Use of elemental profiles determined by energy-dispersive X-ray fluorescence and multivariate analyses to detect adulteration in Ceylon cinnamon.

The price of Cinnamomum verum (Ceylon cinnamon) is around twice as high as that of the other cinnamon varieties commonly grouped under the name cassia cinnamon, making the former spice an attractive target for fraudsters. This work demonstrates that elemental profiles obtained by energy-dispersive X-ray fluorescence in combination with multivariate analyses can be used as a screening method to detect Ceylon cinnamon adulteration. Thirty-six elements were analysed in 52 commercially available cinnamon samples, 29 Ceylon, 8 cassia, and 15 for which no indication about variety was provided. Fifty-eight percent of the samples were either adulterated or did not meet international quality criteria. Four of the ground cinnamon samples labelled as Ceylon cinnamon were found to be pure cassia or a mixture with a high cassia content, and 26 samples were suspected of other types of adulteration including replacement of bark with other parts of the cinnamon tree. Headspace gas chromatography-mass spectrometry and ash determination by thermogravimetric analysis confirmed the conclusions reached by elemental analysis. Only one sample labelled as Ceylon cinnamon and that according to its volatile composition was cassia cinnamon was not flagged as suspicious by elemental analysis.

Feasibility study on the use of elemental profiles to authenticate aromatic rice: the case of Basmati and Thai rice.

Among the thousands of existing rice varieties, aromatic rice has increasingly attracted consumer's preference in recent years. Within aromatic rice, Basmati, cultivated in some regions in Pakistan and India, is highly demanded. Other aromatic rice, cultivated in specific regions, for instance in Thailand (commonly referred to as Jasmine Thai rice), are also highly appreciated by consumers. In this work, the elemental profiles of commercially available rice samples (17 Basmati, 11 Thai, and 7 Long Grain rice) were determined by energy-dispersive X-ray fluorescence (ED-XRF) spectroscopy. The mass fractions of P, Cl, S, K, Fe, Cu, and Zn were significantly different (95% confidence interval) between Basmati and Thai rice and between Thai and Long Grain rice; only Cl, S, and Zn were significantly different between Basmati and Long Grain rice. Multivariate evaluation of the results combining soft independent modelling by class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA) allowed the correct classification (true positives) of 94.1, 85.6, and 100% of the Basmati, Long Grain, and Thai rice, respectively. The specificity (true negatives) of Basmati, Long Grain, and Thai was 94.4, 82.1, and 100%, respectively.

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

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.

Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin.

Honey is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties-orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka-and seven different geographical origins-Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples. Graphical abstract.