Untargeted identification of adulterated Sanqi powder by near-infrared spectroscopy and one-class model

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics

The medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples.

Geographical origin identification of ginseng using near-infrared spectroscopy coupled with subspace-based ensemble classifiers

Ginseng is a well-known traditional herbal medicine and the ginseng available on the market may not actually be produced in a certain place as claimed. Traditional methods of identifying the geographical origin of Ginseng are subjective, time-consuming or destructive. A more efficient approach is desirable. The feasibility of combining near-infrared (NIR) spectroscopy with ensemble learning for discriminating ginseng producing area was explored. A total of 270 samples were collected and evenly partitioned into the training and test sets. Random subspace ensemble (RSE) that uses linear discriminant classifier (LDA) as weak learner (abbreviated RSE-LDA) was used to construct predictive models. Two parameters including the size of subspace and the number of learners in ensemble were optimized. Classic partial least algorithm (PLS) was applied to build the reference model. The sensitivity, specificity, and total accuracy of final RSE-LDA and PLS models were 97.8 %, 100 %, 99.3 %, and 93.3 %, 96.7 %, 95.6 %, respectively. In order to study the impact of training set composition on the results, the samples were randomly divided 200 times and the algorithm was run repeatedly to statistically analyze the sensitivity and specificity on the test set. Similar results were obtained. The effect of training set size was also investigated. It indicates that the combination of NIR spectroscopy with the RSE algorithm is a potential tool of discriminating the origin of Ginseng.

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.

Herb-drug interactions between Panax notoginseng or its biologically active compounds and therapeutic drugs: A comprehensive pharmacodynamic and pharmacokinetic review

ETHNOPHARMACOLOGICAL RELEVANCE

Herbs, along with the use of herb-drug interactions (HDIs) to combat diseases, are increasing in popularity worldwide. HDIs have two effects: favorable interactions that tend to improve therapeutic outcomes and/or minimize the toxic effects of drugs, and unfavorable interactions aggravating the condition of patients. Panax notoginseng (Burk.) F.H. Chen is a medicinal plant that has long been commonly used in traditional Chinese medicine to reduce swelling, relieve pain, clear blood stasis, and stop bleeding. Numerous studies have demonstrated the existence of intricate pharmacodynamic (PD) and pharmacokinetic (PK) interactions between P. notoginseng and conventional drugs. However, these HDIs have not been systematically summarized.

AIM OF THE REVIEW

To collect the available literature on the combined applications of P. notoginseng and drugs published from 2005 to 2022 and summarize the molecular mechanisms of interactions to circumvent the potential risks of combination therapy.

MATERIALS AND METHODS

This work was conducted by searching PubMed, Scopus, Web of Science, and CNKI databases. The search terms included "notoginseng", "Sanqi", "drug interaction," "synergy/synergistic", "combination/combine", "enzyme", "CYP", and "transporter".

RESULTS

P. notoginseng and its bioactive ingredients interact synergistically with numerous drugs, including anticancer, antiplatelet, and antimicrobial agents, to surmount drug resistance and side effects. This review elaborates on the molecular mechanisms of the PD processed involved. P. notoginseng shapes the PK processes of the absorption, distribution, metabolism, and excretion of other drugs by regulating metabolic enzymes and transporters, mainly cytochrome P450 enzymes and P-glycoprotein. This effect is a red flag for drugs with a narrow therapeutic window. Notably, amphipathic saponins in P. notoginseng act as auxiliary materials in drug delivery systems to enhance drug solubility and absorption and represent a new entry point for studying interactions.

CONCLUSION

This article provides a comprehensive overview of HDIs by analyzing the results of the in vivo and in vitro studies on P. notoginseng and its bioactive components. The knowledge presented here offers a scientific guideline for investigating the clinical importance of combination therapies. Physicians and patients need information on possible interactions between P. notoginseng and other drugs, and this review can help them make scientific predictions regarding the consequences of combination treatments.

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.

Flash Extraction, Characterization, and Immunoenhancement Activity of Polysaccharide from Hippophae rhamnoides Linn

Hippophae rhamnoides L. polysaccharide was optimized with flash extraction by response surface design. The optimum process conditions were: rotation rate 5000 r/min, extraction time 15 s, extraction temperature 90 °C and liquid-to-material ratio 38 mL/g, the extraction yield was 15.28±0.02 %. HRP-1 and HRP-2 obtained by 40 % and 60 % graded alcohol precipitation were characterized. The results indicated that HRP-1 and HRP-2 both composed of mannose, ribose, rhamnose, glucuronic acid, galacturonic acid, glucose, galactose, xylose, arabinose with different molar ratio and the molecular weights were 380.59 kDa and 288.24 kDa, respectively. In addition, the in vitro antioxidant and immunoenhancement activities of HRP-1 and HRP-2 were analyzed, and the two fractions showed good free radical scavenging activity against ⋅OH, ABTS⋅⁺ , DPPH⋅, and extremely strong immunomodulatory activity against RAW264.7 cells. Indicating that flash extraction is suitable for extraction of HRP, the structural study of HRP provides a scientific theoretical basis for the development of Hippophae rhamnoides.

