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Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

ABSTRACT: Highlights • Organic food has a different elemental profile than conventional food.• ED-XRF is a suitable method for screening purposes in organic food control.• Multivariate analysis is fundamental to combine all available elemental information. Research has been conducted the last years to assess whether organically grown food is chemically different from produce of conventional agriculture and which markers are appropriate to discriminate between them. Most articles focus on one single food commodity, produced under strict controlled organic farming conditions, leaving open the question whether the difference would be seen when applied to the same commodity under different growing conditions. In this work 118 organic and 151 conventional samples of commercially available paprika powder, cinnamon, coffee, tea, chocolate, rice, wheat flour, cane sugar, coconut water, honey and bovine milk were characterised for their elemental composition using energy dispersive X-ray fluorescence. Resulting profiles were analysed using univariate and multivariate statistical techniques. Organic samples of a given commodity clustered together and were separated from their conventional counterparts. Differences in the elemental composition of food, could be used to develop statistical models for verifying the agronomical production system.

SUBMITTER: Fiamegos Y

PROVIDER: S-EPMC8080890 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The authenticity of tea has become more important to the industry while the supply chains become complex. The quality and price of tea produced in different regions varies greatly. Currently, a rapid analytical method for testing the geographical origin of tea is missing. XRF is emerging as a screening technique for mineral and elemental analysis with applications in the traceability of foodstuffs, including tea. This study aims to develop a reliable multivariate classification model using XRF spectroscopy to obtain the mineral content. A total of 75 tea samples from tea producing countries throughout the world were analysed. After variable shortlisting, 18 elements were used to construct the multivariate models. Tea origin was determined by classifying the tea into 5 major geographical regions producing most of the global tea. PCA showed initial clustering in some regions, although the types of teas included in the study (black, green, white, herbal) showed no discrete cluster membership. The prediction power of each classification model developed was determined by using two multivariate classifiers, SIMCA and PLS-DA, against an independent validation set. The average overall correct classification rates of PLS-DA models were between 54-85% while the results of SIMCA models were between 70-84% resolving the poor clustering initially shown by PCA. This study demonstrated the potential of geographical origin of tea prediction using elemental contents of tea. Naturally, the classification can be linked not only to origin but to the type of tea as well.Practical applicationWholesalers and retailers need a rapid and robust screening tool to confirm the origin and type of tea they sell to consumers. X-Ray fluorescence spectroscopy proved a good technique for achieving this in commercial teas sourced worldwide. Building on multivariate models, broad classification was accomplished both in terms of origin (Asian vs non-Asian) and in tea type with zero sample preparation and low cost of analysis.

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Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

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