Université d'Artois, UMRT 1158 BioEcoAgro, ICV-Institut Charles Viollette, Lens, France.
Sidi Mohamed Ben Abdellah University, Applied Organic Chemistry Laboratory, Fez, Morocco.
J Sci Food Agric. 2021 May;101(7):2901-2911. doi: 10.1002/jsfa.10922. Epub 2020 Dec 1.
Olive oil provides a wide range of health-promoting compounds. The quality of olive oil is an even more complex concept as it is affected by several factors, such as variety, season, stage of maturation, extraction processing, and so on. The main objective of this study was to determine the potential of chemical and mid-infrared spectroscopy techniques to determine the quality and authenticity of virgin olive oil (VOO). For this, we studied 41 VOOs originating from five regions of Morocco (Fez/Meknes, Eastern, Northern, Beni-Mellal/Khenifra, and Marrakech/Safi) and produced using different agricultural and technological conditions during two successive crop seasons (2015-2016 and 2016-2017).
By applying principal component analysis and factorial discriminant analysis with leave-one-out validation to the mid-infrared spectroscopy, clear discrimination between VOO samples according to their geographic origin and variety was observed, with correct classification rates of 91.87% and 91.87% being observed respectively. The application of partial least-squares regression to mid-infrared and chemical data sets allowed excellent prediction of free acidity, peroxide value, k , and chlorophyll level with R of 0.99, 0.97, 0.98, and 0.93 respectively, and good prediction of k (R = 0.84).
The results demonstrate that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool for evaluating the overall quality and authenticity of VOO. © 2020 Society of Chemical Industry.
橄榄油提供了广泛的促进健康的化合物。橄榄油的质量是一个更为复杂的概念,因为它受到多种因素的影响,如品种、季节、成熟阶段、提取加工等。本研究的主要目的是确定化学和中红外光谱技术在确定初榨橄榄油(VOO)的质量和真实性方面的潜力。为此,我们研究了来自摩洛哥五个地区(费兹/梅克内斯、东部、北部、贝尼-梅勒尔/凯尼夫拉和马拉喀什/萨菲)的 41 种 VOO,它们在两个连续的作物季节(2015-2016 和 2016-2017)使用不同的农业和技术条件生产。
通过对中红外光谱应用主成分分析和因子判别分析与留一法验证,观察到 VOO 样品根据其地理来源和品种的明显区分,正确分类率分别为 91.87%和 91.87%。将偏最小二乘回归应用于中红外和化学数据集,允许对游离酸度、过氧化物值、k 和叶绿素水平进行极好的预测,R 分别为 0.99、0.97、0.98 和 0.93,对 k 的预测也很好(R = 0.84)。
结果表明,中红外光谱结合化学计量学工具可作为评估 VOO 整体质量和真实性的快速筛选工具。 © 2020 化学工业协会。