Dr. Seung-Chul Yoon Profile

Dr. Seung-Chul Yoon

Electronics Engineer at Agricultural Research Service

SPIE Involvement:

Conference Program Committee | Author | Science Fair Judge

Publications (20)

Proceedings Article | 7 June 2024 Presentation

Enhancing food safety: real-time detection of foreign materials using high-speed NIR hyperspectral imaging

Seung-Chul Yoon, Zirak Khan

Proceedings Volume PC13060, PC1306002 (2024) https://doi.org/10.1117/12.3013745

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Proceedings Article | 14 May 2019 Presentation

Multi-sensor data fusion for detection of woody breast myopathy in the poultry industry (Conference Presentation)

Seung-Chul Yoon, Brian Bowker, Kurt Lawrence, Hong Zhuang

Proceedings Volume 11016, 1101605 (2019) https://doi.org/10.1117/12.2519084

KEYWORDS: Breast, Data fusion, Machine vision, Cameras, Visualization, Imaging systems, Information fusion, Sensors

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Proceedings Article | 15 May 2018 Presentation

Real-time machine vision system for online detection of wooden breast myopathy in chicken fillets (Conference Presentation)

Seung-Chul Yoon, Brian Bowker, Kurt Lawrence, Hong Zhuang

Proceedings Volume 10665, 106650H (2018) https://doi.org/10.1117/12.2305149

KEYWORDS: Breast, Machine vision, Imaging systems, Computing systems, Connective tissue, CMOS cameras, Light sources and illumination, Shape analysis

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Proceedings Article | 6 June 2017 Presentation

Big data analytics in hyperspectral imaging for detection of microbial colonies on agar plates (Conference Presentation)

Seung-Chul Yoon, Bosoon Park, Kurt Lawrence

Proceedings Volume 10217, 102170M (2017) https://doi.org/10.1117/12.2262800

KEYWORDS: Hyperspectral imaging, Analytics, Pathogens, Light scattering, Machine learning, Mining, Algorithm development, Agriculture, Optical imaging, Reflectivity

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SPIE Journal Paper | 10 August 2015 Open Access

Hyperspectral imaging using RGB color for foodborne pathogen detection

Seung-Chul Yoon, Tae-Sung Shin, Kurt Lawrence, Gerald Heitschmidt, Bosoon Park, Gary Gamble

JEI, Vol. 24, Issue 04, 043008, (August 2015) https://doi.org/10.1117/12.10.1117/1.JEI.24.4.043008

KEYWORDS: Hyperspectral imaging, RGB color model, Reflectivity, Pathogens, Image classification, Cameras, Performance modeling, Reconstruction algorithms, Algorithm development, Statistical modeling

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Proceedings Article | 13 March 2015 Paper

Hyperspectral imaging using a color camera and its application for pathogen detection

Seung-Chul Yoon, Tae-Sung Shin, Gerald Heitschmidt, Kurt Lawrence, Bosoon Park, Gary Gamble

Proceedings Volume 9405, 940506 (2015) https://doi.org/10.1117/12.2083137

KEYWORDS: Hyperspectral imaging, RGB color model, Cameras, Pathogens, Image classification, Reflectivity, Calibration, Algorithm development, Reconstruction algorithms, Xenon

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This paper reports the results of a feasibility study for the development of a hyperspectral image recovery (reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of foodborne pathogens. The target bacterial pathogens were the six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown in Petri dishes of Rainbow agar. The purpose of the feasibility study was to evaluate whether a DSLR camera (Nikon D700) could be used to predict hyperspectral images in the wavelength range from 400 to 1,000 nm and even to predict the types of pathogens using a hyperspectral STEC classification algorithm that was previously developed. Unlike many other studies using color charts with known and noise-free spectra for training reconstruction models, this work used hyperspectral and color images, separately measured by a hyperspectral imaging spectrometer and the DSLR color camera. The color images were calibrated (i.e. normalized) to relative reflectance, subsampled and spatially registered to match with counterpart pixels in hyperspectral images that were also calibrated to relative reflectance. Polynomial multivariate least-squares regression (PMLR) was previously developed with simulated color images. In this study, partial least squares regression (PLSR) was also evaluated as a spectral recovery technique to minimize multicollinearity and overfitting. The two spectral recovery models (PMLR and PLSR) and their parameters were evaluated by cross-validation. The QR decomposition was used to find a numerically more stable solution of the regression equation. The preliminary results showed that PLSR was more effective especially with higher order polynomial regressions than PMLR. The best classification accuracy measured with an independent test set was about 90%. The results suggest the potential of cost-effective color imaging using hyperspectral image classification algorithms for rapidly differentiating pathogens in agar plates.

