Article pubs.acs.org/JAFC
Cite This: J. Agric. Food Chem. 2017, 65, 9353-9359
Trace-Level Volatile Quantitation by Direct Analysis in Real Time Mass Spectrometry following Headspace Extraction: Optimization and Validation in Grapes Jillian A. Jastrzembski, Madeleine Y. Bee, and Gavin L. Sacks* Department of Food Science, Stocking Hall, Cornell University, Ithaca, New York 14853, United States S Supporting Information *
ABSTRACT: Ambient ionization mass spectrometric (AI-MS) techniques like direct analysis in real time (DART) offer the potential for rapid quantitative analyses of trace volatiles in food matrices, but performance is generally limited by the lack of preconcentration and extraction steps. The sensitivity and selectivity of AI-MS approaches can be improved through solid-phase microextraction (SPME) with appropriate thin-film geometries, for example, solid-phase mesh-enhanced sorption from headspace (SPMESH). This work improves the SPMESH-DART-MS approach for use in food analyses and validates the approach for trace volatile analysis for two compounds in real samples (grape macerates). SPMESH units prepared with different sorbent coatings were evaluated for their ability to extract a range of odor-active volatiles, with poly(dimethylsiloxane)/ divinylbenzene giving the most satisfactory results. In combination with high-resolution mass spectrometry (HRMS), detection limits for SPMESH-DART-MS under 4 ng/L in less than 30 s acquisition times could be achieved for some volatiles [3-isobutyl2-methoxypyrazine (IBMP) and β-damascenone]. A comparison of SPMESH-DART-MS and SPME-GC-MS quantitation of linalool and IBMP demonstrates excellent agreement between the two methods for real grape samples (r2 ≥ 0.90), although linalool measurements appeared to also include isobaric interference. KEYWORDS: low-level odorants, volatile analysis, wine grapes, DART, ambient ionization
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INTRODUCTION Ambient ionization mass spectrometric (AI-MS) techniques, like direct analysis in real time mass spectrometry (DART-MS), can improve simplicity and throughput for a wide range of routine chemical analyses.1 In comparison to other popular AI approaches, such as desorption electrospray ionization (DESI), DART is better suited for volatile or semivolatile low molecular weight compounds (
