Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics

Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network

The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions

Introduction

The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established.

Methods

The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models.

Results

The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%.

Discussion

This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

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.

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.

ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances

Due to the diversity and fast evolution of new psychoactive substances (NPS), both public health and safety are threatened around the world. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), which serves as a simple and rapid technique for targeted NPS screening, is challenging with the rapid structural modifications of NPS. To achieve the fast non-targeted screening of NPS, six machine learning (ML) models were constructed to classify eight categories of NPS, including synthetic cannabinoids, synthetic cathinones, phenethylamines, fentanyl analogues, tryptamines, phencyclidine types, benzodiazepines, and "other substances" based on the 1099 IR spectra data items of 362 types of NPS collected by one desktop ATR-FTIR and two portable FTIR spectrometers. All these six ML classification models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extra trees (ET), voting, and artificial neural networks (ANNs) were trained through cross validation, and f1-scores of 0.87-1.00 were achieved. In addition, hierarchical cluster analysis (HCA) was performed on 100 synthetic cannabinoids with the most complex structural variation to investigate the structure-spectral property relationship, which leads to a summary of eight synthetic cannabinoid sub-categories with different "linked groups". ML models were also constructed to classify eight synthetic cannabinoid sub-categories. For the first time, this study developed six ML models, which were suitable for both desktop and portable spectrometers, to classify eight categories of NPS and eight synthetic cannabinoids sub-categories. These models can be applied for the fast, accurate, cost-effective, and on-site non-targeted screening of newly emerging NPS with no reference data available.

An integrated strategy of spectrum-effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba

The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum-effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R² > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum-effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

A quick method for fraud detection in egg labels based on egg centrifugation plasma

The aim of this work was to develop a quick and cheap method for fraud detection in egg labels according to the four legal farming method of the EU. The plasma obtained from egg centrifugation was investigated for this purpose. Initial protein content in egg, plasma protein content, plasma colour parameters (L*, a* and b*) and plasma UV-VIS-NIR (Ultraviolet-Visible-Near-infrared) spectra were evaluated. The classification algorithms applied were SVM (Support-Vector-Machine), LDA (Linear-Discriminant-Analysis) and QDA (Quadratic-Discriminant-Analysis). The analysis of the protein content did not detect differences. Colour parameters and spectral measurements showed significant differences between eggs. Spectra analysis with QDA gave sensitivity of 100% in the calibration set. The validation set scored 87.5% sensitivity and 94.07% specificity using the visible spectra. This work established plasma spectral measurements combined with classification algorithms as a powerful tool to discriminate the four farming systems. This work presents a fast tool for the egg label control.

Rapid Determination of Geniposide and Baicalin in Lanqin Oral Solution by Near-Infrared Spectroscopy with Chemometric Algorithms during Alcohol Precipitation

The selection of key variables is an important step that improves the prediction performance of a near-infrared (NIR) real-time monitoring system. Combined with chemometrics, NIR spectroscopy was employed to construct high predictive accuracy, interpretable models for the rapid detection of the alcohol precipitation process of Lanqin oral solution (LOS). The variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) was innovatively introduced into the variable screening process of the model of geniposide and baicalin. Compared with the commonly used synergy interval partial least squares regression, competitive adaptive reweighted sampling, and random frog, VCPA-IRIV achieved the maximum compression of variable space. VCPA-IRIV-partial least squares regression (PLSR) only needs to use about 1% of the number of variables of the original data set to construct models with Rp values greater than 0.95 and RMSEP values less than 10%. With the advantages of simplicity and strong interpretability, the prediction ability of the PLSR models had been significantly improved simultaneously. The VCPA-IRIV-PLSR models met the requirements of rapid quality detection. The real-time detection system can help researchers to understand the quality rules of geniposide and baicalin in the alcohol precipitation process of LOS and provide a reference for the optimization of a LOS quality control system.

Epimedii Herba: An ancient Chinese herbal medicine in the prevention and treatment of rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic, progressive inflammatory and systemic autoimmune disease resulting in severe joint destruction, lifelong suffering and considerable disability. Diverse prescriptions of traditional Chinese medicine (TCM) containing Epimedii Herba (EH) achieve greatly curative effects against RA. The present review aims to systemically summarize the therapeutic effect, pharmacological mechanism, bioavailability and safety assessment of EH to provide a novel insight for subsequent studies. The search terms included were "Epimedii Herba", "yinyanghuo", "arthritis, rheumatoid" and "Rheumatoid Arthritis", and relevant literatures were collected on the database such as Google Scholar, Pubmed, Web of Science and CNKI. In this review, 15 compounds from EH for the treatment of RA were summarized from the aspects of anti-inflammatory, immunoregulatory, cartilage and bone protective, antiangiogenic and antioxidant activities. Although EH has been frequently used to treat RA in clinical practice, studies on mechanisms of these activities are still scarce. Various compounds of EH have the multifunctional traits in the treatment of RA, so EH may be a great complementary medicine option and it is necessary to pay more attention to further research and development.

