Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

TLDR

Electronic noses have been extensively researched, yet few have been commercialized due to sensing or pattern‑recognition limitations. The study aims to develop a portable, real‑time electronic nose that combines cross‑reactive colorimetric barcode combinatorics with deep convolutional neural networks to monitor meat freshness. The system uses 20‑type porous nanocomposite colorimetric barcodes that generate scent fingerprints, which are identified by a deep convolutional neural network. The supervised DCNN achieved 98.5 % accuracy on 3,475 barcode images, and its integration into a smartphone app provides a fast, accurate, non‑destructive platform for real‑time meat freshness monitoring.

Abstract

Abstract Artificial scent screening systems (known as electronic noses, E‐noses) have been researched extensively. A portable, automatic, and accurate, real‐time E‐nose requires both robust cross‐reactive sensing and fingerprint pattern recognition. Few E‐noses have been commercialized because they suffer from either sensing or pattern‐recognition issues. Here, cross‐reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes—comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate—form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non‐destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.