Short-wave infrared (SWIR) imaging provides beneficial insight into the state and quality of intricate fresh produce, such as apples, which the naked eye cannot. Identifying bruised fruits has never been easier.
Image Credit: Aleksandar Malivuk/Shutterstock.com
Short-wave infrared (SWIR) is commonly described as radiation within the 0.9 – 1.7 μm wavelength region. SWIR imaging makes it possible to observe the otherwise unobservable. As opposed to long-wave (LWIR) and mid-wave infrared (MWIR) irradiation, which are produced by the item, SWIR light undergoes absorption or reflection by an item, generating the sharp contrast required for high-res imaging.
As Si-based sensors come with an upper threshold of around 1.0 μm, SWIR imaging necessitates specific optics and electronics to function in the short-wave infrared spectrum. Indium gallium arsenide (InGaAs) detectors, which span the normal short-wave infrared spectrum, are the main devices employed in SWIR imaging. Line scan InGaAs detectors are also available commercially.
Short-wave infrared wavelengths can pass across lenses, glass, and various other optical elements. SWIR detector-based systems may be created utilizing the same procedures as those for visible components, lowering production costs and allowing the inclusion of protecting windows and filtering inside the system.
The food business is tightly controlled and monitored, with mechanisms in place to oversee operations and cleanliness at all times. Pressure, temperature, solid or liquid levels, and weight are all measured and controlled by sensing devices.
SWIR technology has established itself among the most successful methods for enhancing quality check operations in the food industry in recent times. Inspecting food is an important part of the business since it guarantees that only the finest specimens make it through the manufacturing process.
Food inspection cannot solely rely on manual labor. Conventional inspections, which involve people physically slicing open and inspecting foodstuff, can be wasteful, time-intensive, expensive, and opinionated.
Imaging techniques, on the other hand, provide quick, non-destructive qualitative examination and classification of food. Visible, infrared, and multispectral imaging techniques have already been employed in fresh produce grading systems.
Bruise damage to fruits, resulting mainly from excess impacts as well as compressive stresses on concentrated locations of the fruit surface, is widespread and recognized as a leading cause of fruit degradation and quality decline.
The bruises reduce fresh product commercial acceptance rates and post-harvesting losses throughout the horticulture supply chain owing to devaluation or downright refusal, adding to waste and the accompanying adverse economic, social, and environmental implications.
Optical screening can identify and discard bruised fruits and vegetables. In fact, more effective sortation reduces waste since fruits such as apples may be classified based on their viability for alternative goods, including jams and marmalades, preserves, and freezer blends. Nonetheless, automated sortation technologies frequently lack accuracy in spotting bruising, forcing businesses to rely on human-based sorting techniques.
Fruit condition and quality must be determined deeper than what consumers view on the surface. Invisible factors that suggest future fruit longevity and quality include sugar levels, hardness, soluble solid concentration, and nutrient value.
That is where SWIR sensors come into play, providing detailed insight that would otherwise be impossible to see. IR imaging is often highly successful at getting visual information under an object's exterior. It has proven very beneficial in machine vision since it allows for the acquisition of knowledge that is just not achievable within the visible spectrum.
SWIR spectrum wavelengths engage with things the same way as visible light waves do. Targets absorb or send back photons at this wavelength, providing high-res images with excellent contrast. While SWIR images possess qualities comparable to visible light ones, like reflection and contrast, they are not available in color, but rather only in black and white.
Hyperspectral imaging, working in the SWIR spectrum, can provide useful insight on all features of the surfaces of fruit under examination simultaneously, such as dimensions and geometry, bruising, color, and the chemical makeup of the fruit surface in a continuous spectrum.
On the other hand, multispectral imaging can scan vast volumes of fresh produce with much greater speed by gathering information within specified, discrete spectral bands, satisfying the industrial demand for fast recognition and sensing.
The SWIR GigE line scanning cameras from Teledyne DALSA include a state-of-the-art InGaAs sensor in a small design for a broad range of machine vision solutions. The outstanding sensitivity and minimal noise of this camera enable consumers to observe their items in a new light.
Similarly, Lynx, a ground-breaking InGaAs line scanning camera developed in 2010, offers great optical responsiveness and a wide dynamic range for commercial-scale image processing as well as optical coherence tomography.
Fivez, C., & Vandersmissen, R. (2022). Sensor-based food inspection and sorting. Retrieved from Xenics: https://www.xenics.com/sensor-based-food-inspection-and-sorting/
Grodzki, M. (2020). Imaging Inside Out: SWIR for Apples. Retrieved from Teledyne Imaging: https://possibility.teledyneimaging.com/imaging-inside-out-swir-for-apples/
Grodzki, M. (2020). SWIR Imaging for Apples, part II: The Multispectral Future of Food Inspection. Retrieved from Teledyne Imaging: https://possibility.teledyneimaging.com/swir-imaging-for-apples-part-ii-the-multispectral-future-of-food-inspection/
How Does Shortwave Infrared Imaging Work? (2019). Retrieved from Phase 1 Vision: https://www.phase1vision.com/blog/how-does-shortwave-infrared-imaging-work#:~:text=Wavelengths%20within%20the%20SWIR%20band,resolution%20imaging%20with%20strong%20contrast.
Nturambirwe, J., Perold, W. J., & Opara, U. L. (2021). Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging. Sensors, 21(15). Available at: https://doi.org/10.3390/s21154990
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