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Schmilovitch, Z., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Mizrach, A., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Alchanatis, V., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Kritzman, G., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Korotic, R., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Irudayaraj, J., Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
Debroy, C., Department of Veterinary Science, Pennsylvania State University, University Park, PA, United States
Bacteria detection methods that are presently used in laboratories and quality control inspections, such as serological testing, biological enrichment, culturing, and gas chromatograph mass spectroscopy (GCMS), are expensive, labor intensive, and time consuming. Therefore, in order to ensure that consumers receive a safe and high-quality product, rapid and reliable methods need to be developed for detection of pathogens. Raman spectroscopy, an optical technique based on light scattering, was investigated as a means of rapid on-site produce safety assessment. In this study, a dispersive system spectrophotometer, with a 785 nm diode laser, was employed. Chemometric methods such as partial least squares (PLS) regression and classification analysis were used to evaluate low-concentration suspensions of Erwinia carotovora pv. carotovora (ECC) and Clavibacter michiganense (CBM). The pathogens chosen represent Gram-positive and Gram-negative bacteria. A clear distinction between samples containing bacteria and clean samples was obtained by this method. The system was able to determine bacterial concentrations within 2% of the level in the basic bacteria suspension, based on PLS regression models. Classification analysis enables researchers to detect the presence of each of the tested bacteria in mixed-bacteria suspensions that contain between 10 and 100 cells/mL of ECC and CBM. © 2005 American Society of Agricultural Engineers.
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Detection of bacteria with low-resolution raman spectroscopy
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Schmilovitch, Z., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Mizrach, A., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Alchanatis, V., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Kritzman, G., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Korotic, R., Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Irudayaraj, J., Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
Debroy, C., Department of Veterinary Science, Pennsylvania State University, University Park, PA, United States
Detection of bacteria with low-resolution raman spectroscopy
Bacteria detection methods that are presently used in laboratories and quality control inspections, such as serological testing, biological enrichment, culturing, and gas chromatograph mass spectroscopy (GCMS), are expensive, labor intensive, and time consuming. Therefore, in order to ensure that consumers receive a safe and high-quality product, rapid and reliable methods need to be developed for detection of pathogens. Raman spectroscopy, an optical technique based on light scattering, was investigated as a means of rapid on-site produce safety assessment. In this study, a dispersive system spectrophotometer, with a 785 nm diode laser, was employed. Chemometric methods such as partial least squares (PLS) regression and classification analysis were used to evaluate low-concentration suspensions of Erwinia carotovora pv. carotovora (ECC) and Clavibacter michiganense (CBM). The pathogens chosen represent Gram-positive and Gram-negative bacteria. A clear distinction between samples containing bacteria and clean samples was obtained by this method. The system was able to determine bacterial concentrations within 2% of the level in the basic bacteria suspension, based on PLS regression models. Classification analysis enables researchers to detect the presence of each of the tested bacteria in mixed-bacteria suspensions that contain between 10 and 100 cells/mL of ECC and CBM. © 2005 American Society of Agricultural Engineers.
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