Alcoholic liver disease (ALD) is a major public health problem worldwide, which needs to be effective prevention. Ginsenoside Rg1 (GRg1), a bioactive ingredient extracted from ginseng, has benefit effects on health. In this study, 11 potential targets of GRg1 against ALD were firstly obtained by network pharmacology. KEGG pathway enrichment showed that GRg1-target-ALD was closely related to Toll-like receptor (TLR) and nuclear factor-kappa B (NF-κB) signaling pathways. In addition, GRg1 decreased antioxidant levels and increased oxidative levels in alcohol-treated mice, which alleviated oxidative stress-induced hepatic damage. GRg1 enhanced intestinal barrier function via upregulating the levels of tight junction protein and immunoglobulin A. GRg1 also reduced alcohol-induced inflammation by suppressing TLR4/NF-κB pathway, which was consistent with the prediction of network targets. Moreover, GRg1 altered GM population, and Verrucomicrobia, Bacteroidetes, Akkermansia, Bacteroides, Lachnospiraceae_NK4A136_group, and Alloprevotella played positive association with intestinal barrier indicators and negative correlation with hepatic inflammation biomarkers. The results suggest that GRg1 administration might be a promising strategy for protection of alcohol-induced liver damage.

The traditional method for analyzing the content of instant tea has disadvantages such as complicated operation and being time-consuming. In this study, a method for the rapid determination of instant tea components by near-infrared (NIR) spectroscopy was established and optimized. The NIR spectra of 118 instant tea samples were used to evaluate the modeling and prediction performance of a combination of binary particle swarm optimization (BPSO) with support vector regression (SVR), BPSO with partial least squares (PLS), and SVR and PLS without BPSO. Under optimal conditions, Rp for moisture, caffeine, tea polyphenols, and tea polysaccharides were 0.9678, 0.9757, 0.7569, and 0.8185, respectively. The values of SEP were less than 0.9302, and absolute values of Bias were less than 0.3667. These findings indicate that machine learning can be used to optimize the detection model of instant tea components based on NIR methods to improve prediction accuracy.

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