LINE‐SCAN HYPERSPECTRAL IMAGING PLATFORM FOR AGRO‐FOOD SAFETY AND QUALITY EVALUATION: SYSTEM ENHANCEMENT AND CHARACTERIZATION M. S. Kim, K. Chao, D. E. Chan, W. Jun, A. M. Lefcourt, S. R. Delwiche, S. Kang, K. Lee ABSTRACT. Line‐scan‐based hyperspectral imaging techniques have often served as a research tool to develop rapid multispectral methods based on only a few spectral bands for rapid online applications. With continuing technological advances and greater accessibility to and availability of optoelectronic imaging sensors and spectral imaging spectrographs, the range of implementation for hyperspectral imaging has been broadening across quality and safety inspection needs in the food and agricultural industries. We have developed a series of food inspection imaging systems based on hyperspectral line‐scan imaging with the use of a low‐light sensitive, electron‐multiplying charge‐coupled device (EMCCD). In this methodology article, the spectral and spatial system performance of the latest generation of the ARS hyperspectral imaging system, which is capable of reflectance and fluorescence measurements in the visible and near‐infrared (NIR) spectral regions from 400 to 1000 nm, is evaluated. Results show that the spectral resolution of the system is 4.4 nm at full‐width at half‐maximum (FWHM) and 6 nm FWHM at our typical operation mode (6-pixel spectral binning). We enhanced the system throughput responses by using spectral weighting filters to better utilize the dynamic range of the analog‐to‐digital converter. With this system throughput adjustment, noise‐equivalent reflectance measurements were significantly reduced by approximately 50% in the NIR region for a range of standard diffuse reflectance targets. The responsivity of the system from 450 to 950 nm was determined to be linear. Keywords. Fluorescence, Hyperspectral, Line‐scan imaging, Reflectance, Spectral calibration. hemical and biological food properties can often techniques have been developed to combine the advantages be nondestructively assessed by spectroscopic of spectroscopy and machine vision in addressing agro‐food methods. Machine vision is already ubiquitous for quality and safety problems (Kim et al., 2001; Lu, 2003; Go‐ sorting items by their appearance, but convention‐ wen et al., 2007; Park et al., 2007). Hyperspectral imaging alC monochromatic or RGB‐based imaging methods em- methods provide full‐spectrum data, often hundreds of spec‐ ployed in conventional machine vision techniques are tral data points, for every pixel in the image of a food product, limited to evaluating basic physical attributes, such as the enabling spectral and spatial analysis for correlation to com‐ size, shape, and color of agricultural commodities (Aleixos position, contaminants, and physical attributes such as size et al., 2002; Blasco et al., 2007; Miller and Drouillard, 2001). and shape. However, high speeds and product volumes pres‐ In contrast, spectroscopic (i.e., hyperspectral) imaging can ent significant challenges to improving real‐time online allow for a thorough characterization of physical, chemical, spectral imaging inspection across agro‐food industries. and biological perturbations indicative of agro‐food product Fundamentally, there are two ways to acquire hyperspec‐ safety and quality. For these reasons, hyperspectral imaging tral imaging data from an object: the band sequential imaging method and the pushbroom (line‐scanning) imaging method. The band sequential imaging method captures a full spatial Submitted for review in July 2010 as manuscript number IET 8659; scene at each wavelength in a series of wavelengths to form approved for publication by the Information & Electrical Technologies a three‐dimensional hyperspectral image cube. The push‐ Division of ASABE in January 2011. broom (line‐scan) method captures a single line of spatial in‐ Company and product names are used for clarity and do not imply any formation containing full‐spectrum data for every spatial endorsement by USDA to the exclusion of other comparable products. pixel in the line, and the composite of a set of many spatial The authors are Moon S. Kim, Research Scientist, Kuanglin Chao, Research Scientist, Diane E. Chan, Agricultural Engineer, Won Jun, line‐scans forms a hyperspectral image cube. Research Associate, and Alan M. Lefcourt, ASABE Member Engineer, Since the early 2000s, applications of the hyperspectral Research Scientist, USDA‐ARS Environmental Microbial and Food Safety techniques as nondestructive means to assess safety and qual‐ Laboratory (EMFSL), Beltsville Agricultural Research Center, Beltsville, ity aspects of agricultural products have been increasing (Go‐ Maryland; Stephen R. Delwiche, Research Scientist, USDA‐ARS Food Quality Laboratory, Beltsville