Identification of Genuine and Adulterated Pinellia ternata by Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopy with Partial Least Squares - Discriminant Analysis (PLS-DA)

Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties

The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr). Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared. Results and Discussion: Gas chromatography-ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis

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.

Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

Identification of Pinelliae Rhizoma and its counterfeit species based on enzymatic signature peptides from toxic proteins

BACKGROUND

Pinelliae Rhizoma (PR), a toxic medication, with long history, is commonly used for eliminating phlegm. Due to the shortage of wild resources and the relative lacking of cultivation technology, it is often confused with its counterfeit species in the market, such as Typhonii Rhizoma (TR), Arisaematis Rhizoma (AR) and tubers of Typhonium flagelliforme (TF) and Pinellia pedatisecta (PP).

PURPOSE

It was aimed to screen signature enzymatic peptides from toxic proteins to identify PR and its four counterfeit species.

STUDY DESIGN

A comparative proteogenomics strategy based on open-source transcriptome data was applied for screening signature peptides from toxic proteins, which were applied for species authentication of PR and its counterfeit species.

METHODS

Firstly, the open-source transcriptome data was used for constructing the annotated protein database, which was used for peptides identification. Secondly, the toxicity of different fractions of PR were evaluated by the rat peritoneal inflammation model. Furthermore, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) were used to profile the main proteins bands of five species, whose sequences were identified based on the in-gel digestion experiment by using ultra-high-performance liquid chromatography/quadrupole-Orbitrap mass spectrometry. Finally, the label-free proteomic analysis was performed to character the proteins and screen the signature peptides of five species, which were validated in commercially available products by dynamic multi reaction monitor (DMRM).

RESULTS

The results in this study confirmed that protein was the main toxic components of PR. Both Pinellia ternata agglutinin (PTA) and trypsin inhibitor (TI) like proteins are the main proteins, which were characterized by proteomic analysis based on four annotated protein database. Meanwhile, seven signature peptides from toxic proteins were screened and validated with good repeatability and specificity in commercial products.

CONCLUSION

Seven signature enzymatic peptides from toxic protein screened by the comparative proteogenomics strategy based on open-source transcriptome data achieved good identification ability of PR and its four counterfeit species.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

Comparative Evaluation of Forsythiae Fructus From Different Harvest Seasons and Regions by HPLC/NIR Analysis and Anti-inflammatory and Antioxidant Assays

Forsythiae Fructus (FF), the dry fruit of Forsythia suspensa (Thunb.) Vahl, has a long history of use in traditional Chinese Medicine for its heat-clearing and detoxifying properties. It possesses clinical therapeutic effects and biological functions showing efficacy in handling different diseases. To investigate the FF differences in Henan, Shanxi, and Shaanxi in August and October, the surface morphology, mid-infrared and near-infrared spectrums, and HPLC were analyzed. Concurrently, the anti-inflammatory and antioxidant effects on LPS-induced J774A.1 cells were evaluated by western blot and RT-qPCR. The results showed that FF from different Harvest Seasons and Regions are provided with different microstructures and mid-infrared and near-infrared spectrums, and the levels of forsythiaside A and phillyrin of FF from Shanxi in August and phillygenin of FF from Shaanxi in August were the highest. Meanwhile, FF from Shanxi and Shaanxi in August markedly reduced the levels of inflammatory cytokines and mediators (TNF-α, IL-1β, NF-κB, and iNOS) and the protein expression levels of phosphorylated total IKKα/β and nuclear NF-κB. In August, SXFF and SAXFF also promoted the mRNA expression levels of HO-1 and NQO1 and the protein expression levels of HO-1 and nuclear Nrf2 and suppressed the protein expression levels of KEAP1. Spearman correlation analysis showed that phillygenin had a strong correlation with the protein expression on LPS-induced J774A.1 cells. In summary, our results showed that FF from harvest seasons and regions contributed to the distinct differences in microstructure, the mid-infrared and near-infrared spectrums, and compound content. More importantly, FF from Shanxi and Shaanxi in August showed marked anti-inflammatory and antioxidant activities, but with some differences, which may be because of different contents of phillygenin and phillyrin of lignans in FF.

Ultrafast identification of Pinelliae Rhizoma using colorimetric direct-VPCR

With the increasing growth of the herbal market, a rapid and easy-to-use system is highly desirable in the high-throughput identification of massive herbal medicine samples. Here, an ultrafast and colorimetric detection system was devised based on simplifying template preparation and a newly developed amplification technique, named colorimetric direct-VPCR. The system was successfully applied to the identification of Pinelliae Rhizoma. Compared to the traditional method, the whole test can be finished within 30 min from the sample treatment to the testing results. The method was evaluated by correctly identifying 72 samples obtained from 9 different habitats, demonstrating its high reliability. In summary, we present an ultrafast (less than 30 min) and colorimetric detection platform (under ultraviolet lamp) based on direct-VPCR for the identification of Pinelliae Rhizoma. The high practicability (100% accuracy) of this pipeline enables it to be a promising method in the routine detection of other herbal materials.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03035-9.

Fraud Detection of Herbal Medicines Based on Modern Analytical Technologies Combine with Chemometrics Approach: A Review

Fraud in herbal medicines (HMs), commonplace throughout human history, is significantly related to medicinal effects with sometimes lethal consequences. Major HMs fraud events seem to occur with a certain regularity, such as substitution by counterfeits, adulteration by addition of inferior production-own materials, adulteration by chemical compounds, and adulteration by addition of foreign matter. The assessment of HMs fraud is in urgent demand to guarantee consumer protection against the four fraudulent activities. In this review, three analysis platforms (targeted, non-targeted, and the combination of non-targeted and targeted analysis) were introduced and summarized. Furthermore, the integration of analysis technology and chemometrics method (e.g., class-modeling, discrimination, and regression method) have also been discussed. Each integration shows different applicability depending on their advantages, drawbacks, and some factors, such as the explicit objective analysis or the nature of four types of HMs fraud. In an attempt to better solve four typical HMs fraud, appropriate analytical strategies are advised and illustrated with several typical studies. The article provides a general workflow of analysis methods that have been used for detection of HMs fraud. All analysis technologies and chemometrics methods applied can conduce to excellent reference value for further exploration of analysis methods in HMs fraud.