A novel EGCG-Histidine complex improves gelling and physicochemical properties of porcine myofibrillar proteins: Insight into underlying mechanisms.

Herein, an EGCG-Histidine complex is prepared, characterized, and further used to improve gel properties of myofibrillar proteins (MP). Results of FTIR, XRD, UV-Vis spectroscopy showed that histidine is covalently bound to EGCG by Michael addition or Schiff base reaction to form EGCG-Histidine complex, and antioxidant activity of EGCG-Histidine complex is significantly increased compared to EGCG or histidine alone (P < 0.05). The addition of EGCG-Histidine complex results in cooking loss of gel decreasing from 66.7 ± 0.23 % to 40.3 ± 2.02 %, and improves rheological properties of MP, and enhances gel strength from 0.10 ± 0.01 N to 0.22 ± 0.03 N, indicating positive effect of EGCG-Histidine complex on MP gel formation, above results is supported by results of SEM, CD spectroscopy, SDS-PAGE, and tryptophan fluorescence. These results indicated that EGCG-Histidine complex can be used as a functional ingredient to improve gel quality of meat products.

Effects of NaCl on the interactions between neomethyl hesperidin dihydrochalcone and pork myofibrillar protein: Their relevance to gelation properties.

Effects of sweetener neomethyl hesperidin dihydrochalcone (NHDC) on the gelation properties of myofibrillar protein (MP) at 0.2 M and 0.6 M NaCl were investigated. The results showed that addition of NHDC did not obviously change the cooking loss and strength of MP gels at 0.2 M NaCl. Whilst at 0.6 M NaCl, addition of high doses of NHDC (50-100 µM/g) remarkably increased cooking loss and decreased strength of MP gels. The expanded MP enhanced the interactions between NHDC and MP, leading to aggregation of MP as evidenced by the increasing particle size and the reducing solubility. The MP aggregates impeded hydrophobic interactions and disulfide bonds crosslinking in the gelation process, thereby damaged the gelation properties of MP at 0.6 M NaCl. This was supported by the poor network of MP gels observed by SEM. In the end, molecular docking elucidated the main types of interaction at molecular level, including hydrogen bonding and hydrophobicity. The results would provide guidance for NHDC as a potential sweetener in meat products.

Aerosol Iron Solubility Specification in the Global Marine Atmosphere with Machine Learning.

Aerosol iron (Fe) solubility is a key factor for the assessment of atmospheric nutrients input to the ocean but poorly specified in models because the mechanism of determining the solubility is unclear. We develop a deep learning model to project the solubility based on the data that we observed in a coastal city of China. The model has five variables: the size range of particles, relative humidity, and the ratios of sulfate, nitrate and oxalate to total Fe (TFe) contents in aerosol particles. Results show excellent statistical agreements with the solubility in the literature over most worldwide seas and margin areas with the Pearson correlation coefficients (r) as large as 0.73-0.97. The exception is the Atlantic Ocean, where good agreement is obtained with the model trained using local data (r: 0.34-0.66). The model further uncovers that the ratio of oxalate/TFe is the most important variable influencing the solubility. These results indicate the feasibility of treating the solubility as a function of the six factors in deep learning models with careful training and validation. Our model and projected solubility provide innovative options for better quantification of air-to-sea input of aerosol soluble Fe in observational and model studies in the global marine atmosphere.