Proceedings Article | 7 March 2014 Paper

Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

Seung-Chul Yoon, Tae-Sung Shin, Bosoon Park, Kurt Lawrence, Gerald Heitschmidt

Proceedings Volume 9024, 90240I (2014) https://doi.org/10.1117/12.2041085

KEYWORDS: RGB color model, Hyperspectral imaging, Reflectivity, Data modeling, Image classification, Performance modeling, Statistical modeling, Cameras, Pathogens, Image restoration

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Proceedings Article | 29 May 2013 Paper

Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

Young Wook Seo, Seung Chul Yoon, Bosoon Park, Arthur Hinton, William Windham, Kurt Lawrence

Proceedings Volume 8721, 87210E (2013) https://doi.org/10.1117/12.2016336

KEYWORDS: Principal component analysis, Hyperspectral imaging, Data analysis, Reflectivity, Microorganisms, Neodymium, Infrared imaging, Plating, Image classification, Thermal modeling

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Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficient=0.88) on the validation set.

Proceedings Article | 16 May 2012 Paper

Hyperspectral imaging for detection of non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups on spread plates of mixed cultures

Seung Chul Yoon, William Windham, Scott Ladely, Gerald Heitschmidt, Kurt Lawrence, Bosoon Park, Neelam Narang, William Cray

Proceedings Volume 8369, 836909 (2012) https://doi.org/10.1117/12.919631

KEYWORDS: Hyperspectral imaging, Reflectivity, Data modeling, Statistical modeling, Absorbance, Pathogens, Image classification, Image segmentation, Imaging systems, Image analysis

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We investigated the feasibility of visible and near-infrared (VNIR) hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on spread plates of mixed cultures. Although the traditional culture method is still the "gold standard" for presumptive-positive pathogen screening, it is time-consuming, labor-intensive, not effective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. A previous study was performed using the data obtained from pure cultures individually inoculated on spot and/or spread plates in order to develop multivariate classification models differentiating each colony of the six non-O157 STEC serogroups and to optimize the models in terms of parameters. This study dealt with the validation of the trained and optimized models with a test set of new independent samples obtained from colonies on spread plates of mixed cultures. A new validation protocol appropriate to a hyperspectral imaging study for mixed cultures was developed. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates were prepared, where O45, O111 and O121 serogroups were inoculated into all six plates and each of O45, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with standard normal variate and detrending (SNVD), first derivative, spectral smoothing, and k-nearest neighbor classification (kNN, k=3) of scores in the principal component subspace spanned by 6 principal components. The independent testing results showed 95% overall detection accuracy at pixel level and 97% at colony level. The developed model was proven to be still valid even for the independent samples although the size of a test set was small and only one experiment was performed. This study was an important first step in validating and updating multivariate classification models for rapid screening of ground beef samples contaminated by non-O157 STEC pathogens using hyperspectral imaging.

Proceedings Article | 5 May 2012 Paper

Identification and characterization of Salmonella serotypes using DNA spectral characteristics by fourier transform infrared

Jaya Sundaram, Bosoon Park, Arthur Hinton, Seung Chul Yoon, Kurt Lawrence

Proceedings Volume 8369, 83690O (2012) https://doi.org/10.1117/12.918585

KEYWORDS: Principal component analysis, FT-IR spectroscopy, Proteins, Absorption, Bacteria, Spectroscopy, Diamond, Crystals, Macromolecules, Statistical analysis

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Proceedings Article | 5 May 2012 Paper

Classification of Shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery

Bosoon Park, William Windham, Scott Ladely, Prudhvi Gurram, Heesung Kwon, Seung-Chul Yoon, Kurt Lawrence, Neelam Narang, William Cray

