Ethan Pearce

Biography

I am a PhD researcher in Analytical Chemistry at the Wolfson Atmospheric Chemistry Laboratories (WACL), University of York, and the Nestlé Product Technology Centre (NPTC) Confectionery in York. My research focuses on developing rapid, non-targeted flavour profiling methods for chocolate to support new product development. I have experience with Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) and Gas Chromatography–Mass Spectrometry (GC-MS). I previously completed an integrated Master’s (MChem) degree in Chemistry at the University of York, where my dissertation explored the application of Liquid Chromatography to organic geochemistry.

Qualifications

Master of Chemistry (MChem) in Chemistry

Memberships and Fellowships

Associate Member of the Royal Society of Chemistry (AMRSC)

Research interests

Mass spectrometry and chromatography
Aroma and flavour chemistry
Non-targeted analysis
Multivariate statistics

Teaching interests

Undergraduate practical chemistry and mathematics

Project title

Rapid flavour profiling for sustainable food product development.

Supervisors

Professor Alastair Lewis

Funding

BBSRC Industrial CASE CSV Studentship with Nestlé and The Food Consortium CTP.

Project outline

Volatile organic compounds (VOCs) are key contributors to the flavours and aromas of food products. Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) is a sensitive, rapid, real-time direct-injection mass spectrometry (DIMS) technique capable of measuring these compounds at mixing ratios from parts per million down to parts per trillion. Applied in a non-targeted manner, SIFT-MS can capture volatile fingerprints of products, detecting compounds that a targeted approach may overlook. Analyte concentrations can also be estimated directly, without additional standards, using known reaction rate coefficients of gas-phase ion–molecule reactions. The potential of SIFT-MS for automated flavour and aroma detection in chocolate products is being evaluated to accelerate product testing, development and in-use performance. Accurate classification of chocolate types has been achieved using supervised and unsupervised multivariate statistical analyses, as well as univariate analyses of specific ions, including various Strecker aldehydes. SIFT-MS enables products to be screened at much higher throughput and lower cost per sample compared with sensory panels. However, to increase confidence in volatile identification, SIFT-MS data is often interpreted alongside conventional gas chromatography–mass spectrometry (GC-MS) or gas chromatography–olfactometry (GC-O) analyses, although these methods have more limited sample throughput.