Hyperspectral Imaging for Defect Detection of Pickling Cucumbers

Hyperspectral Imaging for Food Quality Analysis and Control 12/2010; DOI: 10.1016/B978-0-12-374753-2.10014-0
Source: OAI


This chapter presents the application of hyperspectral imaging for defect detection in pickling cucumbers. Many pickling cucumber processors are currently using machine vision systems in their lines for sorting cucumbers on the basis of size, shape, and color. The systems are not designed for detection of external or internal damage in cucumbers; therefore, they are incapable of detecting bruise damage in pickling cucumbers in the form of water soak lesions, carpel separation, or hollow center. With the stringent quality control requirements, the presence of external and/or internal defect can lead to rejection and make the processor liable for economic loss. Studies on the application of hyperspectral imaging for detection of defects in pickling cucumbers show a potential to use the technology in commercial lines. The simultaneous hyperspectral reflectance and transmittance imaging system can simplify the operation and reduce costs by having multiple inspections (size, shape, color, external, and internal bruise) in one station. However, further research is needed to make the technology applicable in the industry. While hyperspectral data are rich in information, processing the hyperspectral data poses several challenges regarding computation speed requirements, information redundancy removal, relevant information identification, and modeling accuracy. Hyperspectral imaging studies are often conducted as a precursor to the design of a multispectral imaging system using 3-4 wavebands for real-time applications.

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