Diverse ginseng species exhibit highly similar morphological features, leading to potential concerns of adulteration. Difference in chemical composition, combined with sensory qualities, could be key to distinguishing between ginseng species and enabling their quality control. However, comprehensive metabolomic comparisons linking chemical profiles to sensory traits remain limited. This study aimed to differentiate Korean ginseng (Panax ginseng) and American ginseng (Panax quinquefolius) using multi-platform metabolomics approach combined with multivariate statistics by analyzing different tissue parts (whole, body, and root). Korean ginseng was overall enriched in sucrose, citric acid, acetic acid, β-panasinsene, and ginsenoside Ra2, whereas American ginseng contained higher succinic acid, β-farnesene, quinquenoside R1. Electronic tongue profiling further distinguished taste attributes, with Korean ginseng showing stronger sweetness and bitterness and American ginseng exhibiting higher sourness. These integrated findings identified discriminant markers and demonstrated the potential of integrated metabolomics and sensory analysis for ginseng authentication and quality assessment.

Chemotherapy-induced mucositis (CIM) is a debilitating side-effect impacting as many as 90% of cancer patients undergoing treatment. Patients receiving 5-Fluorouracil (5FU), a first-line chemotherapeutic in colorectal cancer, experience significant gastrointestinal distress that perpetuates poor patient quality of life and reduces treatment tolerance, efficacy, and survival. Natural compounds have shown promise in improving CIM through their pleiotropic actions, including immune and mucosal regulation. We examined whether panaxynol, a bioactive compound isolated from American ginseng, can alleviate murine CIM symptomology and severity. Intestinal mucositis was induced in C57BL/6J male and female mice by 5 consecutive intraperitoneal injections of 5FU (35 mg/kg/day); PBS was used as the control. Vehicle or panaxynol (2.5 mg/kg/day) was administered via oral gavage every other day, starting on Day -1, for a total of 4 treatments. Panaxynol significantly improved overall mucositis symptomology, attenuated 5FU-induced cytopenia and anemia, ameliorated the 5FU-induced loss of goblet cells per crypt, suppressed pro-inflammatory immune cells in the colonic lamina propria, and altered microbial diversity and taxonomy. Sex differences were observed, with panaxynol exerting a stronger effect in males, significantly reducing the relative percentage of colonic macrophages and neutrophils. Panaxynol treatment was associated with sex-dependent alterations in gut microbial community structure and modulation of specific taxa, including Dubosiella and Bifidobacterium, alongside male-specific increases in Romboutsia and Alistipes; Akkermansia abundance was primarily influenced by 5FU treatment. These preclinical findings support the potential of panaxynol as a therapeutic candidate for the treatment of CIM and highlight the importance of considering sex as a biological variable.

This study addresses the complicated pretreatment steps in pesticide residue detection of American Ginseng by introducing a simplified QuEChERS purification method. The core of this method is a novel composite material (PSA/WT@MeS) made by loading PSA and multi-walled carbon nanotubes onto a three-dimensional melamine sponge. This material effectively removes multiple interferents from complex matrices. When used in the QuEChERS workflow, it allows purification and separation of American Ginseng samples in a single step, greatly streamlining the traditional process. Combined with gas chromatography-mass spectrometry (GC-MS), 12 common pesticides in American Ginseng were measured. The method showed good linearity (R[2] > 0.99) for all 12 pesticides across the 1-20 µg/kg range. Detection limits ranged from 0.01 to 0.12 µg/kg, and quantification limits from 0.03 to 0.40 µg/kg. Matrix effects were kept within ±20%. At three spiked levels, average recoveries ranged from 87.8% to 109.8%, with relative standard deviations below 9.9%. This approach offers faster pretreatment and better purification. It provides a practical option for detecting pesticide residues in American Ginseng and other complex matrices.

