Chemical antibacterials are widely used to control microbial growth but have raised concerns about health risks. It is necessary to find alternative, non-toxic antibacterial agents for the inhibition of pathogens in foods or food contact surfaces. To develop a non-toxic and “green” food-grade alternative to chemical sanitizers, we formulated a multicomponent antibacterial mixture containing Rosmarinus officinalis L., Camellia sinensis L., citric acid, and ε-polylysine and evaluated its bactericidal efficacy against Staphylococcus aureus, Escherichia coli, Bacillus cereus, Salmonella Enteritidis, and Listeria monocytogenes on food contact surfaces. A combination of the agents allowed their use at levels lower than were effective when tested individually. At a concentration of 0.25%, the multicomponent mixture reduced viable cell count by more than 5 log CFU/area, with complete inactivation 24 h after treatment. The inhibitory efficacy of the chemical antibacterial agent (sodium hypochlorite, 200 ppm) and the multicomponent antibacterial mixture (0.25%) on utensil surfaces against S. aureus, E. coli, S. Enteritidis, and L. monocytogenes were similar, but the multicomponent system was more effective against B. cereus than sodium hypochlorite, with an immediate 99.999% reduction on knife and plastic basket surfaces, respectively, and within 2 h on cutting board surfaces after treatment. A combination of these food-grade antibacterials could be a useful strategy for inhibition of bacteria on food contact surfaces while allowing use of lower concentrations of its components than are effective individually. This multicomponent food-grade antibacterial mixture may be a suitable “green” alternative to chemical sanitizers.

Buckwheat sprouts contain various bioactive compounds including rutin which have a number of biological activities. We have previously shown that buckwheat sprouts (TBWE) treated with methyl jasmonate (MeJA) significantly increased the amount of phenolics and the antioxidant activity. The aim of this study was to demonstrate the effect of TBWE on anti-adipogenesis and pro-oxidant enzyme in 3T3-L1 adipocytes. We also evaluated the anti-oxidative activity of TBWE in adipocytes by using the nitroblue tetrazolium assay. Our data showed that TBWE markedly inhibited adipocyte differentiation and ROS production in 3T3-L1 cells compared with control groups. Moreover, TBWE has strongly shown the inhibition of adipogenic transcription factor as well as pro-oxidant enzymes. Together, we demonstrate that the MeJA treatment significantly increased the amount of phenolic compound, resulting in the suppression of adipogenesis and ROS production in the 3T3-L1 cells. These findings indicate that TBWE has the potential for anti-adipogenesis activity with anti-oxidative properties.

Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set.

In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900-1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp²) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth.