Identification of Herbal Medicines Using Pattern Recognition Techniques with Near-Infrared

Identification of Herbal Medicines Using Pattern RecognitionTechniques with Near-Infrared Reflectance SpectraYoung-Ah Woo,*Hyo-Jin Kim,*,1and JungHwan Cho†*College of Pharmacy,Dongduk Women’s University,Seoul 136–814,Korea;and †College of Pharmacy,Sookmyung Women’s University,Seoul 140–742,KoreaAccepted June 7,1999The objective of this study was to develop a nondestructive,rapid,and accurate analytical methodto identify some herbal medicines that are very similar to the naked eye even though they are different species.Ginseng Radix,Austragali Radix,and Smilacis Rhizoma were used for development of the identification method in this study.Near-infrared/(NIR)reflectance spectra were collected over the region 400to 2500nm.With the use of derivatization of spectra the spectral features unique to each species are easier to differentiate.It was shown that NIR reflectance spectra of samples were moderately classified in principal-component space.To select the best identification method for application,pattern recognition techniques using wavelength distance,residual variance,Mahalano-bis distance,and soft independent modeling of class analogy (SIMCA)were compared.This study shows that the residual variance method and SIMCA provide the most accurate prediction results.Overall,NIR reflectance spectroscopy using pattern recognition is shown to have significant potential as a rapid and accurate method for identification of herbal medicines.©1999Academic PressINTRODUCTIONPattern recognition techniques combined with spectroscopic data have been widely used for qualitative and quantitative analysis (1–3).Especially,in particular,near-infrared (NIR)reflectance spectroscopy is an attractive technique due to its speed and has been developed for numerous applications in analyzing pharmaceutical materials (4)as well as agricultural products (5).The qualitative information available in the NIR spectral region has been used for classification using a combination of Mahalanobis distance and residual variance pattern recognition techniques (6).Using discriminant analysis,40different pharmaceutical raw materials were qualified (7).The qualitative identification of raw materials by NIR spectroscopy using a Mahalanobis distance classification technique has been reported (8,9).Soft independent modeling of class analogy (SIMCA)by NIR spectroscopy has been applied to qualitative identification of raw materials (10).In this study,pattern recognition techniques using NIR reflectance spectroscopy were employed to classify herbal medicines based on their species.Even though some herbal medicines are different species,they appear similar to each other.The traditional test has depended on visual inspection or thin-layer chromatography.These tests are either subjective in nature or require operative skills and are not rapid enough for screening huge volumes of herbal medicines.Since NIR reflectance spectroscopy can give more accurate results than traditional methods,it could successfully provide an identification method with the use of pattern recognition techniques such as those listed above.1To whom correspondence should be addressed.Microchemical Journal 63,61–70(1999)Article ID mchj.1999.1768,available online at on0026-265X/99$30.00Copyright ©1999by Academic PressAll rights of reproduction in any formreserved.61EXPERIMENTALSample PreparationAll herbal medicines were acquired from The Natural Agriculture Products Inspection Office(NAPIO),Seoul,Korea.Three herbal medicines—Austragali Radix(AR),Ginseng Radix(ginseng),and Smilacis Rhizoma(SR)—were used as samples.AR is a dried root of Austragali membraneous Bungae.Ginseng is a dried root of Panax ginseng.SR is a dried rhizome of Smilax china Roxburgh.Ninety-seven Austragali Radix samples,100 Ginseng Radix samples,and93Smilacis Rhizoma samples were studied.All samples were powdered using a cyclone mill(Udymill,USA)fitted with a1-mm screen.The particle size of the powder was maintained below800␮m.NIR Reflectance SpectraNIR reflectance spectra were scanned at2-nm intervals using an NIRSystems6500 monochromator instrument(Foss NIRSystems).The spectrometer uses a tungsten halogen lamp,PbS,and silicon detectors.All spectra were obtained as an average of32scans from 400to2500nm.A circular sample cup with a quartz window that was selected provided a sample that was38mm in diameter and10mm thick.Mathematical Description of Discriminant AnalysesWavelength distance.Atfirst,the mean x៮i and standard deviation(s i)spectrum for the training set are computed:x៮iϭ1͸jϭ1n x ij,(1)s iϭͫ1nϪ1͸jϭ1n͑x ijϪx៮i͒2ͬ1/2.(2)In Eq.(1),n is number of spectra and therefore x៮i is mean at wavelength i.s i represents the standard deviation at wavelength i.t calc,iϭ͉x unk,iϪx៮trn,i͉s iͱn unk⅐n trn n unkϩn trn.(3)After calculating mean and standard deviation from the training set,the unknown spectrum’s deviations from the mean at all1050(400–2500nm,2-nm interval)wave-lengths are uncorrelated and normally distributed.And then,t is calculated.Outliers are determined in the95%confidence interval of t distribution.Residual variance.In the residual variance method,classification of a sample is achieved by calculating the sum of the squares of the difference between the original spectrum and the reproduced spectrum(residual spectrum).When an unknown spectrum is projected into the subspace of the principal-component model,its residual spectrum can 62WOO,KIM,AND CHObe computed to determine if the principal-component model adequately represents all its spectral features.To classify unknown spectra,the residual variances of the training set and the unknown spectra are compared:r unk ϭa unk Ϫs unk ⅐E.(4)In Eq.(4),r represents the residual differences,s the score vector,and E the loading vector.S unk 2ϭ1m ͸j ϭ1m r ij2,(5)S 02ϭ1m ͑n Ϫk Ϫ1͒͸i ϭ1n ͸j ϭ1m r ij 2,(6)F ϾF crit ͑␣ϭ0.05,df ϭ1,n Ϫk Ϫ1͒.