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Relationships between volatile flavor compounds and beef flavor descriptive attributes using principal component analysis
Abstracts
endopeptidases, mainly cathepsins, in the generation of polypeptides as well as the activity of exopeptidases in the generation of small peptides. Myofibrillar proteins are the origin for most of the identified small peptides. In some of them, methionine oxidation has been observed towards the end of the process (6.5 and 9 months of processing). The search was focused on those peptides exclusively present at 9 months of curing discarding those peptides appearing at previous times. So, peptides PAPPKEE, APAPPKEE and KKDVKKPA from myosin light chain and RKKPLNI and KEEEELVAL from troponin T were found only at 9 months of processing. Thus, these peptides could be good markers for a minimum processing time of 9 months which is associated to a good quality of the ham. Conclusion: The use of proteomic tools like nESI-LC–MS/MS constitutes a powerful resource to study the generation of peptides in meat products and thus evaluate the potential of the identified small peptides PAPPKEE, APAPPKEE and KKDVKKPA from myosin light chain and RKKPLNI and KEEEELVAL from troponin T as natural markers of processing time for dry-cured ham.
141
Strecker degradation, Maillard reactions and lipid oxidation were was used to predict any of the flavor descriptive attributes. Each flavor attribute, while derived using different volatile compounds, was the result of reactions in the lean and fat portions of meat during cooking. Principal component analyses were conducted using the 13 trained descriptive major flavor attributes. Conclusion: The purpose of this analysis was to take the compounds used in the stepwise regression and see if they were related to or were accounting for similar variation in the sensory flavor descriptor. Generally, compounds related to the Strecker degradation, Maillard reactions, and lipid oxidation tended to cluster with each other. These analyses may help in reducing the number of compounds used to predict each sensory flavor descriptive attribute in future research. These results indicate that to predict any of the specific sensory flavor attributes, multiple volatile flavor compounds were needed. Keywords: Beef flavor, Beef lexicon, Volatiles doi:10.1016/j.meatsci.2014.09.097
Keywords: Dry-cured ham, Peptides, Proteomics, Quality prediction. doi:10.1016/j.meatsci.2014.09.096
81 Relationships between volatile flavor compounds and beef flavor descriptive attributes using principal component analysis H. Lairda, R.K. Millerb, C.R. Kerthb, aAnimal Science, United States, b Texas A&M University, College Station, United States
Objectives: The flavor of beef has been shown to be an important factor in consumer demand. A dynamic group of attributes contribute to beef flavor that are described in the beef flavor lexicon. These flavors are derived from volatile flavor compounds, however, it is not know what specific flavor volatile compounds are related to individual beef flavor attributes from the beef lexicon. Our objective was to determine what volatile flavor compounds are related to major flavor attributes from the beef lexicon (beef identity, browned/roasted, serumy/bloody, fat-like, metallic, liver-like, umami, and overall sweet flavor aromatics; and sweet, sour, salty and bitter basic tastes). Materials and methods: Differences in beef flavor attributes were induced through the use of beef cuts (USDA Top Choice and Select beef top loin steaks, top sirloin steaks, flat iron steak, and bottom round roasts) that have been shown to differ in flavor. Steaks, 2.54 cm thick, and roasts, 7 cm thick, were cooked to either 58, 70, or 82 °C using a gas grill or George Foreman grill for steaks and a crockpot or roasted in an oven for roasts. Additionally, 16 top loin steaks from high pH (N6.0) were cut and cooked as defined. An expert, trained flavor descriptive attribute panel evaluated the steaks and roasts using the beef lexicon with the Spectrum® Universal 16point scale (0 = none and 15 = extremely intense). Volatiles were captured from the same steaks or roasts evaluated by the panelists using the AromaTrax System. Individual volatile, aromatic compounds were identified by two panelists, each at their own sniff port. Raw meat pH, fatty acid composition, myoglobin content, and nonheme iron content were determined. Aromatic, volatile chemicals defined by the AromaTrax System (n = 413) were used. Results: Prediction equations using stepwise regression were developed that accounted for 82, 70, 92, 81, 73, 72, 61, 67, 69, 70, 69, 77, and 89% of the variability in beef identity, browned/roasted, serumy/bloody, fat-like, metallic, liver-like, umami, overall sweet, and sweet, sour, salty and bitter basic tastes, respectively. Not one single class of compounds from