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.

Determination of the Authenticity and Origin of Panax Notoginseng: A Review

Panax notoginseng, a traditional medicinal and edible plant, is widely used in medicine, health care, cosmetics, and other industries. Affected by the discrepancy between market supply and demand and price, the adulteration of P. notoginseng products with other plant-derived ingredients occurs at times. With the continuous development of technologies such as spectroscopy, chromatography, and DNA barcoding, the detection techniques for rapid and sensitive determination of the authenticity identification and origin of P. notoginseng have become more diversified to meet the needs of different regulatory goals and could effectively control practices that mislead consumers and promote false labeling. This review analyzes and summarizes the existing technologies for determining the authenticity and origin of P. notoginseng from these three aspects: morphological, chemical, and molecular biology methods from the literature since 2001; on this basis, the current problems and future research directions are discussed to provide a reference for the establishment of rapid and accurate methods to verify authenticity and origin to promote the further development and improvement of quality control technology systems for P. notoginseng.

Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Food adulteration detection requires quick and simple methods. Spectral detection can significantly reduce the analysis time, but it needs to construct a detection model. In this study, a one-class classification method based on an autoencoder is proposed for the detection of food adulteration by spectroscopy. In the proposed method, the autoencoder is constructed to extract low-dimensional features from high-dimensional spectral data and reconstruct the original spectrum. Then the coding error and reconstruction error are used to determine the food sample is adulterated or not. The infrared spectral data of milk powder and its adulterated forms are used to verify the performance of the proposed model. Experimental results show that the proposed method has similar effects to soft independent modeling of class analogy and one-class partial least squares, and is significantly better than support vector data description. The proposed method can be flexibly applied to the spectral detection of food adulteration.

Detection of glibenclamide adulterated in antidiabetic Chinese patent medicine by attenuated total reflectance -infrared spectroscopy and chemometrics

There have been many reports of adulterated Chinese patent medicine with synthetic prescription that are claimed to be "pure natural". The present work investigates the feasibility of combining attenuated total reflectance-Mid-infrared (ATR-MIR) spectroscopy and several interval-based PLS algorithms for detecting the glibenclamide illegally adulterated in antidiabetic Chinese patent medicine (Jiangtangning). The full-spectrum PLS, four kinds of traditional interval PLS algorithms (iPLS, biPLS, siPLS and mwPLS) and a modified algorithm, i.e., a combination of mwPLS and window size optimization, named cmwPLS, were used for building calibration models. A total of 21 samples adulterated with 0-3.5% glibenclamide were prepared. The dataset was equally split into a training set and a test set for building and testing the prediction models, respectively. For those interval-based PLS, the whole wavenumber axis was divided into 20 sub-intervals. In terms of the prediction on the test set, the new cmwPLS produce the best model, followed by mwPLS. The modified algorithm can optimize automatically the window width (i.e., the number of adjacent variables used for modeling) and position. It can be concluded that cmwPLS coupled with ATR-MIR technique is a good alternative to other traditional chemical analysis for detecting the adulteration of Chinese patent medicine.

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

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

Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food

Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths.

Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics

The aim of this study is to explore the feasibility of detection and quantification of two cheap adulterants (maltodextrin and starch) in Chinese functional food, hawthorn fruits powder (HFP), by using near infrared (NIR) spectroscopy coupled with chemometrics methods. The partial least squares discriminant analysis (PLS-DA) models were developed to discriminate the adulterated HFP from the authentic HFP, while the partial least squares regression (PLSR) models were employed to determine the contents of adulterants. In order to yield the best results, various spectra pretreatment methods and wavelength selection methods were carefully investigated. The models' qualities were assessed by the self-consistency test, the independent test and the rigorous leave-one-out cross-validation test. The metrics for the PLS-DA discriminative model included error rate, true positive rate, true negative rate and F1 score, while the metrics for the PLSR quantitative model were determination coefficient, root mean square error and residual prediction deviation. Finally, very satisfying results were obtained, which indicate that our method is quite robust and applicable, and thus has great potential for rapid detection of adulteration in powder of many other herbal plants or functional foods.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.