Integrated Portable Shrimp-Freshness Prediction Platform Based on Ice-Templated Metal–Organic Framework Colorimetric Combinatorics and Deep Convolutional Neural Networks

Abstract

Real-time monitoring of food freshness is critical to reducing food waste and pursuing sustainable development. Cross-reactive artificial scent screening systems provide a promising solution for food freshness monitoring, but their commercialization is hindered by the low sensitivity or pattern-recognition inaccuracy. Leveraging the cutting-edge artificial intelligence and high-porosity nanomaterial, a cost-effective and versatile method was developed by incorporating metal–organic frameworks into smart food packaging via a colorimetric combinatorics sensor array. The whole UiO-66 family was screened by density functional theory calculations, and UiO-66-Br (due to the highest binding energy) was selected to construct sensor arrays on an ice-templated chitosan substrate (i.e., ice-templated dye@UiO-66-Br/Chitosan). The physicochemical properties and morphologies of the fabricated sensor arrays were systematically characterized. The limit of detection of 37.17, 25.90, and 40.65 ppm for ammonia, methylamine, and trimethylamine, respectively, was achieved by the prepared composite film. Deep convolutional neural networks (DCNN), a deep learning algorithm family, were further applied to monitor shrimp freshness by recognizing the scent fingerprint. Four state-of-the-art DCNN models were trained using 31,584 labeled images and 13,537 images for testing. The highest accuracy achieved was up to 99.94% by the Wide-Slice Residual Network 50 (WISeR50). Our newly developed platform is integrated, sensitive, and non-destructive, enabling consumers to monitor shrimp freshness in real-time conveniently.