Detectable risks
Common targets include metal, calcified bone, glass, stones, and dense plastics where contrast against the product is sufficient.
Application
Foreign material detection for food production.
GreyscaleAI helps food producers inspect for metal, bone, glass, stone, and dense plastic risks while preserving the x-ray image evidence teams need for QA review.
Application fit depends on the product, package, target material, size, density, orientation, and line conditions. GreyscaleAI uses application-specific validation to establish fit, then preserves reviewable inspection evidence in Insights.
Image evidence
AI review
Event history
The production problem
A reject signal is not enough context.
Food safety teams need to confirm what the machine saw, how the event was classified, and whether the product and package create blind spots. GreyscaleAI combines inspection images, AI review, and event history so teams can move from a reject signal to a reviewable record.
Application caveats
Performance depends on product thickness, moisture, density, orientation, packaging format, contaminant size, and line speed.
Review workflow
Rejected units can be traced back to the underlying x-ray image and machine context for QA investigation.
Why traditional inspection struggles
Foreign material detection depends on more than contaminant type.
A contaminant that is easy to detect in one product can be difficult in another. Product thickness, density, moisture, packaging, orientation, and line speed all affect whether the target creates enough contrast for reliable inspection.
Simple pass/fail inspection
- Rejects product from the line
- Provides limited review context
- Can leave QA dependent on manual notes
- May not explain whether similar events are appearing nearby
Image-backed inspection intelligence
- Stores the x-ray image tied to the event
- Connects the event to product, lot, line, time, and machine context
- Supports AI-assisted review where the application is a fit
- Makes related event review possible in Insights
Detection matrix
Detection depends on product and packaging, not just contaminant type.
This matrix is directional, not a blanket guarantee. Validation should always be done with your actual product, package, contaminant set, and line conditions.
What GreyscaleAI sees
Dense materials create the strongest x-ray signals.
High contrast
Often among the easiest contaminants to detect when product density is moderate.
Product-specific
Detection varies widely with product thickness, natural density, and bone composition.
Dense targets
Usually strong candidates where the product does not mask the contaminant.
Case by case
Some dense plastics can be detected, but contrast is usually lower than metal or glass.
Image evidence
Image evidence helps teams confirm what happened.
The value is not only the reject. Teams need the x-ray image, the highlighted region, the machine context, and a way to review related events before deciding what to do next.
Metal fragment
Metal fragment in a finished product
A compact dense object can be reviewed directly in the x-ray evidence, even when the fragment is difficult to see at card size.
Bone fragment
Bone fragment in a can of seafood
A small bone fragment can be reviewed directly in the x-ray evidence, even when it is difficult to see at card size.
Bone in packaged chicken
Bone in packaged chicken
A small bone can be reviewed directly in the x-ray evidence, even when it is difficult to see at card size.
Insights workflow
Foreign material detection is the start of the workflow, not the end.
GreyscaleAI helps teams move from a reject event to the supporting image, machine context, inspection history, and broader review path needed for action.
Search inspection history
Search by product, lot, line, time window, event type, and related inspection records.
Review selected evidence
Open the x-ray image, highlighted region, AI decision, and machine context tied to the event.
Compare related events
Look for similar events by same product, same lot, same line, or nearby time window.
Decide next action
Use the record to support QA review, reinspection, hold/release decisions, or source investigation.
Related proof
See foreign material detection in production applications.
AI bone detection in poultry production
An anonymized protein application showing how AI models helped distinguish true bone from normal poultry variation.
Protein inspection applications
Protein products often combine foreign material risk, natural product variation, bone-related concerns, and QA review needs.
Related systems and fit
The right detection path depends on the line.
The right inspection system and validation plan depend on the actual product, package, target contaminants, line speed, and operating environment. Review HRX system options, discuss application-specific validation, or explore how inspection requirements change by product category.
Inspection Systems
Review HRX system options for aperture, conveyor, throughput, and production environment.
Talk through fit
Share your product, package, target contaminants, and line speed so GreyscaleAI can discuss validation under real operating conditions.
Seafood applications
Seafood applications can involve foreign material, bone, package issues, image history, and production analytics.
Next step
Need to know if your product is a fit?
Share the product, package, target contaminants, and line speed so GreyscaleAI can talk through validation under real operating conditions.