Country of origin is defined as the country where food or feed is entirely grown, produced, or manufactured, or, if produced in more than one country, where it last underwent a substantial change. In the UK, EU-assimilated legislation states that indication of the country of origin is a mandatory labelling requirement for food and feed, including products such as meat, vegetables, eggs, honey and wine. The country of origin claim plays an important role for consumers who tend to relate certain country of origin labelling to superior quality or brand identity. Patriotism (or ethnocentrism) can also play a role in consumer food choice. In Europe, there are 3500 products with a specific geographical origin and their production methods are officially protected (Protected Designation of Origin = PDO; Protected Geographical Indication = PGI; Geographical Indication (for spirit drinks) = GI). These goods often carry a premium price. In addition to customer preference and sale price, country of origin claims are important to businesses when they seek to (i) monitor food miles (carbon footprint), (ii) ensure sustainable sourcing of, for example soy and palm oil (including new Regulation (EU) 2023/1115 on deforestation-free products), (iii) avoid trading of goods which are subject to sanctions, (iv) reassure consumers over concerns of farming and animal welfare standards, (v) avoid foods which are linked to exploitation of farm workers, enforced, or child labour. ‘Verification’ of geographical origin involves testing against a database to confirm that the data for a sample are consistent with those for that geographical location as claimed on a product label. Verification therefore does not involve testing a sample from an unknown location to unequivocally identify its provenance, as such methods are not available or are extremely limited in scope.

Different fertilisation regimes, i.e. the use of inorganic or organic fertilisers used in agriculture, are thought to cause differential effects on soil bacteria. In this study, glasshouse experiments were used to test the effects caused by addition of inorganic fertiliser or digestate from sewage sludge on soil bacterial community structure and diversity assessed by pyrosequencing of the V1-V3 region of the 16S rRNA gene. Spring wheat (Triticum aestivum L., cultivar Paragon) was used as model crop and its growth (measured by total dry weight) was monitored as well as changes in soil nitrogen and phosphorous at three time points over 128 days. Overall, 40 different bacterial phyla were detected with Proteobacteria, Acidobacteria and Actinobacteria dominating the communities. Additionally, members of the Bacteroidetes, Gemmatimonadetes, Chloroflexi and WS3 were found in all samples. Members of the Planctomycetes, Firmicutes, Nitrospirae, candidate division SPAM and Armatimonadetes were found in all samples but at lower abundances. Within the phylum Proteobacteria the classes Alpha-and Betaproteobacteria were most prevalent. Over the course of the experiment, the major differences between treatments were observed for the Actino-, Proteo-and the Acidobacteria. Sequences related to the Planctomycetes, implicated in nitrogen cycling, decreased in all treatments during the course of the experiment. Statistical analyses revealed that mainly nutrient addition and plant growth influenced the bacterial community structure. The effects of the treatment itself could be attributed to different gain in wheat growth especially when the communities were compared at the end of the trial. Our results indicate that the usage of different fertilisers will not only affect the bacterial community by direct addition of nutrients, but also indirectly. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.

While many compounds have been reported to change in laboratory based drought-stress experiments, little is known about how such compounds change, and are significant, under field conditions. The Pisum sativum L. (pea) leaf metabolome has been profiled, using 1D and 2D NMR spectroscopy, to monitor the changes induced by drought-stress, under both glasshouse and simulated field conditions. Significant changes in resonances were attributed to a range of compounds, identified as both primary and secondary metabolites, highlighting metabolic pathways that are stress-responsive. Importantly, these effects were largely consistent among different experiments with highly diverse conditions. The metabolites that were present at significantly higher concentrations in drought-stressed plants under all growth conditions included proline, valine, threonine, homoserine, myoinositol, γ-aminobutyrate (GABA) and trigonelline (nicotinic acid betaine). Metabolites that were altered in relative amounts in different experiments, but not specifically associated with drought-stress, were also identified. These included glutamate, asparagine and malate, with the last being present at up to 5-fold higher concentrations in plants grown in field experiments. Such changes may be expected to impact both on plant performance and crop end-use.

Honeys from specified botanical sources often command a premium price due to their organoleptic or pharmacoactive properties. To prevent the fraudulent marketing of honey, analytical techniques are required to confirm its origin.NMR spectroscopy has been used to identify biomarkers of botanical and geographical origin for European honey. One-dimensional H-1 NMR spectra were acquired from 374 authentic European honeys collected during 2 years, with the majority of these (220) taken from the island of Corsica. Biomarkers of sweet chestnut, Corsican spring Maquis and Arbousier (strawberry-tree) honeys were identified. Kynurenic acid was found to be a biomarker of sweet chestnut honey. alpha-Isophorone and 2,5-dihydroxyphenyl-acetic acid were confirmed as markers of strawberry-tree honey. Additional compounds specific to strawberry-tree and Corsican spring Maquis honey were partially characterised. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.

Statistical analysis of metabolomic datasets can lead to erroneous interpretation of results due to misalignment of the data. Therefore pre-processing methods for peak alignment and data averaging (binning or bucketing) to improve data quality have been used. Here we introduce adaptive binning. The undecimated wavelet transform is used in an improved method for correcting variation in chemical shifts in nuclear magnetic resonance spectroscopy data. Adaptive binning using theoretical and metabolomics NMR spectra significantly increases the ratio of inter-class to intra-class variation and increases data interpretability when compared to conventional binning.