Identification of Chinese herbal medicines by fluorescence microscopy: fluorescent characteristics of medicinal bark

Application of PLS-NN model based on mid-infrared spectroscopy in the origin identification of Cornus officinalis

Mid-infrared spectroscopy has been increasingly used as a nondestructive analytical technique in Chinese herbal medicine identification in recent years. In this study, a new chemometric model named as PLS-NN model was proposed based on the mid-infrared spectral data of Cornus officinalis samples from 11 origins. It was realized by combining the partial least squares and neural networks for the identification of the origin of Chinese herbal medicines. First, we extracted features from the spectral data in 3448 bands using the partial least squares method, and extracted 122 components that contained more than 95% of the information. Then, we trained the PLS-NN model by neural network using the extracted components as inputs and the corresponding origin classes as outputs. Finally, based on an external test set, we evaluated the generalization ability of the PLS-NN model using metrics such as accuracy, F1-Score and Kappa coefficient. The results show that the PLS-NN model performs well in all three metrics when compared to models such as Decision trees, Support vector machine, Partial least squares Discriminant analysis, and Naive bayes. The model not only realizes the dimensionality reduction of full-spectrum data and improves the training efficiency of the model, but also has higher accuracy compared with the full-spectrum data model. The PLS-NN model was applied to identify the origin of Cornus officinalis with an accuracy of 91.9%.

Comparative Analysis of Acanthopanacis Cortex and Periplocae Cortex Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis

Chinese Herbal Medicines (CHMs) can be identified by experts according to their odors. However, the identification of these medicines is subjective and requires long-term experience. The samples of Acanthopanacis Cortex and Periplocae Cortex used were dried cortexes, which are often confused in the market due to their similar appearance, but their chemical composition and odor are different. The clinical use of the two herbs is different, but the phenomenon of being confused with each other often occurs. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two species for fast and robust discrimination, in order to provide a scientific basis for avoiding confusion and misuse in the process of production, circulation and clinical use. In this study, the odor and volatile components of these two medicinal materials were detected by the E-nose and by gas chromatography-mass spectrometry (GC-MS), respectively. An E-nose combined with pattern analysis methods such as principal component analysis (PCA) and partial least squares (PLS) was used to discriminate the cortex samples. The E-nose was used to determine the odors of the samples and enable rapid differentiation of Acanthopanacis Cortex and Periplocae Cortex. GC-MS was utilized to reveal the differences between the volatile constituents of Acanthopanacis Cortex and Periplocae Cortex. In all, 82 components including 9 co-contained components were extracted by chromatographic peak integration and matching, and 24 constituents could be used as chemical markers to distinguish these two species. The E-nose detection technology is able to discriminate between Acanthopanacis Cortex and Periplocae Cortex, with GC-MS providing support to determine the material basis of the E-nose sensors' response. The proposed method is rapid, simple, eco-friendly and can successfully differentiate these two medicinal materials by their odors. It can be applied to quality control links such as online detection, and also provide reference for the establishment of other rapid detection methods. The further development and utilization of this technology is conducive to the further supervision of the quality of CHMs and the healthy development of the industry.

Real-time monitoring of the penetration of amphiphilic acrylate copolymer in leather using a fluorescent copolymer as tracer

A fluorescent tracer, poly (acrylic-co-stearyl acrylate-co-3-acryloyl fluorescein) [poly (AA-co-SA-co-Ac-Flu)], used for real-time monitoring the penetration of amphiphilic acrylate copolymer, poly (acrylic-co-stearyl acrylate) [poly (AA-co-SA)], in leather was synthesized by radical polymerization of acrylic, stearyl acrylate and fluorescent monomer, 3-acryloyl fluorescein (Ac-Flu). The structure, molecular weight, introduced fluorescent group content and fluorescent characteristics of the fluorescent tracer and target copolymer, amphiphilic acrylate copolymer, were also characterized. The results show that the tracer presents the similar structural characteristics to the target and enough fluorescence intensity with 1.68 wt % of the fluorescent monomer introduced amount. The vertical section of the leather treated with the target copolymer mixing with 7% of the tracer exhibits evident fluorescence, and the change of fluorescence intensity along with the vertical section with treating time increasing can reflect the penetration depth of the target copolymer. The introduction of the fluorescent group in polymer structure through copolymerization with a limited amount of fluorescent monomer, Ac-Flu, is an effective way to make a tracer to monitor the penetration of the target in leather, which provides a new thought for the penetration research of syntans such as vinyl copolymer materials in leather manufacture.

Internal transcribed spacer 2 barcode: a good tool for identifying Acanthopanacis cortex

Acanthopanacis cortex has been used in clinical applications for a long time. Considering some historical and geographical factors, Acanthopanacis cortex is easily confused with other herbs in medicine markets, thereby causing potential safety issues. In this study, we used the internal transcribed spacer 2 (ITS2) barcode to identify 69 samples belonging to six species, including Acanthopanacis cortex and its adulterants. The nearest distance, single-nucleotide polymorphisms (SNPs), and neighbor-joining (NJ) tree methods were used to evaluate the identification ability of the ITS2 barcode. According to the kimura-2-parameter model, the intraspecific distance of Eleutherococcus nodiflorus ITS2 sequences ranged from 0 to 0.0132. The minimum interspecific distance between E. nodiflorus and E. giraldii was 0.0221, which was larger than the maximum intraspecific distance of E. nodiflorus. Three stable SNPs in ITS2 can be used to distinguish Acanthopanacis cortex and its closely related species. The NJ tree indicated that the Acanthopanacis cortex samples clustered into one clade, which can be distinguished clearly from the adulterants of this herb. A secondary structure of ITS2 provided another dimensionality to identify species. In conclusion, the ITS2 barcode effectively identifies Acanthopanacis cortex, and DNA barcoding is a convenient tool for medicine market supervision.