Laboratory of Food Chemistry and Technology (LFCT), School of Chemistry, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece.
Laboratory of Systematic Botany and Phytogeography (LSBPh), School of Biology, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece.
Molecules. 2020 Jan 29;25(3):583. doi: 10.3390/molecules25030583.
The last years, non-targeted fingerprinting by Fourier-transform infrared (FT-IR) spectroscopy has gained popularity as an alternative to classical gas chromatography (GC)-based methods because it may allow fast, green, non-destructive and cost-effective assessment of quality of essential oils (EOs) from single plant species. As the relevant studies for L. (bay laurel) EO are limited, the present one aimed at exploring the diagnostic potential of FT-IR fingerprinting for the identification of its botanical integrity. A reference spectroscopic dataset of 97 bay laurel EOs containing meaningful information about the intra-species variation was developed via principal component analysis (PCA). This dataset was used to train a one-class model via soft independent modelling class analogy (SIMCA). The model was challenged against commercial bay laurel and non-bay laurel EOs of non-traceable production history. Overall, the diagnostic importance of spectral bands at 3060, 1380-1360, 1150 and 1138 cm was assessed using GC-FID-MS data. The findings support the introduction of FT-IR as a green analytical technique in the quality control of these often mislabeled and/or adulterated precious products. Continuous evaluation of the model performance against newly acquired authentic EOs from all producing regions is needed to ensure validity over time.
在过去的几年中,傅里叶变换红外(FT-IR)光谱的非靶向指纹图谱已作为基于经典气相色谱(GC)的方法的替代方法而受到欢迎,因为它可能允许快速、绿色、无损和具有成本效益地评估来自单一植物物种的精油(EOs)的质量。由于有关 L.(月桂)EO 的相关研究有限,本研究旨在探索 FT-IR 指纹图谱在识别其植物完整性方面的诊断潜力。通过主成分分析(PCA)开发了包含有关种内变异的有意义信息的 97 种月桂精油参考光谱数据集。使用该数据集通过软独立建模分类分析(SIMCA)训练单类模型。该模型受到商业月桂和非追溯生产历史的非月桂精油的挑战。总体而言,使用 GC-FID-MS 数据评估了光谱带在 3060、1380-1360、1150 和 1138 cm 处的诊断重要性。这些发现支持将 FT-IR 作为这些经常被错误标记和/或掺假的珍贵产品质量控制的绿色分析技术引入。需要对新获得的来自所有生产地区的真实 EO 对模型性能进行持续评估,以确保随着时间的推移保持有效性。