*4.7. Authentication*

To address the growing consumer awareness, the meat industry has a need for systems to guarantee the authenticity of the meat and the products in order to take measures against increasing fraud/adulteration in the growing complexity of global food chains. Regarding beef, concerns about bovine spongiform encephalopathy (BSE) have increased the awareness of safety. In this field, numerous DNA and protein-based target detection techniques, including species-specific PCR, has been proved to be effective. MEATabolomics is expected to provide useful chemical information regarding authenticity as an alternative method. In this context, analysis of geographic origin of beef by metabolomics was attempted with NMR [66] and imaging MS (IMS) [67]. In the NMR approach, extracts of imported beef from 4 countries were discriminated in the results of PCA and OPLS–DA. The major compounds responsible for the discrimination contained succinate and some of amino acids, suggesting that these compounds could be potential biomarkers to discriminate the geographic origin of beef and that NMR has potential to efficiently work for this analysis. IMS-based direct analysis visualizes distribution of target biomolecules such as lipids, glycolipids, and peptides on biological tissue section samples, without complicated pretreatment procedures. In a matrix-associated laser desorption/ionization–IMS analysis of beef from 3 different Japanese regions, the three types of beef were discriminated by subsequent PCA [67]. In these analyses of geographic origin of beef, although geographic difference in metabolome changes may be due to multiple factors of animal breeds, feeds, and environment, including water and climate, the results revealed availability of metabolomics to distinguish production regions. Geographic differences may also contain the production systems of animals. A study aiming to discriminate the pre-slaughter production system was able to distinguish differences between 1-year cattle fed with barley-based concentrate indoors and with pasture feeding outdoors, using NMR metabolomics followed by PLS–DA, analyzing the *longissimus dorsi* (LD) muscle [68]. The results suggested that NMR approach is suitable for authentication of cattle production history.

Challenges to discriminate beef, pork, and mixture samples by metabolomics were also conducted recently for the establishment of measures against meat adulteration. To this end, different grades of minced beef and pork samples were mixed in various ratios and analyzed by GC–MS and reverse-phase LC–MS approaches [69]. The metabolite content and percentages of fat declared on meat product labeling were correlated each other, and the species of meat was identified by chemometrics using differential metabolite sets. Another volatilomic approach using HS/SPME GC–MS was also applied

for the similar purpose [70]. In this study, multiple volatile compounds correlated not only to beef and pork but also the mixture was identified. With use of datasets divided 70% for model calibration and 30% for model prediction, the overall correct classification rate was 99% on average in both datasets. As the authors concluded, this volatilomic approach could be developed for robust and reliable o ff-line discrimination of meat samples. LC–MS approaches were also applied to classification of chicken meats into normally slaughtered and dead on arrival [71,72]. Such applications along with the development of discrimination analysis is expected to solve the current fraud issues related not only to chicken but also beef and pork.

On the other hand, the irradiation of meat is currently highlighted as an issue for its negative impact in cases when used out of appropriate range of strength, despite positive evaluation of the irradiation e ffect on meat such as disinfestation, growth inhibition, parasite control, reduction of pathogenic bacteria, and shelf-life extension. Metabolomics is expected to be a tool to monitor the negative impact of irradiation, but it needs to be a non-time-consuming, non-invasive, and reproducible method. In this regard, NMR metabolomics seems suitable for such purpose, and, accordingly, has been used in attempts to investigate the influence of irradiation on beef [73,74]. In the analysis targeting lipids, stepwise linear discriminant analysis (sLDA) following the NMR data profiling allowed the classification of 81.9% of the beef samples according to the irradiation dose (0, 2.5, 4.5, and 8 kGy) [73]. Moreover, the NMR analysis targeting hydrophilic compounds, combined with subsequent classification tree (CT) analysis, was able to distinguish between the irradiated and non-irradiated beef samples [74]. In addition, glycerol, lactic acid esters, and tyramine were found to be important biomarkers for the classification. Thus, MEATabolomics has been applied in a variety of research fields associated with meat production methods and the quality traits, including authentication.
