Review of studies on polysaccharides, lignins and small molecular compounds from three Polygonatum Mill. (Asparagaceae) spp. in crude and processed states
Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Cited by 2 publications
References 122 publications
Rapid Identification of Medicinal Polygonatum Species and Predictive of Polysaccharides Using ATR‐FTIR Spectroscopy Combined With Multivariate Analysis
IntroductionMedicinal Polygonatum species is a widely used traditional Chinese medicine with high nutritional value, known for its anti‐fatigue properties, enhancement of immunity, delays aging, improves sleep, and other health benefits. However, the efficacy of different species varies, making the quality control of medicinal Polygonatum species increasingly important. Polysaccharides are important in medicinal Polygonatum species because of their potential functional properties, such as antioxidation, hypoglycemia, protection of intestinal health, and minimal toxicological effects on human health, as well as high polysaccharide levels.ObjectiveThis study developed a qualitative medicinal Polygonatum species model and a polysaccharides predictive model based on attenuated total reflection Fourier transform infrared spectroscopy (ATR‐FTIR) combined with a multivariate analysis approach.Materials and MethodsATR‐FTIR spectral information of 334 medicinal Polygonatum species samples was collected and the spectral information of different modes was analyzed. The ATR‐FTIR spectral differences of three medicinal Polygonatum species were studied by multivariate analysis combined with four spectral preprocessing and three variable selection methods. For the prediction of polysaccharides in Polygonatum kingianum Collett & Hemsl. (PK), we initially determined the actual content of 110 PK polysaccharide samples using the anthrone‐sulfuric acid method, then established partial least squares regression (PLSR) and kernel PLSR models in conjunction with attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy.ResultsIn the visualization analysis, the orthogonal partial least squares‐discriminant analysis (OPLS‐DA) model based on second‐order derivative (SD) preprocessing was most suitable for medicinal Polygonatum species species binary classification, spectral differences between Polygonatum cyrtonema Hua (PC) and other species are evident; in the hard modeling, SD preprocessing improves the accuracy of non‐deep learning models for the classification of three medicinal Polygonatum species. In contrast, residual neural network (ResNet) models were the best choice for species identification without preprocessing and variable selection. In addition, the partial least squares regression (PLSR) model and Kernel‐PLSR model can quickly predict PK polysaccharides content, among them, the Kernel‐PLSR model with SD pretreatment has the best prediction performance, residual prediction deviation (RPD) = 7.2870, Rp = 0.9905.ConclusionIn this study, we employed ATR‐FTIR spectroscopy and various treatments to discern different medicinal Polygonatum species. We also evaluated the effects of preprocessing methods and variable selection on the prediction of PK polysaccharides by PLSR and Kernel‐PLSR models. Among them, the ResNet model can achieve 100% correct classification of medicinal Polygonatum species without complex spectral preprocessing. Furthermore, the Kernel‐PLSR model based on SD‐ATR‐FTIR spectra had the best performance in polysaccharides prediction. In summary, by integrating ATR‐FTIR spectroscopy with multivariate analysis, this research accomplished the classification of medicinal Polygonatum species and the prediction of polysaccharides. The methodology offers the benefits of speed, environmental sustainability, and precision, highlighting its significant potential for practical applications. In future research, on the one hand, it can be further investigated using a portable infrared spectrometer, and on the other hand, infrared spectroscopy can also be applied to the prediction of other chemical components of medicinal Polygonatum species.
