Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics

Development and Application of a Quantitative Model for Proximate and Ultimate Analysis of Flue-Cured Tobacco Based on Near-Infrared Spectroscopy

A methodology for predicting proximate and ultimate analysis data was developed by using near-infrared spectroscopy (NIR) combined with chemometric methods. The quantitative model has high accuracy, as evidenced by low root-mean-square-error of prediction (RMSEP) values (e.g., 0.41% for volatile matter and 0.29% for carbon). The model was further applied to tobaccos with distinct aroma profiles, and the predicted ultimate and proximate data lead to aroma classification with 86.6% accuracy. This methodology can be expanded to the aroma discrimination of imported tobaccos from Brazil, the United States, Canada, and Zimbabwe, demonstrating its broad reliability. Compared with traditional analyses, this NIR-based approach offers a fast and accurate method for large-scale tobacco evaluation, highlighting its potential for enhancing tobacco quality characterization through a quantifiable, digital, and high-throughput process.

Rapid Identification of Medicinal Polygonatum Species and Predictive of Polysaccharides Using ATR-FTIR Spectroscopy Combined With Multivariate Analysis

INTRODUCTION

Medicinal Polygonatum species is a widely used traditional Chinese medicine with high nutritional value, known for its anti-fatigue properties, enhancement of immunity, delays aging, improves sleep, and other health benefits. However, the efficacy of different species varies, making the quality control of medicinal Polygonatum species increasingly important. Polysaccharides are important in medicinal Polygonatum species because of their potential functional properties, such as antioxidation, hypoglycemia, protection of intestinal health, and minimal toxicological effects on human health, as well as high polysaccharide levels.

OBJECTIVE

This study developed a qualitative medicinal Polygonatum species model and a polysaccharides predictive model based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with a multivariate analysis approach.

MATERIALS AND METHODS

ATR-FTIR spectral information of 334 medicinal Polygonatum species samples was collected and the spectral information of different modes was analyzed. The ATR-FTIR spectral differences of three medicinal Polygonatum species were studied by multivariate analysis combined with four spectral preprocessing and three variable selection methods. For the prediction of polysaccharides in Polygonatum kingianum Collett & Hemsl. (PK), we initially determined the actual content of 110 PK polysaccharide samples using the anthrone-sulfuric acid method, then established partial least squares regression (PLSR) and kernel PLSR models in conjunction with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy.

RESULTS

In the visualization analysis, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model based on second-order derivative (SD) preprocessing was most suitable for medicinal Polygonatum species species binary classification, spectral differences between Polygonatum cyrtonema Hua (PC) and other species are evident; in the hard modeling, SD preprocessing improves the accuracy of non-deep learning models for the classification of three medicinal Polygonatum species. In contrast, residual neural network (ResNet) models were the best choice for species identification without preprocessing and variable selection. In addition, the partial least squares regression (PLSR) model and Kernel-PLSR model can quickly predict PK polysaccharides content, among them, the Kernel-PLSR model with SD pretreatment has the best prediction performance, residual prediction deviation (RPD) = 7.2870, Rp = 0.9905.

CONCLUSION

In this study, we employed ATR-FTIR spectroscopy and various treatments to discern different medicinal Polygonatum species. We also evaluated the effects of preprocessing methods and variable selection on the prediction of PK polysaccharides by PLSR and Kernel-PLSR models. Among them, the ResNet model can achieve 100% correct classification of medicinal Polygonatum species without complex spectral preprocessing. Furthermore, the Kernel-PLSR model based on SD-ATR-FTIR spectra had the best performance in polysaccharides prediction. In summary, by integrating ATR-FTIR spectroscopy with multivariate analysis, this research accomplished the classification of medicinal Polygonatum species and the prediction of polysaccharides. The methodology offers the benefits of speed, environmental sustainability, and precision, highlighting its significant potential for practical applications. In future research, on the one hand, it can be further investigated using a portable infrared spectrometer, and on the other hand, infrared spectroscopy can also be applied to the prediction of other chemical components of medicinal Polygonatum species.

Differentiation of Polygonatum Cyrtonema Hua from Different Geographical Origins by Near-Infrared Spectroscopy with Chemometrics

BACKGROUND

The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions.

OBJECTIVE

This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions.

METHODS

The models were produced under different conditions to distinguish the origins distinctly. Ten preprocessing methods have been used to preprocess the original spectra (OS) and to select the most optimal spectral preprocessing method. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables.

RESULTS

The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate.

CONCLUSION

The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua.

HIGHLIGHTS

The utilization of NIR spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating QA measures.

