On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

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.

Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction

Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the process analysis of aqueous extraction. Here, we employed Raman spectroscopy to monitor the physical and chemical properties during the extraction process using convolution neural network (CNN) with background subtraction. Real-time spectra were first preprocessed to eliminate fluorescence background interference. Next, two types of CNN models, the one-dimensional CNN (1D-CNN) based on one preprocessing method, and two-dimensional CNN (2D-CNN) based on a concatenation of differentially pretreated data blocks, were used to receive the preprocessed spectra data. Two case studies were conducted for 1D- and 2D-CNN: the extraction of Aurantii fructus, and the co-extraction of Radix Salvia miltiorrhiza and Rhizoma Ligusticum chuanxiong. Furthermore, partial least squares (PLS) models and sequential preprocessing through orthogonalization (SPORT) models were developed and compared with 1D-CNN and 2D-CNN, respectively. CNN-based methods were superior to other models in terms of prediction accuracy, with 2D-CNN yielding the best results. These results indicated that preprocessing and CNN methods were highly complementary, and could effectively remove the fluorescence effect and artefacts introduced by pretreatment in spectral profile. To the best of our knowledge, this is the first study to demonstrate that a combination of preprocessing and CNN leads to improved prediction performance of analytes when using Raman spectroscopy for online monitoring high-pigmented samples.

Use of ATR-FTIR Spectroscopy and Chemometrics for the Variation of Active Components in Different Harvesting Periods of Lonicera japonica

Lonicera japonica Thunb is a commonly used Chinese herbal medicine, which belongs to the family Caprifoliaceae. The active components varied greatly during bud development. Research on the variation of the main active components is significant for the timely harvesting and quality control of Lonicera japonica. In this study, the attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with the chemometric method was performed to investigate the variability of different harvesting periods of Lonicera japonica. The preliminary characterization from ATR-FTIR fingerprints showed various characteristic absorption peaks of the main active components from the different harvesting times, such as flavonoids, organic acids, iridoids, and volatile oils. Additionally, principal component analysis (PCA) scatter plots showed that there was a clear clustering trend in the samples of the same harvesting period, and the samples of the different harvesting periods could be well distinguished. Finally, further analysis by the orthogonal partial least-squares discriminant analysis (OPLS-DA) showed that there were regular changes in flavonoids, phenolic acids, iridoids, and volatile oils in different harvesting periods. Therefore, ATR-FTIR, as a novel and convenient analytical method, could be applied to evaluate the quality of Lonicera japonica.

Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy

In this paper, a rapid model for the determination of pachymic acid content in Poria was established by partial least squares (PLS) regression and near-infrared spectroscopy (NIR). During the research, a total of 108 batches of Poria samples from different producing regions were used, while their corresponding pachymic acid contents by high-performance liquid chromatography (HPLC) were adopted as reference. These samples were divided randomly into calibration sets for model establishment and validation sets for model validation. The test results from the calibration set showed that the best preprocessing method of the NIR spectra model was the standard normal variate (SNV) + second derivatives (SD), and the most suitable number of principal factors was 9. In this model, the coefficient of determination of the calibration set ( $r_c^2$ ) and validation set ( $r_v^2$ ) was 0.915 and 0.917, respectively. Meanwhile, the root mean square error of calibration (RMSEC) and the root mean square error of validation (RMSEP) with the calibration set were 0.051 mg/g and 0.054 mg/g, respectively. These results indicated this model could rapidly and reliably predict the pachymic acid content in Poria and increase the determination efficiency of pachymic acid in Poria. This is conducive to promote the development of industry.

Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process

When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.

