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

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

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.

Effects of the Aging Period and Method on the Physicochemical, Microbiological and Rheological Characteristics of Two Cuts of Charolais Beef

Wet-aging (WA) and dry-aging (DA) methods are usually used in the beef industry to satisfy the consumers' tastes; however, these methods are not suitable for all anatomical cuts. In this study, WA and DA were applied to improve the quality of two cuts of Charolais beef (Longissimus dorsi and Semitendinosus). For 60 days (i.e., 2 days, 15 days, 30 days and 60 days of sampling), a physicochemical, rheological, and microbiological analysis were performed at WA (vacuum packed; temperature of 4 ± 1 °C) and at DA (air velocity of 0.5 m/s; temperature of 1 ± 1 °C; relative humidity of 78 ± 10%) conditions. The results showed that the aging method influenced the aging loss (higher in the DA), cooking loss (higher in the WA), malondialdehyde concentration (higher in the DA) and fatty acid profile (few changes). No differences in the drip loss and color were observed, which decreased after 30 days of aging. The WBSF and TPA test values changed with increasing an aging time showing that the aging improved the tenderness of meat regardless of the aging method. Moreover, the aging method does not influence the microbiological profile. In conclusion, both WA and DA enhanced the quality of the different beef cuts, suggesting that an optimal method-time and aging combination could be pursued to reach the consumers' preferences.

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm^-1 with a resolution of 4 cm^-1. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R² ≥ 0.95) except for the RT variable (RMSE of 0.891% and R² of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R² of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.

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

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.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.