Qualitative and quantitative analysis of major components of Qiye Shen'an tablet by UPLC Q-TOF/MS and UPLC-TQS-MS/MS

The Qiye Shen'an tablet is formulated using total saponins extracted from Notoginseng stems and leaves. At present, the study on its chemical composition remains scarce and the quality control indicators are limited, which seriously hindering the effective quality control and clinical research. Hence, this study aims to comprehensively identify and characterize the Qiye Shen'an tablet while controlling its main component contents. To achieve a comprehensive understanding of this tablet, an ultra-high performance liquid coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS) method was employed for its separation and characterization. Through the analysis of 99 batches of Qiye Shen'an tablet produced by 9 enterprises, the characteristic quantitative components were further obtained. A total of 113 compounds were characterized and identified, among which 17 representative compounds were selected, and the ultra-high performance liquid-triple quadrupole tandem mass spectrometry (UPLC-TQS-MS/MS) method was established for further quantitative determination. It has been successfully applied to the content determination of 99 batches of Qiye Shen'an tablet, and a new quality control method is being formed. This study provides a new method for chemical spectrum analysis and determination of labeled compounds of Qiye Shen'an tablet, and lays a solid foundation for further study of potential active ingredients and comprehensive quality evaluation.

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.

Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with unequal misclassification costs

BACKGROUND

Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace.

RESULTS

This paper proposes a rapid detection method for PST in mussels using near-infrared spectroscopy (NIRS) technology. Spectral data in the wavelength range of 950-1700 nm for PST-contaminated and non-contaminated mussel samples were used to build the detection model. Near-Bayesian support vector machines (NBSVM) with unequal misclassification costs (u-NBSVM) were applied to solve a classification problem arising from the fact that the quantity of non-contaminated mussels was far less than that of PST-contaminated mussels in practice. The u-NBSVM model performed adequately on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u-NBSVM did not decline as the number of PST samples decreased due to adjustments to the misclassification costs. When the number of PST samples was 20, the G-mean and accuracy reached 0.9898 and 0.9944, respectively.

CONCLUSION

Compared with the traditional support vector machines (SVMs) and the NBSVM, the u-NBSVM model achieved better detection performance. The results of this study indicate that NIRS technology combined with the u-NBSVM model can be used for rapid and non-destructive PST detection in mussels. © 2023 Society of Chemical Industry.

Identification of common buckwheat (Fagopyrum esculentum Moench) adulterated in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) presents great potential in the identification of food adulteration due to its advantages of nondestructive, simple, and easy to operate. In this paper, a method based on NIRS and chemometrics was proposed to predict the content of common buckwheat (Fagopyrum esculentum Moench) flour in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour. Partial least squares regression (PLSR) and support vector regression (SVR) models were used to analyze the spectrum data of adulterated samples and predict the adulteration level. Various preprocessing methods, parameter-optimization methods, and competitive adaptive reweighted sampling (CARS) wavelength-selection methods were used to optimize the model prediction accuracy. The results of PLSR and SVR modeling for predicting of Tartary buckwheat adulteration content were satisfactory, and the correlation coefficients of the optimum identification models were above 0.99. In conclusion, the combinations of NIRS and chemometrics indicated excellent predictive performance and applicability to analyze the adulteration of common buckwheat flour in Tartary buckwheat flour. This work provides a promising method to identify the adulteration of Tartary buckwheat flour and results obtained can give theoretical and data support for adulteration identification of agro-products.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

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.

Rapid, simultaneous and non-destructive determination of multiple adulterants in Panax notoginseng powder by front-face total synchronous fluorescence spectroscopy

The authentication of traditional herbal medicines in powder form is of great significance, as they are always of high values but vulnerable to adulteration. Based on the distinct fluorescence of protein tryptophan, phenolic acids and flavonoids, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of Panax notoginseng powder (PP) adulterated with the powder of rhizoma curcumae (CP), maize flour (MF) and whole wheat flour (WF). For either single or multiple adulterants in the range of 5-40% w/w, prediction models were built based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by both five-fold cross-validation and external validation. The constructed PLS2 models simultaneously predicted the contents of multiple adulterants in PP and gave suitable results, with most of the determination coefficients of prediction (R_p²) >0.9, the root mean square error of prediction (RMSEP) no >4% and residual predictive deviation (RPD) >2. The limits of detections (LODs) were 12.0, 9.1 and 7.6% for CP, MF and WF, respectively. All the relative prediction errors for simulated blind samples were between -22% and + 23%. FFSFS offers a novel alternative to the authentication of powdered herbal plants.

Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning

INTRODUCTION

The cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs. Panax notoginseng is a widely used Chinese medicinal material. The growth and accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing.

