The flavor enhancement mechanism of ultrasound-assisted curing and UV-assisted drying in semi-dried tilapia fillets based on flavoromics, lipidomics, and metabolomics.

This study aimed to investigate the effects of ultrasound-assisted curing and UV-assisted drying on the quality of semi-dried tilapia fillets through flavoromics, lipidomics, and metabolomics. Both treatments enhanced myofibril pore space and reduced moisture content (-14.84 %, P < 0.05), with ultrasound demonstrating greater effectiveness. Additionally, they also facilitated lipid oxidation (P < 0.05), which altered the flavor profile. UV treatment enhancing key aroma compounds (ROAV >1), especially octanal, 1-octen-3-one, ethyl-isovalerate, and 2-pentyl-furan, more effectively than ultrasound (P < 0.05). 420 lipid molecules and 213 metabolites were identified, including 162 differential lipids and 69 differential metabolites (VIP > 1). Correlation analysis indicated that triglycerides, fatty acids, organic acids, and nucleosides were key precursors of flavor. The sensory evaluation demonstrated that ultrasound and UV treatments synergistically enhanced fillet quality. This study introduces an innovative processing method aimed at the industrialized and efficient production of high-quality air-dried aquatic products.

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms.

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.