**1. Introduction**

In the last two decades, the metabolomic approach has been employed widely in the research fields of animal and plant nutrition, physiology, breeds, environment, post-harvest storage and processing [1,2] with the advantages of its high-throughput capacity. The target molecules of food metabolomics are animal and plant metabolites, including low molecular-weight hydrophilic and hydrophobic compounds. These compounds include key metabolites such as flavor-associated compounds, nutrients, and functionality-associated compounds in food, some of which have a molecular weight of >1000. Such compounds characterize nutritional and sensory properties, and therefore the metabolome of a food provides important phenotypic information. In this context, metabolomics is a powerful tool to obtain a deeper understanding of the biologically and agriculturally meaningful information in the global metabolome profiles and the changes caused by factors in food production processes [3].

Currently, two major types of platforms have been applied for metabolomic studies so far: mass spectrometry (MS)-based [4] and non-MS-based techniques such as nuclear magnetic resonance (NMR) [2]. Moreover, various types of separation techniques are incorporated in most of the MS-based approaches, depending on the lipophilicity and polarity of the metabolites of interest. Combined with advanced statistical analyses, multivariate analyses, and bioinformatic databases, metabolomics provides clues not only to discover biomarkers for monitoring and assessment of food quality but also to uncover molecular pathways in which enzymatic reaction generates key metabolites [5]. Correlation analysis between sensory evaluation scores and metabolomic profiles potentially leads to key compounds that are associated with eating quality, such as flavor and texture [6–8], which may enable us to predict the palatability of the foods by using the biomarker metabolites in pre- and post-harvest materials. Metabolomic profile data have also been utilized to explore the responsible genes for specific metabolite-featured phenotypes in genome-wide association studies (GWAS) [9,10].

In recent years, studies in muscle and meat science have utilized metabolomics as in other fields [11–13]. Skeletal muscle characteristics are designed by a functionally cooperative set of genes specific to the spatiotemporal requirement in each muscle. The gene expression is further modulated at levels of transcription, post-transcription, translation, and protein modification during development, growth, and maturation stages of the muscle. Accordingly, muscle metabolites determine the physiological muscle characteristics and meat quality traits as the major phenotypic components. The history of the muscle in the developmental and physiological specialization, feeding processes of animals, circumstances in slaughtering, postmortem aging, and processing processes all have influences on transcriptomic and/or proteomic profiles, and finally on the muscle metabolome profile. This makes the understanding of mechanisms behind meat quality traits by the use of molecular markers more di fficult. Nevertheless, the metabolome profiles have been successfully distinguished with metabolites of biomarker candidates in comparisons such as between physiologically di fferent types of skeletal muscles [14–16], between at-slaughter and postmortem muscles [14,15,17–19], between meats produced by di fferent animal feeding conditions [20–26], and between di fferent types of meat processing [7,27–30]. Thus, to better understand factors determining muscle characteristics and meat quality, the recent metabolomics approach has grea<sup>t</sup> advantages, which potentially supports to capture biomarkers of the phenotype. Since raw and cooked meat are rich in flavor-associated volatile compounds and precursors, MEATabolomics studies in combination with sensory evaluation have been conducted to explore biomarker candidates associated with eating quality of meat.

Especially in studies of postmortem muscle and meat quality, MEATabolomics is expected to provide a clue for drawing maps of the metabolic network in postmortem muscle aging and flavor development during cooking, which cannot be replaced by the other biological or chemical approaches. Muscles, especially beef and pork, are more or less aged after death for the tenderization and flavor component accumulation. During the aging, the muscles experience drastic and irreversible physico-chemical and metabolic changes [31]. These metabolic changes have a large impact on subsequent beef and pork quality, under influences of physiological background before slaughter. Despite the impact on meat quality, the metabolic changes have not easily predicted, because coordinated metabolism in live muscle is no longer maintained due to lack of energy supply and arrest of de novo gene expression after animal death. Postmortem metabolic maps drawn by MEATabolomics could lead to a finding of the metabolic factors responsible for meat quality traits and thereby contribute to the exploration of biomarkers in quality monitoring, processing, and authentication of meat including chicken. Thus, postmortem muscle metabolism and the factors associated with meat quality have been the major and unique challenges to be addressed for MEATabolomics.

