Tuesday Afternoon Session Preview: Food Safety: Advances in MS for Characterization of Additives and Contaminants

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E-Separation Solutions

The six Tuesday afternoon presentations will examine food safety issues and advances in MS for characterization of additives and contaminants, including a quantitative comparison of DART-MS to GC?LC-MS methods for contaminant screening of foods, cosmetics, and packaging; experimental design guidelines for non-targeted analyses; a demonstration of the use of multivariate software in developing sample class prediction models based on a recursive workflow; the development of a mobile ambient mass spectrometry for on-site screening for plasticizers; melamine, and residue pesticides in tainted foods; an ultra-fast method and direct method to quantitatively monitor phenolic off-flavor compounds in wine to prevent product spoilage during aging; and a demonstration that UHPLC?QTOF-MS has the potential to differentiate whiskies on the basis of origin as well as age.

Tuesday, 2:30 - 4:30 PM, Auditorium

The six Tuesday afternoon presentations will examine food safety issues and advances in MS for characterization of additives and contaminants, including a quantitative comparison of DART-MS to GC–LC-MS methods for contaminant screening of foods, cosmetics, and packaging; experimental design guidelines for non-targeted analyses; a demonstration of the use of multivariate software in developing sample class prediction models based on a recursive workflow; the development of a mobile ambient mass spectrometry for on-site screening for plasticizers; melamine, and residue pesticides in tainted foods; an ultra-fast method and direct method to quantitatively monitor phenolic off-flavor compounds in wine to prevent product spoilage during aging; and a demonstration that UHPLC–QTOF-MS has the potential to differentiate whiskies on the basis of origin as well as age.

Presenting first will be Luke Ackerman of the FDA Center for Food Safety, College Park, Maryland. Ackerman’s talk is titled “Screening Large Sample Sets for Contaminants by DART-MS: Limitations & Advantages.” It will detail an investigation of the limitations and advantages of direct MS screening for two contrasting sample sets, 9 phthalates in 212 foods and cosmetics and eight photo-initiators in 42 retail food packages.

Ann M. Knolhoff, of FDA/CFSAN, College Park, Maryland, will follow with a presentation titled “Mass Accuracy and Isotopic Abundance Measurements in Complex Sample Matrices: Capabilities of HR-MS Instrumentation for Non-Targeted Analyses.” The presentation will include an evaluation of an Orbitrap and a q-TOF with various concentrations of a standard mixture in complex sample matrices.

The next presenter will be Marcus Kim, of Agilent Technologies, Santa Clara, California. Kim’s talk, titled “Sample Class Prediction for the Determination of Off-Flavors in Cranberries by GC-MS,” will look at a the use of a GC-MS in EI mode to construct a model that predicted whether a cranberry juice sample would pass the sensory test and if the juice was fermented or musty.

Following Kim will be Christopher Shiea, whose presentation, titled “On-Site Screening for Plasticizers, Melamine, and Residual Pesticides in Tainted Foods via Mobile Ambient Mass Spectrometry (MAMS),” will detail how a MAMS system was used to screen for plasticizers and melamine in tainted alcohols, beverages, and milks.

Elizabeth Crawford, of the Institute of Chemical Technology Prague, Prague, Czech Republic will present “Preventing Wine Spoilage: Rapid Screening and Quantitative Analysis of Off-Flavor Phenolic Compounds by DART Mass Spectrometry.” The talk will show how a DART ambient ionization source was coupled to a high-resolution accurate mass quadrupole mass spectrometer for targeted MS-MS analysis of 4-EP and 4-EG directly from wine samples.

Thomas S. Collins will provide the final presentation. His talk, titled “Non-Volatile Profiling of Whiskies Using UHPLC-QTOF-MS,” will show how data was mined for compounds using a molecular feature extractor, filtered and statistically compared to find distinguishing components in each whiskey.

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