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RISS 인기검색어
- 검색키워드
- 저자 : A. M. Lefcourt (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Lefcourt, Alan M.,Beck, Elizabeth A.,Lo, Y. Martin,Kim, Moon S. Korean Society for Agricultural Machinery 2015 바이오시스템공학 Vol.40 No.2
Purpose: The inability to adequately judge the efficacy of cleaning and sanitation procedures in deli departments is a recognized food safety concern. In a prior study, our research group demonstrated that visual inspection of cleaned produce processing surfaces could be enhanced through the use of a portable fluorescence imaging device that detected residual produce residues. Methods: To explore the feasibility of using fluorescence imaging to similarly detect residual deli residues, spectra of American, Cheddar, Provolone, and Swiss cheeses and of processed chicken, ham, roast beef, and turkey were acquired using a laboratory hyperspectral imaging system. Circular punches of these commodities were placed onto stainless steel and high density polyethylene coupons for imaging. The coupon materials were selected to represent common surfaces found in deli departments. Results: Analysis of hyperspectral fluorescence images showed that cheeses exhibited peaks in the blue-green region and at around 675 nm. Meats exhibited peaks in the blue-green region with one of four ham and one of four chicken brands exhibiting peaks at around 675 nm, presumably due to use of plant-derived additives. When commodities were intermittently imaged over two weeks, locations of spectral peaks were preserved while intensity of peaks at shorter wavelengths increased with time. Conclusion: These results demonstrate that fluorescence imaging techniques have the potential to enhance surface hygiene inspection in deli departments and, given the immediate availability of imaging results, to help optimize routine cleaning procedures.
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( Alan M. Lefcourt ),( Elizabeth A. Beck ),( Y. Martin Lo ),( Moon S. Kim ) 한국농업기계학회 2015 바이오시스템공학 Vol.40 No.2
Purpose: The inability to adequately judge the efficacy of cleaning and sanitation procedures in deli departments is arecognized food safety concern. In a prior study, our research group demonstrated that visual inspection of cleaned produceprocessing surfaces could be enhanced through the use of a portable fluorescence imaging device that detected residualproduce residues. Methods: To explore the feasibility of using fluorescence imaging to similarly detect residual deli residues,spectra of American, Cheddar, Provolone, and Swiss cheeses and of processed chicken, ham, roast beef, and turkey wereacquired using a laboratory hyperspectral imaging system. Circular punches of these commodities were placed ontostainless steel and high density polyethylene coupons for imaging. The coupon materials were selected to representcommon surfaces found in deli departments. Results: Analysis of hyperspectral fluorescence images showed that cheesesexhibited peaks in the blue-green region and at around 675 nm. Meats exhibited peaks in the blue-green region with one offour ham and one of four chicken brands exhibiting peaks at around 675 nm, presumably due to use of plant-derivedadditives. When commodities were intermittently imaged over two weeks, locations of spectral peaks were preserved whileintensity of peaks at shorter wavelengths increased with time. Conclusion: These results demonstrate that fluorescenceimaging techniques have the potential to enhance surface hygiene inspection in deli departments and, given the immediateavailability of imaging results, to help optimize routine cleaning procedures.
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Yang, C.C.,Kim, M.S.,Kang, S.,Cho, B.K.,Chao, K.,Lefcourt, A.M.,Chan, D.E. Applied Science Publishers 2012 Journal of food engineering Vol.108 No.2
This research developed and evaluated three multispectral algorithms derived from hyperspectral line-scan fluorescence imaging using violet LED excitation for the detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680, 684, 720, and 780nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on high-speed apple processing lines. This fast and non-destructive method for detection of fecal contamination can be implemented in the food and agricultural industries to help in risk reduction and food safety assurance for preventing or minimizing the potential foodborne illness.
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