82 Volatile compounds related to ground beef spoilage J. Martina, J. Lytea, J. Legakob, L. Thompsona, K. Surowiecc, J.C. Brooksa, a Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX, United States, bNutrition, Dietetics, and Food Sciences Department, Utah State University, Logan, UT, United States, cDepartment of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
Objectives: Little is known regarding the development and origin of compounds related to beef deterioration—further, less is known regarding the variation in pathways among aerobic and anaerobic package types. Of all analyses, volatile compounds may yield the most information regarding the development of spoilage indicators. Therefore, this project aimed to refine a method intended to quantify and characterize headspace volatiles within high-oxygen and anaerobic modified atmosphere packages (MAPs). Materials and methods: Raw round beef (81:19; lean:fat) was portioned into patties at seven days post-packaging. On the day of production, individual patties (150 g) were placed in polypropylene trays and sealed with a high barrier film to form one of two MAP types (n = 9 per type): 80% O2/20% CO2 (Hi-Ox MAP) or 0.4% CO/69.6% N2/30% CO2 (CO-MAP). Packages were stored at an abusive temperature (22 °C) under continuous fluorescent lighting (1530 lx) to accelerate the development of spoilage-related compounds. Package headspace was sampled at 0, 24, and 48 h using a solid phase microextraction (SPME) technique. Briefly, a single SPME fiber was exposed to the package headspace for 40 min to collect volatile compounds. Compounds were characterized and quantified of volatile compounds using a gas chromatograph/mass spectrophotometer (GC/MS). Volatile compounds were identified using a commercially available MS library and identities were validated using standard reference compounds in addition to comparisons of ion fragmentation patterns of samples and compounds of interest. Q values, calculated based on the similarities of the ion fragmentation patterns, were confirmatory of compound identity when greater than 90. Relative compound abundance was used to measure compound concentration within the headspace. Data were analyzed in a mixed model with storage length and package type as fixed effects and replication (n = 3) as a random effect. An α of 0.05 was used for mean separation. Results: As expected, temperature abuse resulted in rapid quality deterioration and the substantial development of volatile compounds. A total of 20 volatile compounds were identified in the headspace of Hi-Ox and CO-MAP packages. Of particular interest was
endopeptidases, mainly cathepsins, in the generation of polypeptides as well as the activity of exopeptidases in the generation of small peptides. Myofibrillar proteins are the origin for most of the identified small peptides. In some of them, methionine oxidation has been observed towards the end of the process (6.5 and 9 months of processing). The search was focused on those peptides exclusively present at 9 months of curing discarding those peptides appearing at previous times. So, peptides PAPPKEE, APAPPKEE and KKDVKKPA from myosin light chain and RKKPLNI and KEEEELVAL from troponin T were found only at 9 months of processing. Thus, these peptides could be good markers for a minimum processing time of 9 months which is associated to a good quality of the ham. Conclusion: The use of proteomic tools like nESI-LC–MS/MS constitutes a powerful resource to study the generation of peptides in meat products and thus evaluate the potential of the identified small peptides PAPPKEE, APAPPKEE and KKDVKKPA from myosin light chain and RKKPLNI and KEEEELVAL from troponin T as natural markers of processing time for dry-cured ham.
