Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera.

Blumea balsamifera (Ai-na-xiang) is used as an important plant source of natural borneol, which is widely used in the pharmaceutical industry. The aim of this study was to establish the methods based on near infrared (NIR) spectroscopy for determining the geographical origin of B. balsamifera, as well as developing a method for the quantitative rapid analysis of the active pharmaceutical ingredients (APIs). A total of 109 samples were collected from China in 2013 and arbitrarily divided into calibration and prediction sets using the Kennard–Stone algorithm. The l-borneol and total flavone contents of the samples were measured by gas chromatography and ultraviolet-visible spectroscopy, respectively. The NIR spectra were acquired using an integrating sphere and a partial least squares (PLS) model was built using the optimum wavelength regions, which were selected using a synergy interval partial least-squares (SiPLS) algorithm. The root mean square errors of prediction of the l-borneol and total flavone models were 0.0779 and 2.2694 mg/g, with R² of 0.9069 and 0.8013, respectively. A discriminant model to determine the geographical origin of B. balsamifera (Guizhou and Hainan) was also established using a partial least squares discriminant analysis method with an optimum pretreatment method. The prediction accuracy rate of the model was 100%. NIR spectroscopy can be used as a reliable and environmentally friendly method to determine the API and the origin of different B. balsamifera samples.

A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong’e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.

Postoperative abdominal adhesion is one of the most common complications after abdominal surgery. A single drug or physical barrier treatment does not achieve the ideal anti-adhesion effect. We developed a thermosensitive hydrogel (PPH hydrogel) consisting of poloxamer 407 (P407), poloxamer (P188), and hydroxypropyl methylcellulose (HPMC) co-blended. An injectable thermosensitive TA/MMC-PPH hydrogel was obtained by loading tannic acid (TA) with an anti-inflammatory effect and mitomycin C (MMC), which inhibits fibroblast migration or proliferation. The optimal prescriptions of PPH hydrogels with a suitable gelling time (63 s) at 37 °C was 20% (w/v) P407, 18% (w/v) P188, and 0.5% (w/v) HPMC. The scanning electron microscopy (SEM) revealed that the PPH hydrogel had a three-dimensional mesh structure, which was favorable for drug encapsulation. The PPH hydrogel had a suitable gelation temperature of 33 °C, a high gel strength, and complicated viscosity at 37 °C, according to the rheological analysis. In vitro release studies have shown that the PPH hydrogel could delay the release of TA and MMC and conform to the first-order release rate. Anti-adhesion tests performed on rats in vivo revealed that TA/MMC-PPH hydrogel significantly reduced the risk of postoperative adhesion. In conclusion, the TA/MMC-PPH hydrogel prepared in this study showed an excellent performance in both controlled drug release and anti-adhesive effects. It can be used as a protocol to prevent or reduce postoperative abdominal adhesion.

Cotton is a widely cultivated cash crop and represents one of the most significant raw materials for textiles on a global scale. The rapid development of the cotton industry has resulted in the production of substantial amounts of cotton husks, which are frequently underutilized or discarded. This study utilizes agricultural waste, specifically cotton shells, as a precursor for biochar, which is subsequently carbonized and nitrogen-doped with ruthenium oxide to synthesize an innovative composite material known as RuO₂-NC. An electrochemical sensor was developed using this composite material to detect heavy metals, particularly lead and copper ions. The results demonstrate that the electrochemical sensor can accurately quantify concentrations of lead and copper ions across a wide linear range, exhibiting exceptional sensitivity. Furthermore, the sensor was tested on samples from Viola tianshanica Maxim (Violaceae) collected from the Xinjiang Uygur Autonomous Region (XUAR) in China, showing commendable accuracy and sensitivity. This approach promotes eco-friendly recycling of agricultural waste while offering advantages such as straightforward operation and reduced costs, thereby presenting promising prospects for practical applications.