Comprehensive evaluation of Dendrobium officinale from different geographical origins using near-infrared spectroscopy and chemometrics

Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (R_p²) in the independent test sets were 12.7768 g kg^-1 and 0.9586, 1.1346 g kg^-1 and 0.9670, 0.3938 g kg^-1 and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner.

The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs

The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling.

Multivariate Statistical Analysis of Metabolites in Anisodus tanguticus (Maxim.) Pascher to Determine Geographical Origins and Network Pharmacology

Anisodus tanguticus (Maxim.) Pascher, has been used for the treatment of septic shock, analgesia, motion sickness, and anesthesia in traditional Tibetan medicine for 2,000 years. However, the chemical metabolites and geographical traceability and their network pharmacology are still unknown. A total of 71 samples of A. tanguticus were analyzed by Ultra-Performance Liquid Chromatography Q-Exactive Mass Spectrometer in combination with chemometrics developed for the discrimination of A. tanguticus from different geographical origins. Then, network pharmacology analysis was used to integrate the information of the differential metabolite network to explore the mechanism of pharmacological activity. In this study, 29 metabolites were identified, including tropane alkaloids, hydroxycinnamic acid amides and coumarins. Principal component analysis (PCA) explained 49.5% of the total variance, and orthogonal partial least-squares discriminant analysis (OPLS-DA) showed good discrimination (R2Y = 0.921 and Q2 = 0.839) for A. tanguticus samples. Nine differential metabolites accountable for such variations were identified through variable importance in the projection (VIP). Through network pharmacology, 19 components and 20 pathways were constructed and predicted for the pharmacological activity of A. tanguticus. These results confirmed that this method is accurate and effective for the geographic classification of A. tanguticus, and the integrated strategy of metabolomics and network pharmacology can explain well the "multicomponent--multitarget" mechanism of A. tanguticus.

Trends and Application of Chemometric Pattern Recognition Techniques in Medicinal Plants Analysis

Medicinal plants have been used and studied for ages, from very old registers to modern ethnopharmacology, which encompasses analytical chemistry, foods, and pharmacy. Based on international norms and governmental organizations of health, phytomedicine-for example, herbal drugs-needs to guarantee the quality control of products and identify contaminants, biomarkers, and chemical profiles, among other issues. In this sense, is necessary to develop advanced analytical methods that show interesting possibilities and obtain a great amount of data. In order to treat the data, a set of mathematical and statistical procedures named chemometrics is necessary. In terms of herbal drugs, chemometric tools may be used to identify the following in plants: parts, development stages, processing, geographic origin, authentication, and chemical markers. This review describes applications of chemometric pattern recognition tools to analyze herbal drugs in different conditions associated with analytical methods in the last six years (2015-2020).

Infrared spectroscopy technique for quantification of compounds in plant-based medicine and supplement

Quality control of plant-based medicine and supplements must be carried out to ensure uniformity in quality and safety in their use, resulting in the need for effective and accurate analytical methods. Infrared spectroscopy is a method of qualitative and quantitative analysis that is fast, time-saving, cost-effective,accurate, and nondestructive. This method has been applied for quantitative analysis of compounds in complex matrices such as plant-based medicine and supplements supported by chemometrics techniques. The success of infrared spectroscopy applications for quantitative analysis of phytochemicals and adulterants content in plant-based medicine and supplement can happen by several factors. This article highlights the effect of spectral preprocessing and variable selection on quantitative analysis of phytochemical and adulterant in plant-based medicine and supplements using infrared spectroscopy. Literature search was conducted with PubMed, Google Scholar, and Science Direct by selecting quantitative analysis research on plant-based medicines and supplements that utilize spectral preprocessing techniques and variable selection in processing data analysis. The preprocessing spectra and variables selection can affect the accuracy and precision of infrared spectroscopy methods. The variable selection can be done using the wavenumber point technique, the wavenumber interval, or a combination thereof. Variable selection is more commonly used for near-infrared data than for IR data. The optimization of the preprocessing spectra and variables selection technique will be useful in increasing the ability of infrared spectroscopy in predicting compound levels.

Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

A mid-level data fusion approach for evaluating the internal and external changes determined by FT-NIR, electronic nose and colorimeter in Curcumae Rhizoma processing

The aim of this study was to establish a robust and rapid method for identification of the changes of Curcumae Rhizoma (CR) in its physical properties and the components after processing. Color and odor used as the indexes for physical property evaluation in this study were quantified by non-targeted bionic detectors colorimeter and e-nose according to Chinese pharmacopoeia, and all chemical changes of the samples were observed by NIR. The technique of mid-level data fusion was adopted to simulate the recognition mode of human, and the prediction accuracy was 100%. A modified NIR band extraction method was used for feature selection after removing the redundant data which otherwise may compromise the accuracy of analysis, with a result showing bisdemethoxycurcumin and curcumol as the key factors most correlated with the changes during processing. The fused matrix was analyzed to evaluate the weight of different sensors and precisely describe the external changes of the samples, followed by a pairwise correlation analysis to investigate the role of colorimeter, e-nose and NIR in identifying the changes caused by processing as well as their correlation with each other. The results of data fusion revealed that aromatic derivatives produced during processing were closely associated with the changes in external characteristics, i.e., color and smell of the samples, while the changes in proteins did not cause significant differences. Correlation analysis demonstrated that bionic sensors could be classified into two groups: a*, WW and WS sensors, which were related to NIR band at about 6500-6700 cm^-1 formed by NH vibration of amide and protein degradation, were sensitive to the processed CR; while L*, b* and WC sensors were found to be correlated to NIR band around 8000 cm^-1 which was caused by first overtone of C-H combination of aromatic derivatives, thus could be employed as the detectors for raw CR.

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000-4000 cm^-1) and Fourier transform mid-infrared (MIR: 4000-600 cm^-1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen's kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

In vitro screening and toxic mechanism exploring of leading components with potential hepatotoxicity of Herba Epimedii extracts

Herba Epimedii is a famous Chinese edible herb, and due to its potential hepatotoxic effects, the safety associated with this herb has attracted a great deal of attention. In this study, the components of four types of the Herba Epimedii extracts were identified by HPLC-MS/MS. Among these components, 11 components that were present in all four extracts and could be obtained as reference substances were evaluated for their ability of cytotoxicity in HL-7702 and HepG2 cells, resulting in the identification of icarisid I and sagittatoside A as the most relevant with respect to the toxicity of the extracts. The targeted toxicological effects were further investigated using a series of correlated biological indicators to elucidate potentially hepatotoxic mechanisms. The results showed that the extracts and the selected compounds had varying degrees of influence on the leakage of ALT, AST and LDH; the activity of SOD, GSH and MDA; the increase in intercellular ROS; and the decrease in MMP. Among the tested substances, the ethanol extracts exhibited stronger hepatotoxicity, with icarisid I and sagittatoside A correlating with this toxic effect, and the hepatoxic mechanisms of which may be associated with damaged cell structure, increased oxidative stress and induction of apoptosis.

A review of the application of near-infrared spectroscopy to rare traditional Chinese medicine

Near-infrared spectroscopy (NIRS) has become popular in the field of traditional Chinese medicine (TCM) due to its many practical advantages, such as providing rapid, accurate, and simultaneous analysis. This review is intended to provide up-to-date information on qualitative and quantitative NIRS analysis in TCM, especially rare TCM. By performing a substantial survey of the literature from China and abroad, we also combine our own studies on some rare TCMs using NIRS to review the application of NIRS in this field. Basic analytical methods and specific examples of NIRS demonstrates NIRS's ability in authenticity identification, species identification, geographic origin analysis, quantitative analysis, adulteration detection, rapid detection, and on-line monitoring of TCM, and illustrates the feasibility and effectiveness of NIRS applied in the quality control (QC) of TCM. Some disadvantages and prospects of NIRS are also discussed here in detail.