Identification of different bran-fried Atractylodis Rhizoma and prediction of atractylodin content based on multivariate data mining combined with intelligent color recognition and near-infrared spectroscopy

Unclear established standard of bran-fried Atractylodis Rhizoma (BFAR), a commonly used drug in Traditional Chinese Medicine (TCM), compromised its clinical efficacy. In this study, we explored the correlation between color and near-infrared spectroscopy (NIR) feature with content of atractylodin, then established a rapid recognition model for the optimal degree of processing for BFAR preparation. The results of the Pearson analysis indicated that the color values were significantly and positively correlated with atractylodin content. The back propagation artificial neural network algorithm and cluster analysis revealed the color of different BFAR could be accurately divided into three categories; subsequently, the color range for the optimal degrees of stir-frying was established as follows: R[red value (105.79-127.25)], G[green value(75.84-89.64)], B[blue value(33.33-42.73)], L[Lightness (81.26-95.09)].Using NIR, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and cluster analysis, three types of BFAR were accurately identified. The prediction model of atractylodin content was established using partial least squares regression analysis. The R² of the validation set was 0.9717 and the root mean square error was 0.026. In the color judgment model, the processing degree of 8 batches of BFAR from the market is inferior. According to the NIR judgment model, the processing degree of all samples from the market is inferior. In conclusion, the best fire degree of BFAR can be identified quickly and accurately based on our established model. It is a potential method for quality evaluation of Chinese Materia Medica processing.

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.

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics

The feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for the rapid detection of the cellulose and hemicellulose contents in corn stover is discussed. Competitive adaptive reweighted sampling (CARS) and genetic simulated annealing algorithm (GSA) were combined (CARS-GSA) to select the characteristic wavelengths of cellulose and hemicellulose and to reduce the dimensionality and multicollinearity of the NIRS data. The whole spectra contained 1845 wavelength variables. After CARS-GSA optimization, the number of characteristic wavelengths of cellulose (hemicellulose) was reduced to 152 (260), accounting for 8.24% (14.09%) of all wavelengths. The coefficients of determination of the regression models for predicting the cellulose and hemicellulose contents were 0.968 and 0.996, the root mean square errors of prediction (RMSEPs) were 0.683 and 0.648, and the residual predictive deviations (RPDs) were 5.213 and 16.499, respectively. The RMSEP of the cellulose and hemicellulose regression models was 0.152 and 0.190 lower for CARS-GSA than for the full-spectrum, and the RPD was increased by 0.949 and 3.47, respectively. The results showed that the CARS-GSA model substantially reduced the number of characteristic wavelengths and significantly improved the predictive ability of the regression model.

Quantitation of volatile aldehydes using chemoselective response dyes combined with multivariable data analysis

Current work proposed a novel quantitative method of volatile aldehydes (VAs) using chemoselective response dyes (CRDs) combined with multivariate data analysis. Multivariate spectral data of selected CRDs was obtained by visible near-infrared spectroscopy. The Synergy-interval Partial Least Squares (Si-PLS) algorithm processed multivariate spectral data to establish VAs quantitative prediction models at the level of 0.0002 v/v to 0.18 v/v. The prediction coefficient (Rp) values of models ranged from 0.8399 to 0.9886, and the Root Mean Square Error of Prediction (RMSEP) values were less than 0.01. These models were verified by classification of aging rice samples, and 93% samples were correctly identified in prediction set. In addition, Density Functional Theory (DFT) calculations explored the interaction mechanism between selected CRDs and VAs. The optimized Highest Occupied Molecular Orbital-Lowest Unoccupied Molecular Orbital (HOMO-LUMO) energy levels, dipole moment, distance between molecules were found to have strong correlations with the interaction.

Water as a probe to understand the traditional Chinese medicine extraction process with near infrared spectroscopy: A case of Danshen (Salvia miltiorrhiza Bge) extraction process

Extraction process is not only a critical manufacturing unit but also the initial process of various extracts and preparations. Taking the most extensive Chinese herbal medicine Danshen (Salvia miltziorrhiza Bge) as an example, salvianolic acid B (Sal B) is its main active pharmaceutical ingredient but lacks accurate characterization of the extraction process. As one of process analytical technologies, near-infrared spectroscopy (NIRS) technology has been widely applied for monitoring pharmaceutical extraction process. In most past studies, water spectral information is often eliminated due to its high absorption. However, this study proposed a method of using water spectrum to understand the whole extraction process and to quickly determine the content of Sal B. Principal component analysis (PCA) was first utilized to investigate the whole extraction process, then the reconstructed spectrum based on PCA was established and analyzed by Aquaphotomics, and finally the partial least squares regression (PLSR) quantitative model of Sal B was established. PCA and Aquaphotomics results showed the whole extraction process could be considered as a dynamic change from structure breaker to structure maker, and the dominance of highly H-bonded water structures increases with the extraction time. Also, the Sal B quantitative model with water spectrum showed higher accuracy and stability than other methods, which parameters (RMSEC, RMSECV, RMSEP, R²c, R²cv, R²p, RPD) were 0.2408 mg/mL, 0.2939 mg/mL, 0.2584 mg/mL, 0.9536, 0.9300, 0.9494, 4.6298, respectively, and the paired t-test showed that Sal B content measured by NIR and HPLC methods had no significant differences (p > 0.05). In conclusion, all result indicated that water can be used as a probe to understand the traditional Chinese medicine extraction process with NIRS.

