Thermal vs. Infra Red

Technical Comparison: Thermal Imagery vs. Infrared Thermometer in Food Safety Monitoring

FSMS Instrumentation & AI-Enabled Recordkeeping Justification

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1. Purpose

This document compares the functionality, data fidelity, and operational value of:

• Thermal imagery devices (e.g., FLIR Lepton 3.5 modules embedded in Android-based systems such as the BlackVue phone)

• Traditional infrared thermometers (IR guns)

It further demonstrates why thermal imagery, when combined with AI-powered image analysis and integration with the Didge platform, represents a superior method for food safety monitoring, compliance documentation, and audit readiness across Critical Control Points (CCPs) in HACCP and ISO 22000-based systems.

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2. Technology Comparison

How Each Device Measures Temperature

2.1 IR Thermometer (Infrared Gun)

• Emits infrared radiation, captures a single-spot temperature

• Measures a small area (typically 1:1 to 12:1 distance-to-spot ratio)

• Outputs a single value based on average infrared energy in that spot

• Offers no spatial context or photographic record

2.2 Thermal Imagery Device (e.g., FLIR Lepton 3.5)

• Uses a radiometric camera sensor to generate up to 19,200 discrete temperature readings per image (160 × 120 pixels)

• Measures the entire surface area of a tray, plate, storage shelf, or cooked dish

• Supports configurable emissivity values (commonly set to 0.95 for food)

• Stores visual + thermal imagery with timestamp and metadata

• Enables AI analysis of food type, portion count, condition, and surface readings

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3. Comparison Matrix

Aspect	IR Thermometer	Thermal Imagery Device + AI + Didge
Data Resolution	Single-point	19,200+ measurements per image
Surface Mapping	None	Full thermal distribution, spatially accurate
Emissivity Setting	Fixed or manually adjusted	Fully configurable (optimized for food at 0.95)
Visual Evidence	None	Captures both thermal and visible spectrum image
Recordkeeping	Manual, prone to omission	Automated, timestamped, AI-labeled records
Temperature Validation	Basic spot check	Context-rich, gradient-sensitive measurement
AI-Powered Field Completion	✗	Food identification, use-by date extraction, portion counting
Audit Compliance	Not verifiable	Verifiable image, temperature, metadata, and AI interpretation
Submission Workflow	Manual entry	Integrated auto-submission via Didge

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4. Perceived Instability

Addressing “Perceived Instability” in Thermal Imagery

Thermal variability is often misunderstood as instability, especially when compared to the fixed average reading from an IR thermometer.

In reality:

• A thermal image detects real-world thermal gradients (e.g., center hotter than edge of a tray)

• Variation is a strength, offering deeper insight into actual food condition

• Thermal imagery can reveal hot spots, cold zones, or undercooked areas missed by IR guns

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5. Benefits

Benefits of Android-Based Thermal Imaging Systems

5.1 Integrated Hardware Advantages

• Uses a modern Android device, providing:

  • High-resolution photography

  • Built-in Wi-Fi / 4G for real-time uploads

  • QR code scanning for location routing

  • Touchscreen for user-friendly interface

5.2 Software and AI Integration with ChatGPT Ecosystem

When thermal images are uploaded, they are analyzed by AI systems (e.g., ChatGPT) to extract structured data:

Data Extracted by AI	Use Cases
Food type and category	Buffet validation, dish classification, allergen tagging
Use-by / production date	Cold storage validation, expiry control
Portion counts	Portion monitoring, yield tracking
Holding equipment type	Identifies context (tray, bain-marie, gastronorm, plate, etc.)
Visual anomalies (e.g., contamination)	Risk detection, incident documentation

This transforms a single image into a rich, automated data source for CCP recordkeeping.

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6. Integration

Thermal imagery, AI outputs, and submission workflows are all embedded into the Didge FSMS platform, allowing:

Real-Time, Structured Recordkeeping

• Automatic form population based on image data

• Real-time submission of operation instances

• QR code routing (e.g., which fridge, cold room, or holding unit the image relates to)

• Storage of image, data, timestamp, and CCP linkage

Audit-Ready Documentation

• Web-based reports per submission

• Data tables for batch validation, export to PDF, CSV, Excel

• Image-linked verification trail (e.g., “this food was at 4.3°C in Cold Room 1 at 10:32 AM”)

End-User Simplicity

• Operators simply:

  • Scan QR code

  • Take image

  • AI and Didge handle the rest

• Eliminates the need for manual thermometer readings or handwritten logs

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7. Summary

Why Thermal Imagery + AI + Didge is the Future of FSMS Monitoring

Key Advantage	Impact
High-resolution surface temperature	Detects cold spots, cooking validation, holding verification
Visual & thermal record combined	Evidence-based submissions, unmatched traceability
AI field population	Reduces human error, increases data richness
Real-time cloud upload via Android	Enables mobile and site-based compliance with full audit support
Seamless integration with Didge	Centralized FSMS management, operational reporting, CCP compliance documentation

Thermal imagery, supported by AI and delivered through the Didge platform, delivers unmatched accuracy, automation, and traceability in food safety monitoring. It provides a future-ready solution that not only meets the compliance demands of ISO 22000, Codex HACCP, and regulatory bodies—but vastly improves operational insight and productivity.

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