Chemometric classification and discrimination of herbal dietary supplements based on ICP-MS elemental profiling

Deep reinforcement learning classification of sparkling wines based on ICP-MS and DOSY NMR spectra

An approach that combines NMR spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS) and advanced tensor decomposition algorithms with state-of-the-art deep learning procedures was applied for the classification of Croatian continental sparkling wines by their geographical origin. It has been demonstrated that complex high-dimensional NMR or ICP-MS data cannot be classified by higher-order tensor decomposition alone. Extension of the procedure by deep reinforcement learning resulted in an exquisite neural network predictive model for the classification of sparkling wines according to their geographical origin. A network trained on half of the sample set was able to classify even 94% of all samples. The model can particularly be useful in cases where the number of samples is limited and when simpler statistical methods fail to produce reliable data. The model can further be exploited for the identification and differentiation of sparkling wines including a high potential for authenticity or quality control.

Multiblock data applied in organic grape juice authentication by one-class classification OC-PLS

A new strategy has been developed to enhance the assessment of the authenticity of whole grape juice within the organic class. This approach is based on the analysis of data from different analytical sources. The novel method employs a multiblock regression technique, specifically the one-class partial least squares (OC-PLS) classifier, to establish a relationship between each predictor block and the response variable. Sequential calculations are performed after orthogonalization with respect to the preceding regression scores. The proposed method has demonstrated effectiveness in detecting targeted samples. The results achieved of the best models for the test set had rates of up to 100 % sensitivity, 89 % specificity, and 83 % accuracy. To compare with the multiblock models, the DD-SIMCA method was employed, but it yielded inferior results when applied to visible data. The multiblock approach proved to be efficient in evaluating from different datasets of varied sources to classification of organic grape juice.

Safety Assessment of Herbal Food Supplements: Elemental Profiling and Associated Risk

Increased usage of herbal food supplements by both the paediatric and adult populations prompted a health risk assessment study. The elemental profiles of 52 supplements collected in Serbia were obtained using ICP-MS. The calculated contribution to permitted daily exposure was in compliance with the guidelines for elemental impurities: up to 46.8% for Pb (infants), 67.2% for Cu (toddlers), 6.8% for As (preschool children), and 8.0% for Hg (adolescents). Hazard quotients indicated high exposure of toddlers to Cu (100.9%) and Zn (112.7%), although by only one supplement, for which hazard indices ranged from 229 to 105% (aligning from toddlers to adolescents). Based on the margin of exposure, As and Pb were not of safety concern. Conversely, the proportion of supplements exceeding the acceptable level of lifetime cancer risk due to As exposure varied from 41.9% in adolescents to 54.3% in adults. Simultaneous use of multiple supplements over a prolonged period of time could further deepen health concerns.

Assessment of the Mineral Composition and the Selected Physicochemical Parameters of Dietary Supplements Containing Green Tea Extracts

Our aim was to assess the mineral composition as well as the physicochemical quality of green tea-based dietary supplements (capsules and tablets) with respect to average weight, size and shape, friability, breaking force and disintegration time. Products were analysed by flame atomic absorption spectrometry for Ca, K, Mg, Na, Cr, Cu, Fe, Zn, Mn, Pb and Cd. Approximately 60% of the analysed supplements met the requirements of European and Polish regulations. The dietary supplements passed weight variation tests, but not all products had compliant capsule sizes. One product in tablet form failed the friability test, and eleven dietary supplements in tablet form failed the disintegration test. The supplements were characterised by a varied elemental composition, with the highest values found for Ca, Fe, Zn and Mn. The recommended daily allowance realisation for Fe and Zn in two dietary supplements (capsule form) exceeded 100%. As a result of the estimation of the monthly intake of toxic metals, it was concluded that the tested products do not pose a risk to consumer health. Significant relationships (p < 0.001) were found for K, Mg, Na, Cu, Fe, Mn and Zn. The application of factor and cluster analyses allowed the verification of the similarity of green tea extracts contained in dietary supplements to the natural raw material, green tea leaves, used as a reference material.