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BACKGROUND: Nε‐carboxymethyllysine (CML), Nε‐carboxyethyllysine (CEL) and α‐aminoadipic acid (AAA) are important foodborne hazards and their intake can cause a variety of diseases in humans. It is extremely important to investigate the formation mechanism of CML, CEL and AAA, as well as their association with each other when aiming to control their production. RESULTS: A multi‐response kinetic model was developed within the glucose‐lysine Maillard reaction model system. The concentrations of glucose, lysine, glyoxal (GO), methylglyoxal (MGO), CML, CEL and AAA were quantified at different temperature (100–160 °C) and at different intervals (0–60 min). The experimental data were fitted to the proposed model to calculate kinetic parameters for the corresponding steps. The results indicated that the production of CML was primarily relied on the direct oxidative cleavage of the Amadori product, rather than the reaction between GO and Lys, whereas CEL and AAA were generated through the reaction of MGO with Lys. Significantly, the reaction between α‐dicarbonyl compounds and Lys preferentially generated CML and CEL, resulting in the lower concentrations of AAA compared to CML and CEL. CONCLUSION: The multi‐response kinetic model developed in the present study can be applied well to the Maillard reaction. The relationship between the formation mechanisms of CML, CEL and AAA is also explained. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Accurate methods for assessing food freshness through colorimetric pH response play a critical role in determining food spoilage and ensuring food quality standards. This study introduces a novel unlabeled DNA sequence, poly-dA20, designed to exploit the colorimetric properties of both the single strand and the fold-back A-motif structure in conjunction with gold nanoparticles (AuNPs) under varying pH conditions. When exposed to storage temperatures of 4 °C and 25 °C, the color variations in the AuNP solution, influenced by pH level changes in mutton and sea bass samples' different storage periods, are easily discernible to the naked eye within a minute. The ratio of UV absorption values at 527 nm and 700 nm (A527/A700) demonstrates a strong linear correlation with both the storage duration and pH of the food samples. Furthermore, a comprehensive analysis combining the total volatile basic nitrogen (TVB-N) value with the A527/A700 ratio is employed for precise assessment of food freshness. The innovative pH-responsive sensing strategy not only provides a new approach for on-site food freshness and spoilage detection systems but also serves as a valuable tool for pH-related biological detection in clinical diagnostic applications. [ABSTRACT FROM AUTHOR]

Context: Amino acids are a highly effective and environmentally friendly adsorbent for SO2. However, there has been no comprehensive study of the binding modes between amino acids and SO2 at the molecular level. In this paper, the binding modes of three amino acids (Asp, Lys, and Val) with SO2 are studied comprehensively and in detail using quantum chemical calculations. The results indicate that each amino acid has multiple binding modes: 22 for Asp, 49 for Lys, and 10 for Val. Both the amino and carboxyl groups in amino acids, as well as those in side chains, can serve as binding sites for chalcogen bonds. The binding energies range from − 6.42 to − 1.06 kcal/mol for Asp, − 12.43 to − 1.63 kcal/mol for Lys, and − 7.42 to − 0.60 kcal/mol for Val. Chalcogen and hydrogen bonds play a crucial role in the stronger binding modes. The chalcogen bond is the strongest when interacting with an amino group, with an adiabatic force constant of 0.475 mDyn/Å. Energy decomposition analysis indicates that the interaction is primarily electrostatic attraction, with the orbital and dispersive interactions dependent on the binding mode. Methods: Amino acids and complexes of amino acids with SO2 were used to do semi-empirical MD using Molclus combined with xtb at the GFN2 level. Optimization and frequency calculations of the structures were conducted using density-functional theory (DFT) B3LYP/6-311G* (with DFT-D3 correction). Single-point energy calculations were performed for all structures using DLPNO-CCSD(T)/aug-cc-pVTZ with tightPNO. Further analysis of the structures was conducted using ESP, AIM, IGMH, and sob-EDA to gain a deeper understanding of the interactions between amino acids and SO2. [ABSTRACT FROM AUTHOR]

Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L−1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg−1 to 8.11 mg kg−1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%–99.8% recovery) than the traditional linear regression model (81.9%–112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis. [ABSTRACT FROM AUTHOR]