Mabood Fazal, Boqué Ricard, Folcarelli Rita, Busto Olga, Al-Harrasi Ahmed, Hussain Javid
Department of Biological Sciences & Chemistry, College of Arts and Sciences, University of Nizwa, Oman.
Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain.
Spectrochim Acta A Mol Biomol Spectrosc. 2015 May 15;143:298-303. doi: 10.1016/j.saa.2015.01.119. Epub 2015 Feb 18.
We have investigated the effect of thermal treatment on the discrimination of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with sunflower oil. Two groups of samples were used. One group was analyzed at room temperature (25°C) and the other group was thermally treated in a thermostatic water bath at 75°C for 8h, in contact with air and with light exposure, to favor oxidation. All samples were then measured with synchronous fluorescence spectroscopy. Fluorescence spectra were acquired by varying the excitation wavelength in the region from 250 to 720nm. In order to optimize the differences between excitation and emission wavelengths, four constant differential wavelengths, i.e., 20nm, 40nm, 60nm and 80nm, were tried. Partial least-squares discriminant analysis (PLS-DA) was used to discriminate between pure and adulterated oils. It was found that the 20nm difference was the optimal, at which the discrimination models showed the best results. The best PLS-DA models were those built with the difference spectra (75-25°C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration. Furthermore, PLS regression models were built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 1.75% of adulteration.
我们研究了热处理对区分纯特级初榨橄榄油(EVOO)样品与掺有葵花籽油的EVOO样品的影响。使用了两组样品。一组在室温(25°C)下进行分析,另一组在75°C的恒温水浴中进行热处理8小时,使其与空气接触并暴露在光线下,以促进氧化。然后用同步荧光光谱法对所有样品进行测量。通过在250至720nm范围内改变激发波长来获取荧光光谱。为了优化激发波长与发射波长之间的差异,尝试了四个固定的差分波长,即20nm、40nm、60nm和80nm。采用偏最小二乘判别分析(PLS-DA)来区分纯油和掺假油。发现20nm的差异是最佳的,此时判别模型显示出最佳结果。最佳的PLS-DA模型是基于差谱(75 - 25°C)构建的,能够在2%的掺假水平下区分纯油和掺假油。此外,还建立了PLS回归模型来量化掺假水平。同样,最佳模型是基于差谱构建的,掺假预测误差为1.75%。