METHODS

In this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of P. notoginseng. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of P. notoginseng at two different levels (district and town levels).

RESULTS

The results indicated that the contents of saponins in P. notoginseng are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for P. notoginseng geographic origin traceability, even if the distance between sampling points is small.

DISCUSSION

The findings of this study could improve the quality of P. notoginseng, provide a reference for cultivating P. notoginseng in the future and alleviate the occurrence of market fraud.

A Review of Machine Learning for Near-Infrared Spectroscopy

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy

A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R²) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil.

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.

Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation

Near-infrared spectroscopy (NIRS) is an excellent process analytical technology (PAT) tool for active pharmaceutical ingredient (API) quantification during fluidized granulation. Therefore, a portable near-infrared spectrometer combined with a new innovative method of extended iterative optimization technique (EIOT) was used to in-line monitor the API content uniformity during fluidized bed granulation. The principal component analysis (PCA) and partial least squares regression (PLSR) were also used to characterize and predict API concentration with changes from 75% to 125% of the label claim to prove the superiority of EIOT. The API content prediction accuracy of the EIOT method was verified through offline High Performance Liquid Chromatography (HPLC) measurement. Also, the spatial distribution of API in granules was visualized by Raman imaging technology. The results showed that the established NIRS method was suitable for the prediction of API content in fluidized bed granulation, which provides a new idea for the determination of API content during granulation.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil

In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm^-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE) and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.7299, 2.2089, 2.3742, 2.5608 and 2.6858 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R²_P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determining the pure oil contents in binary to hexanary blend oil.

Real-time monitoring of the column chromatographic process of Phellodendri Chinensis Cortex part I: end-point determination based on near-infrared spectroscopy combined with machine learning

A novel and rapid approach for end-point determination of berberine hydrochloride, phellodendrine chloride and total alkaloids in a column chromatographic process.

Rapid determination of hemoglobin concentration by a novel ensemble extreme learning machine method combined with near-infrared spectroscopy

A novel ensemble extreme learning machine (ELM) approach that combines Monte Carlo (MC) sampling and least absolute shrinkage and selection operator (LASSO), named as MC-LASSO-ELM, is proposed to determine hemoglobin concentration of blood. It employs MC sampling to randomly select samples from the training set and LASSO further to choose variables from selected samples to establish plenty of ELM sub-models. The final prediction is obtained by combining the predictions of these sub-models. Combined with near-infrared spectroscopy, MC-LASSO-ELM is used to determine the hemoglobin concentration of blood. Compared with ELM, MC-ELM and LASSO-ELM, MC-LASSO-ELM can obtain the best stability and highest accuracy.

Quantitative analysis of polycyclic aromatic hydrocarbons in soil by infrared spectroscopy combined with hybrid variable selection strategy and partial least squares

Infrared spectroscopy (IR) combined with multivariate calibration technology can be used as a potential method to quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in soil, which provides a rapid data support for soil risk assessment. However, IR spectrum contains lots of useless information, its predictive performance is poor. Variable selection is an effective strategy to eliminate irrelevant wavelengths and enhance predictive performance. In this study, IR combined with partial least squares (PLS) was proposed to quantify anthracene and fluoranthene in soil. In order to improve the predictive performance of the PLS calibration model, the synergy interval PLS (siPLS) method was first used for "rough selection" to select feature bands; on this basis, "fine selection" was performed to extract the feature variables. In "fine selection", three different feature variables selection methods, such as successive projection algorithm (SPA), genetic algorithm (GA), and particle swarm optimization (PSO), were compared for their performance in extracting effective variables. The results show that the siPLS-GA calibration model receive a lowest root mean square error (RMSE) and a largest determination coefficient (R²). Results of external validation demonstrate an excellent predictive performance of siPLS-GA calibration model, with the R² = 0.9830, RMSE = 0.5897 mg/g and R² = 0.9849, RMSE = 0.4739 mg/g for anthracene and fluoranthene, respectively. In summary, siPLS combined with GA can accurately extract the effective information of the target substance and improve the predictive performance of the PLS calibration model based on IR spectroscopy.

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).

Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy

With the trend of portable and miniaturization, Raman spectrometer requires more advanced analytical methods providing more rapid and accurate analysis performance for in-situ analysis. In this work, a hybrid variable selection method based on V-WSP and variable importance measurement (VIM) coupled with random forest (RF) was used to improve the quantitative analysis performance of portable laser Raman instruments for quantitative analysis of methanol content in methanol gasoline. First, five preprocessing methods were applied to reduce the infection information in the raw spectra, respectively. Based on the spectra data processed by multivariate scattering correction (MSC), V-WSP was employed to filter the infection or redundant information in Raman spectroscopy, and 579 variables were obtained when the correlation threshold is 0.9600. Then, the variables were further eliminated by VIM. Finally, 43 variables were obtained by the V-WSP-VIM method. In data processing, out of bag (OOB) error estimation and 10-flod cross validation (CV) were applied to optimize the parameters of preprocessing methods, V-WSP, VIM and RF model. The results fully demonstrated that compared with the RF model based on raw spectra, the RF model based on V-WSP-VIM method can achieve a better prediction performance for the quantitative analysis of methanol content in methanol-gasoline, with the coefficients of determination of cross-validation (R²_CV) improving from 0.9100 to 0.9662, the root mean square error of cross-validation (RMSE_CV) reducing from 0.0572 to 0.0365%, the coefficients of determination of prediction set (R²_P) improving from 0.9214 to 0.9407, the root mean square error of prediction set (RMSE_P) reducing from 0.0420 to 0.0382%, the variables reducing from 1044 to 43 and the modeling time reducing from 72.94 to 6.41 s. The results indicates that V-WSP-VIM coupled with RF is an effective method to improve the performance of portable laser Raman spectrometer for quantitative analysis of methanol content in methanol gasoline.

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.

A novel method for the nondestructive classification of different-age Citri Reticulatae Pericarpium based on data combination technique

The quality of Citri Reticulatae Pericarpium (CRP) is closely correlated with the aging time. However, CRPs in different storage ages are similar in appearance, and the young CRP may be labeled as the aged one to obtain the excess profit by some unscrupulous traders. Most traditional analysis methods are laborious and time-consuming, and they can hardly realize the nondestructive classification. In this paper, a novel method based on near-infrared diffuse reflectance spectroscopy (NIRDRS) and data combination technique for the nondestructive classification of different-age CRPs was proposed. The CRPs in different storage ages (5, 10, 15, 20, and 25 years) were measured. The near-infrared spectra of outer skin and inner capsule were obtained. Principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and Fisher's linear discriminant analysis (FLD), with different data pretreatment methods, were used for the classification analysis. Data combination of the outer skin and inner capsule spectra was discussed for further improving the classification results. The results show that multiple sensors provide more useful and complementary information than a single sensor does for improving the prediction accuracy. With the help of data combination strategy, 100% prediction accuracy can be obtained with both second-order derivative-FLD and continuous wavelet transform-multiplicative scatter correction-FLD methods.

Novel Similarity Methods Evaluation and Feasible Application for Pharmaceutical Raw Material Identification with Near-Infrared Spectroscopy

Raw material identification (RMID) is necessary and important to fulfill the quality and safety requirements in the pharmaceutical industry. Near-infrared (NIR) spectroscopy is a rapid, nondestructive, and commonly used analytical technique that could offer great advantages for RMID. In this study, two brand new similarity methods S1 and S2, which could reflect the similarity from the perspective of the inner product of the two vectors and the closeness with the cosine of the vectorial angle or correlation coefficient, were proposed. The ability of u and v factors to distinguish the difference between small peaks was investigated with the spectra of NIR. The results showed that the distinguishing ability of u is greater than v, and the distinguishing ability of S2 is greater than S1. Adjusting exponents u and v in these methods, which are variable and configurable parameters greater than 0 and less than infinity, could identify small peaks in different situations. Meanwhile, S1 and S2 could rapidly identify raw materials, suggesting that the on-site and in situ pharmaceutical RMID for large-volume applications can be highly achievable. The methods provided in this study are accurate and easier to use than traditional chemometric methods, which are important for the pharmaceutical RMID or other analysis.

A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets

OBJECTIVES

This study is aimed to explore the blending process of Dahuang soda tablets. These are composed of two active pharmaceutical ingredients (APIs, emodin and emodin methyl ether) and four kinds of excipients (sodium bicarbonate, starch, sucrose, and magnesium stearate). Also, the objective is to develop a more robust model to determine the blending end-point.

METHODS

Qualitative and quantitative methods based on near-infrared (NIR) spectroscopy were established to monitor the homogeneity of the powder during the blending process. A calibration set consisting of samples from 15 batches was used to develop two types of calibration models with the partial least squares regression (PLSR) method to explore the influence of density on the model robustness. The principal component analysis-moving block standard deviation (PCA-MBSD) method was used for the end-point determination of the blending with the process spectra.

RESULTS

The model with different densities showed better prediction performance and robustness than the model with fixed powder density. In addition, the blending end-points of APIs and excipients were inconsistent because of the differences in the physical properties and chemical contents among the materials of the design batches. For the complex systems of multi-components, using the PCA-MBSD method to determine the blending end-point of each component is difficult. In these conditions, a quantitative method is a more suitable alternative.