Application of Near-Infrared Spectroscopy Analysis Technology to Total Nucleosides Quality Control in the Fermented Cordyceps Powder Production Process

Product quality control is a prerequisite for ensuring safety, effectiveness, and stability. However, because of the different strain species and fermentation processes, there was a significant difference in quality. As a result, they should be clearly distinguished in clinical use. Among them, the fermentation process is critical to achieving consistent product quality. This study aims to introduce near-infrared spectroscopy analysis technology into the production process of fermented Cordyceps powder, including strain culture, strain passage, strain fermentation, strain filtration, strain drying, strain pulverizing, and strain mixing. First, high performance liquid chromatography (HPLC) was used to measure the total nucleosides content in the production process of 30 batches of fermented Cordyceps powder, including uracil, uridine, adenine, guanosine, adenosine, and the process stability and interbatch consistency were analyzed with traditional Chinese medicine (TCM) fingerprinting, followed by the near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) to establish a quantitative analysis model of total nucleosides for online process monitoring of fermented Cordyceps powder preparation products. The model parameters indicate that the established model with good robustness and high measurement precision. It further clarifies that the model can be used for online process monitoring of fermented Cordyceps powder preparation products.

A Raman spectroscopy analysis method for rapidly determining saccharides and its application to monitoring the extraction process of Wenxin granule manufacturing procedure

Saccharides are the major constituents of many herbs, and they are often utilized as quality indicators of many botanical drugs, such as Chinese medicines. A method for the rapid determination of saccharides in the in-process extract solutions is beneficial for process monitoring and ensuring consistency in the quality of the end-products during the manufacturing of Chinese medicines. In this work, a method based on Raman spectroscopy and a competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model was established for the rapid quantification of saccharides. The accuracy and precision of this method were confirmed by employing one monosaccharide (glucose), one oligosaccharide (maltotriose), and two polysaccharides (Codonopsis radix polysaccharides and Polygonati rhizome polysaccharides) as reference substances. The determined results correlated well with the reference values of the four substances with the coefficient of determination of prediction (R_p²) ≥ 0.9939 and the root-mean-square error of prediction (RMSEP) ≤ 1.1052 mg/mL. Then, the method was applied to monitoring the simulated extraction process for Wenxin granule manufacture using total saccharides as a quality indicator. The CARS-PLS model exhibited satisfactory fitting and predictive capability, with R_p² and RMSEP values of 0.9743 and 1.4931 mg/mL, respectively. Our work demonstrated that Raman spectroscopy coupled with chemometrics can offer a reliable and nondestructive alternative for the determination of different types of saccharides, in addition to being useful for real-time monitoring of the extraction process during the manufacturing of Wenxin granules. The presented approach is expected to be applicable to other Chinese medicines.

Near-Infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Steaming Process of Gastrodiae rhizoma with Multiparameters and Chemometrics

