Laboratory of Organic and Analytical Chemistry, University of Sultan Moulay Slimane, Beni-Mellal, Morocco.
Laboratory of Analytical Chemistry, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco.
J Sci Food Agric. 2022 Jan 15;102(1):95-104. doi: 10.1002/jsfa.11335. Epub 2021 Jun 3.
Argan oil is one of the purest and rarest oils in the world, so that the addition of any further product is strictly prohibited by international regulations. Consequently, it is necessary to establish reliable analytical methods to ensure its authenticity. In this study, three multivariate approaches have been developed and validated using fluorescence, UV-visible, and attenuated total reflectance Fourier transform mid-infrared (FT-MIR) spectroscopies.
The application of a partial least squares discriminant analysis model showed an accuracy of 100%. The quantification of adulteration have been evaluated using partial least squares (PLS) regression. The PLS model developed from fluorescence spectroscopy provided the best results for the calibration and cross-validation sets, as it showed the highest R (0.99) and the lowest root mean square error of calibration and cross-validation (0.55, 0.79). The external validation of the three multivariate approaches by the accuracy profile shows that these approaches guarantee reliable and valid results of 0.5-32%, 7-32%, and 10-32% using fluorescence, FT-MIR and UV-visible spectroscopies respectively.
This study confirmed the feasibility of using spectroscopic sensors (routine technique) for rapid determination of argan oil falsification. © 2021 Society of Chemical Industry.
阿甘油是世界上最纯净、最稀有的油之一，因此国际法规严格禁止添加任何其他产品。因此，有必要建立可靠的分析方法来确保其真实性。在这项研究中，使用荧光、紫外-可见和衰减全反射傅里叶变换中红外（FT-MIR）光谱学开发并验证了三种多变量方法。
偏最小二乘判别分析模型的应用显示出 100%的准确性。使用偏最小二乘（PLS）回归评估了掺假的定量。荧光光谱学开发的 PLS 模型为校准和交叉验证集提供了最佳结果，因为它显示出最高的 R（0.99）和最低的校准和交叉验证均方根误差（0.55、0.79）。通过精度概况对三种多变量方法的外部验证表明，这些方法分别使用荧光、FT-MIR 和紫外-可见光谱学保证了可靠和有效的 0.5-32%、7-32%和 10-32%的结果。
这项研究证实了使用光谱传感器（常规技术）快速确定阿甘油掺假的可行性。© 2021 化学工业协会。
