Rapid discrimination of Notoginseng powder adulteration of different grades using FT-MIR spectroscopy combined with chemometrics

Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in 'Yali' pear

The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in 'Yali' pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm-2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm-2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in 'Yali' pear, which would be helpful for supporting postharvest decision systems.

Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology

Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.

Quantitative analysis of the illegal addition of Atenolol in Panax notoginseng based on NIR-MIR spectral data fusion and calibration transfer

To address the issue of the common illegal addition of Atenolol in Panax notoginseng, we propose an approach that realizes multivariate calibration transfer between different particle sizes based on near-infrared (NIR) and mid-infrared (MIR) spectral data fusion. To achieve high prediction accuracy, we construct three data fusion schemes (full-spectrum fusion, feature-level fusion, and decision-level fusion) that combine NIR and MIR spectral data. Among three data fusion schemes, the feature-level fusion based on the UVE-SPA-PLS model for 120-mesh spectral data achieves optimal prediction accuracy. Here, a Piecewise Direct Standardization (PDS) algorithm has been applied to calibration transfer from 100-mesh and 80-mesh to 120-mesh to reduce the influence of particle size and improve the robustness of the model. The correlation coefficient (R2) of 100-mesh, and 80-mesh prediction sets can reach 0.9861 and 0.9823, respectively. The corresponding root mean square error (RMSE) are 0.1545 and 0.2045, respectively. This research provides a method for illegal additions in precious herbs and reduces the effect of particle size on spectral modeling, enabling high-precision quantitative detection. In addition, it has important application prospects in reducing experimental losses of precious medicinal materials and ensuring the safe use of Chinese and Western medicines, which provides an alternative method for non-destructive testing.

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R²_p) range of 0.910 to 0.971 for different adulterants.

Quantitative comparison and rapid discrimination of Panax notoginseng powder and Caulis clematidis armandii using NMR combined with pattern recognition

BACKGROUND

The market demand for Panax notoginseng (P. notoginseng) is growing rapidly because of its useful properties in food and medicine. However, the frequent adulteration of P. notoginseng seriously affects the health of consumers and is a great challenge to food safety. In this study, low- and high-field nuclear magnetic resonance (LF/HF-NMR) were applied to detect the transverse relaxation distribution of P. notoginseng contaminated with different ratios of Caulis clematidis armandii (CCA) and the components in P. notoginseng and CCA, respectively.

RESULTS

Fifty-seven kinds of major and minor components in P. notoginseng and CCA were identified and quantified from their high-resolution NMR spectra, and there were significant differences in ginsenosides, sucrose, and glucose between P. notoginseng and CCA. Furthermore, the partial least squares regression analysis results indicated that LF-NMR parameters (T₂₁ and S₂₁ ) changed linearly as the ratio of CCA increased, and these changes were attributed to the variations in polysaccharide and sucrose in adulterated P. notoginseng.

CONCLUSION

In the relaxation time-based pattern recognition models, the authentic P. notoginseng powder could be classified with 100% accuracy from adulterated P. notoginseng when the adulteration ratio was greater than 30%, demonstrating the possibility of LF-NMR, in combination with pattern recognition, for rapid discrimination of food authenticity. © 2022 Society of Chemical Industry.

Anticoagulant activity analysis and origin identification of Panax notoginseng using HPLC and ATR-FTIR spectroscopy

INTRODUCTION

Panax notoginseng is one of the traditional precious and bulk-traded medicinal materials in China. Its anticoagulant activity is related to its saponin composition. However, the correlation between saponins and anticoagulant activities in P. notoginseng from different origins and identification of the origins have been rarely reported.

OBJECTIVES

We aimed to analyze the correlation of components and activities of P. notoginseng from different origins and develop a rapid P. notoginseng origin identification method.

MATERIALS AND METHODS

Pharmacological experiments, HPLC, and ATR-FTIR spectroscopy (variable selection) combined with chemometrics methods of P. notoginseng main roots from four different origins (359 individuals) in Yunnan Province were conducted.

RESULTS

The pharmacological experiments and HPLC showed that the saponin content of P. notoginseng main roots was not significantly different. It was the highest in main roots from Wenshan Prefecture (9.86%). The coagulation time was prolonged to observe the strongest effect (4.99 s), and the anticoagulant activity was positively correlated with the contents of the three saponins. The content of ginsenoside Rg₁ had the greatest influence on the anticoagulant effect. The results of spectroscopy combined with chemometrics show that the variable selection method could extract a small number of variables containing valid information and improve the performance of the model. The variable importance in projection has the best ability to identify the origins of P. notoginseng; the accuracy of the training set and the test set was 0.975 and 0.984, respectively.

