Fast discrimination and quantification analysis of Curcumae Radix from four botanical origins using NIR spectroscopy coupled with chemometrics tools

Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.

Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion

INTRODUCTION

Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI.

OBJECTIVE

Realization of rapid and accurate identification of the origin of G. elata BI.

MATERIALS AND METHODS

Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI.

SAMPLES

Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images.

RESULTS

The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness.

CONCLUSION

Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.

The Extraction, Determination, and Bioactivity of Curcumenol: A Comprehensive Review

Curcuma wenyujin is a member of the Curcuma zedoaria (zedoary, Zingiberaceae) family, which has a long history in traditional Chinese medicine (TCM) due to its abundant biologically active constituents. Curcumenol, a component of Curcuma wenyujin, has several biological activities. At present, despite different pharmacological activities being reported, the clinical usage of curcumenol remains under investigation. To further determine the characteristics of curcumenol, the extraction, determination, and bioactivity of the compound are summarized in this review. Existing research has reported that curcumenol exerts different pharmacological effects in regard to a variety of diseases, including anti-inflammatory, anti-oxidant, anti-bactericidal, anti-diabetic, and anti-cancer activity, and also ameliorates osteoporosis. This review of curcumenol provides a theoretical basis for further research and clinical applications.

Identification of Perna viridis contaminated with diarrhetic shellfish poisoning toxins in vitro using NIRS and a discriminative non-negative representation-based classifier

Diarrhetic shellfish poisoning (DSP) toxins are one of the most widespread marine biotoxins that affect aquaculture and human health, and their detection has become crucial. In this study, near-infrared reflectance spectroscopy (NIRS) with non-destructive characteristics was used to identify DSP toxins in Perna viridis. The spectral data of the DSP toxin-contaminated and non-contaminated Perna viridis samples were acquired in the 950-1700 nm range. To solve the discrimination of spectra with crossover and overlapping, a discriminative non-negative representation-based classifier (DNRC) has been proposed. Compared with collaborative and non-negative representation-based classifiers, the DNRC model exhibited better performance in detecting DSP toxins, with a classification accuracy of 99.44 %. For a relatively small-scale sample dataset in practical applications, the performance of the DNRC model was compared with those of classical models. The DNRC model achieved the best results for both identification accuracy and F-measure, and its detection performance did not significantly decrease with decreasing sample size. The experimental results validated that a combination of NIRS and the DNRC model can facilitate rapid, convenient, and non-destructive detection of DSP toxins in Perna viridis.

Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms

Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs' manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture

BACKGROUND

Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form.

OBJECTIVE

In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation.

METHOD

For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 μg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS.

RESULTS

The models' validation criteria displayed excellent recoveries and lower errors of prediction.

CONCLUSIONS

The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results.

HIGHLIGHTS

Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.

Multi-Element Analysis and Origin Discrimination of Panax notoginseng Based on Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS)

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

Rapid prediction method of α-Glycosidase inhibitory activity of Coreopsis tinctoria extract from different habitats by near infrared spectroscopy

α-Glucosidase is one of the main enzymes causing elevated blood glucose, and Coreopsis tinctoria extract can be used as a natural inhibitor of α-Glucosidase. Therefore, a new method was proposed for predicting the inhibitory activity on α-Glucosidase of Coreopsis tinctoria extract based on near infrared spectroscopy. The absorbance of the inhibitory system was measured by ultraviolet spectroscopy, which was used to study the inhibitory activity on a-glucosidase of Coreopsis tinctoria extract. The near infrared spectra of the solid samples were collected. By selecting spectral preprocessing and optimizing spectral bands, a rapid prediction model of the inhibitory activity was established by partial least squares regression. The root mean square error of cross-validation (RMSECV), correlation coefficient (R) value and the ratio of prediction to deviation (RPD) value were used as indicators of the evaluation model. The near infrared spectrum model was established by combining the best spectral preprocessing of the continuous wavelet transform (CWT) and the best spectral band. The root mean square error of cross-validation (RMSECV) of this model was 0.815%, the correlation coefficient (R) value was 0.942, and the ratio of prediction to deviation (RPD) was 3.0. The root mean square error of prediction (RMSEP) of the model by prediction set was 0.819%, the correlation coefficient (R) value was 0.950, and the RPD was 3.2. The model shows that the fitting relationship between the predicted inhibition value and the reference inhibition value of the near infrared spectral model is good. The results showed that there was a good correlation between near infrared spectroscopy and the inhibitory activity of Coreopsis tinctoria extract. Thus, the established model was robust and effective and could be used for rapid quantification of α-Glucosidase inhibitory activity. The prediction method is simple and rapid, and can be extended to study the inhibition of other medicinal plants.

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.

Rapid discrimination and screening of volatile markers for varietal recognition of Curcumae Radix using ATR-FTIR and HS-GC-MS combined with chemometrics

ETHNOPHARMACOLOGICAL RELEVANCE

Curcumae Radix (Yujin) has a long medicinal use history in China, which is used to cure diseases like jaundice, cholelithiasis caused by dampness-heat of gallbladder and liver, and so on. It comes from the dried tuberous roots of C. kwangsiensis (Guiyujin), C. longa (Huangyujin), C. phaeocaulis (Lvyujin) and C. wenyujin (Wenyujin). Though there are differences in chemical compositions and pharmacological activities among the four species of Yujin, they have not been differentiated well in clinical application due to their similar morphological characterizations.

