Tracking structural changes of protein residues by two-dimensional correlation surface-enhanced Raman spectroscopy. (15th July 2022)

• Record Type: Journal Article
• Authors: Jiang, Yifan; Du, Shanshan; Xu, Min; Yu, Ting; Zhou, Baomei; Yu, Fanfan; Jiang, Hao; Yang, Lina; Su, Mengke; Liu, Honglin
• Is Part Of: Food chemistry. Volume 382(2022)
• Publication Date: 2022-07-15
• Subjects: 2DC-SERS -- Food quality -- Nitrification -- Protein -- DD-SIMCA; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.132237
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: In-situ tracking structural changes of protein residues by two-dimensional correlated SERS. High-sensitive SERS quantitation of nitrites/nitrosamines in nitrified edible bird's nest (EBN) Rapid and reliable strategy for inherently discriminating natural EBN and artificially dyed fakes. Algorithm-assisted rapid spectral recognition for different EBN samples. Abstract: In-situ tracking structural changes of protein residues was developed by two-dimensional correlation surface-enhanced Raman spectroscopy (2DC-SERS). The change order of SERS fingerprints during artificial nitrification of edible bird's nest (EBN) was interpreted as the structural changes of amino acid residues. It inherently realizes reliable recognition of natural EBN and artificially dyed fakes. Both this direct structural tracking of protein residues and the indirect azo dye testing of nitrites/nitrosamines could be used as indicators for discriminating different EBN before and after the artificial dyeing. Limit of detection (LOD) for nitrite and NDMA is about 40.6 ppb and 88.1 ppb, respectively. A conceptual logical circuit of the OR gate was constructed by considering the protein structural indicator (INPUT1) and the nitrite indicator (INPUT2) as two independent inputs for automatic recognition of different EBN samples. A data-driven analog soft independent modeling (DD-SIMCA) model could quickly distinguish normal EBN from A-EBN with 98% specificity.