Biospeckle laser for real-time monitoring of water and food samples: Enhancing contamination detection and quality control in field applications

Abstract

Laser Biospeckle (BSL) is a nondestructive, real-time monitoring technique that has become a critical tool for assessing water and food quality. This review highlights BSL’s effectiveness in detecting microbial contamination in water and food samples. BSL can detect bacterial contamination at concentrations as low as 2 × 108 CFU/mL within seconds, offering significant advantages over traditional plate counting methods. Additionally, it identifies fungal infections before they become visually detectable, with a sensitivity exceeding 95%. This review compares BSL performance in laboratory and field environments, focusing on microbiological detection and physicochemical parameter analysis (e.g., pH, turbidity, and conductivity), which are critical for industrial and environmental safety. The integration of artificial intelligence (AI) has significantly enhanced BSL’s capabilities, particularly in automated data analysis. AI-driven methods, such as convolutional neural networks (CNNs), achieve contamination classification accuracy above 98%, improving detection speed and reducing human intervention. Furthermore, AI enables real-time automated monitoring, essential for on-site industrial applications. This review also outlines future research directions, including portable BSL sensor development and advances in AI-driven data processing. Addressing these challenges could establish BSL as a transformative tool for food safety and environmental monitoring, enhancing consumer health and regulatory compliance.