Evaluation of WBSF, Color, Cooking Loss of Longissimus Lumborum Muscle with Fiber Optic Near-Infrared Spectroscopy (FT-NIR), Depending on Aging Time

Wyrwisz, Jarosław; Moczkowska, Małgorzata; Kurek, Marcin Andrzej; Karp, Sabina; Atanasov, Atanas G.; Wierzbicka, Agnieszka

doi:10.3390/molecules24040757

Cited by 14 publications

(7 citation statements)

References 43 publications

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“…Cooking loss is an effective index to evaluate the water-holding capacity in muscle (29). It has been well-established that water is the major lost substance during cooking (30,31). Generally, acid excretion time is positively related to cooking loss, which is partly explained by the changes of material composition, pH value, and heating temperature in muscle.…”

Section: Discussionmentioning

confidence: 99%

Longissimus Dorsi Muscle Transcriptomic Analysis of Simmental and Chinese Native Cattle Differing in Meat Quality

et al. 2020

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With the rapid development of economy, the demand for beef, with regard to quantity and quality, by consumers has been increasing in China. Chinese native cattle are characterized by their abundant genetic resources, unique origins, large breeding stocks, and robust environmental adaptability. Thus, to explore the genetic mechanisms on regulating meat quality in Chinese native cattle is of great importance to satisfy increased requirements for beef production. In this study, we investigated three breeds of cattle, namely Yunling, Wenshan, and Simmental, at the age of 12 months. Animals were classified into three groups (n = 5/breed). Growth traits including body weight and body size and plasma hormone levels were measured. Body weight of Wenshan cattle was significantly lower than that of Yunling and Simmental cattle (P < 0.05). Again, body size indexes, such as withers height, body slanting length, chest circumference, and hip and rump length, were significantly lower in Wenshan cattle than those in Yunling and Simmental cattle (P < 0.05). However, there were no significant differences in those indexes between Yunling and Simmental cattle (P > 0.05). Cattle were slaughtered at the age of 18 months and then meat color, pH, pressing losses, muscle tenderness, and cooking losses were measured at 0, 1, 2, 3, 5, and 7 days. Data revealed differences in meat quality among the three breeds analyzed. Based on transcriptomic sequencing and bioinformatic analysis, we observed 3,198 differentially expressed genes related to meat quality, of which 1,750 genes were upregulated. Moreover, we found two important signaling pathways closely linked to meat quality, namely adipocytokine signaling pathway [e.g., Leptin receptor (LEPR)] and protein processing in the endoplasmic reticulum [e.g., signal transducer and activator of transcription 3 (STAT3), heat shock protein (HSPA12A), and calpain 1 (CAPN1)]. The results of transcripts were further verified by qRT-PCR. Using correlation analysis between gene expression levels and shear force, we also identified two functional genes (e.g., HSPA12A and CAPN1) associated with meat quality. Overall, this study provides new sights into novel targets and underlying mechanisms to modulate meat quality in Chinese native cattle.

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Section: Discussionmentioning

confidence: 99%

Longissimus Dorsi Muscle Transcriptomic Analysis of Simmental and Chinese Native Cattle Differing in Meat Quality

et al. 2020

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“…The prediction accuracy of the model is average, and the RPD value is only occasionally greater than 2 [27].…”

Section: Snv Msc Sg + Plsmentioning

confidence: 95%

“…Similarly, Wyrwisz et al developed an online meat tenderness measurement system using NIR spectroscopy combined with fiber optic systems. Despite efforts, model accuracy remained a concern, with occasional RPD values surpassing 2 [27]. Nonetheless, stepwise regression in the MLR method exhibited promising results, hinting at NIR spectroscopy's adaptability with different algorithms and instruments for enhanced detection [28].…”

Section: Nir Spectroscopymentioning

confidence: 99%

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Li,

Wang,

Yang

et al. 2024

Preprint

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“…Similarly, Wyrwisz et al developed an online meat tenderness measurement system using NIR spectroscopy combined with fiber optic systems. Despite efforts, the model accuracy remained a concern, with occasional RPD values surpassing 2 [27]. Nonetheless, stepwise regression in the MLR method exhibited promising results, hinting at NIR spectroscopy's adaptability to different algorithms and instruments for enhanced detection [28].…”

Section: Visible-near Infrared (Vis-nir) Spectroscopymentioning

confidence: 99%

“…Research on tenderness based on non-destructive testing technology. MSC, SG + PLS The prediction accuracy of the model is average, and the RPD value is only occasionally greater than 2[27].…”

mentioning

confidence: 99%

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Li,

Wang,

Yang

et al. 2024

Foods

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Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

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Evaluation of WBSF, Color, Cooking Loss of Longissimus Lumborum Muscle with Fiber Optic Near-Infrared Spectroscopy (FT-NIR), Depending on Aging Time

Cited by 14 publications

References 43 publications

Longissimus Dorsi Muscle Transcriptomic Analysis of Simmental and Chinese Native Cattle Differing in Meat Quality

Longissimus Dorsi Muscle Transcriptomic Analysis of Simmental and Chinese Native Cattle Differing in Meat Quality

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

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