Steaming is a vital unit operation in traditional Chinese medicine (TCM), which greatly affects the active ingredients and the pharmacological efficacy of the products. Near-infrared (NIR) spectroscopy has already been widely used as a strong process analytical technology (PAT) tool. In this study, the potential usage of NIR spectroscopy to monitor the steaming process of Gastrodiae rhizoma was explored. About 10 lab scale batches were employed to construct quantitative models to determine four chemical ingredients and moisture change during the steaming process. Gastrodin, p-hydroxybenzyl alcohol, parishin B, and parishin A were modeled by different multivariate calibration models (SMLR and PLS), while the content of the moisture was modeled by principal component regression (PCR). In the optimized models, the root mean square errors of prediction (RMSEP) for gastrodin, p-hydroxybenzyl alcohol, parishin B, parishin A, and moisture were 0.0181, 0.0143, 0.0132, 0.0244, and 2.15, respectively, and correlation coefficients (R _p ²) were 0.9591, 0.9307, 0.9309, 0.9277, and 0.9201, respectively. Three other batches' results revealed that the accuracy of the model was acceptable and that was specific for next drying step. In addition, the results demonstrated the method was reliable in process performance and robustness. This method holds a great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online monitoring and quality control in the TCM industrial steaming process.

Rapid and simultaneous quality analysis of the three active components in Lonicerae Japonicae Flos by near-infrared spectroscopy

Lonicerae Japonicae Flos (LJF) has historically been widely utilized as a tea and health food. To better understand and evaluate its quality evaluate its quality, a near-infrared spectroscopy (NIRS) method was developed for the rapid and simultaneous analysis of the 3 main active components (chlorogenic acid, isochlorogenic acid A and isochlorogenic acid C). The NIRS model was built using 2 different strategies: partial least squares (PLS) as a linear regression method and artificial neural networks (ANN) as a nonlinear regression method. Furthermore, the NIRS method was applied to analyze the 4 main quality factors, which included 5 processing methods (shade drying, sun drying, vacuum drying, freeze drying and hot-air drying), 2 kinds of harvest time (flower bud stage and florescence stage), 2 species and 8 geographical origins. Collectively, NIRS is a promising method for the quality analysis of LJF.

Recent advances of novel technologies for quality consistency assessment of natural herbal medicines and preparations

Quality consistency is one of the basic attributes of medicines, but it is also a difficult problem that natural medicines and their preparations must face. The complex chemical composition and comprehensive pharmacological action of natural medicines make it difficult to simply apply the commonly used evaluation methods in chemical drugs. It is thus urgent to explore the novel evaluation methods suitable for the characteristics of natural medicines. With the rapid development of analytical techniques and the deepening understanding of the quality of natural herbs, increasing numbers of researchers have proposed many new ideas and technologies. This review mainly focuses on the basic principles, technical characteristics and application examples of the chemical evaluation, biological evaluation methods and their combination in quality consistency evaluation of natural herbs. On the bases of chemical evaluation and clinical efficacy, new methods reflecting their pharmacodynamic mechanism and safety characteristics will be developed, and gradually towards accurate quality control, to achieve the goal of quality consistency. We hope that this manuscript can provide new ideas and technical references for the quality consistency of natural drugs and their preparations, thus better guarantee their clinical efficacy and safety, and better promote industrial development.

Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method

Polysaccharide is the main active compound of Lilium, and showed many activities, such as hypoglycemic, antioxidant, immune-modulatory. There are three types' Lilium in China market, i.e. Lilium lancifolium Thunb (JD), Lilium davidiivar. Unicolor Salisb (L. davidii var)(LZBH), and Lilium brownii F.E. Brown var. viridulum Baker (BH). Near infrared spectroscopy (NIR) technique has become popular in the fields of quality control, due to its advantages, such as fast, non-destructive, and can detect several ingredients, simultaneously. In this study, a classification model was established based on NIR technique and random forest method to accurately distinguish three types' Lilium species, and the classification accuracy reached 94.37%. Furthermore, taking the effects of neighbor wavelength into account, a new weighted partial least square algorithm was proposed to establish an accurate and quantitative model for predicting the polysaccharide contents of these samples. In the model establishing process, some signal pre-treatment methods were optimized, and the validation results with highest determination coefficient (R²) and low root mean square errors of prediction (RMSEP) were, 0.9455 and 0.9098, respectively. The obtained results showed that combined NIR technique with chemometrics was an effective and green method for quality control.

Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process

Cornu Caprae Hircus (goat horn, GH) is one of the frequently used medicinal animal horns in traditional Chinese medicine (TCM). Hydrolysis is one of the key steps for GH pretreatment in pharmaceutical manufacturing. However, the physicochemical complexity of the hydrolysis samples imposes a challenge for hydrolysis process analysis and monitoring. In this study, convolutional neural networks (CNNs), one of the most popular deep learning methods, were used to develop quantitative calibration models based on on-line Raman spectroscopy for monitoring the GH hydrolysis process. Partial least squares (PLS) calibration models were also developed for model performance comparison. For CNN modeling, raw Raman spectra were used as inputs and hyperparameters in the CNN structure were optimized. Results show for four of the seven analytes, the optimized CNN models using raw spectra as inputs outperform the optimized PLS models developed with preprocessed spectra. Therefore, compared with the commonly used PLS algorithm, CNN modeling is also a practicable regression method and can be employed for the analytical purpose of this study. Models with better performance are expected to be obtained by improving the CNN model structure and using more effective hyperparameter optimization approaches in further studies. To the best of our knowledge, this is the first reported case study of combining CNNs and on-line Raman spectroscopy for a regression task.

The Incidence and Risk Factors for Adverse Drug Reactions Related to Tanreqing Injection: A Large Population-Based Study in China

Background: Tanreqing injection (TRQ) is a traditional Chinese medicine commonly used in China to treat pulmonary diseases presenting as phlegm-heat syndrome. Robust data on the safety of TRQ from real-world observational cohorts are currently lacking.

Objective: To evaluate as the incidence, type, and predictors of adverse events (AEs) and adverse drug reactions (ADRs) of TRQ in clinical practice in China.

Methods: We conducted a population-based cohort, multicenter study to evaluate the incidence, manifestation, outcomes, and risk factors of AEs and ADRs following TRQ use in China. Between April 2014 and May 2015 a total of 30,322 consecutive inpatients/emergency attendance patients from 90 hospitals across China administrated TRQ were followed-up for 7 days. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated using logistic regression to identify predictors of ADRs.

Results: The incidence of AEs and ADRs was 1.4 and 0.3%, respectively. Skin and subcutaneous tissue disorders were the most common ADRs. All ADRs were mild or moderate in severity, except for one serious case of anaphylactic reaction. The majority of ADRs (72.8%) occurred in the first 2 h after TRQ administration. Two-thirds of patients (66.1%) in the study were prescribed TRQ off-label, including infants aged ≤24 months. A history of food allergy (OR 4.50, 95% CI: 1.35–15.00), drug allergy (OR 2.77, 95% CI: 1.56–4.94), and fast infusion speed (off-label use) (OR 2.10, 95% CI: 1.27–3.50) were associated with an increased risk of ADRs.

Conclusion: TRQ is well tolerated in the general population, yet off-label use is prevalent. Efforts are required to educate prescribers to adhere to the drug label in order to minimize potential patient harm.

Lonicerae japonicae flos and Lonicerae flos: a systematic review of ethnopharmacology, phytochemistry and pharmacology

Lonicerae japonicae flos (called Jinyinhua, JYH in Chinese), flowers or flower buds of Lonicera japonica Thunberg, is an extremely used traditional edible-medicinal herb. Pharmacological studies have already proved JYH ideal clinical therapeutic effects on inflammation and infectious diseases and prominent effects on multiple targets in vitro and in vivo, such as pro-inflammatory protein inducible nitric oxide synthase, toll-like receptor 4, interleukin-1 receptor. JYH and Lonicerae flos [called Shanyinhua, SYH in Chinese, flowers or flower buds of Lonicera hypoglauca Miquel, Lonicera confusa De Candolle or Lonicera macrantha (D.Don) Spreng] which belongs to the same family of JYH were once recorded as same herb in multiple versions of Chinese Pharmacopoeia (ChP). However, they were listed as two different herbs in 2005 Edition ChP, leading to endless controversy since they have close proximity on plant species, appearances and functions, together with traditional applications. In the past decades, there has no literature regarding to systematical comparison on the similarity concerning research achievements of the two herbs. This review comprehensively presents similarities and differences between JYH and SYH retrospectively, particularly proposing them the marked differences in botanies, phytochemistry and pharmacological activities which can be used as evidence of separate list of JYH and SYH. Furthermore, deficiencies on present studies have also been discussed so as to further research could use for reference.

