Synergy Interval Partial Least Squares Research Articles

NIR Rapid Assessments of Blumea balsamifera (Ai-na-xiang) in China.

Blumea balsamifera (Ai-na-xiang) is used as an important plant source of natural borneol, which is widely used in the pharmaceutical industry. The aim of this study was to establish the methods based on near infrared (NIR) spectroscopy for determining the geographical origin of B. balsamifera, as well as developing a method for the quantitative rapid analysis of the active pharmaceutical ingredients (APIs). A total of 109 samples were collected from China in 2013 and arbitrarily divided into calibration and prediction sets using the Kennard–Stone algorithm. The l-borneol and total flavone contents of the samples were measured by gas chromatography and ultraviolet-visible spectroscopy, respectively. The NIR spectra were acquired using an integrating sphere and a partial least squares (PLS) model was built using the optimum wavelength regions, which were selected using a synergy interval partial least-squares (SiPLS) algorithm. The root mean square errors of prediction of the l-borneol and total flavone models were 0.0779 and 2.2694 mg/g, with R2 of 0.9069 and 0.8013, respectively. A discriminant model to determine the geographical origin of B. balsamifera (Guizhou and Hainan) was also established using a partial least squares discriminant analysis method with an optimum pretreatment method. The prediction accuracy rate of the model was 100%. NIR spectroscopy can be used as a reliable and environmentally friendly method to determine the API and the origin of different B. balsamifera samples.

Determination of resin and moisture content in melamine-formaldehyde paper using near infrared spectroscopy

This paper describes the use of near infrared spectroscopy as a tool for the determination of moisture and resin content on papers impregnated with melamine-formaldehyde resins for high-pressure laminate production. The papers had different colours and grammages. The near infrared analysis range comprised wavelengths between 12,000 cm−1 and 4000 cm−1. Several multivariate calibration procedures and pre-processing techniques were tested for selection of the best spectral interval, including interval partial least-square, forward interval partial least-square and synergy interval partial least-square. The performance of calibration models was evaluated computing the root mean-squared error of cross-validations and the coefficient of determination (R2). An external validation procedure was done using different decorative papers (red, pearl, yellow, violet and pale green). The performances of the best models were compared using the statistical criterion root mean square error of prediction. It was shown that the developed models can be applied in the determination of resin content independently of the grammage and colour of the papers. However, regarding the volatile content, the models seemed to be affected by external factors, such as the presence of dyes and pigments, and were only applicable to papers having spectra similar to those used in the calibration model.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine.

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard-Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995-10664, 7991-6661, and 4326-3999 cm-1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42-33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms

Determining amylose content in rice with near infrared (NIR) spectroscopy, associated with a suitable multivariate regression method, is both feasible and relevant for the rice business to enable Process Analytical Technology applications for this critical factor, but it has not been fully exploited. Due to it being time-consuming and prone to experimental errors, it is urgent to develop a low-cost, nondestructive and ‘on-line’ method able to provide high accuracy and reproducibility. Different rice varieties and specific chemometrics tools, such as partial least squares (PLS), interval-PLS, synergy interval-PLS and moving windows-PLS, were applied to develop an optimal regression model for rice amylose determination. The model performance was evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The high performance of the siPLS method (R=0.94; RMSEP=1.938; 8941–8194cm−1; 5592–5045cm−1; and 4683–4335cm−1) shows the feasibility of NIR technology for determination of the amylose with high accuracy.

Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution

Total fungi count (TFC) is a quality indicator of cocoa beans when unmonitored leads to quality and safety problems. Fourier transform near infrared spectroscopy (FT-NIRS) combined with chemometric algorithms like partial least square (PLS); synergy interval-PLS (Si-PLS); synergy interval-genetic algorithm-PLS (Si-GAPLS); Ant colony optimization – PLS (ACO-PLS) and competitive-adaptive reweighted sampling-PLS (CARS-PLS) was employed to predict TFC in cocoa beans neat solution. Model results were evaluated using the correlation coefficients of the prediction (Rp) and calibration (Rc); root mean square error of prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The developed models performance yielded 0.951≤Rp≤0.975; and 3.15≤RPD≤4.32. The models’ prediction stability improved in the order of PLS<CARS-PLS<ACO-PLS<Si-PLS<Si-GAPLS. FT-NIRS combined with Si-GAPLS may be employed for in-situ and noninvasive quantification of TFC in cocoa beans for quality and safety monitoring.

Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS

Olive oil adulteration with waste cooking oil was detected and quantified by combining optical Raman scattering spectroscopy and chemometrics. Spectra of 96 olive oil samples with waste cooking oil (2.5%, 5%, 10%, 20%, 30% and 50%) were collected by the portable Raman spectroscopy system. iPLS and SiPLS quantitative analysis models were established. The results revealed that spectral data after SNV processing are the best for synergy interval partial least square (SiPLS) modeling and forecast. The root mean squared error of calibration (RMSEC) is 0.0503 and the root mean squared error of validation (RMSEV) is 0.0485. The lower limit of application (LLA) of the proposed method is c[WCO]=0.5%. According to linear regression calculation, the theoretical limit of detection (LOD) of the proposed method is about c[WCO]=0.475%. The established model could make effective quantitative analysis on adulteration of waste cooking oil. It provides a quick accurate method for adulteration detection of waste cooking oil in olive oil.

Gas Bubble Effects and Elimination in Ultrasonic Measurement of Particle Concentrations in Solid–Liquid Mixing Processes

In this paper, the online measurement of particle concentrations in suspensions containing gas bubbles is studied using the ultrasonic spectra and the synergy interval partial least squares regression (Si-PLS) model in the solid–liquid mixing processes. At first, a comparison is made among the ultrasonic signals obtained from suspensions and pure water with and without gas bubbles and the gas bubble effects of high agitation speeds on ultrasonic measurement are evaluated. A moving average and standard deviation method is developed to deal with the real-time ultrasonic signals, aiming to decrease the fluctuations and noises of signals and improve the stability of signals. Then, based on the optimal spectral subintervals selected with the Si-PLS model, the prediction model for the particle concentrations is built, which considers the gas bubble effects as an interferent. The optimal model, of which the root-mean-square error of the prediction subset is 0.38 wt%, is successfully applied to measure the concentration of TiO2 suspensions in the range of 0–15.79 wt% when the agitation speed is less than 1200 r/min. The online measurement results suggest that the proposed method has the potential to be a useful tool for eliminating gas bubble effects and measuring particle concentrations in the solid–liquid mixing processes.

Determination of Retrogradation Degree in Starch by Mid-infrared and Raman Spectroscopy during Storage

Retrogradation behavior is an important physicochemical property of starch during storage. A fast and sensitive method was developed for determining the retrogradation degree (RD) in corn starch by mid-infrared (MIR), Raman spectroscopy, and combination of MIR and Raman. MIR and Raman spectra were collected from different retrogradation starch and then processed by partial least squares (PLS), interval PLS (iPLS), synergy interval PLS (siPLS), and backward interval PLS (biPLS). Two different levels of fusion data extracted from MIR and Raman spectra were analyzed by PLS. The developed models demonstrated that both MIR and Raman techniques combined with chemometrics can be used to determine the RD in starch. The PLS model built by medium-level fusion approach achieved the most satisfied performance with a correlation coefficient of 0.9658. Integrating MIR and Raman technique combined with chemometrics improved the prediction performance of RD in comparison with a single technique.

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

There is a growing need for the effective on-line process monitoring during the manufacture of traditional Chinese medicine to ensure quality consistency. In this study, the potential of near infrared (NIR) spectroscopy technique to monitor the extraction process of Flos Lonicerae Japonicae was investigated. A new algorithm of synergy interval PLS with genetic algorithm (Si-GA-PLS) was proposed for modeling. Four different PLS models, namely Full-PLS, Si-PLS, GA-PLS, and Si-GA-PLS, were established, and their performances in predicting two quality parameters (viz. total acid and soluble solid contents) were compared. In conclusion, Si-GA-PLS model got the best results due to the combination of superiority of Si-PLS and GA. For Si-GA-PLS, the determination coefficient (Rp2) and root-mean-square error for the prediction set (RMSEP) were 0.9561 and 147.6544μg/ml for total acid, 0.9062 and 0.1078% for soluble solid contents, correspondingly. The overall results demonstrated that the NIR spectroscopy technique combined with Si-GA-PLS calibration is a reliable and non-destructive alternative method for on-line monitoring of the extraction process of TCM on the production scale.

