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Cheese microbiota and metabolites and their inter-relationships that underpin specific cheese quality attributes remain poorly understood. Here we report that multi-omics and integrative data analysis (multiple co-inertia analysis, MciA) can be used to gain deeper insights into these relationships and identify microbiota and metabolite fingerprints that could be used to monitor product quality and authenticity. Our study into different brands of artisanal and industrial cheddar cheeses showed that Streptococcus, Lactococcus and Lactobacillus were the dominant taxa with overall microbial community structures differing not only between industrial and artisanal cheeses but also among different cheese brands. Metabolome analysis also revealed qualitative and semi-quantitative differences in metabolites between different cheeses. This also included the presence of two compounds (3-hydroxy propanoic acid and o-methoxycatechol-o-sulphate) in artisanal cheese that have not been previously reported in any type of cheese. integrative analysis of multi-omics datasets revealed that highly similar cheeses, identical in age and appearance, could be distinctively clustered according to cheese type and brand. furthermore, the analysis detected strong relationships, some previously unknown, which existed between the cheese microbiota and metabolome, and uncovered specific taxa and metabolites that contributed to these relationships. these results highlight the potential of this approach for identifying product specific microbe/metabolite signatures that could be used to monitor and control cheese quality and product authenticity.Cheese microbiota play a pivotal role in the development of cheese flavor and the product quality and safety of cheese. These attributes can be achieved by tightly controlled manufacturing conditions, excellent hygiene and use of commercial starter cultures (usually strains of Lactococcus lactis). Following manufacture, when starter bacteria cease to dominate, cheese must be ripened for full flavour development. This is a highly complex process, with successions within microbial communities, and their associated enzymes and biochemical reactions occurring over time to release numerous flavoursome organic compounds 1 . Ripening can be controlled to some extent by the addition of known adjunct cultures, usually strains of lactobacilli, but adventitious bacteria from the factory environment also contribute to the ripening process and to the composition of the cheese microbiota 2 .Advances in DNA sequencing using DNA extracted directly from samples have enabled improved understanding of the microbial communities found in cheese, and especially of less-abundant microorganisms which can nevertheless have significant impacts on cheese flavour and safety 3,4 . Although microbial communities vary depending on the cheese type, in hard cheeses such as cheddar that undergo ripening, after the starter bacteria die off lactobacilli are dominant, and may be found in combination with a number of other genera not belongin...

In this study, the D-optimal mixture design methodology was applied to determine the optimised proportions of inulin, β-glucan and breadcrumbs in formulation of low-fat beef burgers containing pre-emulsified canola and olive oil blend. Also, the effect of each of the ingredients individually as well as their interactions on cooking characteristics, texture, colour and sensory properties of low-fat beef burgers were investigated. The results of this study revealed that the increase of inulin content in the formulations of burgers led to lower cooking yield, moisture retention and increased lightness, overall acceptability, mouldability and desired textural parameters. In contrast, incorporation of β-glucan increased the cooking yield, moisture retention and decreased lightness, overall acceptability, mouldability and desired textural parameters of burger patties. The interaction between inulin and β-glucan improved the cooking characteristics of the burgers without significantly negative effect on the colour or sensory properties. The results of the study clearly stated that the optimum mixture for the burger formulation consisted of (in g per 100 g): inulin 3.1, β-glucan 2.2 and breadcrumbs 2.7. The texture parameters and cooking characteristics were improved by using the mixture of inulin, β-glucan and breadcrumbs, without any negative effects on the sensory properties of the burgers.