Lipid matrix-specific pretreatment method for enhancing the extractability and allergenicity maintenance of bovine milk allergens in ELISA detection

The presence of various components in the food matrix makes allergen detection difficult and inaccurate, and pretreatment is an innovative breakthrough point. Food matrices were categorised based on their composition. Subsequently, a pretreatment method was established using a combination of ultrasound-assisted n-hexane degreasing and weakly alkaline extraction systems to enhance the detection accuracy of bovine milk allergens. Results showed that more allergens were obtained with less structural destruction, as demonstrated using immunological quantification and spectral analysis. Concurrently, allergenicity preservation was confirmed through liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, a KU812 cell degranulation model, and western blotting. The method exhibited good accuracy (bias, 8.47%), repeatability (RSDr, 1.52%), and stability (RSDR, 5.65%). In foods with high lipid content, such as chocolate, the allergen content was 2.29-fold higher than that of commercial kits. Laser confocal scanning microscopy (LCSM) and scanning electron microscopy (SEM) analyses revealed a significant decrease in fat content after post-pretreatment using our method. In addition, colloidal stability surpassed that achieved using commercial kits, as indicated through the PSA and zeta potential results. The results demonstrated the superiority of the extractability and allergenicity maintenance of lipid matrix-specific pretreatment methods for improving the accuracy of ELISA based allergen detection in real food. [Display omitted] •Pretreatment provides a novel breakthrough point for improving detection accuracy in food matrix.•Lipid matrix-specific pretreatment methods for 3 main bovine milk allergens were established.•More allergens were obtained with less structure destruction and better allergenicity maintenance.•Established methods extracted 2.29 times more allergens than that of commercial kits in real food.•Methods showed good accuracy (Bias 8.47%), repeatability (RSDr 1.52%), and stability (RSDR 5.65%).