CNN-assisted accurate smartphone testing of PAD 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 (mu PAD) that specifically responded to these two factors to evaluate sausage spoilage during storage. A dataset consisting of 4096 images of mu PADs 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 mu PADs in food safety and quality control.