Optimization of parameters affecting signal intensity in an LTQ-orbitrap in negative ion mode: A design of experiments approach

• Introducing DoE, to an LTQ-orbitrap for the optimization of the signal intensity.
• A method for determining all factors affecting the sensitivity in an LTQ orbitrap.
• Optimization of an Orbitrap-based MS analysis for improving the signal intensity.
• Employing a 3-step optimization strategy, the important parameters were highlighted.

A multistage optimization of all the parameters affecting detection/response in an LTQ-orbitrap analyzer was performed, using a design of experiments methodology. The signal intensity, a critical issue for mass analysis, was investigated and the optimization process was completed in three successive steps, taking into account the three main regions of an orbitrap, the ion generation, the ion transmission and the ion detection regions. Oleuropein and hydroxytyrosol were selected as the model compounds. Overall, applying this methodology the sensitivity was increased more than 24%, the resolution more than 6.5%, whereas the elapsed scan time was reduced nearly to its half.A high-resolution LTQ Orbitrap Discovery mass spectrometer was used for the determination of the analytes of interest. Thus, oleuropein and hydroxytyrosol were infused via the instruments syringe pump and they were analyzed employing electrospray ionization (ESI) in the negative high-resolution full-scan ion mode. The parameters of the three main regions of the LTQ-orbitrap were independently optimized in terms of maximum sensitivity. In this context, factorial design, response surface model and Plackett–Burman experiments were performed and analysis of variance was carried out to evaluate the validity of the statistical model and to determine the most significant parameters for signal intensity.The optimum MS conditions for each analyte were summarized and the method optimum condition was achieved by maximizing the desirability function. Our observation showed good agreement between the predicted optimum response and the responses collected at the predicted optimum conditions.

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