Nondestructive and multiplex differentiation of pathogenic microorganisms from spoilage microflora on seafood using paper chromogenic array and neural network.

Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a paper chromogenic array coupled with a machine learning neural network (PCA-NN) to detect viable pathogens in the presence of background microflora and spoilage microbe in seafood via volatile organic compounds sensing. Morganella morganii and Shewanella putrefaciens were used as the model pathogen and spoilage bacteria. The study evaluated microbial detection in monoculture and cocktail multiplex detection. The accuracy of PCA-NN detection was first assessed on standard media and later validated on cod and salmon as real seafood models with pathogenic and spoilage bacteria, as well as background microflora. In this study PCA-NN method successfully identified pathogenic microorganisms from microflora with or without the prevalent spoilage microbe, Shewanella putrefaciens in seafood, with accuracies ranging from 90% to 99%. This approach has the potential to advance smart packaging by achieving nondestructive pathogen surveillance on food without enrichment, incubation, or other sample preparation.

Long-term quality retention and decay inhibition of chestnut using thymol loaded chitosan nanoparticle.

Thymol (TMO) was loaded into chitosan nanoparticles (CSNPs) to inhibit chestnuts decay during storage. Three chestnut treatments were evaluated, including the CK (uncoated control), CSNPs (coated with chitosan nanoparticles), and TMO-CSNPs (coated with thymol-loaded chitosan nanoparticles). Quality assessments of chestnuts were conducted periodically for up to 180 days, which included starch content, amylase activity, water content, respiration rate, weight loss rate, microbiological indicators, decay rate, and quality evaluation. Results indicated that TMO-CSNPs had significantly less nutrient loss in soluble sugar (10.61%) and starch content (27.72%) compared with CK, which was attributed to low metabolic activities as evident in low amylase activity and respiration rate. Moreover, TMO-CSNPs significantly inhibited the growth of mold and yeast (4.17 log CFU g-1 on day 180) and kept the lowest decay rate (5.33%). This study demonstrates the potential of food nanomaterial as an alternative strategy to promote food security and supply chain resilience.