Browsing by Subject "statistical optimization"

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    A statistical approach to optimizing paper spray mass spectrometry parameters
    (Wiley, 2020-04) Skaggs, Christine; Kirkpatrick, Lindsey; Wichert, William R. A.; Skaggs, Nicole; Manicke, Nicholas E.; Chemistry and Chemical Biology, School of Science
    Rationale Paper spray mass spectrometry (PS‐MS) was used to analyze and quantify ampicillin, a hydrophilic compound and frequently utilized antibiotic. Hydrophilic molecules are difficult to analyze via PS‐MS due to their strong binding affinity to paper substrates and low ionization efficiency, among other reasons. Methods Solvent and paper parameters were optimized to increase the extraction of ampicillin from the paper substrate. After optimizing these key parameters, a Resolution IV 1/16 fractional factorial design with two center points was employed to screen eight different design parameters simultaneously. Results Pore size, sample volume, and solvent volume were the most significant factors affecting average peak area under the curve (AUC) and the signal‐to‐blank (S/B) ratio for the 1 μg/mL ampicillin calibrant. After optimizing the key parameters, a linear calibration curve with a range of 0.2 μg/mL to 100 μg/mL was generated (R2 = 0.98) and the limit of detection (LOD) and lower limit of quantification (LLOQ) were calculated to be 0.07 μg/mL and 0.25 μg/mL, respectively. Conclusions The statistical optimization procedure undertaken here increased the mass spectral signal intensity by more than a factor of 40. This statistical method of screening followed by optimization experiments proved faster and more efficient, and produced more drastic improvements than typical one‐factor‐at‐a‐time experiments.