis (PLS-DA) for adulterated olive oil was 100%, while the accuracy of EEMs based on unfold-PLS-DA was only 73%. The accuracy of EEMs combined with back-propagation artificial neural network based on self-weighted alternating trilinear decomposition is 100%. In comparison, FTIR and Vis-NIR are superior for the detection of olive oil adulteration due to the convenience of instrument operation and modeling.
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