IoT Product Engineering and Edge AI for Food Quality Monitoring: Cost-Effective C12880MA NIR Spectrometer
Introduction: The Challenge of Cost-Effective Quality Control
High-end HSI (Hyperspectral Imaging) sensors are expensive and complex, creating a barrier to entry for many manufacturers. Customers required a lower-cost sensor path that retains actionable sensitivity for standard food QC tasks like moisture, sugar content, and simple adulteration detection, demanding expert IoT product engineering.
Solution Overview: Edge AI Embedded Systems for Spectroscopic QC
EurthTech pursued a pragmatic approach using the Hamamatsu C12880MA compact NIR spectrometer. The system uses a diffuse reflectance sampling jig for inline checks, where an AI-powered embedded system performs signal processing and chemometric modeling locally. This approach enables higher throughput QC in commodity flows, serving as a direct Edge AI embedded system solution for quality assurance.
Technical Implementation: Embedded Systems Development and AI Engineering
The embedded systems development focused on robust opto-mechanical integration, including stabilized LED illumination with thermal control and a mechanical shutter to ensure factory-floor reliability. The signal chain includes dynamic integration time and multiple averaged captures to improve SNR. For quality metrics (moisture, Brix), we built PLS (Partial Least Squares) regression models. For adulteration, we implemented LDA/RandomForest classification models—a powerful example of our AI engineering solutions.
Results & Impact: AI Engineering and Operational ROI
In a pilot on packaged produce, the system achieved per-sample processing time of <2 seconds, matching typical packing-line speeds. The C12880MA approach reduced hardware cost dramatically versus HSI while enabling effective QC for many commodity flows. This high-ROI solution confirms our ability to deliver tailored AI solutions that balance cost and performance for industrial applications.
Scalability & GIS Context: We defined a per-device calibration routine to ensure inter-device consistency for manufacturing scale. While not strictly GIS-focused, this technology is vital for maintaining the quality assurance layer within a broader Digital twin smart city supply chain model. This capability is foundational for delivering a complete smart city solutions provider platform that monitors both logistics and quality.