Portable Food-Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural...

Area of Science:

• Analytical Chemistry
• Materials Science
• Computer Science

Background:

• Electronic noses (E-noses) are artificial scent screening systems with extensive research.
• Commercialization of E-noses is limited due to challenges in sensing and pattern recognition.
• Portable, accurate, real-time E-nose systems require robust cross-reactive sensing and fingerprint pattern recognition.

Purpose of the Study:

• To develop a novel system for monitoring meat freshness by integrating cross-reactive colorimetric barcode combinatorics with deep convolutional neural networks (DCNNs).
• To create a system that concurrently provides scent fingerprinting and fingerprint recognition for food quality assessment.
• To establish a user-friendly platform for rapid, non-destructive, real-time food freshness identification.

Main Methods:

• Developed colorimetric barcodes using 20 distinct porous nanocomposites (chitosan, dye, cellulose acetate) to generate scent fingerprints.
• Utilized deep convolutional neural networks (DCNNs) for identifying scent fingerprints generated by the colorimetric barcodes.
• Trained a fully supervised DCNN model with 3475 labeled barcode images to predict meat freshness.
• Integrated the DCNN model into a smartphone application for real-time barcode scanning and analysis.

Main Results:

• The developed system achieved an overall accuracy of 98.5% in predicting meat freshness.
• The DCNN successfully identified scent fingerprints generated by the colorimetric barcodes.
• The smartphone application enabled rapid barcode scanning and real-time food freshness identification.
• The system demonstrated to be fast, accurate, and non-destructive.

Conclusions:

• The combination of cross-reactive colorimetric barcode combinatorics and DCNNs offers a robust solution for monitoring meat freshness.
• The developed system provides a simple, accurate, and non-destructive platform for real-time food quality assessment.
• This technology has the potential to benefit consumers and stakeholders across the food supply chain by ensuring food safety and reducing waste.