A novel method based on infrared spectroscopic inception-resnet networks for the detection of the major fish allergen parvalbumin

We have developed a novel approach that involves inception-resnet network (IRN) modeling based on infrared spectroscopy (IR) for rapid and specific detection of the fish allergen parvalbumin. SDS-PAGE and ELISA were used to validate the new method. Through training and learning with parvalbumin IR spectra from 16 fish species, IRN, support vector machine (SVM), and random forest (RF) models were successfully established and compared. The IRN model extracted highly representative features from the IR spectra, leading to high accuracy in recognizing parvalbumin (up to 97.3%) in a variety of seafood matrices. The proposed infrared spectroscopic IRN (IR-IRN) method was rapid (similar to 20 min, cf. ELISA similar to 4 h) and required minimal expert knowledge for application. Thus, it could be extended for large-scale field screening and identification of parvalbumin or other potential allergens in complex food matrices.

Automated summary

A novel approach using IRN modeling based on IR spectroscopy was developed for rapid and specific detection of the fish allergen parvalbumin. The IRN model achieved high accuracy in recognizing parvalbumin in seafood matrices and could be applied for large-scale screening and identification of potential allergens in complex food matrices.