In-situ and real-time monitoring of enzymatic process of wheat gluten by miniature fiber NIR spectrometer

The degree of hydrolysis (DH), peptide content and angiotensin I-converting enzyme (ACE) inhibitory activity of hydrolysates are the three most important indexes for process control in proteins enzymatic hydrolysis and ACE inhibitory peptides preparation. In this study, a miniature fiber optical spectrometer in conjunction with multivariate analysis for in-situ and real-time monitoring of the parameters (DH, peptide content and ACE inhibitory activity) in wheat gluten (WG) hydrolysis process. Synergy interval partial least square (Si-PLS) algorithm was performed to variables selection and the regression model was calibrated. The performance of a model was evaluated by the correlation coefficient (Rp) and the root mean square error (RMSEP) in the prediction set. The results showed that Rp=0.9270, RMSEP=1.73% for DH; Rp=0.9673, RMSEP=0.79mg/ml for peptide content; Rp=0.9507, RMSEP=5.12% for ACE inhibitory. This work demonstrated that miniature fiber optical spectrometer combined Si-PLS model can be used as an alternative method for in-situ and real-time monitoring the enzymatic process of WG.

Portable spectroscopy system determination of acid value in peanut oil based on variables selection algorithms

Abstract The acid value (AV) is an essential parameter for the quality and safety evaluation of peanut oil. In this study, for efficiently and real-time monitor of acid value (AV) in peanut oil, a portable spectroscopy system was first developed and combined with variables selection algorithms to measure acid value (AV) in peanut oils. Developed portable spectroscopy system was applied for transmittance spectrum data acquisition after which partial least squares (PLS) and several variables selection algorithms synergy interval partial least square (Si-PLS), genetic algorithm (GA), genetic algorithm combined with Si-PLS namely GA-Si-PLS, ant colony optimization (ACO) algorithms were systemically studied and comparatively used for modeling. The performances of these models were evaluated according to correlation coefficients squared in the prediction set ( R P ) and root mean square error of prediction (RMSEP). The results showed that the variables selection methods could select more significant variables and improve the model performance, especially for the GA-Si-PLS model with the best performance than other variables selection algorithms with R P = 0.9426 and RMSEP = 0.2980. Finally, the paper draws a conclusion that the developed portable spectroscopy system combined with a suitable variables selection methods could be used for the simultaneous and rapid measurement of acid value in peanut oil.

Temperature effects and compensation in ultrasonic concentration measurement of multicomponent mixture

The ultrasonic technique has been successfully used to measure the concentrations of mixtures, but ultrasonic sensor is sensitive to the temperature of mixtures, which makes the ultrasonic concentration measurement very difficult. In this study, a novel ultrasonic method for compensating the temperature effects and measuring the concentrations of multicomponent mixtures comprising insoluble and soluble substances is proposed. At first, the temperature effects on the ultrasonic signals and ultrasonic concentration measurement of mixtures are investigated. Then, the stepwise temperature compensation (STC) model based on the synergy interval partial least squares (Si-PLS) model and linear interpolation as well as the global temperature compensation (GTC) model based on the Si-PLS model are separately established in combination with the ultrasonic spectra. The overall results indicate that the combination of ultrasonic spectra and temperature compensation models can compensate for the temperature effects effectively and measure the component concentrations of multicomponent mixtures in the range from 16°C to 40°C. The GTC model, with which the root mean square error of the prediction subset is 0.54wt.%, is superior to the STC model, especially when the temperatures of samples are difficult to be acquired or have to be estimated from the ultrasonic spectra. The proposed method can broaden the application scope of ultrasonic concentration measurement, which makes it easier to monitor production process and product quality of multicomponent mixtures with one ultrasonic sensor only.

Assessing saffron (Crocus sativus L.) adulteration with plant-derived adulterants by diffuse reflectance infrared Fourier transform spectroscopy coupled with chemometrics

Saffron, the dried red stigmas of the plant Crocus sativus L., is well-known as one of the most important and expensive spices worldwide. It is thus highly susceptible to fraudulent practices that employ, among others, plant-derived adulterants. This study presents an application of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and chemometric techniques for evaluating adulteration of saffron with six characteristic adulterants of plant origin, i.e. C. sativus stamens, calendula, safflower, turmeric, buddleja, and gardenia. The proposed method involved a three-step process for the detection of adulteration as well as for the identification and quantification of adulterants. Partial least squares discriminant analysis (PLS-DA) was applied to perform authentication of saffron based on mid-infrared fingerprints (4000–600cm−1), resulting in 99% correct classification of pure saffron and saffron adulterated at 5–20% (w/w) levels. Adulterant identification in positive samples was performed with high sensitivity and specificity by a six-class PLS-DA model, with spectroscopic data from the region 2000–600cm−1. Subsequently, partial least squares (PLS) regression models were built for the quantification of each adulterant. By using synergy interval PLS (siPLS) for variable selection, models with improved performance were developed, with detection limits ranging from 1.0% to 3.1% (w/w). The results obtained illustrate that this strategy based on DRIFTS has the potential to complement existing methodologies for the rapid and cost-effective assessment of typical saffron frauds.

