Exploring new dimensions: Single and multi-block analysis of essential oils using DBDI-MS and FT-IR for enhanced authenticity control

Abstract

BackgroundEssential oils (EOs) are complex mixtures of volatile hydrocarbons with a wide range of applications in the pharmaceutical, fragrance and food industry. The composition of EOs is highly variable and can affect their quality and pharmaceutical efficacy. Moreover, the high economic value of EOs, such as those obtained from Rosa damascena, make falsification and misclassification a lucrative business. Consequently, adulterations can lead to serious health consequences for consumers. While current quality control methods for EOs involve analysing their chromatographic profile or comparing their Fourier transform infrared (FT-IR) spectra, these methods can be time-consuming or lack sensitivity. To address these issues, we compared state-of-the-art quality control methods, including gas chromatography flame ionization detection (GC-FID) quantification and enantiomeric ratio determination, FT-IR spectrometry with dielectric barrier discharge ionization coupled to triple quadrupole mass spectrometer (DBDI-MS), in a chemometric single- and multi-block approach. ResultsOur results show that the best classification accuracy of 94.7% for R. damascena samples was obtained using GC-FID combined with partial least square discriminant analysis (PLS-DA). Comparatively, the enantiomeric ratios did not improve classification accuracy. In contrast, fragmentation data from DBDI-MS (Q3), which was acquired in a fraction of the analysis time and without extensive sample preparation, achieved a classification accuracy of 84.2%. We also found that combining FT-IR with parent ion DBDI-MS (Q1) data in a multi-block sequentially orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) model improved classification accuracy, compared to their respective single-block PLS-DA models. SignificanceOverall, our study demonstrates that DBDI, as an ambient ionization method, has significant potential for high-throughput screening. When combined with MS, it can produce comparable classification accuracies to conventional methods, while offering the added benefits of speed and convenience. As such, DBDI-MS is a promising tool for EO quality control.