Deep Learning Used with a Colorimetric Sensor Array to Detect Indole for Nondestructive Monitoring of Shrimp Freshness

Abstract

Intelligent colorimetric freshness indicator is a lowcost way to intuitively monitor the freshness of fresh food. A colorimetric strip sensor array was prepared by p-dimethylaminocinnamaldehyde (PDL)-doped poly(vinyl alcohol) (PVA) and chitosan (Chit) for the quantitative analysis of indole, which is an indicator of shrimp freshness. As a result of indole simulation, the array strip turned from faint yellow to pink or mulberry color with the increasing indole concentration, like a progress bar. The indicator film exhibited excellent permeability, mechanical and thermal stability, and color responsiveness to indole, which was attributed to the interactions between PDL and Chit/PVA. Furthermore, the colorimetric strip sensor array provided a good relationship between the indole concentration and the color intensity within a range of 50−350 ppb. The pathogens and spoilage bacteria of shrimp possessed the ability to produce indole, which caused the color changes of the strip sensor array. In the shrimp freshness monitoring experiment, the color-changing progress of the strip sensor array was in agreement with the simulation and could distinguish the shrimp freshness levels. The image classification system based on deep learning were developed, the accuracies of four DCNN algorithms are above 90%, with VGG16 achieving the highest accuracy at 97.89%. Consequently, a "progress bar" strip sensor array has the potential to realize nondestructive, more precise, and commercially available food freshness monitoring using simple visual inspection and intelligent equipment identification.