Cooking

Critical Control Point (CCP)

Food Safety Management System (FSMS) – Technical Documentation

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1. Introduction

Why Cooking is a Critical Control Point

Cooking is a pivotal step in the food production process and is universally designated as Critical Control Point (CCP ) in most HACCP-based systems. It is the point at which microbiological hazards are actively reduced or eliminated, and therefore it plays a central role in ensuring food safety prior to service.

The fundamental control at this stage is temperature, as cooking food to an adequate internal temperature destroys most foodborne pathogens. The effectiveness of this kill step directly impacts the safety of ready-to-eat (RTE) foods and is subject to strict regulatory oversight.

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2. Hazards

Microbiological Hazards and Cooking Control

The primary hazards addressed at this CCP are biological, particularly:

• Salmonella spp.

• Listeria monocytogenes

• Escherichia coli O157:H7

• Campylobacter spp.

These pathogens may be present in raw meats, poultry, seafood, or even vegetables that have been contaminated through cross-contact. If not properly cooked to the correct internal temperature, pathogens can survive and pose a significant health risk.

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3. HACCP Audit Table

Cooking CCP

Step	Hazard	Control Measure	CCP?	Critical Limit	Monitoring Method
Cooking	Survival of pathogenic microorganisms	Time/temperature control	Yes	≥ 75°C core or surface temp	Infrared thermal imaging (wet dishes only)
	Under-cooked or uneven heating	Visual assessment + image capture	Yes	Full surface heated uniformly	Image analysis and thermal verification

Note: The critical temperature limit of 75°C ensures rapid destruction of most pathogens and is recognized in both ISO 22000, Codex Alimentarius, and local food safety regulations as a validated control measure.

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4. Applicability

Wet Dishes vs. Solid Foods

Thermal image-based surface temperature verification is appropriate only for wet food items that:

• Exhibit even heat distribution on the surface

• Are typically stirred or cooked through, such as:

  • Curries

  • Soups and sauces

  • Braises

  • Stews or casseroles

  • Minced dishes

  • Poured/ladled items

⚠️ This method is not appropriate for:

• Whole joints or roasts

• Large cuts of meat or poultry

• Items where core temperature validation is required

For these foods, a probe thermometer should be used, and internal temperature must be recorded manually or via validated sensors.

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5. AI-Enabled Workflow

Thermal Imagery for CCP #3

Step 1: Capture Thermal Image

• An image is taken immediately post-cooking using a thermal camera (e.g., FLIR device).

• The image captures:

  • Surface temperature distribution

  • Food presentation

  • Background and surrounding conditions

Step 2: AI Processing

• The image is uploaded to the Didge Platform, where:

  • The AI identifies the food type (e.g., “beef curry”)

  • Classifies the category of food (e.g., protein-based, vegetarian)

  • Verifies the surface temperature from thermal data

  • Detects possible contaminants or foreign matter

  • Recognizes portion counts (e.g., 4 scoops in a tray)

  • Identifies serving method (e.g., plate, gastronorm tray, foil container)

  • Flags visual anomalies (e.g., undercooked areas, cross-contamination

Step 3: Auto-Populated Form Submission

• All extracted data is populated into a form in Didge:

  • Food item

  • Temperature (°C)

  • Number of portions

  • Serving vessel

  • Allergen presence (where identified on packaging or labels)

  • Contamination status (if flagged)

  • Timestamp and image metadata

• The form is automatically submitted as a new operation instance for traceability and audit.

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6. Metadata and Traceability

Each cooking image submission includes:

• Timestamp

• Operator (if user ID linked)

• Image

• Temperature data

• Cooking context (food type, container)

This enables traceability at the batch or dish level, critical for:

• Corrective action tracking

• Product recalls

• Allergen tracing

• Verification of cooking step compliance

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7. Audit & Compliance

The data captured via thermal imaging and AI is stored in the cloud-based Didge Platform, supporting:

• ISO 22000 compliance for cooking temperature validation

• Codex HACCP verification for CCP #3

• Local food authority inspections and audits

Didge provides:

• Data tables of cooking instances (sortable, filterable)

• Web reports for individual submissions

• Exports to PDF, Excel, CSV for audit folders

• Performance summaries (e.g., dishes cooked above 75°C per day)

This approach offers a much larger, high-fidelity dataset than traditional clipboard or manual thermometer logs, reducing the risk of:

• Falsified or incomplete records

• Human error

• Audit non-compliance

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8. Summary

Smarter Cooking Validation Through AI + Thermal Imagery

The integration of thermal imaging and AI for CCP #3 (Cooking) provides:

• Real-time surface temperature validation of wet cooked items

• Visual documentation of cooking conditions and portioning

• Automated, tamper-resistant submissions

• Food classification and allergen checks

• Metadata-enhanced traceability

• Full FSMS and HACCP compliance

When paired with proper probe verification for solid foods, this system represents a comprehensive, modern cooking control strategy that dramatically increases accountability, data integrity, and audit readiness.

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