Application of near infrared spectroscopy to the analysis and fast quality assessment of traditional Chinese medicinal products

Laser-Induced Breakdown Spectroscopy-Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model's robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications.

Fast analysis of straw proximates based on partial least squares using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a rapid measurement technique based on the spectroscopic absorption bands of specific functional groups within biomass. Its main advantages include simple preparation, precise analysis, and the ability to analyze multiple components simultaneously. Fast analysis of straw proximates (moisture, ash, and fixed carbon) has been investigated by means of NIRS. A total of 144 samples were collected, the spectral data were analyzed by partial least squares (PLS) regression and support vector regression (SVR) with four wavelength selection methods. PLS combined with competitive adaptive reweighted sampling (CARS) provided excellent predictive performance for moisture, ash, and fixed carbon. For moisture prediction, the values of RP2, RMSEP and RPD were 0.7202, 0.8196, and 2.11, respectively. For ash prediction, the values of RP2, RMSEP and RPD were 0.9307, 0.5901, and 3.69, respectively. For fixed carbon prediction, the values of RP2, RMSEP and RPD were 0.8504, 0.2735, and 2.76, respectively. Fast analysis of proximates of corn stover was possible using this NIRS system.

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

NIR quantitative model trans-scale calibration from small scale to pilot scale via directed DOSC-SBC algorithm

In order to solve the problem of inapplicability of NIR quantitative models due to the large difference between the modeling samples and the samples to be tested, Directed DOSC-SBC（DDOSC-SBC）algorithm is proposed in this paper based on Direct Orthogonal Signal Correction combined with Slope/Bias Correction (DOSC-SBC) algorithm. To obtain the suitable spectral matrix transfer parameters for the test set during DDOSC spectral preprocessing, several representative test samples in the test set were selected, then the spectral systematic errors between the modeling set and the test set were corrected with the SBC method in order to realize the trans-scale prediction of the NIR quantitative model. NIR data and the critical quality attributes（CQAs）were detected in the small scale and pilot scale pharmaceutical process of the fluidized bed granulation of dextrin and water extraction of honeysuckle. After the small scale model was calibrated via the directed DOSC-SBC algorithm which was guided by representative pilot scale samples, the small scale model was able to predict the pilot scale test samples more accurately. The NIR quantitative model trans-scale calibration from small scale to pilot scale was also successfully realized with a RPD value higher than 3.5 and RSEP value lower than 10%. DDOSC-SBC algorithm is a successful model trans-scale calibrated method that can be applied to NIR real-time monitoring of CQAs in the preparation process of Chinese herbal medicine.

Rapid authentication of different herbal medicines by heating online extraction electrospray ionization mass spectrometry

The rapid and accurate authentication of traditional Chinese medicines (TCMs) has always been a key scientific and technical problem in the field of pharmaceutical analysis. Herein, a novel heating online extraction electrospray ionization mass spectrometry (H-oEESI-MS) was developed for the rapid and direct analysis of extremely complex substances without the requirement for any sample pretreatment or pre-separation steps. The overall molecular profile and fragment structure features of various herbal medicines could be completely captured within 10-15 s, with minimal sample (<0.5 mg) and solvent consumption (<20 μL for one sample). Furthermore, a rapid differentiation and authentication strategy for TCMs based on H-oEESI-MS was proposed, including metabolic profile characterization, characteristic marker screening and identification, and multivariate statistical analysis model validation. In an analysis of 52 batches of seven types of Aconitum medicinal materials, 20 and 21 key compounds were screened out as the characteristic markers of raw and processed Aconitum herbal medicines, respectively, and the possible structures of all the characteristic markers were comprehensively identified based on Compound Discoverer databases. Finally, multivariate statistical analysis showed that all the different types of herbal medicines were well differentiated and identified (R²X > 0.87, R²Y > 0.91, and Q² > 0.72), which further verified the feasibility and reliability of this comprehensive strategy for the rapid authentication of different TCMs based on H-oEESI-MS. In summary, this rapid authentication strategy realized the ultra-high-throughput, low-cost, and standardized detection of various complex TCMs for the first time, thereby demonstrating wide applicability and value for the development of quality standards for TCMs.

Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm

The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is meaningful. In this paper, a visual classification and identification model of Raman spectroscopy combined with principal component analysis (PCA) and support vector machine (SVM) algorithms was developed to rapidly classify and identify carbonate and sulfate mineral medicines. The results reveal that although the Raman spectra are too similar to distinguish by naked eye, the PCA-SVM algorithm can perform accurate classification and identification, and its accuracy, precision, recall and F1-score parameters all reach 100%. The proposed method is rapid, accurate, nondestructive, convenient, portable, and low cost, and has important application value for the classification, identification and quality supervision of various carbonate and sulfate mineral medicines.

Application of chloroplast genome in the identification of Traditional Chinese Medicine Viola philippica

BACKGROUND

Viola philippica Cav. is the only source plant of "Zi Hua Di Ding", which is a Traditional Chinese Medicine (TCM) that is utilized as an antifebrile and detoxicant agent for the treatment of acute pyogenic infections. Historically, many Viola species with violet flowers have been misused in "Zi Hua Di Ding". Viola have been recognized as a taxonomically difficult genera due to their highly similar morphological characteristics. Here, all common V. philippica adulterants were sampled. A total of 24 complete chloroplast (cp) genomes were analyzed, among these 5 cp genome sequences were downloaded from GenBank and 19 cp genomes, including 2 "Zi Hua Di Ding" purchased from a local TCM pharmacy, were newly sequenced.

RESULTS

The Viola cp genomes ranged from 156,483 bp to 158,940 bp in length. A total of 110 unique genes were annotated, including 76 protein-coding genes, 30 tRNAs, and four rRNAs. Sequence divergence analysis screening identified 16 highly diverged sequences; these could be used as markers for the identification of Viola species. The morphological, maximum likelihood and Bayesian inference trees of whole cp genome sequences and highly diverged sequences were divided into five monophyletic clades. The species in each of the five clades were identical in their positions within the morphological and cp genome tree. The shared morphological characters belonging to each clade was summarized. Interestingly, unique variable sites were found in ndhF, rpl22, and ycf1 of V. philippica, and these sites can be selected to distinguish V. philippica from samples all other Viola species, including its most closely related species. In addition, important morphological characteristics were proposed to assist the identification of V. philippica. We applied these methods to examine 2 "Zi Hua Di Ding" randomly purchased from the local TCM pharmacy, and this analysis revealed that the morphological and molecular characteristics were valid for the identification of V. philippica.

CONCLUSIONS

This study provides invaluable data for the improvement of species identification and germplasm of V. philippica that may facilitate the application of a super-barcode in TCM identification and enable future studies on phylogenetic evolution and safe medical applications.

Recent advances of novel technologies for quality consistency assessment of natural herbal medicines and preparations

Quality consistency is one of the basic attributes of medicines, but it is also a difficult problem that natural medicines and their preparations must face. The complex chemical composition and comprehensive pharmacological action of natural medicines make it difficult to simply apply the commonly used evaluation methods in chemical drugs. It is thus urgent to explore the novel evaluation methods suitable for the characteristics of natural medicines. With the rapid development of analytical techniques and the deepening understanding of the quality of natural herbs, increasing numbers of researchers have proposed many new ideas and technologies. This review mainly focuses on the basic principles, technical characteristics and application examples of the chemical evaluation, biological evaluation methods and their combination in quality consistency evaluation of natural herbs. On the bases of chemical evaluation and clinical efficacy, new methods reflecting their pharmacodynamic mechanism and safety characteristics will be developed, and gradually towards accurate quality control, to achieve the goal of quality consistency. We hope that this manuscript can provide new ideas and technical references for the quality consistency of natural drugs and their preparations, thus better guarantee their clinical efficacy and safety, and better promote industrial development.

Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO₂ loading, and water content in CO₂ absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R²) of 0.9955 and RMSECV of 0.75%. CO₂ calibration model achieved R² of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R² of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO₂, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO₂ solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

Medicinal Plant Analysis: A Historical and Regional Discussion of Emergent Complex Techniques

The analysis of medicinal plants has had a long history, and especially with regard to assessing a plant's quality. The first techniques were organoleptic using the physical senses of taste, smell, and appearance. Then gradually these led on to more advanced instrumental techniques. Though different countries have their own traditional medicines China currently leads the way in terms of the number of publications focused on medicinal plant analysis and number of inclusions in their Pharmacopoeia. The monographs contained within these publications give directions on the type of analysis that should be performed, and for manufacturers, this typically means that they need access to more and more advanced instrumentation. We have seen developments in many areas of analytical analysis and particularly the development of chromatographic and spectroscopic methods and the hyphenation of these techniques. The ability to process data using multivariate analysis software has opened the door to metabolomics giving us greater capacity to understand the many variations of chemical compounds occurring within medicinal plants, allowing us to have greater certainty of not only the quality of the plants and medicines but also of their suitability for clinical research. Refinements in technology have resulted in the ability to analyze and categorize plants effectively and be able to detect contaminants and adulterants occurring at very low levels. However, advances in technology cannot provide us with all the answers we need in order to deliver high-quality herbal medicines and the more traditional techniques of assessing quality remain as important today.

Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of r_p² (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.

Near-infrared spectroscopy (NIRS) as a tool to monitor exhaust air from poultry operations

Intensive poultry operation systems emit a considerable volume of inorganic and organic matter in the surrounding environment. Monitoring cleaning properties of exhaust air cleaning systems and to detect small but significant changes in emission characteristics during a fattening cycle is important for both emission and fattening process control. In the present study, we evaluated the potential of near-infrared spectroscopy (NIRS) combined with chemometric techniques as a monitoring tool of exhaust air from poultry operation systems. To generate a high-quality data set for evaluation, the exhaust air of two poultry houses was sampled by applying state-of-the-art filter sampling protocols. The two stables were identical except for one crucial difference, the presence or absence of an exhaust air cleaning system. In total, twenty-one exhaust air samples were collected at the two sites to monitor spectral differences caused by the cleaning device, and to follow changes in exhaust air characteristics during a fattening period. The total dust load was analyzed by gravimetric determination and included as a response variable in multivariate data analysis. The filter samples were directly measured with NIR spectroscopy. Principal component analysis (PCA), linear discriminant analysis (LDA), and factor analysis (FA) were effective in classifying the NIR exhaust air spectra according to fattening day and origin. The results indicate that the dust load and the composition of exhaust air (inorganic or organic matter) substantially influence the NIR spectral patterns. In conclusion, NIR spectroscopy as a tool is a promising and very rapid way to detect differences between exhaust air samples based on still not clearly defined circumstances triggered during a fattening period and the availability of an exhaust air cleaning system.

From quality markers to data mining and intelligence assessment: A smart quality-evaluation strategy for traditional Chinese medicine based on quality markers

BACKGROUND

The quality of traditional Chinese medicine (TCM) forms the foundation of its clinical efficacy. The standardization of TCM is the most important task of TCM modernization. In recent years, there has been great progress in the quality control of TCM. However, there are still many issues related to the current quality standards, and it is difficult to objectively evaluate and effectively control the quality of TCM.

PURPOSE

To face these challenge, we summarized the current quality marker (Q-marker) research based on its characteristics and benefits, and proposed a reasonable and intelligentized quality evaluation strategy for the development and application of Q-markers.

METHODS

Ultra-performance liquid chromatography-quadrupole/time-of-flight with partial least squares-discriminant analysis was suggested to screen the chemical markers from Chinese medicinal materials (CMM), and a bioactive-guided evaluation method was used to select the Q-markers. Near-infrared spectroscopy (NIRS), based on the distinctive wavenumber zones or points from the Q-markers, was developed for its determination. Then, artificial intelligence algorithms were used to clarify the complex relationship between the Q-markers and their integral functions. Internet and mobile communication technology helped us to perform remote analysis and determine the information feedback of test samples.

