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

• IRN, SVM and RF models based on IR spectra of parvalbumin were constructed and compared. • IRN model had the greatest accuracy for recognizing fish pavalbumin (up to 97.3%). • IRN model was based on highly representative featured from IR spectra of the parvalbumin allergen. • IRN model could detect parvalbumin accurately in seafood matrices. • Infrared spectroscopic IRN method was rapid (~20 min) and effective. 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 (~20 min, cf. ELISA ~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.