http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
( 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.
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.
A Simple Multispectral Imaging Algorithm for Detection of Defects on Red Delicious Apples
Lee, Hoyoung,Yang, Chun-Chieh,Kim, Moon S.,Lim, Jongguk,Cho, Byoung-Kwan,Lefcourt, Alan,Chao, Kuanglin,Everard, Colm D. Korean Society for Agricultural Machinery 2014 바이오시스템공학 Vol.39 No.2
Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.
( Hoyoung Lee ),( Chun Chieh Yang ),( Moon S. Kim ),( Jongguk Lim ),( Byoung Kwan Cho ),( Alan Lefcourt ),( Kuanglin Chao ),( Colm D. Everard ) 한국농업기계학회 2014 바이오시스템공학 Vol.39 No.2
Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.