Agricultural Research Center, Beltsville, wen et al., 2007). Researchers at the USDA Agricultural Maryland; and Sukwon Kang, ASABE Member, Agricultural Engineer, Research Service (ARS) have developed several versions of and Kangjin Lee, Agricultural Engineer, National Institute of Agricultural hyperspectral imaging systems along with image analysis Engineering, Rural Development Administration, Suwon, Korea. techniques to address food safety and quality concerns for Corresponding author: Moon S. Kim, USDA‐ARS EMFSL, Beltsville food production and in food processing (Kim et al., 2001; Agricultural Research Center, Bldg 303 BARC‐East, 10300 Baltimore Ave., Beltsville, MD 20705‐2350; phone 301‐504‐8462; fax: 301‐504‐ Yang et al., 2006; Kim et al., 2008; Jun et al., 2009). The ARS 9466; e‐mail: [email protected]. hyperspectral imaging systems utilize the pushbroom line‐ Transactions of the ASABE Vol. 54(2): 703-711 2011 American Society of Agricultural and Biological Engineers ISSN 2151-0032 703 scan approach. Due to speed restrictions for data acquisition MATERIALS AND METHODS and processing, hyperspectral imaging has often been used as LINE‐SCAN HYPERSPECTRAL IMAGING SYSTEM a research tool to develop more rapid multispectral methods, The line‐scan hyperspectral imaging platform shown in based on only a few spectral bands, that can operate at higher figure 1 was designed for acquisition of reflectance measure‐ speeds for online applications (Kim et al., 2001). However, ments in the range of approximately 400 to 1000 nm and newer line‐scan‐based imaging technologies can effectively fluorescence measurements from approximately 420 to deliver high‐speed online safety and quality inspection of 700Ănm (with UV‐A excitation). Another consideration was food and agricultural products on high‐throughput process‐ flexibility for imaging objects of varying sizes. The platform ing lines in both hyperspectral and multispectral domains is able to accommodate imaging of individual grains of wheat (Chao et al., 2007). We have also developed a new line‐scan to whole chicken carcasses (i.e., adjustable field of view from hyperspectral imaging platform for high‐speed inspection on 80 to 300 mm). commercial processing lines that is capable of simultaneous multiple inspection algorithms for addressing different safety Sensing Components and quality problems (Kim et al., 2008). The line‐scan hyperspectral imaging system utilizes a In spite of the ubiquitous applications of hyperspectral low‐light sensitive electron‐multiplying charge‐coupled‐ imaging techniques and their development by independent device (EMCCD) camera (MegaLuca, Andor Technology, laboratories and/or commercial entities in agricultural fields, Inc., Belfast, Northern Ireland). The EMCCD has 1002 × details encompassing wavelength calibrations and character‐ 1004 pixels and is thermoelectrically cooled to ‐20°C via a ization of spectral‐dependent response are limited. Unlike two‐stage Peltier device. The EMCCD is equipped with a conventional spectroscopic systems, hyperspectral imaging 12.5 MHz pixel readout rate and 14‐bit A/D digitizer. Image captures spectral information at each spatial pixel location, data are transferred to a PC via USB 2.0. Both vertical and and the use of hypercube image data should not casually pro‐ horizontal pixels can be binned, and binning is achieved in ceed under the assumption that individual pixel responses are the hardware prior to data transfer to PC. equal. An imaging spectrograph (400 to 1000 nm, VNIR Hyper‐ In this methodology article, we present detailed system in‐ spec, Headwall Photonics, Inc., Fitchburg, Mass.) and a formation along with system characterization and illustrate C‐mount object lens (Schneider Optics, Van Nuys, Cal.) are a method to adjust spectral throughput to further enhance the attached to the EMCCD. The instantaneous field of view spectral‐spatial responses of the system. Characterization of (IFOV) is limited to a thin line by the spectrograph slit size. the system included wavelength calibration and examination A range of aperture slits (10, 25, 60, and 100 mm) is available, of the effects of pixel binning (in the spectral dimension) on and the slit can be readily changed. Typically, the 60 mm slit spectral bandwidth (resolution). Wavelength‐dependent re‐ is used most often in our system. Through the slit, light from sponse characteristics included (1) noise, in terms of signal‐ the scanned line of the IFOV is dispersed by the dispersive to‐noise ratio (SNR) or noise‐equivalent reflectance (NER), grating and projected onto the EMCCD. Therefore, for each and (2) responsivity, in terms of linearity with respect to line‐scan, a two‐dimensional (spatial and spectral) image is changes in exposure time for a fixed