ICYTAC   23898
INSTITUTO DE CIENCIA Y TECNOLOGIA DE ALIMENTOS CORDOBA
Unidad Ejecutora - UE
artículos
Título:
Development of a method for the elemental analysis of milk powders using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and its potential use in geographic sourcing
Autor/es:
HOFFMAN, TRICIA; GRIBOFF, JULIETA; OGRINC, NIVES; ALMIRALL, JOSE; JA?IMOVI?, RADOJKO; JAGODIC, MARTA; PODKOLZIN, IVAN; BAY, LIAN JIE; MONFERRÁN, MAGDALENA; WUNDERLIN, DANIEL
Revista:
TALANTA
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2018 vol. 186 p. 670 - 677
ISSN:
0039-9140
Resumen:
Milk has been reported as one of the most adulterated foodstuffs in the developed and developing world. One way to detect adulteration is to determine whether the country of origin on the label could be the actual country of origin. Such profiling may be accomplished through the use of elemental analysis techniques, however this is a preliminary study and this goal is not yet met. In this study, a laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) method was developed for the analysis of solid milk powder and compared to k0-instrumental neutron activation analysis (k0-INAA) for a reference milk material (IAEA-153) as well as several milk samples from different countries. The analytical figures of merit for both the LA-ICP-MS and the k0-INAA analysis are reported. Precision of ~ 10% RSD or better was achieved for most elements for both techniques and bias of ~ 10% was achieved for both techniques for most elements with LA-ICP-MS producing lower limits of detection (~ 1 mg/kg) for Sr. The comparison of LA-ICP-MS to k0-INAA showed overlap of the 95% confidence intervals for all comparison samples. A total of 68 authentic milk powder samples representing 5 different countries (Argentina, Russia, Singapore, Slovenia, and the United States) were analyzed to determine whether multivariate elemental differences between the countries were sufficiently larger than within country differences in order to visualize groupings by country. Principle component analysis (PCA) using Na, Mg, Ca, Rb, and Sr show different groups for the United States, Argentina, Singapore, and Slovenia samples of limited representation for each country. However the large number and geographic distribution of samples from Russia were not able to be distinguished from the samples from the United States and Slovenia.