A Review of High-Throughput Optical Sensors for Food Detection Based on Machine Learning.

Area of Science:

• Food Science and Technology
• Analytical Chemistry
• Sensor Technology

Background:

• Growing global food industry necessitates advanced food safety and quality monitoring.
• High-throughput detection technologies are crucial for meeting consumer demands and regulatory standards.
• Digital intelligent optical sensors offer speed, precision, and non-destructive analysis capabilities.

Purpose of the Study:

• To systematically review the construction principles of intelligent optical sensors.
• To explore the applications of these sensors in food inspection and analysis.
• To highlight the integration of advanced technologies for improved food safety.

Main Methods:

• Utilizing nanomaterials (quantum dots, metal nanoparticles, upconversion nanoparticles) for signal transduction.
• Employing machine learning algorithms (SVM, random forests, CNN) for data analysis and model optimization.
• Integrating multiple detection mechanisms like spectral analysis, fluorescence, and hyperspectral imaging.

Main Results:

• Demonstrated efficient detection of pesticide residues, heavy metals, microorganisms, and food freshness.
• Showcased the versatility of optical sensors across various food quality and safety parameters.
• Highlighted the synergistic effect of combining optical sensing with advanced data processing.

Conclusions:

• Intelligent optical sensors are a promising technology for high-throughput food testing.
• Future advancements will focus on multifunctional integration, miniaturization, and intelligent operation.
• Integration with cloud computing and IoT will enable comprehensive supply chain food safety monitoring.