The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky–Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

Ganoderma lucidum is an edible and medicinal fungus. Triterpenoids, particularly ganoderic acids, represent the principal bioactive constituents in Ganoderma lucidum . Bacteria‐enzyme synergistic fermentation broth of Ganoderma lucidum (FBG) was optimized as a potential alternative to traditional water‐extracted Ganoderma lucidum (WEG). The optimized parameters with Lactobacillus rhamnosus fermentation exhibited the highest content of ganoderic acid F (GAF). Administration of FBG led to a pronounced decline in lipid deposition and contents of intracellular total cholesterol (TC), triglycerides (TG), low‐density lipoprotein cholesterol (LDL‐C), reactive oxygen species (ROS), and malonic dialdehyde (MDA). The FBG treatment also markedly increased the activities of high‐density lipoprotein cholesterol (HDL‐C), superoxide dismutase (SOD), and catalase (CAT). The results of network pharmacology analysis and molecular docking suggested that GAF exhibits a robust interaction with the core targets sodium leak channel, nonselective (NALCN) and eukaryotic translation elongation factor 1 alpha 2 (EEF1A2). Enzyme‐linked immunosorbent assay (ELISA) results demonstrated that GAF could effectively reduce lipid accumulation while upregulating EEF1A2, phosphorylated AMP‐activated protein kinase (p‐AMPK), and peroxisome proliferator‐activated receptor alpha (PPAR‐α) protein expression. Collectively, these findings indicate that GAF alleviates free fatty acid (FFA)‐induced hepatic lipid accumulation through activation of the EEF1A2/p‐AMPK/PPAR‐α signaling pathway, suggesting a potential mechanism involving AMPK‐related pathways.

Inflammatory bowel disease (IBD) poses a significant global health burden with rising incidence, particularly in Asia. This study employed an integrative network pharmacology approach combined with molecular docking to elucidate the therapeutic mechanism of ganoderic acid A (GAA) against IBD. Potential GAA targets were retrieved from pharmacogenomic databases, while IBD-related genes were curated from OMIM and GeneCards databases. Weighted gene co-expression network analysis of IBD transcriptomic datasets (GSE38713, GSE126124) identified disease-associated modules, with the yellow module exhibiting the strongest positive correlation. Functional enrichment analyses demonstrated significant involvement of overlapping targets in lipid metabolism, the inflammatory response, and the mitogen-activated protein kinase (MAPK) signaling cascade pathway. We identified 14 IBD-GAA-ferroptosis-related genes and 54 key module genes. Intersection analysis revealed 5 overlapping targets, including tumor necrosis factor-α(TNF-α), peroxisome proliferators-activated receptor γ (PPARγ), MAPK14, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic α (PIK3CA), and Caspase 3 (CASP3). Molecular docking confirmed high-affinity binding of GAA to these targets, with binding energies ranging from −7.3 to −10 kcal/mol. Crucially, experimental evaluation demonstrated the pivotal role of GAA in alleviating disease pathology. GAA treatment suppressed the significantly elevated levels of TNF-α and p-MAPK14 in the organoids using a cytokine/LPS-induced IBD model. These findings collectively suggest a potential involvement of GAA in pathways associated with ferroptosis regulation, although direct experimental evidence for ferroptosis markers remains to be established. The observed multi-target effects on immune regulation and cellular proliferation/differentiation provide a foundation for further mechanistic investigation.