Machine learning-driven smart colorimetric sensor arrays for food freshness monitoring: Advances, challenges, and future prospects.

Abstract

Ensuring food safety necessitates rapid, non-destructive, and reliable monitoring strategies, among which colorimetric sensor arrays (CSAs), based on analyte-induced color changes, have emerged as cost-effective tools for real-time food quality monitoring. Recent advances in sensing materials, fabrication strategies, and colorimetric mechanisms have expanded the analytical capability of CSAs, particularly through integration with machine learning. Automated feature extraction, pattern recognition, and predictive modeling significantly improve sensitivity, specificity, and classification accuracy, enabling more reliable food freshness assessment. Both traditional algorithms and deep learning models have been applied to CSA data analysis, supporting their implementation in portable, smartphone-based, and embedded sensing platforms. However, practical deployment is still limited by sensor degradation, environmental variability, response inconsistency, and the lack of standardized imaging and evaluation protocols. Future efforts should prioritize adaptive and lightweight algorithms, standardized data workflows, multimodal sensing systems, and sustainable, high-stability materials to enhance robustness, reproducibility, and industrial applicability.