A developed Fourier transform infrared spectroscopy (FT-IR) was employed to investigate changes of protein conformation, which played significant roles in maintaining stable protein networks of white croaker surimi gel, exploring the relationship between protein conformation and surimi gel networks. Spectra of surimi and gels with different grades (A, AA, FA and SA) were analyzed by tri-step FT-IR method and peak-fitting of deconvolved and baseline corrected amide I bands (1600~1700 cm⁻¹). The result showed that α-helix was the main conformation of surimi proteins. During surimi gelation, α-helix of myosin partially transformed into β-sheet, β-turn and random coil structures. β-sheet and random coil structures were the main protein conformations maintaining the structure of surimi gel, of which β-sheet made the main contribution to gel strength. Scanning electron microscopy (SEM) result revealed that surimi gels had a fibrous and homogeneous network structure. Moreover, ordered interconnections between three-dimensional proteins networks of gels were inclined to emerge in higher grade surimi, in agreement with the gel strength results. It was demonstrated that the tri-step FT-IR spectroscopy combined with peak-fitting could be applicable for exploration of surimi protein conformation changes during gelation to deepen understanding of its effect on gel quality.

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy combined with chemometrics were explored to classify Cabernet Sauvignon wines from different countries (Australia, Chile and China). Commercial wines (n = 540) were scanned in transmission mode using MIR and NIR, and their characteristic fingerprint bands were extracted at 1750-1000 cm⁻¹ and 4555-4353 cm⁻¹. Through the identification system of Tri-step infrared spectroscopy, the correlation between macroscopic chemical fingerprints and geographical regions was explored more deeply. Furthermore, Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra were used to visualize or discriminate differences between samples and to realize geographical origin traceability of Cabernet Sauvignon wines. Through “external test set (n = 157)” validation, SIMCA models correctly classified 97%, 97% and 92% of Australian, Chilean and Chinese Cabernet Sauvignon wines, while the DA models correctly classified 86%, 85% and 77%, respectively. Based on unique digital fingerprints of spectroscopy (FT-MIR and FT-NIR) associated with chemometrics, geographical origin traceability was achieved in a more comprehensive, effective and rapid manner. The developed database models based on IR fingerprint spectroscopy with chemometrics could provide scientific basis and reference for geographical origin traceability of Cabernet Sauvignon wines (Australia, Chile and China).