The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens

Chun Chieh Yang, Moon S. Kim, Yung-Kun Chuang, Hoyoung Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

Original languageEnglish
Title of host publicationSensing for Agriculture and Food Quality and Safety V
Volume8721
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventSensing for Agriculture and Food Quality and Safety V - Baltimore, MD, United States
Duration: Apr 30 2013May 1 2013

Conference

ConferenceSensing for Agriculture and Food Quality and Safety V
CountryUnited States
CityBaltimore, MD
Period4/30/135/1/13

Fingerprint

feces
Contamination
contamination
Imaging
Imaging System
Imaging systems
Water
Imaging techniques
Spinach
Line
spinach
Fluorescence
leaves
Food safety
Food processing
Hyperspectral Imaging
water
Hyperspectral Image
Vegetables
Image Analysis

Keywords

  • Food safety
  • Hyperspectral image
  • Leafy greens
  • Line scan
  • Machine vision
  • Multispectral image

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Yang, C. C., Kim, M. S., Chuang, Y-K., & Lee, H. (2013). The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens. In Sensing for Agriculture and Food Quality and Safety V (Vol. 8721). [87210G] https://doi.org/10.1117/12.2016030

The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens. / Yang, Chun Chieh; Kim, Moon S.; Chuang, Yung-Kun; Lee, Hoyoung.

Sensing for Agriculture and Food Quality and Safety V. Vol. 8721 2013. 87210G.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yang, CC, Kim, MS, Chuang, Y-K & Lee, H 2013, The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens. in Sensing for Agriculture and Food Quality and Safety V. vol. 8721, 87210G, Sensing for Agriculture and Food Quality and Safety V, Baltimore, MD, United States, 4/30/13. https://doi.org/10.1117/12.2016030
Yang CC, Kim MS, Chuang Y-K, Lee H. The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens. In Sensing for Agriculture and Food Quality and Safety V. Vol. 8721. 2013. 87210G https://doi.org/10.1117/12.2016030
Yang, Chun Chieh ; Kim, Moon S. ; Chuang, Yung-Kun ; Lee, Hoyoung. / The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens. Sensing for Agriculture and Food Quality and Safety V. Vol. 8721 2013.
@inproceedings{178e4a44874243a89e034645c8b2b3db,
title = "The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens",
abstract = "This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.",
keywords = "Food safety, Hyperspectral image, Leafy greens, Line scan, Machine vision, Multispectral image",
author = "Yang, {Chun Chieh} and Kim, {Moon S.} and Yung-Kun Chuang and Hoyoung Lee",
year = "2013",
doi = "10.1117/12.2016030",
language = "English",
isbn = "9780819495129",
volume = "8721",
booktitle = "Sensing for Agriculture and Food Quality and Safety V",

}

TY - GEN

T1 - The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy greens

AU - Yang, Chun Chieh

AU - Kim, Moon S.

AU - Chuang, Yung-Kun

AU - Lee, Hoyoung

PY - 2013

Y1 - 2013

N2 - This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

AB - This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

KW - Food safety

KW - Hyperspectral image

KW - Leafy greens

KW - Line scan

KW - Machine vision

KW - Multispectral image

UR - http://www.scopus.com/inward/record.url?scp=84881152649&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84881152649&partnerID=8YFLogxK

U2 - 10.1117/12.2016030

DO - 10.1117/12.2016030

M3 - Conference contribution

SN - 9780819495129

VL - 8721

BT - Sensing for Agriculture and Food Quality and Safety V

ER -