Alzheimer's disease (AD) is the most common type of dementia affecting the aged population worldwide, yet its social perceptions have been less studied. To investigate the perceptions and attitudes toward AD in the Chinese population, a cross-sectional face-to-face survey of 2,000 randomly selected adults was conducted in five representative cities of China. This survey focused on the fear of AD, and the relationship between this variable and each studied factor was analyzed using univariate analysis and multivariate regression analysis. In general, 76.6% of the total respondents had personal fear of developing AD, and such fear was closely related to the proximity to AD and perceived severity of AD, as well as other factors such as gender and self-perceived health. The results strongly suggested that more attention should be paid to public health education of AD, which can only be achieved with the cooperation of government, media, medical institutions, and the community so as to eliminate people's confusion about AD, relieve their psychological burden, and optimize their health-seeking behavior.

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Pu-erh tea processed from the sun-dried green tea leaves can be divided into ancient tea (AT) and terrace tea (TT) according to the source of raw material. However, their similar appearance makes AT present low market identification, resulting in a disruption in the tea market rules of fair trade. Therefore, this study analyzed the classification by principal component analysis/hierarchical clustering analysis and conducted the discriminant model through stepwise Fisher discriminant analysis and decision tree analysis based on the contents of water extract, phenolic components, caffeine, and amino acids, aiming to investigate whether phytochemicals coupled with chemometric analyses distinguish AT and TT. Results showed that there were good separations between AT and TT, which was caused by 16 components with significant (p < 0.05) differences. The discriminant model of AT and TT was established based on six discriminant variables including water extract, (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, theacrine, and theanine. Among them, water extract comprised multiple soluble solids, representing the thickness of tea infusion. The model had good generalization capability with 100% of performance indexes according to scores of the training set and model set. In conclusion, phytochemicals coupled with chemometrics analyses are a good approach for the identification of different raw materials.