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Technologies

Sorting technologies explained as part of one production system.

Every Mayson sorter is configured from the inspection layers its material actually needs — visible-light imaging, AI-assisted recognition, multi-view inspection, lighting-and-ejector control, and near-infrared (NIR) material recognition. Not every machine uses all of them: the right combination is selected for the stream, the reject classes, and the line it runs in. Each section below sets out what the technology can see, where it fits, and how the result is confirmed on your own material.

Request a material testView sorting platforms

How they work together

Lighting, imaging, recognition, and ejection are planned as one sorting path.

Each technology page explains what the inspection layer does, where it fits, and how it connects to the products and materials it supports.

  1. 01FeedMaterial is metered into an even, stable flow.
  2. 02PresentChute or belt presents particles to the view.
  3. 03IlluminateTuned lighting makes target defects visible.
  4. 04CaptureCameras image every particle in position.
  5. 05RecogniseRecipe and AI logic classify accept vs reject.
  6. 06EjectTimed air pulses remove the rejected stream.

Optical technology

Visible-Light Inspection

Visible-light analysis identifies material by color, contour, gloss, and surface variation.High-speed inspection supports recipe-based classification and precisely timed rejection.

Live material stream
RunningThroughput 3,842 pcs/min
ID 04217Class: RedConf: 99.2%
Analysis dashboard
Color segmentation
Clear
Blue
Green
White
Red
RGB distribution
Channel values
R212
G46
B48
Contour and shape
Area
24.7 mm²
Perimeter
21.3 mm
Circularity
0.68
Aspect ratio
1.23
ClassificationRed
Confidence99.2%
✓ Accept for red stream
Spectral signature400500600700nm
Color space (CIE LAB)
L*
52.6
a*
68.3
b*
43.1

High accuracy

Consistent results with minimal false calls

Maximum uptime

Stable performance in demanding environments

Adaptive recipes

Programs tuned to real material samples

Traceable and repeatable

Data-rich insight for quality control

Intelligent recognition

AI-Assisted Recognition

Recipe-linked models work alongside conventional sorting rules to recognise subtle, variable, or hard-to-parameterise defect classes with greater accuracy.

How it works

  1. 01
    Define from real data

    your team builds defect categories from real sample images and production experience, not generic presets.

  2. 02
    Configure recognition logic

    recognition rules and model-assisted logic are tuned around the target material and its reject classes, so each decision stays explainable.

  3. 03
    Keep claims practical

    public wording stays measured until product-specific evidence is reviewed; the model assists the operator rather than replacing judgement.

AI recognition cockpit: a live feed, a segmentation-to-feature-vector analysis pipeline, class probabilities with an anomaly score, and accept/reject decision routing
Recognition logic

What it identifies

Complex visual defectsMixed appearance classesProduct-specific reject groups

Suitable materials

Plastics recyclingBeans and seedsNutsFresh and frozen products

Application scope

  • Training examples and sample variation must be reviewed before any application-specific claim is published.
  • AI wording must not imply autonomous quality decisions made without operator validation.
  • A material test should confirm whether the defect categories are stable enough for production.

Inspection architecture

Multi-View Inspection

Multiple synchronised viewpoints reveal surfaces, edges, and orientation-dependent defects that a single camera angle can miss — building a more complete understanding of every product.

How it works

  1. 01
    Configure viewpoints

    cameras are positioned around the product so each angle exposes a different visible surface, matched to its geometry and flow.

  2. 02
    Correlate observations

    views are combined into a surface-visibility map so defect cues are cross-checked across angles, not judged from one.

  3. 03
    Confirm critical surfaces

    a part is only classified once the surfaces that matter are visible and the confidence threshold is met.

Multi-view inspection cockpit: top, side A, and side B camera views of the same product, a fused surface-visibility map, and a summary of defects a single view would miss
System configuration

What it identifies

Side-specific visible defectsFine cracks, chips, and seam defects on edgesSurface marks exposed only by orientation

Suitable materials

NutsBeans and seedsFresh and frozen productsPlastics recycling

Application scope

  • It is a configuration pattern, not a universal claim for every product family.
  • Stable material flow and consistent particle spacing and rotation must be tested.
  • Performance metrics require verified test records for the specific product.

Control layer

Lighting and Ejector Control

The practical control layer that synchronises inspection visibility, decision timing, and reject actuation for consistent, accurate sorting.

How it works

  1. 01
    See clearly

    precision lighting reveals target defects and reduces visual ambiguity across different materials.

  2. 02
    Time precisely

    detection, particle position, and actuation are synchronised to the millisecond so the right pulse meets the right particle.

  3. 03
    Eject accurately

    compressed-air actuation is tuned to lift the rejected stream out cleanly without disturbing accepted product.

Live control dashboard: a lighting recipe, detection event, position tracking, and eject-fire pipeline with synchronised signal-flow and timing waveforms
Control system

What it identifies

Recipe timing windowsReject actuation timingLighting-sensitive visible defects

Suitable materials

Rice and paddyPlastics recyclingGlass recycling

Application scope

  • Hardware details must match the actual product configuration.
  • Compressed air, product speed, and material spread all affect final rejection behaviour.
  • Recipes must be documented per material test, not copied across unrelated streams, and reject-accuracy claims need approved test data.

Spectral technology

NIR Inspection Technology

Near-infrared (NIR) analysis identifies polymer type by measuring spectral response across multiple wavelengths — enabling high-speed, accurate plastic sorting.

How it works

  1. 01
    Polymer signature recognition

    each polymer has a distinct spectral fingerprint that is captured across the near-infrared range.

  2. 02
    Multi-wavelength analysis

    broad spectral scanning separates plastics that look identical to the human eye or a colour camera.

  3. 03
    Real-time classification

    within milliseconds each particle is classified and routed, so sorting keeps pace with production speed.

NIR inspection cockpit: a material live feed, the measured spectral fingerprint against a polymer library, a per-polymer identification table with confidence, and accept/reject routing
Spectral recognition

What it identifies

Polymer type by spectral signature (PET, HDPE, PP, PE, PVC, PS, ABS, PC)Additives, coatings, and fillers that shift the signalComposition conflicts such as moisture, ageing, or contamination

Suitable materials

Plastics recyclingPlastic recycling line

Application scope

  • NIR cannot reliably identify black or carbon-black plastics, which absorb the signal.
  • Best performance is on light-coloured and translucent plastics with clean, dry surfaces.
  • Accuracy depends on validated spectral libraries, stable flow, and periodic calibration.

Material test

Technology fit is defined by the material, not the brochure.

Send a representative sample and we will show which inspection path best fits the material, output target, and production line.

Request a material testTalk to an engineer