Impact of farming type, variety and geographical origin on bananas bacterial community

Improved PCR-DGGE analysis by emulsion-PCR for the determination of food geographical origin: A case study on Greek PDO "avgotaracho Mesolonghiou"

Greek avgotaracho Mesolonghiou (fish eggs from Flathead Mullet) is a highly valuable food product which holds Protected Destination of Origin status. The aim of this work was to use PCR-DGGE technique to examine whether there is a correlation between bacteria population in fish eggs and geographical origin. Cluster analysis of fish eggs from three geographical locations (Mesolonghi, Australia and Mauritania) discriminated samples according to their provenance. Moreover, we utilized emulsion-PCR amplification in DGGE analysis in order to investigate whether we could obtain further information about food products’ bacteria communities. PCR-DGGE proved to be a suitable method for fish eggs traceability, moreover emulsion PCR-DGGE provides better results. Emulsion-PCR can face up the existing limitations of conventional PCR and thus can be demonstrated as alternative molecular technique for complex and processed matrices, regarding food traceability and authentication.

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Traceability of Greek PDO “avgotaracho Mesolonghiou” by PCR-DGGE is presented.

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Emulsion PCR for DGGE” improves analysis of microbial communities.

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Emulsion PCR for DGGE improves geographical traceability of food.

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Emulsion PCR as an alternative molecular method for food traceability and authentication.

Preliminary Study on Tracing the Origin and Exploring the Relations between Growing Conditions and Isotopic and Elemental Fingerprints of Organic and Conventional Cavendish Bananas (Musa spp.)

The stable isotopic ratios and elemental compositions of 120 banana samples, Musa spp. (AAA Group, Cavendish Subgroup) cultivar Williams, collected from six countries (Colombia, Costa Rica, Dominica Republic, Ecuador, Panama, Peru), were determined by isotope ratio mass spectrometry and inductively coupled plasma mass spectrometry. Growing conditions like altitude, temperature, rainfall and production system (organic or conventional cultivation) were obtained from the sampling farms. Principal component analysis (PCA) revealed separation of the farms based on geographical origin and production system. The results showed a significant difference in the stable isotopic ratios (δ¹³C, δ¹⁵N, δ¹⁸O) and elemental compositions (Al, Ba, Cu, Fe, Mn, Mo, Ni, Rb) of the pulp and peel samples. Furthermore, δ¹⁵N was found to be a good marker for organically produced bananas. A correlation analysis was conducted to show the linkage of growing conditions and compositional attributes. The δ¹³C of pulp and peel were mainly negatively correlated with the rainfall, while δ¹⁸O was moderately positively (R values ~0.5) correlated with altitude and temperature. A moderate correlation was also found between temperature and elements such as Ba, Fe, Mn, Ni and Sr in the pulp and peel samples. The PCA results and correlation analysis suggested that the differences of banana compositions were combined effects of geographical factors and production systems. Ultimately, the findings contribute towards understanding the compositional differences of bananas due to different growing conditions and production systems linked to a defined origin; thereby offering a tool to support the traceability of commercial fruits.

Improved geographical origin discrimination for tea using ICP-MS and ICP-OES techniques in combination with chemometric approach

BACKGROUND

There is an urgent need to strengthen the testing and certification of geographically iconic foods, as well as to use discriminatory science and technology for their regulation and verification. Multi-element and stable isotope analyses were combined to provide a new chemometric approach for improving the discrimination tea samples from different geographical origins. Different stoichiometric methods [principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), back propagation based artificial neural network (BP-ANN) and linear discriminant analysis (LDA)] were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in an experimental test.

RESULTS

Multi-element and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry easily distinguished the geographical origins. However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse compared to that of the three supervised learning methods (PLS-DA, BP-ANN and LDA). BP-ANN and LDA, with 100% recognition and prediction abilities, were found to be better than PLS-DA. ⁸⁶ Sr and ¹¹² Cd were the markers enabling the successful classification of tea samples according to their geographical origins. Under the validation by 'blind' dataset, the prediction accuracies of the BP-ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%.

CONCLUSION

In summary, determination of mineral elements and stable isotopes using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry techniques coupled with chemometric methods, especially the LDA method, is a good approach for improving the authentication of a diverse range of tea. The present study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. © 2020 Society of Chemical Industry.