Fruit and vegetable sorting has evolved far beyond surface-level inspection. As food safety regulations tighten and quality expectations rise, producers need inspection technologies that can detect internal defects, subtle material differences, and hidden contamination at full production speed. For example, supermarket customers are unlikely to purchase visibly bruised fruit such as apples, even when the internal quality remains suitable for consumption. With accurate sorting in place, these apples can be redirected to alternative uses such as juices, purees, or processed foods rather than being discarded, maximizing product value for retailers while significantly reducing food waste across the supply chain.
This is where Short-Wave Infrared (SWIR) imaging has become a game-changer.
Unlike visible or standard monochrome imaging, SWIR exploits the characteristic absorption behavior of water, sugars, fats, and organic compounds in the 1000–1700 nm wavelength range. These spectral properties make SWIR uniquely suited for identifying defects, foreign materials, and quality attributes that are invisible to conventional vision systems.
Below, we explore how SWIR cameras enable robust inspection across several high value produce categories.
Why SWIR for Food Sorting?
SWIR imaging provides three critical advantages in food inspection:
These properties make SWIR particularly effective for grading, defect detection, and foreign material identification.
Kiwi quality assessment
Detecting Blemishes, Soft Spots, and Puncture Damage
Kiwi fruit presents a unique inspection challenge: a rough, fibrous exterior that can mask internal damage. Visible inspection often fails to distinguish between cosmetic surface variation and true quality defects.
SWIR cameras enable:
Citrus quality evaluation (Lemons &Oranges)
Going Beyond Surface Color
Citrus fruits are visually uniform, which makes detection particularly difficult using RGB or monochrome cameras alone. SWIR enables consistent detection of both surface and subsurface defects.
Key Defects Explained
Dried Fruit quality control
Foreign Material Detection and Quality Assurance
Dried fruits present a different challenge: low moisture content and highly variable shapes and textures. Traditional visible inspection struggles to reliably detect dense contaminants.
SWIR excels at:
These materials have distinctly different SWIR absorption characteristics compared to organic dried fruit, making them stand out clearly—even when similar in color.
Nut quality sorting
From Foreign Materials to Aflatoxin Risk Reduction
Nut quality sorting requires reliable differentiation between acceptable product and defects based on internal structure and material composition rather than surface appearance.
Key SWIR Capabilities
Aflatoxin (Risk Screening)
Aflatoxin is a toxic compound produced by certain fungi (notably Aspergillus species) that can grow on nuts under improper storage conditions. It is:
While SWIR does not chemically “measure” aflatoxin, it is highly effective at detecting mold-damaged and structurally compromised nuts, which are strongly correlated with aflatoxin risk.
The Role of SWIR Imaging in Food Inspection
Across fresh produce, dried fruits, and nuts, SWIR imaging consistently delivers what conventional vision systems cannot: reliable material differentiation, subsurface defect detection, and robust foreign material identification.
As production speeds increase and quality standards tighten, SWIR cameras are no longer a niche technology, they are becoming an essential component of advanced food sorting and grading systems.
By seeing beyond surface appearance, SWIR enables producers to make better decisions, reduce waste, and deliver safer, higher-quality food to the market.
JAI will soon be able to provide even more dedicated solutions designed specifically for the demands of fruit and vegetable sorting and grading. These solutions will leverage SWIR imaging to address real-world inspection challenges across fresh produce, dried fruits, and nuts, enabling reliable detection of defects, contaminants, and quality attributes.