Near-infrared (NIR) spectroscopy has become a key analytical tool for rapid and non-destructive quantification in agricultural, pharmaceutical, and medicinal materials. However, the predictive performance of deep learning models remains heavily constrained by the limited availability of labeled NIR spectra, as chemical reference measurements are labor-intensive, costly, and slow. Existing augmentation strategies either generate unrealistic spectral variations or introduce label drift that degrades model reliability. Thus, the central challenge is to achieve accurate and robust NIR quantitative modeling when only a limited number of samples can be labeled by reference chemical measurements.

American ginseng (Panax quinquefolium L.) is a traditional Chinese medicinal herb that has been used in China for hundreds of years. The significant hypoglycemic activity of American ginseng has made it widely used in health food as a valuable medicinal herb with homologous origin. However, its effect and mechanism of improving insulin resistance (IR) remain unclear. In the present study, an IR-HepG2 cells model induced by high concentrations of insulin was constructed and treated with ethanol extract of dried American ginseng (EDAG) at different concentrations to reveal the in vitro ameliorative effect of EDAG on IR by monitoring glucose consumption, glycogen content, triglyceride (TG) content, and total cholesterol (TC) content of the cells. The cell metabolite changes were tracked by liquid chromatography-mass spectrometry (LC-MS)-based metabolomics combined with multivariate statistical analysis, the metabolic biomarkers and related metabolic pathways were analyzed, and further elucidate its mechanism of action. The results showed that treatment with EDAG increased glucose consumption and glycogen content, and decreased intracellular TC and TG content in IR-HepG2 cells to different degrees. The results of cell metabolomics indicated that EDAG treatment reversed metabolic disorders by regulating sphingolipid metabolism, linoleic acid metabolism, and arginine and proline metabolism, etc. The present study explored the in vitro IR improvement mechanism of EDAG based on the LC-MS cell metabolomics approach, which confirms the great potential of LC-MS technology in the evaluation of drug efficacy and can be an effective tool for the related analysis of other Chinese herbal medicines.

Immunosenescence is characterized by an age-associated decline in immune function, particularly involving T-cell dysfunction, which increases susceptibility to infections and chronic diseases. This study investigated the anti-aging and immunomodulatory effects of American ginseng extract (G1899) using in vitro and in vivo models of aging. Cellular senescence was induced in HepG2 cells by D-galactose treatment, followed by exposure to G1899 (20 and 100 μg/mL). Senescence-associated markers were assessed to evaluate cellular aging. An aging mouse model was established in male C57BL/6 mice through intraperitoneal administration of D-galactose (500 mg/kg) and tert-butyl hydroperoxide (0.4 mmol/kg), and G1899 was orally administered at 400 mg/kg. Thymic immune cell subsets and aging-related protein expression were analyzed using flow cytometry and Western blotting. G1899 significantly reduced p21 expression and senescence-associated β-galactosidase activity in senescent HepG2 cells. In aging-induced mice, G1899 restored CD4[+] and CD8[+] T-cell populations, normalized naïve T-cell levels, and reduced anergic CD28-negative T cells. Furthermore, G1899 regulated the expression of key aging-related proteins, including FOXO1, Sirt1, p53, and CD38. These findings demonstrate that G1899 attenuates age-related immune alterations by restoring thymic T-cell homeostasis and regulating aging-associated molecular pathways.

American ginseng fibrous roots, often discarded as bioprocessing waste, constitute a valuable source of ginsenosides with high recovery potential. This study established a systematic strategy to enrich ginsenosides from the fibrous roots using macroporous adsorption resins and to investigate the adsorption mechanisms. Among the nine resins tested, the non-polar HPD100 resin, characterized by its styrene-divinylbenzene copolymer matrix and high specific surface area, exhibited superior performance, with an adsorption capacity of 194.1 ± 5.8 mg/g and desorption efficiency above 98.0 ± 1.9%. Dynamic column experiments optimized the operating conditions, achieving 74.9 ± 3.0% purity and 84.9 ± 3.7% recovery at a flow rate of 3 bed volumes per hour, 2.3 ± 0.2 mg/mL sample concentration, and 80% ethanol eluent. The resin maintained stable performance over five adsorption-desorption cycles. Kinetic and thermodynamic analyses revealed that adsorption followed a pseudo-second-order model and Langmuir isotherm, with spontaneous and endothermic characteristics. Structural analyses, including scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, revealed hydrogen bonding, van der Waals forces, and π-π stacking as key interactions. Molecular dynamics simulations revealed a thermally enhanced binding effect, demonstrating that elevated temperatures strengthen ginsenoside-resin interactions by increasing binding energy even as hydrogen bonding diminishes. This work elucidates the fundamental adsorption mechanism and establishes a theoretical basis for the high-value valorization of American ginseng by-products through a rationally designed, temperature-controlled strategy.