Proceedings Volume 8369, 83690L (2012) https://doi.org/10.1117/12.919695

KEYWORDS: Microscopes, Hyperspectral imaging, Pathogens, Glasses, Bacteria, Image classification, Imaging systems, Image acquisition, Scattering, Crystals

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Proceedings Article | 3 June 2011 Paper

Improved egg crack detection algorithm for modified pressure imaging system

Seung Chul Yoon, Kurt Lawrence, Deana Jones, Gerald Heitschmidt, Bosoon Park

Proceedings Volume 8027, 80270U (2011) https://doi.org/10.1117/12.883750

KEYWORDS: Imaging systems, Image processing, Motion estimation, Cameras, Control systems, Algorithm development, Detection and tracking algorithms, Agriculture, Image segmentation, Image compression

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Proceedings Article | 3 June 2011 Paper

AOTF hyperspectral microscopic imaging for foodborne pathogenic bacteria detection

Bosoon Park, Sangdae Lee, Seung-Chul Yoon, Jaya Sundaram, William Windham, Arthur Hinton, Kurt Lawrence

Proceedings Volume 8027, 802707 (2011) https://doi.org/10.1117/12.884012

KEYWORDS: Hyperspectral imaging, Microscopes, Pathogens, Image quality, Luminescence, Imaging systems, Tumors, Image acquisition, Safety, Composites

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Proceedings Article | 3 June 2011 Paper

Rapid detection of salmonella using SERS with silver nano-substrate

J. Sundaram, B. Park, A. Hinton, W. Windham, S. Yoon, K. Lawrence

Proceedings Volume 8027, 80270B (2011) https://doi.org/10.1117/12.883996

KEYWORDS: Surface enhanced Raman spectroscopy, Silver, Raman spectroscopy, Calibration, Bacteria, Metals, Biopolymers, Data modeling, Pathogens, Raman scattering

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Proceedings Article | 20 April 2010 Paper

Development of real-time line-scan hyperspectral imaging system for online agricultural and food product inspection

Seung Chul Yoon, Bosoon Park, Kurt Lawrence, William Windham, Gerald Heitschmidt

Proceedings Volume 7676, 76760J (2010) https://doi.org/10.1117/12.850460

KEYWORDS: Imaging systems, Hyperspectral imaging, Detection and tracking algorithms, Cameras, Algorithm development, Image processing, Line scan image sensors, Multispectral imaging, Forward error correction, Reflectivity

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This paper reports a recent development of a line-scan hyperspectral imaging system for real-time multispectral imaging applications in agricultural and food industries. The hyperspectral imaging system consisted of a spectrograph, an EMCCD camera, and application software. The real-time multispectral imaging with the developed system was possible due to (1) data binning, especially a unique feature of the EMCCD sensor allowing the access to non-contiguous multispectral bands, (2) an image processing algorithm designed for real-time multispectral imaging, and (3) the design and implementation of the real-time application software. The imaging system was developed as a poultry inspection instrument determining the presence of surface feces on poultry carcasses moving at commercial poultry processing line speeds up to 180 birds per minute. The imaging system can be easily modifiable to solve other real-time inspection/sorting problems. Three wavelengths at 517 nm, 565 nm and 802 nm were selected for real-time fecal detection imaging. The fecal detection algorithm was based on dual band ratios of 565nm/517nm and 802nm/517nm followed by thresholding. The software architecture was based on a ping pong memory and a circular buffer for the multitasking of image grabbing and processing. The software was written in Microsoft Visual C++. An image-based internal triggering (i.e. polling) algorithm was developed to determine the start and end positions of birds. Twelve chickens were used for testing the imaging system at two different speeds (140 birds per minute and 180 bird per minute) in a pilot-scale processing line. Four types of fecal materials (duodenum, ceca, colon and ingesta) were used for the evaluation of the detection algorithm. The software grabbed and processed multispectral images of the dimension 118 (line scans) x 512 (height) x 3 (bands) pixels obtained from chicken carcasses moving at the speed up to 180 birds per minute (a frame rate 286 Hz). Intensity calibration, detection algorithm, displaying and saving were performed within the real-time deadlines.