This review provides an overview of past applications, recent findings, and topics achieved by MS- and NMR-based MEATabolomics approaches associated with animal muscle physiology and meat quality traits. We especially focus on studies of meat quality traits and the factors influencing meat quality, such as postmortem aging, as well as animal breed and feeding, processing, spoilage, and authentication. Using those keywords, a total of 78 studies were collected by search in databases such as PubMed, ScienceDirect, and Springer Link, from which 56 studies were selected and featured.

#### **2. MEATabolomic Methodologies and Approaches**

The MS-based metabolomics need a separation step suitable for target metabolites of interest (Table 1). Of the current separation techniques, capillary electrophoresis (CE)–MS has high performance in the acquisition of polar and charged metabolite information with high resolution and sensitivity, but not in non-ionic molecules [32,33]. CE has an advantage over gas chromatography (GC) and high-performance liquid chromatography (HPLC; LC) for the resolution of ionic compounds, including their isomers, owing to separation in CE by their charge-to-mass ratio. GC–MS has been widely used for decades due to its established high-separation e fficiency, selective and sensitive mass detection, and a broad range of target molecules, mainly fatty acids and sugars, although it needs derivatization of sample molecules, especially for non-volatile compounds. Derivatization artifacts may be generated by decomposition of thermolabile molecules in GC, during the analysis after enhancement of volatility of molecules. On the other hand, LC–MS targets compounds with a relatively narrow range but has the flexibility to change the type of targets with replaceable separation columns. As the common stationary columns in LC, C18 reversed-phase is the most frequently used for separation of hydrophobic molecules, while the polar phase, such as silica and amide, is used for hydrophilic molecules. Running time of LC separation can be much reduced to less than 10 min in the case of ultra HPLC (UHPLC, UPLC).

**Table 1.** Features of the most commonly used separation techniques in mass spectrometry (MS)-based metabolomics.


The separated molecules are required to be ionized generally by electrospray ionization (ESI) in CE, electron ionization (EI) or chemical ionization (CI) in GC, and ESI or atmospheric pressure chemical ionization (APCI; API) in LC. MS detection techniques have been developed to various types, and, currently, the most frequently used technique for matching to the upstream CE, GC, and LC separation steps is time-of-flight (TOF) to achieve higher sensitivity, accuracy, rate of measurement, and mass and dynamic range for the acquisition of more metabolite information [4]. Fourier transform (FT) type MS also has a high-throughput performance with high resolution and a broad range of target molecules and is thereby used for metabolomics. Of the FTMS, FT-ion cyclotron resonance (FT-ICR) MS utilizes a magnetic field to detect resonance of cyclotron motion of metabolite ion, while the orbitrap type is based on a system using an electric field. In some cases, tandem MS (MS/MS) is employed to acquire more structural information for the characterization of compounds. These MS-based approaches are utilized not only for targeted metabolomics but also for untargeted metabolomics [26,34,35]. Recently, rapid evaporative ionization mass spectrometry (REIMS) has been introduced in MEATabolomics [26,36]. This method allows direct MS analysis of a biological sample with no preparative steps, under normal atmospheric conditions, due to being based on the ambient ionization [37,38]. A trend of MEATabolomic approach can be seen in the times of employment for each technique in the 56 studies collected in this review: CE–MS, GC–MS, LC–MS, NMR, and REIMS approaches have been employed in 5, 17, 17, 22, and 1 studies, respectively, with some cases combining multiple approaches (Table 2).







chromatography; HS/SPME: head space–solid phase microextraction; LDA: linear discriminant analysis; MSEA: metabolite set enrichment analysis; OPLS–DA: orthogonal PLS–discrimination analysis; Pathway: pathway enrichment analysis; Pm.: postmoretm; PCA: principal component analysis; PLS: partial least square analysis; PLS–DA: PLS–discrimination analysis; REIMS: rapid Evaporative Ionization Mass Spectrometry; RF: random forest; sLDA: stepwise linear discriminant analysis; UPLC: ultra-performance LC.

