Search

This study systematically investigated the impact of covalent binding of tannic acid (TA) to different proteins on protein structure and the stability of a nanoemulsion of Perilla essential oil (PEO). Additionally, the antimicrobial activity of the nanoemulsion was assessed. The interactions between TA and various proteins were explored using fluorescence spectroscopy and Fourier transform infrared (FTIR) spectroscopy, revealing that TA exhibited the most binding sites (n = 1.65) to whey protein isolate (WPI), forming a stable protein-TA complex. The emulsion prepared using TA had high stability. One-factor-at-a-time experiments showed that the emulsion prepared under the conditions of 675 W ultrasonic power and an oil-to-water ratio of 1:9 had the smallest droplet size of (292.3 ± 1.96) nm, a relatively high absolute zeta potential value of (–62.5 ± 0.29) mV, and the highest PEO encapsulation efficiency of (74.51 ± 0.35)%. The interaction between WPI-TA complex and PEO could stabilize the structure of the emulsion, and the emulsion maintained high stability at 4 ℃ for 28 days. Furthermore, emulsion encapsulation significantly reduced the loss of PEO, with the loss of PEO in the emulsion being only one-third of that of the pure essential oil under the same conditions. This study provides new ideas for future research on the synthesis of protein-polyphenol complexes and the encapsulation of volatile substances, laying a foundation for expanding the application of nanoemulsion in the food industry.

BACKGROUND: This study evaluates the effect of mechanical properties on the in vitro dynamic gastrointestinal digestion of hydrogels containing starch (HCSs) as a model for studying the nutrient digestibility of solid foods. It provides a useful theoretical basis for the processing of specific foods. RESULT: Four types of HCSs with two levels of fracture stress (17.4–20.9 kPa and 55.5–57.6 kPa) and two levels of fracture strain (25.4–28.5% and 53.7–57.4%) were prepared. For these HCSs, the degree of gastric disintegration of hydrogels reduced significantly when fracture strain exceeded 30% (P < 0.05). The gastric emptying of HCS particles was also affected by mechanical properties. For example, even at the same level of fracture stress (ca. 20 kPa), the dry solids retention ratio decreased markedly from 0.90 to 0.43 with a decrease in fracture strain from 53.7% to 25.4% (P < 0.05). For the starch hydrolysis of HCSs after gastric digestion, more than 70% of starch in the particles of all types of HCSs emptied did not undergo digestion. The starch hydrolysis of HCSs during small intestinal digestion was also influenced by their mechanical properties. Fracture strains of HCSs, rather than their fracture stress, affected starch digestibility in hydrogels. CONCLUSION: The gastric disintegration, the gastric emptying, and the starch hydrolysis of HCSs are suppressed when fracture strain exceeded 30%. Even with the amount of nutritional components contained in hydrogels being the same, the in vitro gastrointestinal digestion behavior of HCSs depends on their mechanical properties. This behavior has the potential to be used in the design of processed foods with controlled bioaccessibility. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Spoilage bacteria play a remarkable role in the spoilage of chilled chicken. In this paper, a total of 42 isolates belonging to 16 species of four genera were isolated from chilled chicken and displayed different characterizations of psychrotrophic spoilage. Six isolates of J7, J8, Q20, Q23, R1, and R9 with differences in proteolytic capabilities were further characterized for in situ spoilage potential evaluation. Pseudomonas lundensis J8 exhibited the strongest spoilage potential in situ, displaying a fast growth rate, increased pH velocity, high total volatile basic nitrogen, and high peptide content in the chicken samples. The volatile flavor analysis of chicken samples via electronic nose indicated that the content of characteristic odors representing spoilage, including sulfides, organic sulfide, and hydride, increased during storage. Additionally, the principle component and correlation analyses revealed that the spoilage odors produced by different species of bacteria were significantly different and positively correlated with the results of protease activity in vitro. The characteristics of spoilage bacteria in chilled chicken provided a comprehensive insight into microbial assessment during storage. [ABSTRACT FROM AUTHOR]

Objective . Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach . To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Main results . In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Significance . FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility., (© 2024 IOP Publishing Ltd.)