Citation: Chia, S.; Teo, G.; Tay, S.J.; Loo, L.S.W.; Wan, C.; Sim, L.C.; Yu, H.; Walsh, I.; Pang, K.T. An Integrative Glycomic Approach for Quantitative Meat Species Profiling. Foods 2022, 11, 1952. https:// doi.org/10.3390/foods11131952 Academic Editors: Gianfranco Picone and Nazimah Hamid Received: 18 May 2022 Accepted: 24 June 2022 Published: 30 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). foods Article An Integrative Glycomic Approach for Quantitative Meat Species Profiling Sean Chia 1,† , Gavin Teo 1,† , Shi Jie Tay 1 , Larry Sai Weng Loo 2,3 , Corrine Wan 1 , Lyn Chiin Sim 1 , Hanry Yu 2,3,4,5 , Ian Walsh 1 and Kuin Tian Pang 1, * 1 Bioprocessing Technology Institute, Agency for Science Technology and Research (A*STAR), Singapore 138668, Singapore; sean_chia@bti.a-star.edu.sg (S.C.); gavin_teo@bti.a-star.edu.sg (G.T.); tay_shi_jie@bti.a-star.edu.sg (S.J.T.); corrine_wan@bti.a-star.edu.sg (C.W.); sim_lyn_chiin@bti.a-star.edu.sg (L.C.S.); ian_walsh@bti.a-star.edu.sg (I.W.) 2 Institute of Bioengineering and Bioimaging, Agency for Science Technology and Research (A*STAR), Singapore 138669, Singapore; larry_loo@ibb.a-star.edu.sg (L.S.W.L.); hyu@ibb.a-star.edu.sg (H.Y.) 3 Department of Physiology, the Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore 4 Mechanobiology Institute, National University of Singapore, Singapore 117411, Singapore 5 CAMP, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore * Correspondence: zach_pang@bti.a-star.edu.sg † These authors contributed equally to this work. Abstract: It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost theglobal food industry around USD 6.2 to USD 40 billion annually. To over- come this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic ap- proach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication. Keywords: O-glycan; N-glycan; glycomic; meat species 1. Introduction With the growing human population and the increasing demand for food, food adul- teration has become a global problem estimated to affect 10–20% of all food consumed in the world [1,2]. Such contamination by either additions or substitutions of meat from a different species is a significant dietary issue, particularly for individuals with allergies or those of a certain religious conviction [3,4]. It is thus prudent to develop techniques in authenticating meat products as a means of ensuring safe trade and ethics [2,3]. Currently, many methods have been developed for the means of food fraud de- tection, including microscopic, spectroscopic (NMR, FTIR), and DNA-based techniques (PCR) [2,5–7]. Indeed, amidst these techniques, biomarkers identification by means of omics technology allows such quantification and distinction at a molecular level [8,9]. In fact, the significant popularity and application of these technologies in resolving food Foods 2022, 11, 1952. https://doi.org/10.3390/foods11131952 https://www.mdpi.com/journal/foods