Fermented foods are considered the main sources of biogenic amines (BAs) in the human diet while lactic acid bacteria (LAB) are the main producers of BAs. Normal water (NW) and hydrogen-rich water (HRW) were used for preparing red beet pickles, i.e., NWP and HRWP, respectively. The formation of BAs, i.e., aromatic amines (tyramine, 2-phenylethylamine), heterocyclic amines (histamine, tryptamine), and aliphatic di-amines (putrescine), was analyzed in both beet slices and brine of NWPs and HRWPs throughout the fermentation stages. Significant differences in redox value (Eh7) between NWP and HRWP brine samples were noticed during the first and last fermentation stages with lower values found for HRWPs. Total mesophilic aerobic bacteria (TMAB), yeast–mold, and LAB counts were higher for HRWPs than NWPs for all fermentation stages. Throughout fermentation stages, the levels of all BAs were lower in HRWPs than those of NWPs, and their levels in brines were higher than those of beets. At the end of fermentation, the levels (mg/kg) of BAs in NWPs and HRWPs were, respectively: tyramine, 72.76 and 61.74 (beet) and 113.49 and 92.67 (brine), 2-phenylethylamine, 48.00 and 40.00 (beet) and 58.01 and 50.19 (brine), histamine, 67.89 and 49.12 (beet) and 91.74 and 70.92 (brine), tryptamine, 93.14 and 77.23 (beet) and 119.00 and 93.11 (brine), putrescine, 81.11 and 63.56 (beet) and 106.75 and 85.93 (brine). Levels of BAs decreased by (%): 15.15 and 18.35 (tyramine), 16.67 and 13.44 (2-phenylethylamine), 27.65 and 22.7 (histamine), 17.09 and 21.76 (tryptamine), and 21.64 and 19.5 (putrescine) for beet and brine, respectively, when HRW was used in pickle preparation instead of NW. The results of this study suggest that the best method for limiting the formation of BAs in pickles is to use HRW in the fermentation phase then replace the fermentation medium with a new acidified and brined HRW followed by a pasteurization process.

Many microbiological, enzymatic, physicochemical, and biochemical changes occur during the production and storage of fresh and processed meat products, which tend to deteriorate more easily by their nature. Nevertheless, consumers mainly prefer cost-effective foods that have a longer shelf life and minimally modified natural properties, can be easily prepared, and in which fewer additives are used. For these reasons, the food packaging industry has turned towards developing new applications with different functions that are not found in traditional packaging methods, such as antimicrobial packaging systems, especially for the protection and improvement of food quality and safety. In this type of packaging, in addition to improving food safety and quality, the shelf life of the product is extended by slowing down the growth rate of microorganisms. Thus, the existing growth of microorganisms during the transportation and storage of the product is also prevented. Therefore, the preservatives taken into the human body with foods are reduced, and the negative effects on health are also avoided. In these systems, the application of antimicrobial food packaging components to the packaging material can be performed by the addition of antimicrobial agents into the polymer, coating polymer surfaces with antimicrobial agents, immobilizing antimicrobial agents on the polymer, and using polymers with antimicrobial properties. In this review, antimicrobial packaging and application methods were generally explained, and innovative packaging systems and their use in meat and meat products were evaluated.

The objective of this study was to examine the impact of different concentrations of black mulberry leaf extract (BMLE) on the microbial quality, lipid oxidation, biogenic amine content, color stability, and sensory attributes of raw chicken meat during a 12-day chilled storage period. The raw chicken meat was treated with 0.1% BHT (positive control), 0.1%, 0.3%, and 0.5% BMLE, and the outcomes were then compared to the results obtained from raw chicken meat with no additive (control). In comparison to the control group, the inclusion of BMLE resulted in a decrease ( ) in pH and thiobarbituric acid reactive substances (TBARS), as well as an improvement in redness () ( ). The addition of BMLE significantly extended the shelf life of raw chicken meats compared to the control, as it limited microbiological development and lipid oxidation during storage ( ). Additionally, the BMLE exhibited the most potent inhibitory impact on the buildup of these four BAs (tyramine, cadaverine, histamine, and tyramine) in raw chicken samples at the 12-day storage period ( ). Despite the 0.5% BMLE groups’ lowest results for microbial counts, TBARS, and biogenic amines, the concentration of 0.3% BMLE proved to be the most advantageous in terms of sensory acceptability. These findings suggested that BMLE, rather than artificial chemicals, could be utilized in raw chicken products as a promising natural antioxidant and antibacterial agent.

In this study samples of raw pistachio nuts were prepared containing from 0% to 60% adulteration with dried peas. This training set of samples were analysed by Raman spectroscopy at frequencies from 200 to 2000 cm^-1 and an integrated AI model was used to detect this adulteration using 18,000 laboratory data points. Novel hybrid Self-organising Maps (SOM) and Convolutional Neural Network (CNN) methodologies were developed to intuitively examine pistachio adulteration with peas. SOM, an unsupervised learning (UL) method, was used to convert the data into lower-dimensional digital images. The numerical values depicted in the images showed the extent of pure and contaminated pistachio nuts in the mixtures. Image classification is a key subject in computer vision which aims to distinguish multiple clusters based on distinct features of images. Since image pixel values arranged in a two-dimensional grid and hexagonal lattices show similarities among pistachio adulteration characteristics, CNN was employed to efficiently sort and classify images for detecting pistachio-pea features. It involves determining a function that correlates the pixel intensities of an image with a specified class on the lattices. The outcomes of training and validation were satisfactory. This innovative hybridisation method is distinguished by its ability to cluster and represent high-dimensional unsupervised datasets as two-dimensional images, while also distinguishing between fraudulent and authentic pistachios. The CNN was constructed with a 4-layer architecture for this work, achieving maximum training and validation accuracies of 97% for pistachio adulteration with peas. This research will yield sophisticated AI software that facilitates the seamless online classification of adulterated pistachio in the future.