Deep learning-based colorimetric indicator on polylactic acid packaging for nondestructive monitoring of fresh-cut fruits and vegetables.

The perishability of fruits and vegetables (F&v) presents a significant challenge in maintaining food quality and safety. However, current methods for monitoring the freshness of fresh-cut F&v remains limited. This study introduces a novel deep learning-based colorimetric indicator system designed for the nondestructive monitoring of freshness in fresh-cut F&v packed in polylactic acid (PLA) bags. The system employed an ethylcellulose-based indicator (EMT), which showed a distinct color transition in response to carbon dioxide (CO2) levels (0 %-30 %) during storage. In addition to its sensitivity, the EMT exhibited remarkable stability and reusability. Moreover, using fresh-cut green pepper as a model, the relationship of 'physiological state-freshness-indicator color' was constructed through the application of feature extraction algorithms (PCA and FLDA) in machine learning for the first time. The correlation was harnessed in conjunction with deep learning algorithms for image recognition and analysis. This approach mitigated or eliminated recognition errors arising from individual differences in human visual perception and variations in shooting conditions. The results indicated that the system could accurately, quickly, and nondestructively assess the freshness of fresh-cut green pepper, and the average accuracy of MobileNetV3-Small recognition could reach 96.09 % under k-fold cross-validation. The proposed strategy offered a highly accurate, real-time, and nondestructive method for monitoring produce freshness, with potential applications in food safety, health monitoring, and environmental protection.