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[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]

• Specific fingerprints were extracted by Tri-step IR for red wine authentication. • Multivariate data fusion of IR with E-nose endowed better quantitative model. • Fusion models based on extracted spectral regions rendered the best r p and RPD. A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200–950 cm−1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), r p increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner. [ABSTRACT FROM AUTHOR]

Abstract: Seven kinds of amino acids were used to modify the non-water-soluble extracellular melanin (LEM346) from Lachnum YM-346. It was found that arginine–melanin (ALEM346) had the highest solubility, being 4.55% (gg−1) in 30°C distilled water. Elemental analysis, infrared spectrum and mass spectrum analysis revealed that LEM346 molecule contained indole quinone structure, its molecular formula speculated to be C18H8O6N2. Infrared spectrum analysis showed that ALEM346 had characteristic absorption peaks at 1672.346 and 1637.679cm−1. Mass spectrum analysis indicated that ALEM346 contained three types of arginine–melanin molecules. When the ALEM346 concentration was 500mgL−1, its total antioxidant capacity was equivalent to α-tocopherol of 46.65mmolL−1, the DPPH and O2 − scavenging rates and the Fe2+ chelating rate were 89.05%, 93.81% and 80.18%, respectively, suggesting that the antioxidant activity of ALEM346 was stronger than that of LEM346. [Copyright &y& Elsevier]

• 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]