CONCLUSION

This method is a powerful analytical tool for the activity analysis and identification of Chinese medicinal materials from different origins.

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.

A rapid quality grade discrimination method for Gastrodia elata powderusing ATR-FTIR and chemometrics

Gastrodia elata is an obligate fungal symbiont used in traditional Chinese medicine. There are currently 4 grades of the plant based on the "Commodity Specification Standard of 76 Kinds of Medicinal Materials". The traditional discrimination methods for determining the medicinal grade of G. elata powders are complex and time-consuming which are not suitable for rapid analysis. We developed a rapid analysis method for this plant using attenuated total reflection and Fourier-transform infrared spectroscopy (ATR-FTIR) together with machine learning algorithms. The original spectroscopic data was first pre-treated using the multiplicative scatter correction (MSC) method and 4 principal components were extracted using extremely randomized trees (Extra-trees) and principal component analysis (PCA) algorithms, and different kinds of classification models were established. We found that multilayer perceptron classifier (MLPC) modeling was superior to support vector machine (SVM) and resulted in validation and prediction accuracies of 99.17% and 100%, respectively and a modeling time of 2.48 s. The methods established from the current study can rapidly and effectively distinguish the 4 different types of G. elata powders and thus provides a platform for rapid quality inspection.

An evaluation of IR spectroscopy for authentication of adulterated turmeric powder using pattern recognition

Turmeric powder is a widely consumed spice, making it an attractive target for adulteration, which is not easily detected. The study examined the simultaneous use of IR spectroscopy in combination with controlled (PCA) and uncontrolled (PLS-DA and CMCA) pattern recognition techniques to detect and classify Sudan Red, starch and metanil yellow fraud in turmeric powder nondestructively. The results showed that the two major peaks in turmeric powder at 1625 cm^-1 and 1600 cm^-1 are not present in Sudan Red, starch and metanil yellow because these materials lack this functional group. Data distribution at the two PC locations showed clearly scattered clusters according to the four mixing studied models (turmeric powder, turmeric powder-Sudan Red mixture, turmeric powder-starch mixture and turmeric powder-metanil yellow mixture), but there was a clear overlap between turmeric powder and turmeric powder - Sudan red mixture. Both PLS-DA and SIMCA supervised methods showed satisfactory discrimination. The results also showed that in all the sample groups, when the samples were classified by PLS-DA, the values were higher compared to the SIMCA model. The overall precision of the SIMCA and PLS-DA classifier were 82% and 92%, respectively. However, when considering only two main categories adulterated (the samples at the groups 2, 3 and 4) and pure (the samples at the group 1), an acceptable degree of separation between the resulting classes was obtained. Consequently, IR spectroscopy with pattern recognition methods was found to be a promising tool for nondestructive grouping of turmeric powder samples with different types of adulteration in turmeric powder.

Terahertz, infrared and Raman absorption spectra of tyrosine enantiomers and racemic compound

The application of terahertz (THz)-based techniques in biomolecule study is very promising but still in its infancy. In the present work, we employed THz time-domain spectroscopy (THz-TDS) and THz time-domain attenuated total reflection (THz-TD-ATR) spectroscopy to investigate the properties of tyrosine (Tyr) enantiomers (L- and D-Tyr) and racemate (DL-Tyr) in solid state and aqueous solutions, respectively. THz absorption spectra of solid L- and D-Tyr show similar absorption spectra with peaks at 0.95, 1.92, 2.06 and 2.60 THz, which are obviously different from the spectrum of DL-Tyr with peaks at 1.5, 2.15 and 2.40 THz. In contrast, although THz absorption spectra of L-Tyr solution and D-Tyr solution are similar and different from the spectrum of DL-Tyr solution, both of them have no observable peaks. Interestingly, it was found that the solution containing equal amounts of L- and D-Tyr has a similar spectrum as that of DL-Tyr solution, as far as the mass concentrations of the two types of solutions are kept the same. On other hand, solid L-, D- and DL-Tyr were also investigated with infrared spectroscopy and Raman spectroscopy, respectively. The results show that the spectra of L- and D-Tyr can be regarded the same but they are slightly different from the spectrum of DL-Tyr. With the aid of principal component analysis (PCA), the difference between L-/D-Tyr and DL-Tyr can be confirmed without any ambiguity. Overall, this work systematically interrogated and evaluated the performance of THz-based techniques in the detection of the chirality of tyrosine.

Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth

Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of M. micrantha were collected using hyperspectral imaging in the spectral range of 450-998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild.

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.

