1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat

Abstract

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using ¹H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

Keywords: NMR; biomarkers; halal meat; metabolomics; multivariate data analysis.

Figure 1

H-NMR spectra of MS (Chevon); DM (Donkey); BM (Beef); CM (Chicken). 1, Acetate; 2, ADP/AMP/ATP; 3, alanine; 4, α-Mannose; 5, Asparagine; 6, Aspartic acid; 7, Biotin; 8, Butyric acid; 9, betaine; 10, carnosine; 11, carnitine; 12, choline; 13, creatine; 14, dimethylamine; 15, formate, 16, fumarate; 17, glucose; 18, glutamate; 19, glutamine; 20, glycerol; 21, glutathione; 22, glycine; 23, 3-hydroxybutyrate; 24, hypoxanthine; 25, IMP; 26, inosine; 27, isoleucine; 28, leucine; 29, lactate; 30, malate; 31, methionine; 32, niacinamide; 33, phenyalanine; 34, valine; 35, phosphocholine; 36, pyruvate; 37, uracil.

Figure 2

Score plot (PC1 vs. PC2) of PCA based on the whole range of ¹H-NMR signals (δ 0.5–δ 10.0) of CM = Chicken; MS = Chevon; BM = Beef; DM = Donkey.

Figure 3

(A) Score plot (PLS1 vs. PLS2) of OPLS-DA based on whole range of1¹H-NMR signals (δ 0.5–δ 10.0) of CM = Chicken; MS = Chevon; BM = Beef; DM = Donkey. (B) Validation through 200 permutation test, (C) OPLS-DA Loading scatter plot.

Figure 4

(A) Score plot (PLS1 vs. PLS2) of OPLS-DA based on whole range of ¹H-NMR signals (δ 0.5–δ 10.0) of MS = Chevon; BM = Beef; DM = Donkey. (B) Validation through 200 permutation test. (C) Loading scatter plot of OPLS-DA.

Figure 5

Relative quantification of the major differentiating metabolites based on the mean peak area of the related signals. CM = Chicken; MS = Chevon; BM = Beef; DM = Donkey. * depicts the differences between meat groups. Statistical differences of metabolites between groups were calculated by using one-way analysis of variance and a Tukey multiple comparison test. Statistical icons: * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.