Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products
Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present st...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Armstrong, Cheryl M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Analytical and bioanalytical chemistry - Berlin : Springer, 2002, 416(2023), 3 vom: 14. Apr., Seite 621-626 |
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| Übergeordnetes Werk: |
volume:416 ; year:2023 ; number:3 ; day:14 ; month:04 ; pages:621-626 |
| Links: |
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| DOI / URN: |
10.1007/s00216-023-04668-w |
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| Katalog-ID: |
SPR054257034 |
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| 100 | 1 | |a Armstrong, Cheryl M. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products |
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| 520 | |a Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. | ||
| 650 | 4 | |a Microbial detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sample preparation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Homogenization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sampling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Poultry |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a He, Yiping |4 aut | |
| 700 | 1 | |a Chen, Chin-Yi |4 aut | |
| 700 | 1 | |a Counihan, Katrina |4 aut | |
| 700 | 1 | |a Lee, Joe |4 aut | |
| 700 | 1 | |a Reed, Sue |4 aut | |
| 700 | 1 | |a Capobianco, Joseph |0 (orcid)0000-0003-3651-4284 |4 aut | |
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10.1007/s00216-023-04668-w doi (DE-627)SPR054257034 (SPR)s00216-023-04668-w-e DE-627 ger DE-627 rakwb eng Armstrong, Cheryl M. verfasserin aut Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. Microbial detection (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 Homogenization (dpeaa)DE-He213 Sampling (dpeaa)DE-He213 Poultry (dpeaa)DE-He213 He, Yiping aut Chen, Chin-Yi aut Counihan, Katrina aut Lee, Joe aut Reed, Sue aut Capobianco, Joseph (orcid)0000-0003-3651-4284 aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 416(2023), 3 vom: 14. Apr., Seite 621-626 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:416 year:2023 number:3 day:14 month:04 pages:621-626 https://dx.doi.org/10.1007/s00216-023-04668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 416 2023 3 14 04 621-626 |
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10.1007/s00216-023-04668-w doi (DE-627)SPR054257034 (SPR)s00216-023-04668-w-e DE-627 ger DE-627 rakwb eng Armstrong, Cheryl M. verfasserin aut Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. Microbial detection (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 Homogenization (dpeaa)DE-He213 Sampling (dpeaa)DE-He213 Poultry (dpeaa)DE-He213 He, Yiping aut Chen, Chin-Yi aut Counihan, Katrina aut Lee, Joe aut Reed, Sue aut Capobianco, Joseph (orcid)0000-0003-3651-4284 aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 416(2023), 3 vom: 14. Apr., Seite 621-626 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:416 year:2023 number:3 day:14 month:04 pages:621-626 https://dx.doi.org/10.1007/s00216-023-04668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 416 2023 3 14 04 621-626 |
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10.1007/s00216-023-04668-w doi (DE-627)SPR054257034 (SPR)s00216-023-04668-w-e DE-627 ger DE-627 rakwb eng Armstrong, Cheryl M. verfasserin aut Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. Microbial detection (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 Homogenization (dpeaa)DE-He213 Sampling (dpeaa)DE-He213 Poultry (dpeaa)DE-He213 He, Yiping aut Chen, Chin-Yi aut Counihan, Katrina aut Lee, Joe aut Reed, Sue aut Capobianco, Joseph (orcid)0000-0003-3651-4284 aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 416(2023), 3 vom: 14. Apr., Seite 621-626 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:416 year:2023 number:3 day:14 month:04 pages:621-626 https://dx.doi.org/10.1007/s00216-023-04668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 416 2023 3 14 04 621-626 |
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10.1007/s00216-023-04668-w doi (DE-627)SPR054257034 (SPR)s00216-023-04668-w-e DE-627 ger DE-627 rakwb eng Armstrong, Cheryl M. verfasserin aut Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. Microbial detection (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 Homogenization (dpeaa)DE-He213 Sampling (dpeaa)DE-He213 Poultry (dpeaa)DE-He213 He, Yiping aut Chen, Chin-Yi aut Counihan, Katrina aut Lee, Joe aut Reed, Sue aut Capobianco, Joseph (orcid)0000-0003-3651-4284 aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 416(2023), 3 vom: 14. Apr., Seite 621-626 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:416 year:2023 number:3 day:14 month:04 pages:621-626 https://dx.doi.org/10.1007/s00216-023-04668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 416 2023 3 14 04 621-626 |
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10.1007/s00216-023-04668-w doi (DE-627)SPR054257034 (SPR)s00216-023-04668-w-e DE-627 ger DE-627 rakwb eng Armstrong, Cheryl M. verfasserin aut Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. Microbial detection (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 Homogenization (dpeaa)DE-He213 Sampling (dpeaa)DE-He213 Poultry (dpeaa)DE-He213 He, Yiping aut Chen, Chin-Yi aut Counihan, Katrina aut Lee, Joe aut Reed, Sue aut Capobianco, Joseph (orcid)0000-0003-3651-4284 aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 416(2023), 3 vom: 14. Apr., Seite 621-626 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:416 year:2023 number:3 day:14 month:04 pages:621-626 https://dx.doi.org/10.1007/s00216-023-04668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 416 2023 3 14 04 621-626 |
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Enthalten in Analytical and bioanalytical chemistry 416(2023), 3 vom: 14. Apr., Seite 621-626 volume:416 year:2023 number:3 day:14 month:04 pages:621-626 |
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Enthalten in Analytical and bioanalytical chemistry 416(2023), 3 vom: 14. Apr., Seite 621-626 volume:416 year:2023 number:3 day:14 month:04 pages:621-626 |
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Armstrong, Cheryl M. @@aut@@ He, Yiping @@aut@@ Chen, Chin-Yi @@aut@@ Counihan, Katrina @@aut@@ Lee, Joe @@aut@@ Reed, Sue @@aut@@ Capobianco, Joseph @@aut@@ |
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| author |
Armstrong, Cheryl M. |
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Armstrong, Cheryl M. misc Microbial detection misc Sample preparation misc Homogenization misc Sampling misc Poultry Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products |
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Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products Microbial detection (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 Homogenization (dpeaa)DE-He213 Sampling (dpeaa)DE-He213 Poultry (dpeaa)DE-He213 |
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misc Microbial detection misc Sample preparation misc Homogenization misc Sampling misc Poultry |
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misc Microbial detection misc Sample preparation misc Homogenization misc Sampling misc Poultry |
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Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products |
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Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products |
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Armstrong, Cheryl M. |
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Analytical and bioanalytical chemistry |
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Armstrong, Cheryl M. He, Yiping Chen, Chin-Yi Counihan, Katrina Lee, Joe Reed, Sue Capobianco, Joseph |
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use of a commercial tissue dissociation system to detect salmonella-contaminated poultry products |
| title_auth |
Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products |
| abstract |
Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
| abstractGer |
Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
| abstract_unstemmed |
Abstract Successful detection of bacterial pathogens in food can be challenging due to the physical and compositional complexity of the matrix. Different mechanical/physical and chemical methods have been developed to separate microorganisms from food matrices to facilitate detection. The present study benchmarked a commercial tissue digestion system that applies both chemical and physical methods to separate microorganisms from tissues against stomaching, a standard process currently utilized by commercial and regulatory food safety laboratories. The impacts of the treatments on the physical properties of the food matrix were characterized along with the compatibility of the methods with downstream microbiological and molecular detection assays. The results indicate the tissue digestion system can significantly reduce the average particle size of the chicken sample relative to processing via a stomacher (P < 0.001) without adversely affecting either real-time PCR (qPCR) or plate counting assays, which are typically used to detect Salmonella. Furthermore, inoculated chicken treated with the GentleMACS resulted in a significant increase (P < 0.003) in the qPCR’s detection capabilities relative to stomached controls. Cohen kappa (κ) coefficient and McNemar’s test indicate the plating assays and PCR results agree with measurements obtained via the 3 M Molecular Detection System as defined in the MLG standard (κ > 0.62; P > 0.08). Collectively, the results demonstrate that the technique enables detection of pathogens in meat at lower levels of contamination using current industry standard technologies. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
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Use of a commercial tissue dissociation system to detect Salmonella-contaminated poultry products |
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https://dx.doi.org/10.1007/s00216-023-04668-w |
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He, Yiping Chen, Chin-Yi Counihan, Katrina Lee, Joe Reed, Sue Capobianco, Joseph |
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He, Yiping Chen, Chin-Yi Counihan, Katrina Lee, Joe Reed, Sue Capobianco, Joseph |
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| doi_str |
10.1007/s00216-023-04668-w |
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2024-07-04T00:43:26.047Z |
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| score |
7.401044 |