Ginsenosides, the principal bioactive constituents of American ginseng (Panax quinquefolius), require precise monitoring during column chromatography to ensure product quality. Conventional assays are time-consuming and unsuited for real-time process control, particularly for low-abundance compounds. Here, we present a rapid, non-destructive near-infrared (NIR) optical sensing system coupled with an Evolutionary Attention-based Long Short-Term Memory (EA-LSTM) model to predict ginsenoside concentrations dynamically. The approach exploits the high-speed acquisition and reproducibility of NIR spectra while leveraging EA-LSTM's temporal feature extraction and adaptive attention to focus on chemically informative elution segments. Offline spectra from eight elution batches, referenced by UPLC, enabled model training and validation, with high-frequency sampling simulated to mimic industrial deployment. The system demonstrated high accuracy (R[2] = 0.9841, RPD = 7.94) and robustness under dynamic conditions (R[2] = 0.9694, RPD = 5.72, response time = 0.34 ms), highlighting its potential for real-time, non-destructive monitoring and intelligent control in natural product purification.

Near-infrared (NIR) spectroscopy combined with artificial intelligence (AI) is widely used in food and pharmaceutical industries for rapid analysis, but its reliability is limited by small labeled datasets due to the difficulty of measuring sample chemical compositions. This study proposes DeepSS, a novel deep learning-based chemometrics approach, to address this challenge. DeepSS employs a pyramid encoder to extract low-dimensional latent features from NIR spectra and introduces a data pairing strategy to augment training data by combining latent features of sample pairs and subtracting their labels. During testing, pseudo labels are generated by pairing test samples with training features, followed by a decision strategy for final predictions. Benchmarked against four models using datasets from tablets, apples, and American ginseng, DeepSS reduced prediction errors by 1.51 %-14.29 % for key components. DeepSS demonstrates reliable performance on small labeled NIR datasets, improving prediction accuracy and efficiency in food and pharmaceutical analysis.

Rapid and reliable detection of adulteration in American ginseng (Panax quinquefolius L.) is essential for ensuring product integrity and safeguarding consumer health, as the powder is frequently adulterated with low-cost herbal substitutes. This study proposes a novel analytical framework integrating near-infrared (NIR) spectroscopy with machine learning modeling to identify adulteration involving Panax quinquefolius L, Angelica dahurica, Astragalus membranaceus, Panax notoginseng, and wheat flour. A total of 1680 samples were prepared, including 6 types of adulterants and 4 adulteration levels (20 %, 40 %, 60 %, and 80 %, w/w). Spectral data were preprocessed using noise reduction, baseline drift correction, and scatter correction. Principal component analysis (PCA) provided preliminary visualization of clustering trends, whereas four supervised classifiers-k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and kernel extreme learning machine (KELM)-were comparatively assessed for classification performance. Among them, KELM achieved the best performance with an overall accuracy of 0.9560, precision/recall of 0.9629, and a Kappa coefficient of 0.9551 on the testing set. This study establishes an integrated NIR-chemometric framework using a hybrid-KELM for rapid, non-destructive, and interpretable authentication of American ginseng powder, where SHAP-based wavelength analysis highlights key spectral regions and provides practical guidance for developing simplified, application-specific NIR devices and broader detection of economically motivated adulteration in herbal products.