AIM OF THE STUDY

In this study, the four species of Yujin were rapidly and accurately discriminated. The potential volatile markers for varietal recognition were identified.

MATERIALS AND METHODS

Attenuated total reflection fourier transformed infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to rapidly discriminate the four species of Yujin. Headspace-gas chromatography-mass spectrometry (HS-GC-MS) technology coupled with chemometrics was employed to characterize volatile profiling, differentiate species and select potential markers for varietal recognition of Yujin.

RESULTS

By applying PCA (principal components analysis) and HCA (hierarchical cluster analysis), HS-GC-MS realized complete differentiation of the four species of Yujin, while ATR-FTIR only recognized Guiyuijin. Back propagation neural network (BP-NN), KNN (K-nearest neighbor) and LDA (linear discriminant analysis) models based on spectral data achieved 100% discriminant accuracies. Support vector machines (SVM), KNN and PLS-DA (partial least square discriminant analysis) models based on volatile compounds also realized 100% discriminant accuracies. Additionally, the potential volatile markers for varietal recognition of Yujin were screened using PLS-DA, including 2 for Guiyujin, 6 for Lvyujin, 9 for Wenyujin and 13 for Huangyujin.

CONCLUSIONS

The present study developed reliable methods for the varietal discrimination and volatile compounds characterization of Yujin, which will provide references for its quality control and clinical efficacy.

UV spectroscopy and HPLC combined with chemometrics for rapid discrimination and quantification of Curcumae Rhizoma from three botanical origins

Curcumae Rhizoma (Ezhu in Chinese) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Ezhu from three botanical origins (Curcuma kwangsiensis, Curcuma phaeocaulis, and Curcuma wenyujin), ultra-violet (UV) spectroscopy and high performance liquid chromatography (HPLC) combined with chemometric tools were employed in this study. Firstly, the analysis method for the simultaneous determination of eleven compounds in Ezhu was developed by HPLC, and the UV spectra of thirty-eight batches of Ezhu were acquired. Then, principal component analysis (PCA), an unsupervised pattern recognition method, was applied on the HPLC and UV spectral data. PCA did not show a clear separation between C. phaeocaulis and C. wenyujin samples with HPLC data. By contrast, the supervised techniques, decision tree (DT) and linear discriminant analysis (LDA), achieved the complete discrimination for the three species of Ezhu with 100 % correct classification rate (CCR), showing excellent performance. Based on UV spectral data, PCA presented good performance for discriminating the three species of Ezhu. LDA, support vector machine (SVM) and k-nearest neighbors (KNN) models provided 96.3 % CCR for the calibration set and 100 % CCR for the validation set. Moreover, the partial least squares (PLS) and back propagation-artificial neural network (BP-ANN) quantitative models established on UV spectral data were satisfactory in predicting the contents of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane. The residual predictive deviation (RPD) for zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane of PLS models were 3.169, 1.502 and 1.735, and that of BP-ANN models were 3.467, 2.481 and 2.370, respectively. The present work proposed a rapid and reliable method for the discrimination of Ezhu from three botanical origins and the prediction of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane contents in Ezhu, which will help a lot in the quality control of Ezhu and other multi-origin herbal medicines.

High performance liquid chromatography fingerprint and headspace gas chromatography-mass spectrometry combined with chemometrics for the species authentication of Curcumae Rhizoma

Curcumae Rhizoma (Ezhu), a multi-origin Chinese medicine, originates from the dry rhizomes of C. kwangsiensis, C. phaeocaulis and C. wenyujin. The three species have great variation in chemical components and therapeutic effects. To improve safety and effectiveness in clinical use, a strategy integrating chromatographic analysis and chemometrics for the species authentication of Ezhu was proposed. Firstly, systematic analysis of chemical compositions in Ezhu was achieved using high performance liquid chromatography (HPLC) fingerprint and headspace gas chromatography-mass spectrometry (HS-GC-MS). HPLC fingerprints showed that seventeen peaks in common for C. kwangsiensis and eleven peaks in common for C. wenyujin both presented a good similarity (> 0.9, only several samples < 0.8). Eleven common peaks in C. phaeocaulis and the similarity values of most samples were higher than 0.700. Additionally, there were ten common peaks in all Ezhu samples and they had relatively poor similarity with the correlation coefficients ranging from 0.364 to 0.881. For HS-GC-MS, thirty-six volatile components were identified in the three species of Ezhu, mainly monoterpenes and sesquiterpenes. Subsequently, chemometrics including unsupervised principal component analysis (PCA), supervised linear discriminant analysis (LDA), K-nearest neighbors (KNN), back propagation neural network (BP-NN) and orthogonal partial least squares-discrimination analysis (OPLS-DA) was applied to extract useful information from chromatographic profiles. Based on HPLC fingerprint data, PCA could hardly differentiate Ezhu with the three species, and LDA, KNN and BP-NN models provided more than 85 % correct identification. With HS-GC-MS data, PCA could only distinguish C. wenyujin from the other two species, and LDA, KNN, BP-NN and OPLS-DA models achieved excellent classification with 100 % accuracy. Finally, five volatile components (eucalyptol, humulene, β-elemene, (+)-2-bornanone and linalool) with variable importance for the projection (VIP) values higher than 1 in the OPLS-DA model were selected as potential chemical markers for the species authentication of Ezhu. And the constructed OPLS-DA model using these markers obtained 100 % accuracy. Consequently, a rapid, precise and feasible strategy was established for the discrimination and quality control of Ezhu with different species.