CNN-assisted accurate smartphone testing of μPAD for pork sausage freshness.

The accumulation of lipid oxidation and biogenic amines is the primary cause of pork sausage spoilage during storage. Therefore, this study aimed to develop a colorimetric microfluidic paper analytical device (μPAD) that specifically responded to these two factors to evaluate sausage spoilage during storage. A dataset consisting of 4096 images of μPADs was collected, and a convolutional neural networks (CNN)-based classification model (Resnet50) was trained and tested with an accuracy, F1 score, and recall rate of 97.10%, 97.14%, and 99.17%, respectively. Additionally, the CNN-based model was integrated into a smartphone application with user-friendly interfaces catering to both professional and non-professional users. Furthermore, the classification method was validated by predicting the shelf life of sausages stored at different temperatures. Compared to conventional methods, this approach provided a cost-effective, rapid, and portable detection method for assessing pork sausage freshness while broadening the application of smartphone-based colorimetric μPADs in food safety and quality control. • A microfluidic paper-based analytical device (μPAD) was prepared. • A CNN model (Resnet50) was trained and embedded in a smartphone application. • TBARs and biogenic amines were colorimetric indicators of pork sausages' freshness. • The predictive model's accuracy was above 97% and validated by shelf-life. • The operation was low-cost and user-friendly. [ABSTRACT FROM AUTHOR]