CONCLUSIONS

Our results demonstrated that the effect of density plays an important role in improving the performance of the model, and a robust modeling method has been developed.

Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs

Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR), 35 Atractylodis Macrocephalae Rhizoma (AMR) and 39 Angelicae Dahuricae Radix (ADR), were collected and divided into the training and prediction sets. Principal component analysis (PCA) was used for observing the sample cluster tendency of the calibration set. Different preprocessing methods were investigated and the optimal preprocessing combination was selected according to spectral signal characteristics and three-dimensional PCA (3D PCA) clustering results. The final discriminant model was built using extreme learning machine (ELM). The exploratory studies on the raw spectra and their 3D PCA scores indicate that the classification of the five herbs cannot be achieved by PCA of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. Furthermore, their combination in the order of CWT + autoscaling + SG smoothing can enhance the spectral resolution and obtain the best clustering result. These results are also validated using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in both the calibration set and the prediction set. Therefore, the developed method could be used as a rapid, economic and effective method for discriminating the five herbs used in this study.

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.

Rapid prediction of atrazine sorption in soil using visible near-infrared spectroscopy

Sorption is an important process for determining the fate, effects, and ecological risks of pesticides in terrestrial and aquatic environments. Within a watershed, soil properties vary greatly because of landscape and management practices, leading to spatial variation of pesticide sorption coefficients (K_d). A method for the rapid determination of the sorption variability of atrazine in soils of the Baima river catchment using visible near-infrared (Vis-NIR) spectroscopy is studied in this work. Partial least square regression (PLS) was used to build calibration models. To achieve optimum models, several methods of spectral preprocessing and variable selection were investigated. The results show that the combination of standard normal variant transform (SNV) and Monte Carlo uninformative variable elimination (MC-UVE) can significantly improve the model. For validation samples, the correlation coefficient between the predicted value and the reference value determined by high-performance liquid chromatography (HPLC) analysis is 0.8090. Moreover, positive correlations are observed between the pesticide adsorption coefficient and the organic carbon (OC) and total nitrogen (TN) contents, respectively. Prediction models for OC and TN were built. The correlation coefficients of OC and TN between the predicted values and the reference values are 0.9285 and 0.6599, respectively. The results show that Vis-NIR can be used as a rapid and simple method to predict soil composition and pesticide sorption.

Quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae by near infrared spectroscopy coupled with chemometrics

OBJECTIVE

To establish a fast, simple and reliable method for quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae (referred as BZ below) by near infrared spectroscopy coupled with chemometrics.

METHOD

Twelve batches of raw medicinal materials of BZ were collected from three main producing location in China. According to the Pharmacopoeia of the People's Republic of China, these raw decoction pieces were stir-fried in wheat bran using a stir-frying machine for 3, 6, 9, 12 and 15 min, respectively. The resulted 60 samples were categorized into three classes (i.e., light, moderate and dark) by experienced pharmacists according to their surface color. After that, these slices were smashed to acquire near infrared spectra and to determine the contents of atractylenolide I, II and III by HPLC method. Qualitative and quantitative models were constructed to relate the spectra to the color labels and to the contents of three atractylenolides. Various chemometrics methods, including calibration methods like principal component analysis, partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR), spectra pretreatment methods like standard normal variate, multiplicative scatter correction, derivation and smoothing, feature selection methods like particle swarm optimization, genetic algorithm (GA) and other fourteen methods were compared in detail. The PLS-DA models were evaluated by jackknife tests with calculating parameters such as error rate (ERR), true positive rate (TPR), true negative rate (TNR) and F1 score, meanwhile the PLSR models were evaluated by five fold cross-validation tests with calculating parameters such as coefficients of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD).

RESULTS

The PLS-DA models with spectra pretreated by 1D5S or 1D9S and wavelengths selected by InfFS, Relief-F, MutInfFS, fisher or CFS performed best, yielding 0.00 of ERR, 1.00 of TPR, 1.00 of TNR, and 1.00 of F1 for all three classes. As for quantitative models, the PLSR models by 1D5S spectra pretreatment and GA wavelengths selection performed best, where R²_C and R²_P were all >0.95, RMSE_C and RMSE_P were all <0.04%, MAE_C and MAE_P were all <0.04%, and RPD were all >5.

CONCLUSION

The present qualitative and quantitative models can be successfully used to distinguish the degree of suitability of processed BZ, and to determine the contents of three atractylenolides, which thus are of great help for quality evaluation and control of processed BZ and other decoction pieces.

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.