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

[Display omitted] • Paper chromogenic array (PCA) integrated with machine learning (ML) was developed. • PCA exhibit distinguishable pattern shifts when reacting volatile metabolites. • PCA pattern recognition achieved using a cross-validated neural network. • PCA accurately identify multiplexed pathogens from indigenous microflora. • The nondestructive PCA-ML holds great potential as a smart food safety system. 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. [ABSTRACT FROM AUTHOR]