Application of Identification and Evaluation Techniques for Ethnobotanical Medicinal Plant of Genus Panax: A Review

Genus Panax, as worldwide medicinal plants, has a medical history for thousands of years. Most of the entire genus are traditional ethnobotanical medicine in China, Myanmar, Thailand, Vietnam and Laos, which have given rise to international attention and use. This paper reviewed more than 210 articles and related books on the research of Panax medicinal plants and their Chinese patent medicines published in the last 30 years. The purpose was to review and summarize the species classification, geographical distribution, and ethnic minorities medicinal records of the genus Panax, and further to review the analytical tools and data analysis methods for the authentication and quality assessment of Panax medicinal materials and Chinese patent medicines. Five main technologies applied in the identification and evaluation of Panax have been introduced and summarized. Chromatography was the most widely used one. Further research and development of molecular identification technology had the potential to become a mainstream identification technology. In addition, some novel, controversial, and worthy methods including electronic noses, electronic eyes, and DNA barcoding were also introduced. At the same time, more than 80% of the researches were carried out by a combination of chemometric pattern-recognition technologies and multi-analysis technologies. All the technologies and methods applied can provide strong support and guarantee for the identification and evaluation of genus Panax, and also conduce to excellent reference value for the development and in-depth research of new technologies in Panax.

Identification of unhealthy Panax notoginseng from different geographical origins by means of multi-label classification

Root-knot nematode is a common plant-parasitic pest with a highly destructive that infects more than 2000 plant species. Panax notoginseng (P. notoginseng) is one of the most susceptible traditional medicine. More importantly, it is difficult to distinguish the powders of P. notoginseng infected with root-knot nematode from those of healthy P. notoginseng due to the color and shape are same after being ground into powder. In this paper, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) was used to identify P. notoginseng samples. Multiplicative scatter correction (MSC) was applied to preprocess the spectral data. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were employed to select feature variables. Density-based spatial clustering of application with noise (DBSCAN) was adopted to discover groups within the data. Also, we found that the geographical origin is a pivotal factor to consider when identifying unhealthy P. notoginseng. Therefore, we introduced a novel multi-label classification (MLC) method to identify healthy and unhealthy P. notoginseng powders from three different geographical origins. In addition, binary relevance method (BR), classifier chain (CC), ensembles of classifier chains (ECC), and multilayer perceptron classifier (MLPC) were applied to create classification models, ECC exhibits superior performance in particular.

Quantifying several adulterants of notoginseng powder by near-infrared spectroscopy and multivariate calibration

The authentication of traditional Chinese medicine (TCM) is critically important for public-health and economic terms. Notoginseng, a classical TCM of high economic and medical value, could be easily adulterated with Sophora flavescens powder (SFP), corn flour (CF) or other analogues of low-grade (ALG) because of their similar tastes, appearances and much lower cost. The main objective of this study was to evaluate the feasibility of applying of near-infrared (NIR) spectroscopy and multivariate calibration for identifying and quantifying several common adulterants in notoginseng powder. Two datasets were prepared for experiment. The competitive adaptive reweighted sampling (CARS) was used to select informative variables. Two different schemes were used for sample set partition. Model population analysis (MPA) was made. The results showed that, the constructed partial least squares (PLS) model using a reduced set of variables from CARS can provide superior performance to the full-spectrum PLS model. Also, the sample set partition is very of great importance. It seems that the combination of NIR spectroscopy, CARS and PLS is feasible to quantify common adulterants in notoginseng powder.

Fast discrimination of the geographical origins of notoginseng by near-infrared spectroscopy and chemometrics

Notoginseng is a type of highly valued Traditional Chinese medicine (TCM) due to its hemostatic and cardiovascular functions. Notoginseng of Yunnan in China usually commands a premium price and is often the subject of fraudulent practices. The feasibility of combining near-infrared (NIR) spectroscopy with chemometrics was investigated to discriminate notoginseng of different geographical origins. A total of 250 samples of four different provinces in China were collected and divided equally into the training and test sets. Principal component analysis (PCA) was used for observing possible trend of grouping. Two chemometric algorithms including partial least squares-discriminant analysis (PLSDA) and soft independent modeling of class analogy (SIMCA) were used to construct the discriminant models. Standard normal variate (SNV) and first derivative were used for pre-processing spectra. On the independent test set, the PLSDA model outperforms the SIMCA model. When combining both pre-processing methods, the constructed PLSDA model achieved 100% sensitivity and 100% specificity on both the training set and the test set. It indicates that SNV+first derivative pre-processing and PLSDA algorithm can serve as the potential tool of fast discriminating the geographical origins of notoginseng.