Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Component analysis using UPLC-Q-TOF/MS and quality evaluation using fingerprinting and chemometrics for hops

Hops, extensively cultivated in China for their food and medicinal applications, currently lack well-defined chemical markers to evaluate variations in their quality. The study aimed to explore variations in the quality of Chinese hops by the chemical characteristics of hops, employing UPLC-Q-TOF/MS, integrated with chemical fingerprinting and chemometrics. The results indicated that Chinese hops are abundant in polyphenols and bitter acids. The integration of UPLC-Q-TOF/MS, Chemical fingerprinting, and chemometrics revealed to be an accurate and effective approach for assessing the quality of Chinese hops. In this study, ten important chemical markers were found to be useful in differentiating various hop varieties. Moreover, the support vector machine showed a prediction accuracy of 92.3077% in identifying Chinese hop varieties. The strategy of the study lays the groundwork for classifying Chinese hop varieties and serves as a prerequisite for future quality control studies, particularly focusing on chemical compositions.

Quality Evaluation of Tripterygium Glycoside Tablets Based on Quantitative Band-Selective 2D 1H-13C HSQC and 1H NMR Fingerprinting

Tripterygium glycoside tablets (TGTs) are preparations extracted and purified from Tripterygium wilfordii Hook. F and are extensively utilized in the treatment of autoimmune diseases, such as rheumatoid arthritis (RA). However, variations in production processes among manufacturers can lead to challenges in quality control and clinical utilization of TGTs. A band-selective 2D 1H-13C HSQC quantification method was applied for the determination of 13 active ingredients in TGTs. This method was validated following the guidelines of USP-NF 2022. The results demonstrated that the quantitative method exhibited excellent signal resolution, as well as sufficient accuracy, sensitivity, and stability. In addition, the 1H NMR spectra of TGTs from three manufacturers underwent analysis using principal component analysis and orthogonal partial least-squares discriminant analysis. The results revealed significant differences among the TGTs from the three manufacturers, with manufacturer 2 and manufacturer 3 demonstrating superior product consistency compared to manufacturer 1. A quality evaluation system for TGTs was developed based on band-selective 2D 1H-13C HSQC and 1H NMR, encompassing both quality markers and fingerprinting. This system offers reliable approaches and insights for enhancing the quality control of natural products.

A Robust TabNet-Based Multi-Classification Algorithm for Infrared Spectral Data of Chinese Herbal Medicine with High-Dimensional Small Samples

Robust classification algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet. Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials. The paper also provides ideas for multi-classification modeling data with small sample size and high-dimensional feature.

Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC × GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC × GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by "burr" signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC × GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC × GC data.

Large-Scale Production of Specialized Metabolites In Vitro Cultures

For centuries plants have been intensively utilized as reliable sources of food, flavoring, and pharmaceutical ingredients. However, plant natural habitats are being rapidly lost due to the climate change and agriculture. Plant biotechnology offers a sustainable approach for the bioproduction of specialized plant metabolites. The unique structural features of plant-derived specialized metabolites, such as their safety profile and multi-target spectrum, have led to the establishment of many plant-derived drugs. However, there are still many challenges to overcome regarding the production of these metabolites from plant in vitro systems and establish a sustainable large-scale biotechnological process. These challenges are due to the peculiarities of plant cell metabolism, the complexity of plant specialized metabolite pathways, and the correct selection of bioreactor systems and bioprocess optimization. In this book chapter, we attempted to focus on the advantages of plant in vitro systems and in particular plant cell suspensions for their cultivation as a source of plant-derived specialized metabolites. A state-of-the-art technological platform for plant cell suspension cultivation from callus induction to lab-scale cultivation, extraction, and purification is presented. Possibilities for bioreactor cultivation of plant cell suspensions in benchtop and large-scale volumes are highlighted, including several examples and patents for industrial production of specialized metabolites.