Food Chemistry: X最新文献

This research utilized gas chromatography-mass spectrometry (GC-MS), electronic tongue (E-tongue), electronic nose (E-nose), and sensory analysis to investigate the flavor profiles of beef jerky from four specific regions of China: Xinjiang, Tibet, Sichuan, and Inner Mongolia. Physical analysis revealed that beef jerky from Inner Mongolia had lower moisture content and water activity, while Tibet and Inner Mongolia samples exhibited lower pH values. Seventy-two volatile compounds were identified and quantified among all samples, indicating significant regional variations in aldehydes, alcohols, and esters. Heatmap cluster analysis clearly explained the unique regional flavor profiles. Furthermore, partial least squares regression analysis using E-nose and E-tongue showed significant correlations with the volatile compounds and sensory attributes. These findings indicate the efficacy of a multi-faceted analytical approach for flavor differentiation and emphasize the need for further research to understand the mechanisms behind developing characteristic flavors in beef jerky.

This study combined HS-SPME-GC-MS and high-throughput sequencing to explore how honey-processing methods with varying mucilage retentions impact volatile compounds and microbial communities in green coffee beans. HS-SPME-GC-MS revealed that the RH group (75 % to 80 % mucilage retention) had the highest relative content of volatile compounds. According to rOAV >1, 13 key aroma compounds were identified, contributing to flavors like "mellow" and "fruity". High-throughput sequencing identified seven dominant bacterial genera and four dominant fungal genera, with higher diversity of fungi than bacteria across treatments. Correlation analysis indicated that bacteria and fungi contribute to aroma formation, with bacteria more active in low-mucilage and fungi in high-mucilage treatments. Overall, the RH group was optimal for the aroma quality and bioactivity of green coffee beans. The findings of this research offers insights into aroma compound-microbe interactions in coffee mucilage fermentation, helping coffee producers optimize process parameters for better-quality coffee products.

This study investigates how different air temperatures and infrared intensities affect the physicochemical properties of dried okra at different airflow rates. The model was developed using machine learning, and Okra's physicochemical properties were optimized using a self-organizing map (SOM). The results showed that higher infrared intensity and air temperature improved rehydration and colour but reduced water activity and vitamin C levels. In contrast, faster airflow helped preserve quality by counteracting the negative effects of higher temperatures and infrared intensity. The SOM algorithm identified five optimal drying conditions, revealing that lower temperatures, lower infrared intensity, and higher airflow provided optimal conditions for improving the quality of okra slices. Interestingly, the machine learning model's predictions closely matched the test data sets, providing valuable insights for understanding and controlling the factors affecting okra drying performances. This study used machine learning to optimize the drying process of okra, a new approach for improving food drying techniques. It offers valuable insights for the food industry in its quest to improve efficiency without sacrificing product quality.

The cryoprotective effects of mannan oligosaccharides (MOS) and curdlan (CU) on the quality of grass carp surimi after freeze-thaw cycles (FTCs) were assessed using the response surface methodology. The optimal contents of MOS (6.79 %, w/w) and CU (0.45 %, w/w) produced minimum thawing losses and the highest gel strength of surimi after five times FTCs. MOS, CU, and their mixture demonstrated cryoprotective effects on grass carp surimi. Compared to MOS or CU alone, MOS-CU displayed synergistic cryoprotective effects, as evidenced by the better prevention of thawing losses of surimi, the superior retardation of the aggregation and denaturation of MP, the amelioration of the gel strength and WHC of surimi gel. Moreover, the MOS-CU mixture demonstrated cryoprotective effects equivalent to those of commercial cryoprotectant on grass carp surimi from zero to five times FTCs and even outperformed CC after seven times FTCs.

Virtual screening techniques have gained much attention as a means of studying bioactive peptides. This study aimed to screen DPP-IV inhibitor peptides in goat milk after simulated digestion in vitro combined with molecular docking and dynamics simulations. By evaluating the docking energy and active sites, and by analyzing RMSD, RMSF, and Rg values, two novel peptides, GPFPLL and LPYPY, were successfully screened and identified. GPFPLL and LPYPY were found to exhibit high inhibitory activity against DPP-IV (IC₅₀ of 130.68 ± 10.38 μM and 179.52 ± 18.89 μM, respectively). Both GPFPLL and LPYPY stably bound to S1 and S1' in DPP-IV, and both demonstrated competitive inhibition of DPP-IV. The inhibition of DPP-IV by GPFPLL and LPYPY after in vitro digestion reached 31.90 % ± 1.80 % and 39.37 % ± 0.90 %, respectively. In a Caco-2 cell experiment, GPFPLL and LPYPY exhibited significant inhibition of DPP-IV, reaching 46.53 % ± 3.48 % and 65.98 % ± 2.87 %, respectively, when the concentration of each peptide was 2 mg/mL. The results of this study suggest that using molecular docking and dynamics simulations to screen novel peptides is an effective approach, and the identified peptides GPFPLL and LPYPY show potential for diabetes management.