CHAPTERS

The quality control research, evaluation, standard establishment and quality control of TCM must be based on the systematic analysis of Q-markers to study and describe the material basis of TCM efficacy, define the chemical markers in the plant body, and understand the process of herb drug acquisition, change and transmission laws affecting metabolism and exposure. Based on the advantages of chemometrics, new sensor technologies, including infrared spectroscopy, hyperspectral imaging, chemical imaging, electronic nose and electronic tongue, have become increasingly important in the quality evaluation of CMM. Inspired by the concept of Q-marker, the quantitation can be achieved with the help of artificial intelligence, and these subtle differences can be discovered, allowing the quantitative analysis by NIRS and providing a quick and easy detection method for CMM quality evaluations.

CONCLUSION

The concept of Q-markers focused on unique CMM differences, dynamic changes and their transmission and traceability to establish an overall quality control and traceability system. Based on the basic attributes, an integration model and artificial intelligence research path was proposed, with the hope of providing new ideas and perspectives for the TCM quality management.

Detection of Cistanches Herba (Rou Cong Rong) Medicinal Products Using Species-Specific Nucleotide Signatures

Cistanches Herba is a medicinal plant that has tonification properties and is commonly used in Asia. Owing to the imbalance between supply and demand, adulterants are frequently added for profit. However, there is no regulatory oversight because quality control tools are not sufficient for identifying heavily processed products. Thus, a novel molecular tool based on nucleotide signatures and species-specific primers was developed. The ITS2 regions from 251 Cistanches Herba and adulterant samples were sequenced. On the basis of SNP sites, four nucleotide signatures within 30~37 bp and six species-specific primers were developed, and they were validated by artificial experimental mixtures consisting of six different species and different ratios. This method was also applied to detect 66 Cistanches Herba products on the market, including extracts and Chinese patent medicines. The results demonstrated the utility of nucleotide signatures in identifying adulterants in mixtures. The market study revealed 36.4% adulteration: 19.7% involved adulteration with Cynomorium songaricum or Cistanche sinensis, and 16.7% involved substitution with Cy. songaricum, Ci. sinensis, or Boschniakia rossica. The results also revealed that Cy. songaricum was the most common adulterant in the market. Thus, we recommend the use of species-specific nucleotide signatures for regulating adulteration and verifying the quality assurance of medicinal product supply chains, especially for processed products whose DNA is degraded.

Recent advances and effective strategies in the discovery and applications of natural products

Natural products are the most representative form of conventional therapy as compared to any other traditional or alternative medicine systems.

Identification of a Quality Marker (Q-Marker) of Danhong Injection by the Zebrafish Thrombosis Model

Quality-marker (Q-marker) is an emerging concept to ensure the quality and batch-to-batch consistency of Chinese medicine (CM). However, significant difficulties remain in the identification of Q-markers due to the unclear relationship between complex chemical compositions and the pharmacological efficacy of CM. In the present study, we proposed a novel strategy to identify the potential Q-marker of danhong injection (DHI) by an in vivo zebrafish thrombosis model. The anti-thrombotic effects of DHI and its major constituents were evaluated by the zebrafish model of arachidonic acid (AA)-induced thrombosis. The results indicated that DHI can attenuate tail venous thrombus and recover the decrease of heart red blood cell (RBC) intensity in a dose-dependent manner. The result that DHI prevented the formulation of thrombosis in zebrafish was also validated in the zebrafish thrombosis model with green fluorescence protein (GFP)-labeled hemoglobin. The major components of DHI, namely danshen (DS) and honghua (HH), as well as the major chemical constituents of DHI, also exerted anti-thrombotic effects, among which rosmarinic acid (RA) and p-coumaric acid (pCA) showed moderate anti-thrombotic effects. This is the first time that pCA from HH has been found as an active compound exerting an anti-thrombotic effect in a dose-dependent manner, whose IC₅₀ value is approximately 147 μg/mL. By analyzing 10 batches of normal DHI samples and five abnormal samples by high-performance liquid chromatography (HPLC), we found the contents of pCA and RA can be positively correlated to the anti-thrombotic effect of DHI, suggesting that pCA and RA could be potential Q-markers of DHI to ensure batch-to-batch consistency. Our findings illustrated that discovering major active compounds from CM by in vivo pharmacological models can be a useful approach to identifying Q-markers of CM, and in vivo pharmacological models can be a potential tool to evaluate batch-to-batch consistency of CMs.