Determination of Geographical Origin and Anthocyanin Content of Black Goji Berry (Lycium ruthenicum Murr.) Using Near-Infrared Spectroscopy and Chemometrics

In order to rapidly and efficiently determine geographical origin and characterization categories in five varieties of black Goji berry (Lycium ruthenicum Murr.), near-infrared (NIR) spectroscopy and chemometrics were utilized for data acquisition. Using this data, synergy interval partial least squares (Si-PLS), linear discriminant analysis (LDA), K-nearest neighbors (KNN), back propagation artificial neural network (BP-ANN) and least-squares support vector machine (LS-SVM) regression were systematically evaluated and compared during model development. LS-SVM was initially performed to calibrate the discrimination model to identify the geographical origins and categories of the black Goji berry samples. Compared with other models, the recognition rate of LS-SVM was more than 98.18 %, which showed excellent generalization for identification results. Total anthocyanin content was closely related with the quality of black Goji berry. Synergy interval partial least squares (Si-PLS) was applied to develop the prediction model of total anthocyanin content. The model was optimized by a leave-one-out cross-validation, and model performance was evaluated by assessing the root mean square error of the prediction (RMSEP) and correlation coefficient (R t ) in the prediction set. Experimental results showed that the optimum results of the Si-PLS model were achieved as follows: RMSEP = 0.602 mg/g and R t = 0.899 in the prediction set. The overall results sufficiently demonstrate that spectroscopy coupled with the Si-PLS regression tool has the potential to successfully discriminate black Goji berry varieties.

Research on modeling method to analyze Lonicerae Japonicae Flos extraction process with online MEMS-NIR based on two types of error detection theory

To establish a rapid quantitative analysis method for online monitoring of chlorogenic acid in aqueous solution of Lonicera Japonica Flos extraction by using micro-electromechanical near infrared spectroscopy (MEMS-NIR). High performance liquid chromatography(HPLC) was used as reference method．Kennard-Stone (K-S) algorithm was used to divide sample sets, and partial least square(PLS) regression was adopted to establish the multivariate analysis model between the HPLC analysis contents and NIR spectra. The synergy interval partial least squares (SiPLS) was used to selected modeling waveband to establish PLS models. RPD was used to evaluate the prediction performance of the models. MDLs was calculated based on two types of error detection theory, on-line analytical modeling approach of Lonicera Japonica Flos extraction process was expressed scientifically by MDL. The result shows that the model established by multiplicative scatter correction(MSC) was the best, with the root mean square with cross validation(RMSECV), root mean square error of correction(RMSEC) and root mean square error of prediction(RMSEP) of chlorogenic acid as 1.707, 1.489, 2.362, respectively, the determination coefficient of the calibration model was 0.998 5, and the determination coefficient of the prediction was 0.988 1．The value of RPD is 9.468.The MDL (0.042 15 g•L⁻¹) selected by SiPLS is less than the original,which demonstrated that SiPLS was beneficial to improve the prediction performance of the model. In this study, a more accurate expression of the prediction performance of the model from the two types of error detection theory, to further illustrate MEMS-NIR spectroscopy can be used for on-line monitoring of Lonicera Japonica Flos extraction process.

Ultrasonic characterization of aqueous mixture comprising insoluble and soluble substances with temperature compensation

This paper presents an ultrasonic technique for simultaneously determining the concentrations of insoluble and soluble substances in aqueous mixtures at different temperatures. First of all, the phase velocity spectra and the attenuation spectra of aqueous mixtures which are sensitive to the concentrations of substances are analyzed. Then, a synergy interval partial least squares (Si-PLS) model is applied to build the sub-models for the concentrations of substances at different temperature points. Finally, the overall model is constructed to build the relations among the sub-models by linear interpolation, in which the temperature effects are taken into account. The experimental and analytical results show that the overall model has the potential to simultaneously determine the concentrations of substances in aqueous mixtures at different temperatures. The proposed technique is time-saving in analyzing online signals, and thus will find wide applications in laboratory and industry for investigating aqueous mixtures and monitoring online processes.

