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Muscle protein tropomyosins of invertebrates are major allergens responsible for wide spread allergic reactions against invertebrates such as shellfish and insects. In order to develop a sandwich enzyme-linked immunoadsorbent assay (ELISA) for detection and quantification of the invertebrate pan-allergen tropomyosin, a specific monoclonal antibody (MAb), CE7B2, was produced. We have successfully established a sandwich ELISA for measuring invertebrate tropomyosin concentrations in food and food materials. The sandwich ELISA system using the MAb CE7B2 is a useful tool to detect and quantify levels of tropomyosin in food. The method is also helpful to detect mite and cockroach tropomyosins, the important indoor allergens., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

[Display omitted] • A novel model, IR-CAM, was developed by infrared spectroscopic chemometrics. • TM could be identified within 10 min by shared and unique fingerprint peaks group. • Rapid species differentiation of TM at the Class/Family level was achieved by IR-CAM. • The IR-CAM was a new tool for identification and traceability of food allergens. To solve the lack of rapid and accurate methods for allergen identification and traceability, an infrared spectroscopic chemometric analytical model (IR-CAM) was established by combining infrared spectroscopy with principal component and cluster analysis. By comparing the second derivative infrared (SD-IR) spectra of 5 proteins and 14 crustaceans and shellfish tropomyosin (TM), 8 shared peaks and unique fingerprint peaks in the amide III region were found for crabs, shrimps, and shellfish. Based on the unique fingerprint peaks coexisting with shared peaks, allergen TM in crustaceans and shellfish could be identified within 10 min (cf. ELISA ∼ 4 h). Concurrently, the species differentiation of TM at the Class/Family level was achieved based on IR-CAM. Validation by fermented aquatic products TM (n = 60) demonstrated that the developed IR-CAM could simultaneously identify and differentiate TM in crustaceans and shellfish accurately. It could be applied for allergen detection and traceability of aquatic products on an antibody-free basis. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]