Rosmarinic acid counteracts activation of hepatic stellate cells via inhibiting the ROS-dependent MMP-2 activity: Involvement of Nrf2 antioxidant system

Recently, oxidative stress is involved in hepatofibrogenesis. Matrix metalloproteinase-2 (MMP-2) is required for activation of hepatic stellate cells (HSCs) in response to reactive oxygen species (ROS). This study was designed to explore the hypothesis that the inhibitory effect of rosmarinic acid (RA) on HSCs activation might mainly result from its antioxidant capability by increasing the synthesis of glutathione (GSH) involved in nuclear factor kappa B (NF-κB)-dependent inhibition of MMP-2 activity. Here, we demonstrate that RA reverses activated HSCs to quiescent cells. Concomitantly, RA inhibits MMP-2 activity. RNA interference-imposed knockdown of NF-κB abolished down-regulation of MMP-2 by RA. RA-mediated inactivation of NF-κB could be blocked by the diphenyleneiodonium chloride (DPI; a ROS inhibitor). Conversely, transfection of dominant-negative (DN) mutant of extracellular signal-regulated kinases 2 (ERK2), c-Jun N-terminal kinase 1 (JNK1), or p38α kinase had no such effect. Simultaneously, RA suppresses ROS generation and lipid peroxidation (LPO) whereas increases cellular GSH in HSC-T6 cells. Furthermore, RA significantly increased antioxidant response element (ARE)-mediated luciferase activity, nuclear translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) and catalytic subunits from glutamate cysteine ligase (GCLc) expression, but not modulatory subunits from GCL (GCLm). RA-mediated up-regulation of GClc is inhibited by the shRNA-induced Nrf2 knockdown. The knocking down of Nrf2 or buthionine sulfoximine (a GCL inhibitor) abolished RA-mediated inhibition of ROS. Collectively, these results provide novel insights into the mechanisms of RA as an antifibrogenic candidate in the prevention and treatment of liver fibrosis.

DNA Barcoding and Pharmacovigilance of Herbal Medicines

Pharmacovigilance of herbal medicines relies on the product label information regarding the ingredients and the adherence to good manufacturing practices along the commercialisation chain. Several studies have shown that substitution of plant species occurs in herbal medicines, and this in turn poses a challenge to herbal pharmacovigilance as adverse reactions might be due to adulterated or added ingredients. Authentication of constituents in herbal medicines using analytical chemistry methods can help detect contaminants and toxins, but are often limited or incapable of detecting the source of the contamination. Recent developments in molecular plant identification using DNA sequence data enable accurate identification of plant species from herbal medicines using defined DNA markers. Identification of multiple constituent species from compound herbal medicines using amplicon metabarcoding enables verification of labelled ingredients and detection of substituted, adulterated and added species. DNA barcoding is proving to be a powerful method to assess species composition in herbal medicines and has the potential to be used as a standard method in herbal pharmacovigilance research of adverse reactions to specific products.

Species authentication and geographical origin discrimination of herbal medicines by near infrared spectroscopy: A review

Near infrared (NIR) spectroscopy as a rapid and nondestructive analytical technique, integrated with chemometrics, is a powerful process analytical tool for the pharmaceutical industry and is becoming an attractive complementary technique for herbal medicine analysis. This review mainly focuses on the recent applications of NIR spectroscopy in species authentication of herbal medicines and their geographical origin discrimination.