141
Strecker degradation, Maillard reactions and lipid oxidation were was used to predict any of the flavor descriptive attributes. Each flavor attribute, while derived using different volatile compounds, was the result of reactions in the lean and fat portions of meat during cooking. Principal component analyses were conducted using the 13 trained descriptive major flavor attributes. Conclusion: The purpose of this analysis was to take the compounds used in the stepwise regression and see if they were related to or were accounting for similar variation in the sensory flavor descriptor. Generally, compounds related to the Strecker degradation, Maillard reactions, and lipid oxidation tended to cluster with each other. These analyses may help in reducing the number of compounds used to predict each sensory flavor descriptive attribute in future research. These results indicate that to predict any of the specific sensory flavor attributes, multiple volatile flavor compounds were needed. Keywords: Beef flavor, Beef lexicon, Volatiles doi:10.1016/j.meatsci.2014.09.097
Keywords: Dry-cured ham, Peptides, Proteomics, Quality prediction. doi:10.1016/j.meatsci.2014.09.096
81 Relationships between volatile flavor compounds and beef flavor descriptive attributes using principal component analysis H. Lairda, R.K. Millerb, C.R. Kerthb, aAnimal Science, United States, b Texas A&M University, College Station, United States
Objectives: The flavor of beef has been shown to be an important factor in consumer demand. A dynamic group of attributes contribute to beef flavor that are described in the beef flavor lexicon. These flavors are derived from volatile flavor compounds, however, it is not know what specific flavor volatile compounds are related to individual beef flavor attributes from the beef lexicon. Our objective was to determine what volatile flavor compounds are related to major flavor attributes from the beef lexicon (beef identity, browned/roasted, serumy/bloody, fat-like, metallic, liver-like, umami, and overall sweet flavor aromatics; and sweet, sour, salty and bitter basic tastes). Materials and methods: Differences in beef flavor attributes were induced through the use of beef cuts (USDA Top Choice and Select beef top loin steaks, top sirloin steaks, flat iron steak, and bottom round roasts) that have been shown to differ in flavor. Steaks, 2.54 cm thick, and roasts, 7 cm thick, were cooked to either 58, 70, or 82 °C using a gas grill or George Foreman grill for steaks and a crockpot or roasted in an oven for roasts. Additionally, 16 top loin steaks from high pH (N6.0) were cut and cooked as defined. An expert, trained flavor descriptive attribute panel evaluated the steaks and roasts using the beef lexicon with the Spectrum® Universal 16point scale (0 = none and 15 = extremely intense). Volatiles were captured from the same steaks or roasts evaluated by the panelists using the AromaTrax System. Individual volatile, aromatic compounds were identified by two panelists, each at their own sniff port. Raw meat pH, fatty acid composition, myoglobin content, and nonheme iron content were determined. Aromatic, volatile chemicals defined by the AromaTrax System (n = 413) were used. Results: Prediction equations using stepwise regression were developed that accounted for 82, 70, 92, 81, 73, 72, 61, 67, 69, 70, 69, 77, and 89% of the variability in beef identity, browned/roasted, serumy/bloody, fat-like, metallic, liver-like, umami, overall sweet, and sweet, sour, salty and bitter basic tastes, respectively. Not one single class of compounds from
82 Volatile compounds related to ground beef spoilage J. Martina, J. Lytea, J. Legakob, L. Thompsona, K. Surowiecc, J.C. Brooksa, a Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX, United States, bNutrition, Dietetics, and Food Sciences Department, Utah State University, Logan, UT, United States, cDepartment of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
Objectives: Little is known regarding the development and origin of compounds related to beef deterioration—further, less is known regarding the variation in pathways among aerobic and anaerobic package types. Of all analyses, volatile compounds may yield the most information regarding the development of spoilage indicators. Therefore, this project aimed to refine a method intended to quantify and characterize headspace volatiles within high-oxygen and anaerobic modified atmosphere packages (MAPs). Materials and methods: Raw round beef (81:19; lean:fat) was portioned into patties at seven days post-packaging. On the day of production, individual patties (150 g) were placed in polypropylene trays and sealed with a high barrier film to form one of two MAP types (n = 9 per type): 80% O2/20% CO2 (Hi-Ox MAP) or 0.4% CO/69.6% N2/30% CO2 (CO-MAP). Packages were stored at an abusive temperature (22 °C) under continuous fluorescent lighting (1530 lx) to accelerate the development of spoilage-related compounds. Package headspace was sampled at 0, 24, and 48 h using a solid phase microextraction (SPME) technique. Briefly, a single SPME fiber was exposed to the package headspace for 40 min to collect volatile compounds. Compounds were characterized and quantified of volatile compounds using a gas chromatograph/mass spectrophotometer (GC/MS). Volatile compounds were identified using a commercially available MS library and identities were validated using standard reference compounds in addition to comparisons of ion fragmentation patterns of samples and compounds of interest. Q values, calculated based on the similarities of the ion fragmentation patterns, were confirmatory of compound identity when greater than 90. Relative compound abundance was used to measure compound concentration within the headspace. Data were analyzed in a mixed model with storage length and package type as fixed effects and replication (n = 3) as a random effect. An α of 0.05 was used for mean separation. Results: As expected, temperature abuse resulted in rapid quality deterioration and the substantial development of volatile compounds. A total of 20 volatile compounds were identified in the headspace of Hi-Ox and CO-MAP packages. Of particular interest was