El Orche Aimen, Bouatia Mustapha, Mbarki Mohamed
Laboratory of Chemical Processes and Applied Materials, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco.
Laboratory of Analytical Chemistry & Bromatology, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco.
J Anal Methods Chem. 2020 Jul 11;2020:8860161. doi: 10.1155/2020/8860161. eCollection 2020.
One of the most important issues in the field of quality assurance of olive oils is the detection of the freshness of olive oil. In this study, 400 nm laser-induced fluorescence spectroscopy was used with supervised and unsupervised multivariate analysis methods to develop a rapid method able to discriminate between freshly produced olive oils and oil that has been stored for a period of time ranging from 12 to 24 months. The fluorescence spectral data were firstly processed by the PCA. This method shows strong discrimination of the three oil classes using the first three components which present 96% of the total variability of the initial data, and then supervised classification models were constructed using the discriminant partial least square regression PLS-DA, support vector machine SVM, and linear discriminant analysis LDA. These methods show a high capacity in the classification of the three classes of olive oil. The validation of these classification models by external samples shows a high capacity of classification of the samples in their class with an accuracy of 100%. This study demonstrated the feasibility of the fluorescence spectroscopy fingerprint (routine technique) for the classification of olive oils according to their freshness and storage time.
橄榄油质量保证领域最重要的问题之一是检测橄榄油的新鲜度。在本研究中，使用400nm激光诱导荧光光谱法结合有监督和无监督多变量分析方法，开发了一种能够区分新生产的橄榄油和储存了12至24个月的橄榄油的快速方法。荧光光谱数据首先通过主成分分析（PCA）进行处理。该方法利用前三个成分对三类油表现出很强的区分能力，这三个成分呈现了初始数据总变异性的96%，然后使用判别偏最小二乘回归（PLS-DA）、支持向量机（SVM）和线性判别分析（LDA）构建有监督分类模型。这些方法在三类橄榄油的分类中表现出很高的能力。通过外部样品对这些分类模型进行验证，结果表明样品在其类别中的分类能力很高，准确率达到100%。本研究证明了荧光光谱指纹（常规技术）根据橄榄油的新鲜度和储存时间进行分类的可行性。