Rapid authentication of starch adulterations in ultrafine granular powder of Shanyao by near-infrared spectroscopy coupled with chemometric methods

Near-infrared reflectance (NIR) spectroscopy combined with chemometric techniques was developed for classification and quantification of cheaper starches (corn and wheat starch) in ultrafine granular powder of Shanyao (UGPSY). By performing orthogonal partial least squares discrimination analysis (OPLS-DA), NIR could efficiently distinguish among authentic UGPSY and UGPSY adulterated with cornstarch and wheat starch. In addition, the starch content in adulterated UGPSY was determined by NIR coupled with an appropriate multivariate calibration method. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were performed comparatively to calibrate the regression model. Experimental results showed that the performance of the siPLS model is the best compared to PLS and iPLS. These results show that the combination of NIR spectroscopy and chemometric methods offers a simple, fast and reliable method for the classification and quantification of the ultrafine granular powder of the herb.

Spectroscopic quantitation of tetrazolium formazan in nano-toxicity assay with interval-based partial least squares regression and genetic algorithm

Spectrophotometric quantitation of formazan in tetrazolium-based nanoparticle (NP) toxicity assay requires a robust calibration model immune to optical interference. For the first time, variant of partial least squares (PLS) regression models, such as, full-spectrum (250–700nm) PLS, interval PLS (iPLS), backward interval PLS (biPLS), and synergy interval PLS (siPLS) models have been adopted for formazan quantitation. Models were evaluated based on root mean square error of cross-validation (RMSECV), and prediction (RMSEP). The spectral variables in optimal iPLS, biPLS and siPLS models, as well as variables retained above a selection frequency threshold (for all intervals), were further refined in a genetic algorithm (GA). The results suggest that the optimal biPLS (140 variables, 5 LVs, RMSECV: 0.4438, RMSEP: 0.2936) and siPLS (88 variables, 5 LVs, RMSECV: 0.4401, RMSEP: 0.316) models were superior either to the full-spectrum PLS (4 LVs, RMSECV: 0.9674, RMSEP: 0.4618) or traditional single wavelength calibration (414nm, RMSECV: 2.0864, RMSEP: 2.1628). Minimum RMSEP (0.2976) was observed when GA was performed on spectral variables retained (above a threshold frequency) from the cumulative frequency distribution of all siPLS models. Finally, applicability of the selected PLS regression models in real NP toxicity assay is demonstrated.

Rapid detection of total nitrogen content in soy sauce using NIR spectroscopy

Xu J., Huang F., Li Y., Chen Z., Wang Y. (2015): Rapid detection of total nitrogen content in soy sauce using NIR spectroscopy. Czech J. Food Sci., 33: 518–522. A method for the rapid and nondestructive determination of total nitrogen content in soy sauce was explored. Pre diction models were established using near-infrared spectroscopy combined with each of the following techniques: partial least squares (PLS), interval PLS, synergy interval PLS (siPLS), and backward interval PLS. Results showed that each improved forecast model was better than the PLS model. The siPLS method exhibited the best performance. The full spectrum of light soy sauce was divided into 20 subintervals. The combination of four subintervals, namely, 6, 11, 13, and 18, showed the best effect, with a correlation coefficient of 0.9977 and an RMSECV of 0.0198. The full spectrum of dark soy sauce was divided into 20 subintervals. The combination of three subintervals, namely, 14, 17, and 19, showed the best effect, with a correlation coefficient of 0.9818 and an RMSECV of 0.0640. Therefore, the siPLS method can realise a rapid and accurate detection of total nitrogen content in soy sauce.

Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao).

BackgroundThis study aims to analyze the active pharmaceutical ingredients (APIs) of licorice (Radix Glycyrrhizae; gancao), including glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids, in multi-ingredient and multi-phase extraction by online near-infrared technology with fiber optic probes and chemometric analysis.MethodsHigh-performance liquid chromatography and ultraviolet spectrophotometry determined the APIs content in different extraction phases by online near-infrared analysis, which included sample set selection by the Kennard–Stone algorithm, optimization of spectral pretreatment methods (i.e., orthogonal signal correction and wavelet denoising spectral correction), and model calibration by the partial least-squares algorithm, moving-window partial least-squares algorithm and synergy interval partial least-squares (SiPLS) algorithm. The relative errors and F values were used to assess the models in different extraction phases.ResultsThe root-mean-square error of correction, root-mean-square error of cross-validation and root-mean-square error of prediction of APIs in the SiPLS model was less than 0.07. The F values of glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids were 10,765, 32,431, 649 and 6080, respectively, which were larger than 6.90 (P < 0.01).ConclusionThe study demonstrated the feasibility of online NIR analysis in the multi-ingredient and multi-phase extraction of APIs from licorice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13020-015-0069-2) contains supplementary material, which is available to authorized users.