Lipid profile migration during the tilapia muscle steaming process revealed by a transactional analysis between MS data and lipidomics data

Lipids are crucial food components impacting nutrition and flavor. Lipidomics, using LC-MS, analyzes lipid profiles; UHPLC-QE Orbitrap MS offers high sensitivity and resolution. Tilapia, a significant global food source, is often processed into frozen fillets. Previous studies explored the effects of various thermal processing methods on tilapia lipid profiles, but the migration of substances from muscle to juice during steaming remained unstudied. This research aimed to develop a transactional analysis combining UHPLC-QE Orbitrap MS and lipidomics data to analyze lipid migration from tilapia muscle to juice during steaming. The method involved extracting lipids from tilapia muscle and juice at different steaming times, analyzing them via UHPLC-QE Orbitrap MS, and developing a transactional analysis to correct lipidomics data and normalize MS data. This corrected data was then used to analyze and statistically evaluate lipid migration profiles.

LC-MS-based lipidomics has been applied to analyze various food products such as goat milk, soymilk, bovine milk, and bee pollen. The technique has also been used to study tilapia muscle lipid profiles after different thermal processing methods and the impact of dry salting. However, these studies often require extensive LC-MS workloads and don't address the issue of false positives in lipidomics data analysis. Previous research examined the effects of steaming on tilapia muscle aroma, metabolites, and lipid profiles, but none investigated substance migration to the juice produced during the steaming process.

Frozen tilapia fillets were thawed, cut, and steamed for 0, 10, 30, and 60 minutes. Lipids were extracted from both the muscle and the resulting juice using a modified Folch and Bligh method. UHPLC-QE Orbitrap MS was used to analyze the extracted lipids. A transactional analysis procedure was developed, involving: 1) obtaining UHPLC-QE Orbitrap MS data using TraceFinder 4.1 software; 2) importing this data into LipidSearch™ 4.1.3 software for lipid identification; 3) screening identified lipidomics data based on parameters like MainS/N, IDNum, MainMScore, and MainArea; 4) normalizing lipidomics data by dividing peak area by the initial tilapia muscle weight; 5) screening UHPLC-QE Orbitrap MS data by removing lipids not found in the normalized lipidomics data; 6) normalizing the screened MS data; 7) correcting normalized lipidomics data by removing false positives absent from normalized MS data; 8) statistically analyzing normalized UHPLC-QE Orbitrap MS data using MetaboAnalyst 5.0; and 9) analyzing individual lipids and lipid classes in the muscle, juice, and combined system. Statistical analyses included PLS-DA, VIP scores, and heat map visualization using MetaboAnalyst 5.0.

The transactional analysis significantly reduced UHPLC-QE Orbitrap MS workload and removed false-positive data (22.4–36.7%) from the lipidomics data. Analysis of the corrected lipidomics data revealed three significant individual lipid changes: disappearance, full migration from muscle to juice, and appearance in juice. Six types of lipid class changes were observed: disappearance, full migration, appearance in juice, appearance in muscle, appearance in both muscle and juice, and retention in muscle. FA, LPC, and TG showed the highest peak area levels in steamed tilapia muscle. Statistical analysis using PLS-DA and heatmaps revealed clear classification of lipid classes and individual lipids in muscles, juice, and combined muscle-juice systems across the different steaming times, except for lipid classes in juice alone. Specifically, 9, 5, and 10 lipid class variables (compared with 52, 116, and 178 individual lipid variables) were significant in differentiating lipid profiles in the muscles, juices, and total muscle-juice systems, respectively.

The developed transactional analysis effectively improved the efficiency and accuracy of lipid profile analysis compared to traditional lipidomics approaches. The significant lipid migration from muscle to juice during steaming highlights the importance of considering juice composition when evaluating the nutritional value of steamed tilapia. The identified changes in lipid classes suggest various mechanisms such as heat-induced hydrolysis, which needs further investigation using targeted lipidomics. The amphipathic nature of certain lipid classes (e.g., DG, PG) suggests they might form liposomes in the juice. The limitations of the study mainly center on the untargeted lipidomics approach, which does not accurately determine lipid origins and products, and the lack of internal standards for accurate quantification in mg/g of tilapia muscle.

This study successfully developed a novel transactional analysis method to analyze lipid profile migration during tilapia steaming. This improved methodology reduced workload and enhanced data accuracy. The findings demonstrate significant lipid migration from muscle to juice and highlight the importance of considering both components for a complete nutritional assessment of steamed tilapia. Future research should employ targeted lipidomics and investigate the mechanisms underlying lipid changes more comprehensively.

The study used an untargeted lipidomics approach, which limits precise determination of lipid origins and products during steaming. The absence of internal standards prevents the accurate quantification of lipid amounts (mg/g of tilapia muscle). Further, the study focuses on tilapia and might not be fully generalizable to other fish species.