After MS measurement, initial putative metabolite can be identified on the basis of the accurate mass–to–charge ratio (m/z) of the mass spectral ion in MS-based metabolomics. This is assisted by the use of metabolite databases such as METLIN (https://metlin.scripps.edu/), the Human Metabolome Database (HMDB; http://www.hmdb.ca/), and MassBank (http://www.massbank.jp/).

NMR is also highlighted as a method for practical use, such as authentication purposes, in an analytical routine. This technique can provide rapid and reproducible measurements in complex mixtures without a time-consuming pretreatment. Although NMR has relatively low ability to profile metabolites compared to the MS-based techniques due to its low resolution and sensitivity, it has the ability to collect distinct information that the other metabolomics cannot access in a non-destructive and non-biased way [2]. Especially, approaches with 1H–high-resolution magic angle spinning (HR–MAS) have also been applied to characterize meat quality [39,46,59]. Unlike the other types of NMR, this NMR enables us to investigate intact tissue specimens (10–50 mg) and allows the spectra to be obtained with a high resolution compatible to that obtained in liquid samples in less than 30 min.

Once the data matrix is produced from the collected raw data, subsequent statistical analyses and data mining are often performed to identify samples or variables (metabolites) that characterize the variations between datasets and may represent biologically meaningful determinants. In most cases, the statistical sample classification is conducted to overview pattern recognition of sample categories, by multivariate analyses such as principal component analysis (PCA), clustering analysis, partial least square analysis (also called projection to latent structures, PLS), PLS–discriminant analysis (PLS–DA), and random forests (RF) [5,75].

PCA is an unsupervised statistical method that reduces dimensions of high dimensional data to visualize sample distribution and grouping on the principal component (PC) plot based on the pattern of the metabolite dataset and thereby is employed by most of the metabolomic studies as the first step of data analysis [75]. Clustering analysis, especially hierarchical clustering analysis (HCA), is also widely used to generate a snapshot profiling of dataset. In HCA, the algorithm divides the measured datasets into subgroups so that datasets with similar metabolomic profiles are placed in each group. PCA and HCA are frequently used for visualization of classification in omics studies, including metabolomics.

PLS has been developed as a supervised extension of PCA [75]. This regression-based method is especially useful when fewer samples are available than measured metabolites. PLS–DA is used to elucidate the metabolites that carry the information of classification, which screens highly contributing metabolites to the classification. PLS and PLS–DA have often been employed to sharpen the separation between the groups, especially when the groups are not su fficiently separated in PCA. Orthogonal PLS (OPLS)–DA is an extension of PLS–DA to cover the defect of PLS–DA, owing to its robustness against noise. RF is a machine learning method used to discriminate two groups. Di fferent from conventional methods such as PCA and PLS–DA, RF allows data structure understood without dimensional reduction with its low bias and low variance. As with other data mining analyses, the application of support vector machine (SVM) and neural networks are attempted for some of MEATabolomic studies [34]. These analytical methods have been applied for sample characterization and determination of biomarker candidates.

Classical statistical analyses such as Student's *t*-test and analysis of variance (ANOVA) are also used to compare metabolite levels between sample groups, with care about the detection of false-positive metabolites in multiple comparisons of the datasets. To reduce false-positive detection caused by the familywise error rate (FWER) in multiple comparisons, procedures of false discovery rate (FDR), Holm, Bonferroni, or Benjamini–Hochberg are applied to adjust the levels of significance detection in metabolomic studies [5]. Obviously, there are advantages and disadvantages to every method and database. Further information and details in these statistical analyses are described for reference in methodological reviews [5,75].

Moreover, recent progress of bioinformatics analytic tools and databases largely contributes to compound annotation, metabolic pathway finding, and biological data interpretation of the extracted compounds (see the websites of The Metabolomics Society, Inc. for more detail information; http://metabolomicssociety.org/). HMDB (http://www.hmdb.ca/) and PubChem (https://pubchem. ncbi.nlm.nih.gov/) are among the most widely used databases, and they play important roles in the annotation of compound signals.

The Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/) is frequently used to connect the metabolome profile with genomic, transcriptomic, enzymatic, and pathway information, which supports prediction and pathway mapping of potential molecular factors responsible for the phenotypic events. Software for analyses of metabolic pathways such as MetaboAnalyst (https://www.metaboanalyst.ca/) has also often been used for biological interpretation in recent years. MetaboAnalyst provides a variety of statistical analysis tools, including visualizing programs, multivariate analyses, and metabolite pathway analyses. These metabolomic approaches, especially CE–MS, GC–MS, LC–MS, and NMR, in combination with subsequent data analyses have recently been increasingly used in animal-based food research fields [10–12,76].

#### **3. Metabolomes Associated with Meat Quality Traits**

Metabolomics is used for the exploration of key compounds contributing to the physico-chemical properties and sensory evaluation scores, and thereby it contributes to accounting for meat palatability and quality traits such as color and water-holding capacity (WHC). As described below, the contents of skeletal muscle metabolites, including amino acids and sugars, are a ffected by animal genetic background, feeding, muscle type, postmortem aging, and meat processing. These metabolomic changes are linked with the physico-chemical properties of muscles and meat, as shown in variations in meat color and WHC between di fferent types of muscles or between di fferently treated muscles. Accordingly, the metabolites are beneficial indicators to predict the physico-chemical meat quality traits, which indicate a result more or less from the influences of animal genetic background, feeding, and postmortem processes (Table 2).

#### *3.1. Meat Color, WHC, and pH Decline*

The color of meat is one of the commercially important meat quality traits since the appearance of meat has a grea<sup>t</sup> influence on the consumer's desire to purchase [77]. The redness and discoloration are determined by the chemical status of myoglobin, which is a ffected by multiple factors that are inherent to both live animal and postmortem conditions. With hydrophilic interaction liquid chromatography–mass spectrometry (HILIC–MS)-based metabolomics, influences of postmortem aging time, packaging atmosphere, and display time on lamb meat color was investigated to identify the metabolites a ffected by those postmortem conditions [40]. Many compounds were found to change with time of aging and display or packaging atmosphere, which indicated the contribution of the compounds, including amino acids, sugars, nucleotides, and organic acids, to the sample discrimination. Furthermore, the abundance of reducing or antioxidant compounds such as L-glutathione and taurine contributed to color stability. Intriguingly, boron complexes of sugar and malate, presumably color stability-related compounds present in plants but not in animals, were detected, although the role of the compounds remains unknown [40]. WHC is also one of the most important traits of meat quality. Various genetic and environmental factors can induce myofibrillar protein denaturation and thereby lower the WHC of meat. Meat with lower WHC generally has higher drip loss, which has a negative impact on juiciness and palatability of the meat. To assess the responsible genes for WHC in pork, metabolomics was applied to characterize pork with high drip loss via metabolic pathway enrichment analysis in an integrative omics approach, as described below (see Section 4.1) [35].

Due to the large impact on meat color, WHC, and other final meat quality traits, postmortem pH decline is also an important factor to be controlled. In a study investigating two chicken lines di ffering in ultimate pH (pHu), the *pectoralis major* muscles of the two lines were discriminated against by high-resolution NMR metabolomics [41]. Subsequent metabolite set enrichment analysis (MSEA) of the data showed that carbohydrate metabolism in the low pHu line and metabolism of amino acid and

protein in the high pHu line were over-representative pathways. The di fference in the metabolome profile between high and low pHu chicken might be due to the ability of the muscles of glycogen storage and carbohydrate use.