Rapid Identification of Medicinal Polygonatum Species and Predictive of Polysaccharides Using ATR‐FTIR Spectroscopy Combined With Multivariate Analysis
IntroductionMedicinal Polygonatum species is a widely used traditional Chinese medicine with high nutritional value, known for its anti‐fatigue properties, enhancement of immunity, delays aging, improves sleep, and other health benefits. However, the efficacy of different species varies, making the quality control of medicinal Polygonatum species increasingly important. Polysaccharides are important in medicinal Polygonatum species because of their potential functional properties, such as antioxidation, hypoglycemia, protection of intestinal health, and minimal toxicological effects on human health, as well as high polysaccharide levels.ObjectiveThis study developed a qualitative medicinal Polygonatum species model and a polysaccharides predictive model based on attenuated total reflection Fourier transform infrared spectroscopy (ATR‐FTIR) combined with a multivariate analysis approach.Materials and MethodsATR‐FTIR spectral information of 334 medicinal Polygonatum species samples was collected and the spectral information of different modes was analyzed. The ATR‐FTIR spectral differences of three medicinal Polygonatum species were studied by multivariate analysis combined with four spectral preprocessing and three variable selection methods. For the prediction of polysaccharides in Polygonatum kingianum Collett & Hemsl. (PK), we initially determined the actual content of 110 PK polysaccharide samples using the anthrone‐sulfuric acid method, then established partial least squares regression (PLSR) and kernel PLSR models in conjunction with attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy.ResultsIn the visualization analysis, the orthogonal partial least squares‐discriminant analysis (OPLS‐DA) model based on second‐order derivative (SD) preprocessing was most suitable for medicinal Polygonatum species species binary classification, spectral differences between Polygonatum cyrtonema Hua (PC) and other species are evident; in the hard modeling, SD preprocessing improves the accuracy of non‐deep learning models for the classification of three medicinal Polygonatum species. In contrast, residual neural network (ResNet) models were the best choice for species identification without preprocessing and variable selection. In addition, the partial least squares regression (PLSR) model and Kernel‐PLSR model can quickly predict PK polysaccharides content, among them, the Kernel‐PLSR model with SD pretreatment has the best prediction performance, residual prediction deviation (RPD) = 7.2870, Rp = 0.9905.ConclusionIn this study, we employed ATR‐FTIR spectroscopy and various treatments to discern different medicinal Polygonatum species. We also evaluated the effects of preprocessing methods and variable selection on the prediction of PK polysaccharides by PLSR and Kernel‐PLSR models. Among them, the ResNet model can achieve 100% correct classification of medicinal Polygonatum species without complex spectral preprocessing. Furthermore, the Kernel‐PLSR model based on SD‐ATR‐FTIR spectra had the best performance in polysaccharides prediction. In summary, by integrating ATR‐FTIR spectroscopy with multivariate analysis, this research accomplished the classification of medicinal Polygonatum species and the prediction of polysaccharides. The methodology offers the benefits of speed, environmental sustainability, and precision, highlighting its significant potential for practical applications. In future research, on the one hand, it can be further investigated using a portable infrared spectrometer, and on the other hand, infrared spectroscopy can also be applied to the prediction of other chemical components of medicinal Polygonatum species.
Lignin: An Adaptable Biodegradable Polymer Used in Different Formulation Processes
Introduction: LIG is a biopolymer found in vascular plant cell walls that is created by networks of hydroxylated and methoxylated phenylpropane that are randomly crosslinked. Plant cell walls contain LIG, a biopolymer with significant potential for usage in modern industrial and pharmaceutical applications. It is a renewable raw resource. The plant is mechanically protected by this substance, which may increase its durability. Because it has antibacterial and antioxidant qualities, LIG also shields plants from biological and chemical challenges from the outside world. Researchers have done a great deal of work to create new materials and substances based on LIG. Numerous applications, including those involving antibacterial agents, antioxidant additives, UV protection agents, hydrogel-forming molecules, nanoparticles, and solid dosage forms, have been made with this biopolymer. Methods: For this review, a consistent literature screening using the Pubmed database from 2019–2024 has been performed. Results: The results showed that there is an increase in interest in lignin as an adaptable biomolecule. The most recent studies are focused on the biosynthesis and antimicrobial properties of lignin-derived molecules. Also, the use of lignin in conjunction with nanostructures is actively explored. Conclusions: Overall, lignin is a versatile molecule with multiple uses in industry and medical science
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
Contact Info
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Product
Resources
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.
