Hyperspectral imaging for meat quality inspection – unlocking new possibilities in automated inspection

Growing demands for food quality and safety are driving meat processors to invest in advanced quality inspection systems. Alongside conventional RGB machine vision systems, hyperspectral imaging (HSI) is gaining increasing importance, enabling the analysis of characteristics that are invisible to both the human eye and standard imaging systems.

What is hyperspectral imaging?

Conventional industrial cameras capture images using three color channels—red, green, and blue (RGB). In contrast, hyperspectral cameras analyze a much broader range of wavelengths, including the near-infrared (NIR) spectrum. This enables the acquisition of information not only about a product’s appearance but also about its physical and chemical properties.

This makes it possible to detect variations that remain invisible to conventional machine vision inspection, significantly expanding the capabilities of automated quality inspection.

Applications in meat processing

In the meat industry, hyperspectral imaging supports automated quality inspection by analyzing both product characteristics and the presence of contaminants or unwanted materials. Depending on the production process, the system can detect bone fragments, cartilage, and foreign materials such as plastic, rubber, or metal, while simultaneously assessing meat quality directly on the production line.

Such solutions help prevent products that fail to meet quality standards—or contain foreign materials—from progressing to subsequent stages of production, enhancing food safety and improving production process reliability.

Poultry fillet inspection – a practical application

A good example of the practical application of hyperspectral imaging is Scanway’s poultry fillet inspection system. Each fillet is analyzed in real time without interrupting the production line. The system simultaneously evaluates meat quality and detects foreign materials, helping manufacturers ensure the highest standards of food safety.

The inspection scope includes the detection of foreign materials such as plastic fragments (PET, PE, PP), metal, rubber, glass, wood, paper, textiles, film, insects, as well as bone fragments, feathers, and cartilage. At the same time, the system inspects product quality by identifying blood spots, discoloration, torn edges, and residual skin and bone fragments.

Depending on the type of defect being inspected, the system uses RGB cameras, hyperspectral imaging, or a combination of both technologies. This approach makes it possible to select the inspection method best suited to the specific quality characteristics being evaluated.

Hyperspectral imaging or RGB? Choosing the right technology matters

Although hyperspectral imaging significantly expands product analysis capabilities, it is not always the optimal solution for every application. Tests conducted by Scanway have shown that for certain meat products, such as kabanos sausages, smoked sausages, and frankfurters, conventional RGB imaging delivers better results. Quality attributes related to the product’s visual appearance—such as discoloration or the degree of smoking—were more effectively identified in the visible spectrum than in the near-infrared range.

For this reason, the choice of inspection technology should always be based on a thorough analysis of the production process and the specific types of defects to be detected.

Technology tailored to the production process

Modern quality inspection is not about choosing a single technology for every application. The best results are achieved by selecting the solution that best fits a specific production process. Depending on the product type and the nature of the defects being inspected, the optimal approach may involve conventional RGB machine vision, hyperspectral imaging, or a combination of both technologies.

This approach improves the effectiveness of quality inspection, reduces the number of defective products, and ensures a high level of food safety while maintaining production efficiency.