#### *3.2. Flavor and Palatability*

Skeletal muscle metabolites include amino acids and sugars that are precursors of volatile compounds associated with meat aroma. The contents of these compounds are altered, depending on animal feeding, the genetic background, and the postmortem aging process. This could cause variations in meat flavor between meats from di fferent animal production or postmortem conditions. Therefore, muscle and meat metabolites are useful indices to predict or evaluate meat flavor and overall palatability in a comparison between meats of di fferent animal breeds, feeding conditions, and/or postmortem processes. After postmortem aging, amino acids and sugars in muscles make a large contribution to the quality of cooked meat, both as intact forms and as products by the Maillard reaction that occurs between amino compounds and reducing sugars during heat treatment. One of the final volatile products, 2,5-dimethyl-4-hydroxy-3(2H)-furanone (DMHF; furaneol), has been identified as a key aroma compound in beef extract by aroma extract dilution analysis (AEDA) [42]. Even though the aroma of furaneol depends on its concentration, it has grea<sup>t</sup> influences on sensory characteristics as a flavor component in meat as well as other foods [78]. In fact, furaneol was detected on Japanese Shorthorn beef during cooking at 180 ◦C by analysis with headspace solid-phase micro-extraction (HS/SPME) GC–MS, as well as other flavor-associated volatile compounds [43]. Moreover, inosine-5-monophosphate (5-IMP) greatly contributes to the enhanced flavor of the meat due to its primary role as an *umami* compound, in a di fferent manner from aroma compounds [79,80]. Thus, the MEATabolomics approach provides tools to access these palatability-associated compounds with the high-throughput analytic systems.

In a beef study combined with flavor-associated evaluation scores, the *longissimus thoracis* (LT) muscles of Japanese Black (JB) and Holstein cattle were discriminated by the metabolome profiles especially with decanoic acid and glutamine [44], indicating that these compounds were associated with the di fference in flavor between the two breeds. Metabolites in beef a ffecting the sensory attributes have also been explored using commercial ground beef di ffering in grinding and packaging methods [60]. The study resulted in the determination of 33 metabolites di fferentiating the grinding and 22 metabolites associated with beef flavor and o ff-flavor. Results from a pork study of di fferences between breeds indicated that high carnosine content in meat was associated with a low flavor/taste score in pork [6]. Sensory score-associated variation of sugar content and composition of fatty acids, amino acids, and nucleotides were also observed in a comprehensive analysis of the metabolites in seven brands of JB beef [61]. The results of these studies sugges<sup>t</sup> the influences of animal genetic and feeding factors on sensory evaluation scores via the meat metabolites. It is quite likely that altered contents of amino acids and sugars in the animal and meat-producing processes cause changes in the content of volatile flavor-associated compounds, including furaneol in the cooked meat. Even in the presence of complicated biological and manufactural factors, these studies revealed that metabolomic information is expected to provide indices to predict sensory phenotypes of meat.

#### *3.3. Chicken Meat Quality Traits*

Chicken meat quality traits are one of the most intensively focused targets in recent MEATabolomics, due to the globally growing market. A Chinese local breed, Wuding chicken meat, was analyzed by the 1H NMR-based approach [45], in which the age of chicken a ffected the levels of metabolites such as lactate, creatine, IMP, glucose, carnosine, anserine, and taurine. Moreover, the abnormal meat quality of chicken is currently an important issue in the poultry industry. Increasing incidents of muscle abnormalities, including white striping (WS), wooden breast (WB), and spaghetti meat (SM) phenotypes, have been linked to a drastic improvement of breast muscle mass [81]. Metabolomics approaches have been applied to especially elucidate the mechanisms underlying the hardness of the WB phenotype. As WB-related biomarker candidates, Abasht et al. [47] identified compounds associated with protein levels, muscle protein degradation, and altered glucose metabolism, using GC–MS and LC–MS/MS followed by RF procedure analysis. Another study with 1H–NMR approach revealed that WB-a ffected broiler chicken breasts had a higher content of leucine, valine, alanine, glutamine, lysine, lactate, succinate, taurine, glucose, and IMP, but lower histidine, β-alanine, acetate, creatine, creatinine, and anserine compared to normal fillets [48]. In another attempt to explore WB myopathy biomarkers, muscle exudate was used as a sample. Linked with discrimination of the samples between WB and non-WB phenotypes, amino acids, nucleotides, and organic acids were identified as the WB-associated metabolites by an NMR approach [49], suggesting the usefulness of those metabolites as the WB markers. This result was partly consistent with the previous study using breast samples with/without WB [48]. WS, on the other hand, has been associated with altered metabolism related to carbohydrates, long-chain fatty acids, and carnitine in a study with GC–MS and LC–MS approaches, suggesting the involvement of altered β-oxidation in the WS phenotype [50]. A HR–MAS NMR approach revealed that muscle dystrophy of pectoralis in chicken is associated with low content of anserine and carnosine [46]. Thus, the results of these MEATabolomics studies have suggested that the chicken breast muscle abnormalities could be caused by the altered metabolism of carbohydrates, protein and amino acids, and β-oxidation.

