Background Strategies to reduce salt intake are encouraged to be implemented in parallel with those that aim to ensure iodine adequacy at the population level. The aim of the present study was to assess and compare knowledge, attitudes and behaviours related to salt and iodine among students in Europe and Asia. Methods A multicentre cross-sectional study was conducted with 2459 university students in total (42.7% males, median age 21 years) from four countries in Europe and two countries in Asia. Data were collected with the use of a self-administered questionnaire, and univariate and multivariate statistical analyses were performed to explore any association between variables. Results Only 6.5% of all participants knew the correct salt recommendations. Nearly a quarter of them (24.4%) found salt recommendations confusing and/or contradictory. There were significant differences between European and Asian participants, with those from Europe being better informed about salt recommendations, but significantly less knowledgeable about iodine. The reported frequency of use of salt and salt-containing sauces either at the table or for cooking, as well as knowledge about ways to reduce salt intake among those who indicated to make conscious efforts to do so, differed significantly between countries. Significant differences between countries were also observed with respect to the type of salt used, with about one third of all participants (34%) not being aware of the kind of salt they used. Conclusion The results of this survey highlight serious salt- and iodine-related knowledge gaps among university students in Europe and Asia. Raising awareness and conducting information campaigns is needed to promote changes in behaviour that would result in a reduction of salt intake and conscious use of iodised salt at the individual level.

Raw goat milk pricing is based on the milk quality especially on fat, solid not fat (SNF) and density. Therefore, there is a need of approach for composition quantization. This study applied radial basis function network (RBFN) to calibrate fat, SNF, and density with visible and near infrared spectra (400~2500 nm). To find the optimal parameters of goal error and spread used in RBFN, a response surface method (RSM) was employed. Results showed that with the optimal parameters suggested by RSM analysis, R 2 difference for training and testing data set was the smallest which indicated the model was less possible of overtraining or undertraining. The R 2 for testing set was 0.9569, 0.8420 and 0.8743 for fat, SNF and density, respectively, when optimal parameters were used in RBFN.