掺假者
山茶花
化学计量学
傅里叶变换红外光谱
偏最小二乘回归
食用油
数学
生物系统
植物油
傅里叶变换
化学
色谱法
材料科学
分析化学(期刊)
计算机科学
统计
化学工程
工程类
生物
数学分析
计算机安全
出处
期刊:Food Chemistry
[Elsevier]
日期:2022-08-01
卷期号:385: 132661-132661
被引量:10
标识
DOI:10.1016/j.foodchem.2022.132661
摘要
A novel improved method for the authentication of edible oil samples based on Fourier-transform infrared (FTIR) spectroscopy coupled with chemometrics has been developed. A discrimination analysis model has been developed. On this basis, 100% correct classification of 135 samples from eleven species has been achieved. Recognition rates with respect to external validation for 91 pure oil samples and 231 blend samples were 100% and 92.6%, respectively. A general quantitative model for detecting edible oil adulteration (taking Camellia oil as an example) has also been built. An optimal backward interval partial least-squares model, based on the spectral regions ν = 3100-2900, 1800-1700, 1500-1400, and 1200-1100 cm-1, has been determined, giving good performances. A specific sub-model using a single adulterant oil has also been constructed, which showed higher prediction accuracy. Based on the developed qualitative and quantitative FTIR methods, adulterant oils in Camellia blends could be rapidly detected, effectively differentiated, and accurately quantified.
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