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• Separation significantly enhanced the antibiofilm activity of the fractions. • Separation significantly increased the antibacterial activity of the fractions. • Through UPLC-MS analysis, 17 phenolic compounds were identified. • Separation dramatically improved antibacterial efficiency of the fraction in tofu. • It provides an efficient way to improve activity/efficiency of natural components. Separation and enrichment of phenolic components from Citrus medica L. var. sarcodactylis were performed to improve the antibiofilm and antibacterial activity/efficiency of the fractions for the first time. Through separation of the crude extracts, the preparation of four fractions was done by chromatography on macroporous resin. It was found that the antibiofilm and antibacterial activity of the fractions were significantly enhanced. The obtained 30% ethanol eluted fraction (EEF) and 60% EEF both significantly inhibited biofilm formation. After the second separation by polyamide resin chromatography, the activity of the fractions was also improved. Complete inhibition (100%) on biofilm formation of S. aureus was achieved at 2.0 mg/mL. The MIC of the fractions on S. aureus was decreased to 2.0 mg/mL. There has been variation of 7.3–185.6 mg/g of phenolic content in the fractions, and a strong correlation between the anti-biofilm, the antibacterial activity and the phenolic content. Chemical composition analysis showed the EEF comprised 17 phenolic compounds. Moreover, the obtained EEF exhibited much higher antibacterial activity in tofu than crude extract. Therefore, chromatography separation significantly improved the antibacterial and antibiofilm activity/efficiency of the fractions both in vitro and in tofu. [ABSTRACT FROM AUTHOR]

The continuous development of e-commerce recommendation systems enables people to better obtain the products they need, saving economic and time costs. When recommending products, massive online user reviews can objectively reflect the efficacy of products and provide more practical reference value. However, due to the fuzziness of expression, there exists great uncertainty about the information in user reviews. The probabilistic linguistic term set (PLTS) is a useful tool that can help solve the information ambiguity and distinguish the importance of different terms. This paper regards PLTS as a data statistical tool to depict the information of user reviews. Based on the theory of PLTS, this paper proposes a hotel recommendation algorithm. First, we analyse the statements of online user hotel reviews with the aid of Jieba and TF-IDF. Second, the statements of reviews are translated into PLTSs and stored in the evaluation matrix. Third, we adopt the maximum deviation method to derive the attribute weights of hotels. Finally, we use probabilistic linguistic cosine similarity to calculate the similarity degrees of hotels and rank the recommended hotels according to similarity. With respect to different hospital products of users, the recommendation orders of hotels are given according to their degrees of similarity. To verify the effectiveness and superiority of the proposed algorithm, this paper selects 10 hotels in the city of Zhengzhou for case application and conducts a comparative analysis with other recommendation methods. [ABSTRACT FROM AUTHOR]