The authors are with the Instrumentation and Sensing Laboratory, U.S. Department of Agriculture, Agricultural Research Service, Building 303 Powder Mill Road, Beltsville, Maryland 20705. USA
Alan M. Lefcourt, Moon S. Kim, and Yud-Ren Chen, "Detection of fecal contamination on apples with nanosecond-scale time-resolved imaging of laser-induced fluorescence," Appl. Opt. 44, 1160-1170 (2005)
Detection of apples contaminated with feces is a public health concern. We found that time-resolved imaging of apples artificially contaminated with feces allowed optimization of timing parameters for detection. Dairy feces were applied to Red Delicious and Golden Delicious apples. Laser-induced fluorescence responses were imaged by use of a gated intensified camera. We developed algorithms to automatically detect contamination iteratively by using one half of the apples and validated them by applying the optimized algorithms to the remaining apples. Results show that consideration of the timing of fluorescence responses to pulsed-laser excitation can enhance detection of feces on apples.
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Number of Red Delicious Apples from a Total of 48 Apples for Testing, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. Algorithms used for detection were iteratively optimized for each image set.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Table 2
Number of Golden Delicious Apples from a Total of 48 Apples for Testing, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. Algorithms used for detection were iteratively optimized for each image set.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Results for combined image sets are the union of detection results for corresponding normal and ratio image sets.
Table 3
Number of Red Delicious Apples from a Total of 48 Apples for Validation, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. The optimized detection algorithms derived for Table 1 were applied to these image sets for validation.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Table 4
Number of Golden Delicious Apples from a Total of 48 Apples for Validation, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. The optimized detection algorithms derived for Table 2 were applied to these image sets for validation.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Results for combined image sets are the union of detection results for corresponding normal and ratio image sets.
Tables (4)
Table 1
Number of Red Delicious Apples from a Total of 48 Apples for Testing, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. Algorithms used for detection were iteratively optimized for each image set.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Table 2
Number of Golden Delicious Apples from a Total of 48 Apples for Testing, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. Algorithms used for detection were iteratively optimized for each image set.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Results for combined image sets are the union of detection results for corresponding normal and ratio image sets.
Table 3
Number of Red Delicious Apples from a Total of 48 Apples for Validation, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. The optimized detection algorithms derived for Table 1 were applied to these image sets for validation.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Table 4
Number of Golden Delicious Apples from a Total of 48 Apples for Validation, for which the Contamination Site Was Successfully Detected by Filter (682 or 700 nm), Treatment (1:2, 1:20, and 1:200 Dilutions of Dairy Feces), and Image Seta
Image sets relate to the gate timing of the ICCD camera. The optimized detection algorithms derived for Table 2 were applied to these image sets for validation.
Numbers in parentheses correspond to the window gate in nanoseconds.
For ratio images, images in the indicated image set were divided by corresponding images acquired with the 590-nm filter.
Results for combined image sets are the union of detection results for corresponding normal and ratio image sets.