Nontargeted Analysis of Lipid Extracts Using 1H NMR Spectroscopy Combined with Multivariate Statistical Analysis to Discriminate between the Animal Species of Raw and Processed Meat

Variations in the Metabolome of Unaged and Aged Beef from Black-and-White Cows and Heifers by 1H NMR Spectroscopy

(1) Background: The selection of raw material and the postmortem processing of beef influence its quality, such as taste. In this study, the metabolome of beef from cows and heifers is examined for differences during aging. (2) Methods: Thirty strip loins from eight heifers and seven cows (breed code: 01-SBT) were cut into ten pieces and aged for 0, 7, 14, 21 and 28 days. Samples from the left strip loins were wet-aged in vacuum, while samples from right strip loins were dry-aged at 2 °C and 75% relative humidity. The beef samples were extracted with methanol-chloroform-water, and the polar fraction was used for ¹H NMR analysis. (3) Results: The PCA and OPLS-DA showed that the metabolome of cows and heifers varied. Eight metabolites revealed significant differences (p < 0.05) in the samples from cows and heifers. The aging time and aging type of beef also affected the metabolome. Twenty-eight and 12 metabolites differed significantly (p < 0.05) with aging time and aging type, respectively. (4) Conclusions: The variations between cows and heifers and aging time affect the metabolome of beef. By comparison, the influence of aging type is present but less pronounced.

Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis

Meat species of raw meat and processed meat products were investigated by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. 1H NMR data was analyzed by using a non-targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published 1H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy.