Food Quality & Sensory Sciencehttp://hdl.handle.net/11019/15002024-03-28T17:02:08Z2024-03-28T17:02:08ZA Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner–Bratzler Shear Force Measured on Longissimus thoracis Muscles of Young Limousin-Sired BullsZhu, YaoGagaoua, MohammedMullen, Anne MariaKelly, Alan L.Sweeney, TorresCafferky, JamieViala, DidierHamill, Ruth M.http://hdl.handle.net/11019/36382024-03-03T04:20:55Z2021-04-27T00:00:00ZA Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner–Bratzler Shear Force Measured on Longissimus thoracis Muscles of Young Limousin-Sired Bulls
Zhu, Yao; Gagaoua, Mohammed; Mullen, Anne Maria; Kelly, Alan L.; Sweeney, Torres; Cafferky, Jamie; Viala, Didier; Hamill, Ruth M.
Beef tenderness is of central importance in determining consumers’ overall liking. To better understand the underlying mechanisms of tenderness and be able to predict it, this study aimed to apply a proteomics approach on the Longissimus thoracis (LT) muscle of young Limousin-sired bulls to identify candidate protein biomarkers. A total of 34 proteins showed differential abundance between the tender and tough groups. These proteins belong to biological pathways related to muscle structure, energy metabolism, heat shock proteins, response to oxidative stress, and apoptosis. Twenty-three putative protein biomarkers or their isoforms had previously been identified as beef tenderness biomarkers, while eleven were novel. Using regression analysis to predict shear force values, MYOZ3 (Myozenin 3), BIN1 (Bridging Integrator-1), and OGN (Mimecan) were the major proteins retained in the regression model, together explaining 79% of the variability. The results of this study confirmed the existing knowledge but also offered new insights enriching the previous biomarkers of tenderness proposed for Longissimus muscle.
peer-reviewed
2021-04-27T00:00:00ZA comparative study of thermally and chemically treated dairy waste: Impacts on soil phosphorus turnover and availability using 33P isotope dilutionKhomenko, OlhaFenton, OwenLeahy, J.J.Daly, Karenhttp://hdl.handle.net/11019/36322024-03-03T04:27:35Z2023-01-01T00:00:00ZA comparative study of thermally and chemically treated dairy waste: Impacts on soil phosphorus turnover and availability using 33P isotope dilution
Khomenko, Olha; Fenton, Owen; Leahy, J.J.; Daly, Karen
Dairy processing sludge (DPS) and DPS-derived secondary products such as struvite, biochar, hydrochar and ash (collectively known as SRUBIAS) are emerging as alternatives to fertilizers produced from mined rock phosphate. However, little is known about how these products affect soil P availability and daily P turnover rates.. A lack of such information prevents precision nutrient management planning using these products out on farms. This study used a novel isotope dilution technique (IPD) with 33P as a tracer to compare P turnover in soils amended with chemically (alum-treated DPS and struvite) and thermally (biochar, hydrochar, ash) treated DPS. Results showed that thermally treated products exhibited poor agronomic performance as P fertilizers, potentially inhibiting P availability when applied to soils. For example, a P deficient soil amended with hydrochar treatment at the highest application rates did not record a build-up of available P to agronomic target values. In ash and biochar treated P deficient soils, available P increased but only with very high application rates of 150 and 80 mg P kg −1. The application of these products as fertilizers could have negative implications for both environmental and agronomic goals. Conversely, chemically treated fertilisers demonstrated better agronomic performance. The same agronomic target value was reached with application rates of only 20 mg P kg −1 soil for DPS and 50 mg P kg −1 soil for struvite. However, the techniques deployed revealed that these products exhibited slower rates of available and exchangeable P build-up when compared with chemical fertilisers. This suggests that these bio-based alternatives require higher application rates or earlier application times compared to conventional chemical fertilizers. Regulations providing advice on P use in agricultural soils need to account for slower P turnover in soils receiving recycled fertilizers. The IPD technique is transferrable to all wastes to examine their performance as fertilizers.
peer-received
2023-01-01T00:00:00ZFeasibility of utilizing color imaging and machine learning for adulteration detection in minced meatRady, Ahmed M.Adedeji, AkinbodeWatson, Nicholas J.http://hdl.handle.net/11019/35792024-03-05T07:22:13Z2021-12-01T00:00:00ZFeasibility of utilizing color imaging and machine learning for adulteration detection in minced meat
Rady, Ahmed M.; Adedeji, Akinbode; Watson, Nicholas J.
Meat products are popular foods and there is a need for cost-effective technologies for rapid quality assessment. In this study, RGB color imaging coupled with machine learning algorithms were investigated to detect plant and animal adulterants with ratios of from 1 to 50% in minced meat. First, samples were classified as either pure or adulterated, then adulterated samples were classified based on the adulterant's type. Finally, regression models were developed to predict the adulteration quantity. Linear discriminant classifier enhanced by bagging ensembling performed the best with overall classification accuracies for detecting pure or adulterated samples up to 99.1% using all features, and 100% using selected features. Classification accuracies for adulteration origin were 48.9–76.1% using all features and 63.8% for selected features. Regression trees were used for adulterant level quantification and the r (RPD) values were up to 98.0%(5.0) based on all features, and 94.5%(3.2) for selected features. Gray-level and co-occurrence features were more effective than other color channels in building classification and regression models. This study presents a non-invasive, and low-cost system for adulteration detection in minced meats.
peer-reviewed
2021-12-01T00:00:00ZDark-cutting beef: A brief review and an integromics meta-analysis at the proteome level to decipher the underlying pathwaysGAGAOUA, MohammedWarner, Robyn D.Purslow, PeterRamanathan, RanjithMullen, Anne MariaLópez-Pedrouso, MariaFranco, DanielLorenzo, José M.Tomasevic, IgorPicard, BrigitteTroy, DeclanTerlouw, E.M. Claudiahttp://hdl.handle.net/11019/35222024-01-25T03:14:20Z2021-11-01T00:00:00ZDark-cutting beef: A brief review and an integromics meta-analysis at the proteome level to decipher the underlying pathways
GAGAOUA, Mohammed; Warner, Robyn D.; Purslow, Peter; Ramanathan, Ranjith; Mullen, Anne Maria; López-Pedrouso, Maria; Franco, Daniel; Lorenzo, José M.; Tomasevic, Igor; Picard, Brigitte; Troy, Declan; Terlouw, E.M. Claudia
Comprehensive characterization of the post-mortem muscle proteome defines a fundamental goal in meat proteomics. During the last decade, proteomics tools have been applied in the field of foodomics to help decipher factors underpinning meat quality variations and to enlighten us, through data-driven methods, on the underlying mechanisms leading to meat quality defects such as dark-cutting meat known also as dark, firm and dry (DFD) meat. In cattle, several proteomics studies have focused on the extent to which changes in the post-mortem muscle proteome relate to dark-cutting beef development. The present data-mining study firstly reviews proteomics studies which investigated dark-cutting beef, and secondly, gathers the protein biomarkers that differ between dark-cutting versus beef with normal-pH in a unique repertoire. A list of 130 proteins from eight eligible studies was curated and mined through bioinformatics for Gene Ontology annotations, molecular pathways enrichments, secretome analysis and biological pathways comparisons to normal beef color from a previous meta-analysis. The major biological pathways underpinning dark-cutting beef at the proteome level have been described and deeply discussed in this integromics study.
peer-reviewed
2021-11-01T00:00:00Z