Proceedings Article | 20 April 2010 Paper

Line-scan hyperspectral imaging for real-time poultry fecal detection

Bosoon Park, Seung-Chul Yoon, William Windham, Kurt Lawrence, G. Heitschmidt, Moon Kim, Kaunglin Chao

Proceedings Volume 7676, 76760I (2010) https://doi.org/10.1117/12.850258

KEYWORDS: Imaging systems, Hyperspectral imaging, Line scan image sensors, Cameras, Inspection, Image processing, Multispectral imaging, Safety, Image acquisition, Light emitting diodes

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Proceedings Article | 15 April 2008 Paper

Modified pressure system for imaging egg cracks

Kurt Lawrence, Seung Chul Yoon, Deana Jones, Gerald Heitschmidt, Bosoon Park, William Windham

Proceedings Volume 6983, 698303 (2008) https://doi.org/10.1117/12.786864

KEYWORDS: Imaging systems, Cameras, Image processing, Sensors, Image filtering, Digital filtering, Light emitting diodes, Optical filters, CCD cameras, Image enhancement

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Proceedings Article | 12 October 2007 Paper

Hyperspectral imaging for detecting pathogens grown on agar plates

Seung Chul Yoon, Kurt Lawrence, Gregory Siragusa, John Line, Bosoon Park, William Windham

Proceedings Volume 6761, 67610A (2007) https://doi.org/10.1117/12.734819

KEYWORDS: Hyperspectral imaging, Organisms, Reflectivity, Calibration, Algorithm development, Pathogens, Imaging systems, Cameras, Lamps, Blood

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Proceedings Article | 23 October 2006 Paper

Bone fragment detection in chicken breast fillets using diffuse scattering patterns of back-illuminated structured light

Seung Chul Yoon, Kurt Lawrence, Douglas Smith, Bosoon Park, William Windham

Proceedings Volume 6381, 63810G (2006) https://doi.org/10.1117/12.686262

KEYWORDS: Bone, Transmittance, Breast, Reflectivity, Light scattering, Image processing, Scattering, Cameras, X-ray imaging, Imaging systems

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Proceedings Article | 15 September 2005 Paper

Thresholding for biological material detection in real-time multispectral imaging

Seung Chul Yoon, Bosoon Park, Kurt Lawrence, William Windham

Proceedings Volume 5913, 59131O (2005) https://doi.org/10.1117/12.615364

KEYWORDS: Algorithm development, Detection and tracking algorithms, Multispectral imaging, Magnesium, Statistical analysis, Contamination, Hyperspectral imaging, Imaging systems, Target detection, Remote sensing

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Showing 5 of 20 publications

Conference Committee Involvement (16)

Sensing for Agriculture and Food Quality and Safety XVII

14 April 2025 | Orlando, Florida, United States

Sensing for Agriculture and Food Quality and Safety XVI

22 April 2024 | National Harbor, Maryland, United States

Sensing for Agriculture and Food Quality and Safety XV

2 May 2023 | Orlando, Florida, United States

Sensing for Agriculture and Food Quality and Safety XIV

5 April 2022 | Orlando, Florida, United States

Sensing for Agriculture and Food Quality and Safety XIII

12 April 2021 | Online Only, Florida, United States

Sensing for Agriculture and Food Quality and Safety XII

27 April 2020 | Online Only, California, United States

Sensing for Agriculture and Food Quality and Safety XI

16 April 2019 | Baltimore, MD, United States

Sensing for Agriculture and Food Quality and Safety X

17 April 2018 | Orlando, FL, United States

Sensing for Agriculture and Food Quality and Safety IX

13 April 2017 | Anaheim, CA, United States

Sensing for Agriculture and Food Quality and Safety VIII

20 April 2016 | Baltimore, MD, United States

Sensing for Agriculture and Food Quality and Safety VII

21 April 2015 | Baltimore, MD, United States

Image Processing: Machine Vision Applications VIII

10 February 2015 | San Francisco, California, United States

Sensing for Agriculture and Food Quality and Safety VI

6 May 2014 | Baltimore, MD, United States

Sensing for Agriculture and Food Quality and Safety V

30 April 2013 | Baltimore, Maryland, United States

Sensing for Agriculture and Food Quality and Safety IV

24 April 2012 | Baltimore, Maryland, United States

Sensing for Agriculture and Food Quality and Safety III

26 April 2011 | Orlando, Florida, United States

Showing 5 of 16 Conference Committees

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