ArticleAnimal bioscience2023
Prediction of tenderness in bovine longissimus thoracis et lumborum muscles using Raman spectroscopy.
Article in Animal bioscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Machine learning-based prediction and quality control of meat and meat products: applications for intelligent quality management in the meat industry.Food science of animal resources · 2026Review
- Advances in Non-Thermal Processing of Meat and Monitoring Meat Protein Gels Through Vibrational Spectroscopy.Foods (Basel, Switzerland) · 2025Review
- Acoustic Characterization Study of Beef Loins Using Ultrasonic Transducers.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThis study was conducted to evaluate Raman spectroscopy technique as a noninvasive tool to predict meat quality traits on Braford longissimus thoracis et lumborum muscle.
methodsThirty samples of muscle from Braford steers were analyzed by classical meat quality techniques and by Raman spectroscopy with 785 nm laser excitation. Water holding capacity (WHC), intramuscular fat content (IMF), cooking loss (CL), and texture profile analysis recording hardness, cohesiveness, and chewiness were determined, along with fiber diameter and sarcomere length by scanning electron microscopy. Warner-Bratzler shear force (WBSF) analysis was used to differentiate tender and tough meat groups.
resultsHigher values of cohesiveness and CL, together with lower values of WHC, IMF, and shorter sarcomere were obtained for tender meat samples than for the tougher ones. Raman spectra analysis allows tender and tough sample differentiation. The correlation between the quality attributes predicted by Raman and the physical measurements resulted in values of R2 = 0.69 for hardness and 0,58 for WBSF. Pearson's correlation coefficient of hardness (r = 0.84) and WBSF (r = 0.79) parameters with the phenylalanine Raman signal at 1,003 cm-1, suggests that the content of this amino acid could explain the differences between samples.
conclusionRaman spectroscopy with 785 nm laser excitation is a suitable and accurate technique to identify beef with different quality attributes.
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