Near infrared system coupled chemometric algorithms for the variable selection and prediction of baicalin in three different processes

Characteristic variables are essential and necessary basis in model construction, and are related to the prediction result closely in near infrared spectroscopy (NIRS) analysis. However, the same compound usually has different characteristic variables for different analysis and it would be lower correlation between variables and structure in many researches. So, the accuracy and reliability are expected to improve by exploring characteristic variables in different spectrum analysis. In this study, competitive adaptive weighted resampling method (CARS) was applied to select characteristic variables related to baicalin from NIRS analysis data, which were applied to analysis of baicalin in three different processes including the herb, extraction process and concentration process of Scutellaria baicalensis. After application of CARS method, 70, 50 and 50 variables were selected respectively from three processes above. The selected variables were firstly analyzed by statistical methods that they were found to be consistent and correlated among three different processes after one-way analysis of variance test and Kendall's W. Partial least-squares (PLS) regression and extreme learning machine (ELM) models were constructed based on optimized data. Models after variable selection were less complicated and had better prediction results than global models. After comparison, CARS-PLS was suitable for the prediction of extraction process, while for the concentration process and herb, CARS-ELM performed better. The Rc value of the herb, extraction and concentration model were 0.9469, 0.9841 and 0.9675, respectively. The RSEP values were 4.54%, 6.96% and 8.37%, respectively. The results help to frame a theoretical basis for characteristic variables of baicalin.

Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques

Near infrared (NIR) spectroscopy with chemometric techniques was applied to discriminate the geographical origins of crude drugs (i.e., dried ripe fruits of Trichosanthes kirilowii) and prepared slices of Trichosanthis Fructus in this work. The crude drug samples (120 batches) from four growing regions (i.e., Shandong, Shanxi, Hebei, and Henan Provinces) were collected, dried, and used and the prepared slice samples (30 batches) were purchased from different drug stores. The raw NIR spectra were acquired and preprocessed with multiplicative scatter correction (MSC). Principal component analysis (PCA) was used to extract relevant information from the spectral data and gave visible cluster trends. Four different classification models, namely K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and support vector machine-discriminant analysis (SVM-DA), were constructed and their performances were compared. The corresponding classification model parameters were optimized by cross-validation (CV). Among the four classification models, SVM-DA model was superior over the other models with a classification accuracy up to 100% for both the calibration set and the prediction set. The optimal SVM-DA model was achieved when C =100, γ = 0.00316, and the number of principal components (PCs) = 6. While PLS-DA model had the classification accuracy of 95% for the calibration set and 98% for the prediction set. The KNN model had a classification accuracy of 92% for the calibration set and 94% for prediction set. The non-linear classification method was superior to the linear ones. Generally, the results demonstrated that the crude drugs from different geographical origins and the crude drugs and prepared slices of Trichosanthis Fructus could be distinguished by NIR spectroscopy coupled with SVM-DA model rapidly, nondestructively, and reliably.

Traceability the provenience of cultivated Paris polyphylla Smith var. yunnanensis using ATR-FTIR spectroscopy combined with chemometrics

The conventional procedures, based on attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), have been developed for the origins traceability of cultivated Paris polyphylla Smith var. yunnanensis (PPY) samples with the help of partial least square discriminant analysis (PLS-DA) and random forest. In this study, a set of 219 batch cultivated PPY samples, containing the cultivation years of 5, 6 and 7, and covering the municipal districts of Chuxiong, Dali, Honghe, Lijiang and Yuxi in Yunnan Province, China, were used to build the discrimination models. Firstly, a visualized analysis was carried out by t-distributed stochastic neighbor embedding (t-SNE) to reduce each data point in a two-dimensional map and make a knowledge of the sample distribution tendency. Secondly, the single spectra data sets of Paridis rhizome and leaf tissues, and the combination of these two data sets with variable selection (mid-level data fusion strategy), were used to establish PLS-DA and random forest models, and parallelly compared the model performance. Results demonstrated that the discrimination ability of PLS-DA preceded the random forest model, and the classification performance was remarkably improved after mid-level data fusion. These results verified each other by 5-, 6- and 7-year old Paridis samples and indicated that the model performance established in the present study was reliable. Besides, five agronomic characters, including the plant height, dry weight of rhizome and leaf tissues, and the allocation of rhizome and leaf were determined and analyzed, results of which indicated that the dry weight and their allocation was significantly different among various origins and fluctuated with the cultivation years. This study was using a comprehensive and green analytical method to discriminate the cultivated Paridis according to their provenances, which was simultaneously benefited for the appropriate cultivation areas selection based on the dry weight of rhizome tissues.

