The Cook CCP
1. Introduction
Cooking isn’t just about flavour — it’s about food safety, trust, and compliance. With RIDGY AI, your kitchen gets a smart assistant that sees what you see: the food, the probe, the temperature, and the conditions that matter most.
Snap a photo, and RIDGY does the heavy lifting: it identifies the food, reads the temperature, checks for risks, and instantly tells you if your cook meets the gold standard — 75 °C for safety and compliance.
No guesswork. No shortcuts. Just clear, reliable data that keeps your team confident, your guests safe, and your HACCP program running like a well-tuned grill.
RIDGY turns everyday cooking checks into effortless compliance, so you can focus on what matters — creating great food with peace of mind.
2. Cooking in HACCP (CCP3)
Cooking is identified as Critical Control Point 3 (CCP3) in many HACCP-based food safety programs. Its purpose is to achieve lethality — destroying harmful microorganisms that cause foodborne illness. Cooking ensures the inactivation of bacteria such as Salmonella spp., E. coli O157:H7, Listeria monocytogenes, and Clostridium perfringens, which are often found in raw meats, poultry, seafood, and other high-risk foods.
Reaching an internal temperature of ≥75 °C provides a validated 5-log reduction in these pathogens, making food safe for consumption. This step is a cornerstone of every food safety program, protecting both customers and businesses.
3. Compliance Criteria
Pass / Cooked (Compliant): Internal or surface temperature ≥75 °C.
Fail / Non-Compliant: Temperature <75 °C.
(No “hot holding” option — anything under 75 °C is non-compliant for CCP3 cooking.)
4. Food Data
Item: The name or type of food (AI-detected or from labels in the image).
Food Classification: Category such as meat, poultry, fish, shellfish, dairy, plant-based.
Storage: Where the food is shown (tray, plate, pass, hotbox, oven).
Hamburger: Flags if it’s a hamburger patty or sandwich (special monitoring in many HACCP programs).
BEO: Banquet Event Order reference, if present in the image.
Allergen Cues (Visible): Detects ingredients linked to allergens (nuts, sesame, shellfish, gluten).
Fish/Shell Segmentation: Recognises fish fillets, prawns, oysters, etc.
Processed/Minced Meat: Identifies ground or reformed meat (e.g., sausages, kofta, meatballs).
Cooking Compliance: Pass/Fail outcome based on the ≥75 °C rule.
5. Temperature Data
Temperature: Overall recorded temperature (in °C).
TDZ (Temperature Danger Zone): Whether the food is in 5–60 °C range (rapid bacterial growth).
Measurement Method: How the reading was taken (probe, Bluetooth probe, FLIR thermal image).
Measurement Type: Whether the value represents internal or surface temperature.
Internal Temperature: Probe reading from the food’s core.
Surface Temperature: External reading from probe or FLIR.
Cooking Method: Method identified (grill, fry, steam, sous-vide, oven, boil).
Visual Cues – Browning/Crust: Detects caramelisation or crust formation, key for whole cuts.
Visual Cues – Visible Pink/Red Areas: Detects raw or undercooked zones.
Juice Clarity: Checks if juices run clear (indicator of doneness in poultry and meats).
6. Conditions Data
Location: Where the food is situated (grill, pass, tray, oven, plate).
Food-Contact Surface: Material in contact with food (stainless tray, foil, cutting board, plate).
Cutting Board Colour: Board colour (linked to regional food safety codes: red for raw meat, green for veg, etc.).
Gloves/Jewellery Visible: Detects if operators wear gloves, rings, watches (hygiene risk).
Utensil Looks Clean: Whether utensils appear visibly clean or soiled.
Foreign Objects: Detects any unexpected materials (plastic, packaging, hair).
Contamination: Flags visible contamination or cross-contact.
Quantity: Counts how many portions/servings are in the frame.
Utensils: Identifies type (tongs, ladle, knife, etc.).
General Equipment Used: Detects visible cooking equipment (grill, fryer, combi oven).
General Condition: Adds context (under heat lamp, on tray, plated).
7. Data Integrity & Risk Mitigation
This process extracts approximately 30 individual data points from each cooked food image. While this may seem extensive, every data point serves a clear purpose: to protect the business, safeguard the brand, and ensure the wellbeing of customers and staff by demonstrating that food has been cooked safely.
In the event of a foodborne illness, customer complaint, or regulatory investigation, the depth and rigor of these captured data provide multiple layers of protection, minimizing operational and reputational risk. Each piece of information contributes to a defensible record that validates food safety practices, strengthens compliance, and ultimately builds consumer trust.
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