Rapid sample preparation procedure for As speciation in food samples by LC-ICP-MS

This paper describes a rapid method for arsenic (As) speciation by LC-ICP-MS in several types of food samples. Prior to analysis, samples were milled and the As species extracted from biological tissues by sonication in only 2 min with a solution containing MeOH (10%, v/v) plus HNO3 (2%, v/v). As species were separated by LC using an anion exchange column. Method detection limits for AsB, As3+,DMA, MMA and As5+ were 1.3, 0.9, 0.6, 0.7 and 0.8 ng g(-1), respectively. Method accuracy and precision were traceable to Certified Reference Materials SRM1577 bovine liver from the National Institute of Standards and Technology, CE278 mussel tissue from the Institute of Reference Materials and Measurements and DOLT-3 dogfish liver tissue and DORM-3 fish protein from the National Research Council of Canada. Finally, the method was applied to speciate As in food samples (egg, fish muscle, beef and chicken) purchased in Brazilian markets.

International collaborative project to compare and monitor the nutritional composition of processed foods

Background: Chronic diseases are the leading cause of premature death and disability in the world with overnutrition a primary cause of diet-related ill health. Excess energy intake, saturated fat, sugar, and salt derived from processed foods are a major cause of disease burden. Our objective is to compare the nutritional composition of processed foods between countries, between food companies, and over time. Design: Surveys of processed foods will be done in each participating country using a standardized methodology. Information on the nutrient composition for each product will be sought either through direct chemical analysis, from the product label, or from the manufacturer. Foods will be categorized into 14 groups and 45 categories for the primary analyses which will compare mean levels of nutrients at baseline and over time. Initial commitments to collaboration have been obtained from 21 countries. Conclusions: This collaborative approach to the collation and sharing of data will enable objective and transparent tracking of processed food composition around the world. The information collected will support government and food industry efforts to improve the nutrient composition of processed foods around the world.