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Recent Submissions

  • PublicationOpen Access
    Preliminary investigation into the development or acquisition of advanced digital post-mortem inspection systems for Irish abattoirs
    (Teagasc, 2024-04-30) Duffy, G; Pinheiro, J; Boyle, L; Burgess, K; Gomes, C; Department of Agriculture Food and the Marine
    Following animal slaughter, the Official Veterinarians (OV) conduct a post-mortem inspection (PMI) of the carcass. Traditional meat inspection (EU 854/2004) is based on visual inspection, palpation and incision to identify clinical illness or pathological lesions in the animal or bird presented for slaughter. However, it is recognised that many currently relevant food safety hazards (microbiological and chemical residues) are “invisible” to such traditional meat inspection methods and accordingly the European Food Safety Authority (EFSA) published Scientific Opinions in 2011, 2012 and 2013, which called for a modernisation of meat safety assurance systems. It was considered that palpation/incisions used in current PMI poses a risk of microbial cross-contamination and the EFSA Opinions called for improved Food Chain Information (FCI), enabling risk-differentiation of animals presented for slaughter, and for low risk animals, a move to visual only (VOI) carcass inspection, while maintaining a detailed inspection with incision and palpation on high risk animals. These recommendations were subsequently adopted into EU Regulations (218/2014; 2017/625; 2019/624; 2019/627). Additionally, an EFSA Scientific Opinion in 2022 recommended monitoring of tail lesions in pig carcasses at slaughter to monitor pig welfare and enable feedback to farmers.
  • PublicationOpen Access
    Utilization of microwave dielectric microscopy for assessing compositional and technological quality of beef patties
    (2024-08-28) Rady, Ahmed M.; Dimitrakis, Georgios; Waltson, Nik; Tiwari, Brijesh; Hamill, Ruth M.
    Monitoring the quality of value-added meat products is a challenging task to ensure the desired nutrients and sensorial by consumers and promote traceability in the meat industry. In this study, a microwave dielectric spectroscopy was feasibly investigates as an offline sensing system for beef patties. The benchtop system that works in the transmission mode (300 kHz to 3 GHz) comprised a parameters test set device coupled with a network analyzer, and the studied model system was beef patties that was formulated through six fat ratios (5-30%), two mincing levels (coarse, fine), and three muscles (round, brisket, and chuck steak), which resulted in testing 360 samples. Critical quality attributes included Water Holding Capacity (WHC), moisture, protein and fat contents. Predictive models were developed using Partial Least Squares Regression (PLSR) and 4-fold cross validation was utilized to conclude the optimal calibration models that was then applied on a separate test set. Results obtained for the test set showed correlation coefficient(Root Mean Square Error of Prediction) or r(RMSEP) values of 84.07%(3.15%) for moisture, 86.45%(3.87%) for fat, 69.98%(1.82%) for protein, and 52.12%(11.68%) for WHC. This study presented a feasible application of microwave dielectric technology as a rapid quality assurance methodology for ensuring transparency and resilient traceability of processed meats.
  • PublicationOpen Access
    Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres
    (2025-04-22) Rady, Ahmed; Fisher, Oliver; El-Banna, Aly A. A.; Emasih, Haitham H.; Watson, Nicholas J.
  • PublicationOpen Access
    Utilization of microwave dielectric microscopy for assessing compositional and technological quality of beef patties
    (2024-08-28) Rady, Ahmed M.; Dimitrakis, Georgios; Watson, Nik; Tiwari, Brijesh; Hamill, Ruth M.
    Monitoring the quality of value-added meat products is a challenging task to ensure the desired nutrients and sensorial by consumers and promote traceability in the meat industry. In this study, a microwave dielectric spectroscopy was feasibly investigates as an offline sensing system for beef patties. The benchtop system that works in the transmission mode (300 kHz to 3 GHz) comprised a parameters test set device coupled with a network analyzer, and the studied model system was beef patties that was formulated through six fat ratios (5-30%), two mincing levels (coarse, fine), and three muscles (round, brisket, and chuck steak), which resulted in testing 360 samples. Critical quality attributes included Water Holding Capacity (WHC), moisture, protein and fat contents. Predictive models were developed using Partial Least Squares Regression (PLSR) and 4-fold cross validation was utilized to conclude the optimal calibration models that was then applied on a separate test set. Results obtained for the test set showed correlation coefficient(Root Mean Square Error of Prediction) or r(RMSEP) values of 84.07%(3.15%) for moisture, 86.45%(3.87%) for fat, 69.98%(1.82%) for protein, and 52.12%(11.68%) for WHC. This study presented a feasible application of microwave dielectric technology as a rapid quality assurance methodology for ensuring transparency and resilient traceability of processed meats.
  • PublicationOpen Access
    Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres
    (2025-04-22) Rady, Ahmed; Fisher, Oliver; El-Banna, Aly A. A.; Emasih, Haitham H.; Watson, Nicholas J.
    Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt.

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