Metabolite profile has been used to understand the causes of variability in beef tenderness, but still little is known about how metabolites contribute to beef quality. Therefore, this study was carried out to evaluate how meat metabolites and their metabolic pathways correlate to variability in beef tenderness. Carcasses from 60 noncastrated male cattle were selected, and three 2.5-cm-thick longissimus thoracis steaks were obtained and aged (0°C to 4°C) for 7d. Warner-Bratzler shear force (WBSF) was performed (steak 1). Based on WBSF data, 2 tenderness classes (n = 30; 15 per class [tender and tough]) were created to perform sarcomere length (steak 2) and metabolom ic analysis (steak 3). Meat ultimate pH did not differ between tenderness classes. However, steaks classified as tender had greater sarcomere length (P = 0.019) than those classified as tough. Acetyl-carnitine (P = 0.026), adenine (P = 0.026), beta-alanine (P = 0.005), fumarate (P = 0.022), glutamine (P = 0.043), and valine (P = 0.030) concentration were higher in tender beef compared with tough beef. The 4 most important compounds differing between tender and tough beef were lactate, glucose, creatine, and glutamine, which may indicate that metabolic pathways such as D-glutamine and D-glutamate metabolism, beta-alanine metabolism, purine metabolism, and tricarboxylic acid cycle affected the tenderness classes. Beta-alanine (r = − 0.45), acetyl-carnitine (r = − 0.40), fumarate (r = − 0.38), valine (r = − 0.34), glucose (r = − 0.32), glutamine (r = − 0.31), and adenine (r = −0.31) were negatively correlated with WBSF values. Metabolite profile in tender beef indicated a greater oxidative metabolism, which promoted modifications in the muscle structure and proteolysis, favoring its tenderization.

Metabolomics is one of the “omics” sciences that can reveal the metabolic phenotype of organisms. This capability makes it a valuable tool for plant investigation, as plants present a vast chemical diversity. From the analytical point of view, two main techniques are frequently used in metabolomics and are often complementary: Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) spectroscopy. Here, we describe NMR and its applications in plant metabolomics. We start by contextualizing the research field to then explore study design, sample collection, sample preparation, NMR data acquisition, and data analysis, showing the key features for achieving quality and relevant results. Within these topics, the most common databases used for plant metabolites identification and assignments are listed, as these help to shorten the laborious task of metabolomics investigation of natural products. Concerning NMR parameters, we discuss the key pulse sequences, recommend acquisition parameters, and examine the data each sequence can provide. Similarly, we delve into data analysis, highlighting the most commonly used chemometric methods and how to achieve high-quality results. Therefore, this review aims to provide a comprehensive guide for NMR-based metabolomics analysis of plants.

To clarify the relationship between beef genetic selection for growth and precocity with muscle metabolism and metabolites, we performed metabolomic analysis using Longissimus lumborum (LL) muscle from Nellore cattle with divergent selection for these traits (high growth, HG; low growth, LG; high precocity, HP; low precocity, LP). Genetic potential for growth affected muscle protein and energetic metabolism. HG animals had a high concentration of arginine, carnosine, and leucine compared to LG animals. HP animals presented a high concentration of glutamine, betaine, creatinine, isoleucine, carnitine, acetyl carnitine, and lower levels of glucose compared to LP animals, affecting protein and fatty acid metabolism. Intensity of selection (high or low) was correlated with changes in protein metabolism, and the type of selection (growth or precocity) affected fat metabolism. In conclusion, both HG and HP appear to be correlated with a high concentration of protein metabolites and changes in protein metabolic pathways, while selection for precocity is more correlated with changes in fat metabolism compared to animals selected for growth.