Fast determination of oxides content in cement raw meal using NIR-spectroscopy and backward interval PLS with genetic algorithm

Determining oxides content in cement raw meal with near infrared (NIR) spectroscopy, associated with partial least square (PLS) regression, is fast and potential for cement industry to realize cement raw material proportioning control. However, it has hardly been studied. Backward interval PLS (biPLS) with genetic algorithm (GA-biPLS) were applied to select characteristic variables closely related to the concentration of oxide of interest to establish calibration model. The optimal GA-biPLS models showed that the determination coefficient (R_p²) and root mean square error of prediction (RMSEP) were 0.8857 and 0.0994 for CaO, 0.8718 and 0.1044 for SiO₂, 0.7417 and 0.0693 for Al₂O₃, 0.5404 and 0.0387 for Fe₂O₃, correspondingly. These results indicate that GA-biPLS can select less variables with better prediction performance by comparison with PLS and biPLS, the NIR spectroscopy combined with GA-biPLS algorithm is a fast, accurate and reliable alternative method for determination of oxides content in cement raw meal.

Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA

Near-infrared spectroscopy (NIRS) is an efficient method for detecting the content of carbon and nitrogen in many materials, which solves the problems of the time-consuming and high-cost traditional chemical analysis method. To quickly detect the carbon-nitrogen ratio (C/N) for the anaerobic fermentation (AF) feedstock using NIRS, a genetic simulated annealing algorithm (GSA) is presented based on a genetic algorithm combined with a simulated annealing algorithm. By combining GSA with backward interval partial least squares (BiPLS), we construct a BiPLS-GSA algorithm to optimize the characteristic wavelength variables of NIRS; this algorithm significantly reduced the number of wavelength variables involved in modeling and effectively improved the detection accuracy and efficiency of the model. The determination coefficients, root mean squared error, mean relative error (MRE) and residual predictive deviation for the validation set in the BiPLS-GSA regression model were 0.9067, 7.6676, 5.5274%, and 3.5626, respectively. Meanwhile, compared to the entire spectrum model, the MRE was decreased by 16.54% in the BiPLS-GSA-based model. The research in this paper improves the adaptability of the prediction model based on optimizing sensitive wavelength variables for C/N, which provides a new way for rapid and accurate measurement of the C/N of AF feedstock.

NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.)

In this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms; synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction; root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.

Protective Effects of Aqueous Extracts of Flos lonicerae Japonicae against Hydroquinone-Induced Toxicity in Hepatic L02 Cells

Hydroquinone (HQ) is widely used in food stuffs and is an occupational and environmental pollutant. Although the hepatotoxicity of HQ has been demonstrated both in vitro and in vivo, the prevention of HQ-induced hepatotoxicity has yet to be elucidated. In this study, we focused on the intervention effect of aqueous extracts of Flos lonicerae Japonicae (FLJ) on HQ-induced cytotoxicity. We demonstrated that HQ reduced cell viability in a concentration-dependent manner by administering 160 μmol/L HQ for 12 h as the positive control of cytotoxicity. The aqueous FLJ extracts significantly increased cell viability and decreased LDH release, ALT, and AST in a concentration-dependent manner compared with the corresponding HQ-treated groups in hepatic L02 cells. This result indicated that aqueous FLJ extracts could protect the cytotoxicity induced by HQ. HQ increased intracellular MDA and LPO and decreased the activities of GSH, GSH-Px, and SOD in hepatic L02 cells. In addition, aqueous FLJ extracts significantly suppressed HQ-stimulated oxidative damage. Moreover, HQ promoted DNA double-strand breaks (DSBs) and the level of 8-hydroxy-2'-deoxyguanosine and apoptosis. However, aqueous FLJ extracts reversed HQ-induced DNA damage and apoptosis in a concentration-dependent manner. Overall, our results demonstrated that the toxicity of HQ was mediated by intracellular oxidative stress, which activated DNA damage and apoptosis. The findings also proved that aqueous FLJ extracts exerted protective effects against HQ-induced cytotoxicity in hepatic L02 cells.

