Effects of Perceived Benefit on Vitamin D Supplementation Intention: A Theory of Planned Behaviour Perspective.

There are many factors that affect vitamin D supplementation, including those from the theory of planned behaviour (TPB); however, how the perceived benefit acts in the model remains unknown. In the current study, we tested the efficacy of the TPB and the impacts of the perceived benefit (PBE) in the model. The subjects were 287 customers who purchased vitD from pharmacies in major cities in Taiwan. A structured questionnaire was used to collect the data. t-tests, analysis of variance (ANOVA), regression analyses, and path analysis via SPSS and AMOS were used to analyse the data. The original TPB model explained 47.5% of the variance of intention with the three variables of attitude (ß = 0.261), perceived behavioural control (ß = 0.183), and subjective norms (ß = 0.169). The model that incorporated PBE increased the explained variance to 59.7%, and PBE became the strongest predictor (ß = 0.310) and a significant mediator linking attitude, subjective norms, perceived control (ANC) with supplementation intention. PBE and attitude were the two most important variables in predicting vitD supplementation intention. We suggest that updated information regarding dietary sources of vitD and its benefits should be included in health- or nutrition-related courses in education programs for the overall health of the nation.

2-DE combined with two-layer feature selection accurately establishes the origin of oolong tea.

Taiwan is known for its high quality oolong tea. Because of high consumer demand, some tea manufactures mix lower quality leaves with genuine Taiwan oolong tea in order to increase profits. Robust scientific methods are, therefore, needed to verify the origin and quality of tea leaves. In this study, we investigated whether two-dimensional gel electrophoresis (2-DE) and nanoscale liquid chromatography/tandem mass spectroscopy (nano-LC/MS/MS) coupled with a two-layer feature selection mechanism comprising information gain attribute evaluation (IGAE) and support vector machine feature selection (SVM-FS) are useful in identifying characteristic proteins that can be used as markers of the original source of oolong tea. Samples in this study included oolong tea leaves from 23 different sources. We found that our method had an accuracy of 95.5% in correctly identifying the origin of the leaves. Overall, our method is a novel approach for determining the origin of oolong tea leaves.