#### **4. Factors A** ff**ecting Muscle and Meat Metabolomes**

As mentioned above, meat is originally skeletal muscles of livestock; consequently, meat quality is greatly a ffected by factors in the livestock production system, such as animal genetic background, the feeding and stress that animals experienced, but also the physiological characteristics of live muscle, postmortem aging, processing, and spoilage of meat. All the influences of these factors can be assessed by the muscle or meat metabolome profile that is the phenotype resulting from animal experiences through the expression of gene transcription, translation, and the protein modifications as well.

#### *4.1. Animal Species, Breeds, and Genetic Backgrounds*

A variety of characteristics in meat depends on species or breeds of animals to a grea<sup>t</sup> extent. The genetic background of animals thus has an influence on the phenotype expression resulting in an inherent metabolome profile. For the purpose of investigating the e ffect of genetic background, metabolomics has so far been applied to capture muscle and meat phenotypic di fferences between species (beef, pork, chicken) [44] or breeds in beef cattle [44] and pigs [6]. The meat of those species was discriminated by PCA of GC–MS metabolome profiles with specific compounds being characteristic to each species [44], although di fferent postmortem muscle metabolism might a ffect the profile. In comparison between Holstein and the highly marbled JB cattle that di ffer in oily flavor, sweet flavor, wagyu beef aroma, and the overall evaluation in a sensory test, their LT muscles were discriminated by OPLS–DA [44]. A higher level of decanoic acid in JB than in Holstein cattle, and di fferences between skeletal muscle, inter-, and intramuscular fat tissues of JB cattle were also observed.

Regarding cattle, in the comparison between di fferent strains of Nellore cattle genetically selected by precocity, a genetic e ffect on *longissimus lumborum* (LL) muscle metabolome was observed by 1H–NMR [51]. The purpose of that study was to investigate changes in metabolites associated with muscle physiology and quality of the LL meat, under the established improving e ffect of high growth or precocity background on daily gain, carcass characteristics, and other industrial meaningful traits. In the subsequent PLS–DA using the detected compounds (metabolites related to glycolysis and the citric acid cycle, amino acids, organic acids, nucleotides, and sugars), the muscles were discriminated between high and low precocity groups. Pathway analysis in the study highlighted the association of the muscle protein metabolism with the intensity of the selection. Thus, these MEATabolomic studies have revealed their potential to uncover species- or breed-specific di fferences in metabolites, through which di fferences in meat quality between breeds can be assessed at the molecular level.

A study comparing 1H–NMR metabolome and sensory evaluation scores between five crossbreds of pigs observed an inter-crossbreed e ffect on metabolites, such as amino acids, lactate, IMP, glycerol, and choline-containing compounds. Some of those compounds were associated with meat quality, including sensory scores [6]. The results also suggested the association of live muscle metabolism, membrane properties, muscle fiber glycolytic potential, lipolysis, and proteolysis with the metabolomic di fference.

Furthermore, in a study on pork investigating the relationship between metabolites and drip loss, metabolomics was applied in an integrative way to understand the association of a high drip loss phenotype with the genetic background [35]. In this single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS), a region of candidate genes was identified on chromosome 18 as one associated with drip loss, and the metabolite glycine. Compared to conventional genetic studies using quantitative trait loci (QTL) and candidate genes for drip loss in pigs, GWAS is expected to improve the e fficiency of candidate gene detection and accuracy of genomic prediction by avoiding detection of false-positive associations. In beef, GWAS was conducted on JB cattle to explore genes responsible for the palatability of beef, which revealed that SNPs in the ecto-5-nucleotidase (NT5E) gene a ffected the content of IMP in the postmortem aged beef, due to the modulating e ffects of NT5E enzymatic activity. This is a result of which GC- and LC-based metabolomics contributed to GWAS on meat [52,53]. In these comprehensive omics approaches such as GWAS, metabolomics is useful to capture phenotypic metabolites for the candidate genes, due to its wide range of phenotypic molecular measurements.