Non-linearity correction in NIR absorption spectra by grouping modeling according to the content of analyte

To correct the non-linearity caused by light scattering in quantitative analysis with near infrared absorption spectra, a new modeling analysis method was proposed: grouping modeling according to the content of analyte. In this study, we tested the proposed method for non-invasive detection of human hemoglobin (Hb) based on dynamic spectrum (DS). We compared the prediction performance of the proposed method with non-grouping modeling method. Experimental results showed that the root mean square error of the prediction set (RMSEP) by the proposed method was reduced by 9.96% and relative standard deviation of the prediction set (RSDP) was reduced by 4.73%. The results demonstrated that the proposed method could reduce the effects of non-linearity on the composition analysis by spectroscopy. This research provides a new method for correcting the non-linearity stemming from light scattering. And the proposed method will accelerate the pace of non-invasive detection of blood components into clinical application.

Simultaneous measurement of contents of liquirtin and glycyrrhizic acid in liquorice based on near infrared spectroscopy

OBJECTIVE

To establish calibration models for simultaneous determination of contents of liquirtin and glycyrrhizic acid, and to investigate the variable selection methods.

METHODS

The contents of liquirtin and glycyrrhizic acid determined by HPLC were as the reference values, which were associated with samples spectra by using near infrared spectrum (NIR) analysis technology. Calibration models were developed using partial least squares (PLS) regression algorithm, and evaluated by the independent dataset test with calculating the metrics of coefficients of determination of calibration and prediction (R²_c, R²_p), the root mean square errors of calibration and prediction (RMSEC, RMSEP), the mean absolute errors of calibration and prediction (MAEC, MAEP), and the residual prediction deviation (RPD). Five variable selection methods including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), particle swarm optimization (PSO) and genetic algorithm (GA), were investigated.

RESULTS

Compared to the original full spectra, both quantification models for liquirtin and glycyrrhizic acid performed better with a clear ranking of GA>PSO>CARS>MCUVE≅VIP>Full. Especially for GA-PLS models, RMSEC and RMSEP were <0.05%, R²_c and R²_p were >0.94, and RPD were both >4, indicating that both the models had good robustness and excellent prediction accuracy.

CONCLUSION

The present calibration models can be utilized to simultaneously determine the contents of liquirtin and glycyrrhizic acid in liquorice samples, and thus are of great help for rapid quality evaluation and control of liquorice.

Near infrared spectroscopy based monitoring of extraction processes of raw material with the help of dynamic predictive modeling

The control of batch-to-batch quality variations remains a challenging task for pharmaceutical industries, e.g., traditional Chinese medicine (TCM) manufacturing. One difficult problem is to produce pharmaceutical products with consistent quality from raw material of large quality variations. In this paper, an integrated methodology combining the near infrared spectroscopy (NIRS) and dynamic predictive modeling is developed for the monitoring and control of the batch extraction process of licorice. With the spectra data in hand, the initial state of the process is firstly estimated with a state-space model to construct a process monitoring strategy for the early detection of variations induced by the initial process inputs such as raw materials. Secondly, the quality property of the end product is predicted at the mid-course during the extraction process with a partial least squares (PLS) model. The batch-end-time (BET) is then adjusted accordingly to minimize the quality variations. In conclusion, our study shows that with the help of the dynamic predictive modeling, NIRS can offer the past and future information of the process, which enables more accurate monitoring and control of process performance and product quality.

Combining near infrared spectroscopy with predictive model and expertise to monitor herb extraction processes

Albeit extensively utilized, herb extraction process (HEP) is hard to be monitored because of its batch nature and the fluctuating quality of raw materials. Process analytical tools like near infrared spectroscopy (NIRS) can offer nondestructive examinations and collect abundant data of the process, which in principle contain the information about the quality of both the product and the process itself. However, extra effort is often required for the data mining of such process measurements, and extracting knowledge of the quality of process can be even harder. In this study, we take the extraction process of licorice as a typical HEP instance, and combine NIRS with classical partial least squared regression (PLSR) and expertise for its on-line monitoring. We show that our scheme effectively extracts information with clear physical meanings, through which we can even uncover the process fault that does not induce evident abnormalities in the product quality. Moreover, the constructed model can continuously evolve with more process data from daily operations, and the idea of the whole framework can be directly generalized to other HEP.