Qualitative analysis of the roots of Salvia miltiorrhiza and Salvia yunnanensis based on NIR, UHPLC and LC-MS-MS

Near infrared spectroscopy (NIR) was applied to discriminate the roots of salvia miltiorhiza Bunge (Danshen for short) and Salvia yunnanensis C. H. Wright (Zidanshen for short) by means of principal component analysis (PCA), improved and simplified K nearest neighbors (IS-KNN). Furthermore, an ultra-high performance liquid chromatographic (UHPLC) coupled with photodiode-array detector was developed for building fingerprints of lipophilic components of Danshen and Zidanshen, respectively. Basing on NIR information, both PCA and IS-KNN method classified the two kinds of Chinese medical herbs with 100% accuracy. The chromatographic fingerprints of the lipophilic components of Danshen and Zidanshen have 10 and 12 common peaks, respectively. Liquid chromatography coupled with mass spectroscopy (LC-MS-MS) was applied to identify these peaks. Among these, three small peaks in the fingerprints of Zidanshen are not found in Danshen, one of which was identified as α-lapachone, and the other two compounds were not yet identified; a small peak after tanshinone IIA in the fingerprints of Danshen was not found in Zidanshen, which was identified as miltirone. The two herbs have 10 common lipophilic components. The similarity between the two reference chromatograms of Zidanshen and Danshen is 0.902, but the mean similaritie between Zidanshen (or Danshen) fingerprints and its own reference chromatogram is 0.973 (or 0.976). The contents of main lipophilic components are significantly lower in Zidanshen than in Danshen (P < 0.01 or P < 0.05). The results indicate that the two Chinese medical materials are not only different in NIR spectra, but also different in species and quantities of lipophilic components. NIR spectra analysis can identify Danshen and Zidanshen rapidly and accurately. UHPLC coupled with MS analysis demonstrates the detail differences between the two herbs both in species and contents of their lipophilic components.

An additional data fusion strategy for the discrimination of porcini mushrooms from different species and origins in combination with four mathematical algorithms

Porcini are a source of popular food products with many beneficial functions and the internal quality of these mushrooms is largely determined by many factors. An additional data fusion strategy based on low-level data fusion for two portions (cap and stipe) and mid-level data fusion for two spectroscopic techniques (UV and FTIR) was developed to discriminate porcini mushrooms from different species and origins. Based on a finally obtained data array, four mathematical algorithms including PLS-DA, k-NN, SVM and RF were comparatively applied to build classification models. Each calibrated model was developed after selecting the best debug parameters and then a test set was used to validate the established model. The results showed that the SVM algorithm based on a GA procedure searching for parameters had the best performance for discriminating different porcini samples with the highest cross-validation, specificity, sensitivity and accuracy of 100.00%. Our study proved the feasibility of two spectroscopic techniques for the discrimination of porcini mushrooms originated from different species and origins. This proposed method can be used as an alternative strategy for the quality detection of porcini mushrooms.

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis

Rhodiola is an increasingly widely used traditional Tibetan medicine and traditional Chinese medicine in China. The composition profiles of bioactive compounds are somewhat jagged according to different species, which makes it crucial to identify authentic Rhodiola species accurately so as to ensure clinical application of Rhodiola. In this paper, a nondestructive, rapid, and efficient method in classification of Rhodiola was developed by Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics analysis. A total of 160 batches of raw spectra were obtained from four different species of Rhodiola by FT-NIR, such as Rhodiola crenulata, Rhodiola fastigiata, Rhodiola kirilowii, and Rhodiola brevipetiolata. After excluding the outliers, different performances of 3 sample dividing methods, 12 spectral preprocessing methods, 2 wavelength selection methods, and 2 modeling evaluation methods were compared. The results indicated that this combination was superior than others in the authenticity identification analysis, which was FT-NIR combined with sample set partitioning based on joint x-y distances (SPXY), standard normal variate transformation (SNV) + Norris-Williams (NW) + 2nd derivative, competitive adaptive reweighted sampling (CARS), and kernel extreme learning machine (KELM). The accuracy (ACCU), sensitivity (SENS), and specificity (SPEC) of the optimal model were all 1, which showed that this combination of FT-NIR and chemometrics methods had the optimal authenticity identification performance. The classification performance of the partial least squares discriminant analysis (PLS-DA) model was slightly lower than KELM model, and PLS-DA model results were ACCU = 0.97, SENS = 0.93, and SPEC = 0.98, respectively. It can be concluded that FT-NIR combined with chemometrics analysis has great potential in authenticity identification and classification of Rhodiola, which can provide a valuable reference for the safety and effectiveness of clinical application of Rhodiola.