A multipurpose tool to evaluate the nutritional quality of individual foods: Nutrimap.

BACKGROUND/OBJECTIVES: With obesity and nutrition-related diseases rising, public health authorities have recently insisted nutritional quality be included when advertising and labelling food. The concept of nutritional quality is, however, difficult to define. In this paper we present an innovative, science-based nutrient profiling system, Nutrimap, which quantifies nutritional assets and weaknesses of foods. METHODS: The position of a food is defined according to its nutritional composition, food category, the consumer's nutritional needs, consumption data and major public health objectives for nutrition. Amounts of each of 15 relevant nutrients (in 100 kcal) are scored according to their ability to 'rebalance' or 'unbalance' the supply in the whole diet, compared with current recommendations and intakes. These scores are weighted differently in different food categories according to the measured relevance of the category to a nutrient's supply. Positive (assets) and negative (weaknesses) scores are totalled separately. RESULTS: Nutrimap provides an overall estimate of the nutritional quality of same-category foods, enabling easy comparisons as exemplified for cereals and fruit/vegetables. Results are consistent with major nutritional recommendations and match classifications provided by other systems. Simulations for breakfasts show that Nutrimap can help design meals of controlled nutritional value. CONCLUSIONS: Combining objective scientific bases with pragmatic concerns, Nutrimap appears to be effective in comparing food items. Decision-makers can set their own limits within the Nutrimap-defined assets and weaknesses of foods and reach categorisations consistent with their objectives--from regulatory purposes to consumer information or support for designing meals (catering) or new products (food industry).

Nutrient profiling: comparison and critical analysis of existing systems.

BACKGROUND: Nutrient profiling systems aim at positioning foodstuffs relative to each other according to their contribution to a balanced diet. The accuracy and performance of methodologies are still debated. We present here a critical analysis of the structure and efficiency of the current schemes. METHODS: The literature survey detected only four systems addressing the issue on an 'across the board' approach and with enough detail to enable analysis. The building principles of these systems were compared and their performance was estimated via their classification of a series of 125 foodstuffs on the basis of nutritional composition. These classifications were compared with one another and with an empirical classification by expert nutritionists. RESULTS: All systems gave a similar overview, with fruits and vegetables ranked as the most favourable foods and fatty and sugary foods as the least favourable ones, but numerous discrepancies existed in every system, mainly related to their choice of nutrients and thresholds. The FSA scoring system seemed the most consistent approach, although it still generated some questionable rankings. Expert classification did not clearly validate any scheme, and cannot be considered as a true reference. CONCLUSION: Nutrient profiling systems are confirmed to be powerful tools to translate nutritional information related to the whole diet into the level of individual foods. However, the performance of the existing schemes remains moderate. Alternative approaches, such as considering food categories or introducing more stringent validation steps by a panel of expert nutritionists, could be ways to reach more efficient and consensual tools.

Structural and kinetic bases for the recognition of tRNATyr by tyrosyl-tRNA synthetase.

The aminoacylation of transfer RNA is a key step of translation since it relates amino acids to anticodons. To understand how the tyrosyl-tRNA synthetase (TyrTS) from Bacillus stearothermophilus recognizes tRNA(Tyr), we constructed 14 new mutant TyrTS by site-directed mutagenesis, determined their kinetic properties and used these and previous data to construct a detailed structural model of the complex between TyrTS and the acceptor arm of tRNA(Tyr). In the model Arg207, Lys208, Asn 146 and Glu 152 interact with phosphate groups. A contact between guanine 1 and Trp 196 is unspecific. Adenine 73, the fourth base from the 3' end, is specifically recognized through Trp 196 and the main-chain carbonyl of Ala150. At the active site, adenine 76 might interact with Lys82 and Arg86. There is a tight complementarity in shape between the tRNA and the synthetase. TyrTS and tRNA(Tyr) form an additional contact, in the vicinity of adenine 73, when their complex goes from the initial state to the transition state. The rate of aminoacylation, through the precise recognition of adenine 73, could thus be an important factor of discrimination by TyrTS among tRNAs.