(7)A sample’s residual variance,s unk 2,can be compared with the expected residual variance,s 02,for the training set.The unknown spectra are then classified according to the probability levels obtained from the F test.This is performed in the 95%confidence interval of the F distribution.Mahalanobis distance.The multidimensional distance measures were computed withd unk 2ϭ͑S unk ϪS ៮͒T ͸Ϫ1͑S unk ϪS ៮͒,(8)where d unk2ϭsquared distance of an unknown sample from the centroid,S unk ϭunknown sample’s vector of scores,S,training set’s vector of mean scores,and ¥ϭtraining set’s variance–covariance matrix.Squared distances follow a ␹2distribution.With the 95%confidence interval,outliers are detected.Soft independent modeling of class analogy.SIMCA considers each class separately.For each class separately,a principal-component analysis is performed which leads to a PC model for each class (so-called disjoint class models).The following equations are demonstrated for class K ,for example:s 0K ϭͫ͸j ϭ1m ͸i ϭ1n K ͑e ij K ͒2/͑n K Ϫp K Ϫ1͒͑m Ϫp K ͒ͬ1/2,(9)s u K ϭͫ͸j ϭ1m͑e uj K ͒2/͑m Ϫp K ͒ͬ1/2,(10)63NIR REFLECTANCE SPECTRA OF HERBAL MEDICINESF ϭ͑s u K ͒2͑s 0K ͒2,(11)͑s lim K ͒ϭ͑s 0K ͒⅐F 0.05.(12)In Eq.(9),s 0K is the standard deviation of the training set for class K ,n k is the number of objects,p k is the number of significant principal components in class K ,and m is thenumber of variables.s u K ,the standard deviation of the unknown spectrum,is calculatedusing the residual e uj value.In SIMCA,outliers are detected in the 95%confidence interval of the F distribution.If the F value is larger than the critical F value at a given level of significance,one can conclude that the distance of u from class K is significantlylarger than that of class K as a whole.If (s u K )2Ͻ(s lim K )2,unknown spectrum (u )isdetermined to be a member of K .RESULTS AND DISCUSSIONNear-Infrared Reflectance Spectral FeaturesFigure 1shows average NIR spectra of three herbal medicines.It is difficult to find specific bands in the NIR spectra based on species since there are hundreds of components in herbal medicines,and NIR bands are composed of overtones and combinations of fundamental vibrations of –CH,–NH,and –OH groups from the midinfrared (MID-IR).The differences in the spectral baseline in Fig.1are mostly due to differences in particle size.It is generally known that the larger the particle,the greater the baseline offset.However,much qualitative information can be elucidated from the NIR spectrum.Second derivatives were used to remove baseline drift.As a result,the unique spectral features for each species became more apparent,as shown in Fig.2.The spectral differences of the three samples were observed in several different regions,around 1700,2150,and2250FIG.1.Average NIR spectra of Austragali Radix,Ginseng Radix,and Smilacis Rhizoma.64WOO,KIM,AND CHOnm.The identification rule using NIR reflectance spectroscopy is based on these quali-tative differences in the spectra.Wavelength Distance AnalysisFor wavelength distance analysis,samples for training set and test set were randomly selected.Table 1shows data set preparation for AR,Gin,and SR.The full ranges from 400to 2500nm of NIR spectra were used for the performance of wavelength distance analysis.Wavelength distance analysis was performed in the 95%confidence level of the t test.Figure 3shows the result when AR data are used as the training set for constructing a model.All 20AR data in the test set were identified as AR and all 20SR data in the test set were not misidentified as AR.However,11ginseng data were misidentified as AR.Figure 4shows the result when ginseng data are used as the training set for constructing a model.All 20ginseng data were identified as ginseng.However,all 20AR data and 10SR data in the test set were misidentified as ginseng.Figure 5shows the result when SR data are used as the training set for constructing a model.Whereas all 20SR data in the test set were identified as SR and AR data in the test set were misidentified as SR,7FIG.2.Second-derivative spectra of Austragali Radix,Ginseng Radix,and Smilacis Rhizoma.TABLE 1Data Set Preparation for Wavelength Distance,Residual Variance,and Mahalanobis Distance MethodsSample Number of spectrain training setNumber of spectra in test set Austragali Radix 7720Ginseng Radix 8020Smilacis Rhizoma732065NIR REFLECTANCE SPECTRA OF HERBAL MEDICINESginseng data were misidentified as SR.By this method,there is not 100%differentiation of these samples.Principal-Component AnalysisPrincipal-component analysis (PCA)based on singular value decomposition is used as a first step to obtain information from NIR spectra.It is carried out by calculatingprincipal FIG.3.Prediction results using wavelength distance method,residual variance method,and Mahalanobis distance method when Austragali Radix was used as trainingset.FIG.4.Prediction results using wavelength distance method,residual variance method,and Mahalanobis distance method when Ginseng Radix was used as training set.66WOO,KIM,AND CHOcomponents used for reconstructing a model instead of variables.Each principal compo-nent explains independent variation within the samples.The qualitative differences between the three herbal samples were observed using the first three principal compo-nents,PC1,PC2,and PC3,as shown in Fig.6.Spectral data points were moderately classified based on species.SR and AR data points are clearly differentiated,while Gin data points are not completely separated from SR and AR.Pattern recognition techniques using residual variance,Mahalanobis distance,and SIMCA,performed based on PCA,may improve the capability of the identification method,and are compared below.Residual Variance AnalysisFor residual variance analysis,data for training set and test set were randomly selected in Table 1.The full ranges from 400to 2500nm of the NIR spectrum were used to construct the PCs used in the residual variance analysis.To construct a model,5,8,and 11principal components were used for AR,ginseng,and SR,respectively.Residual variance analysis was performed at a 95%confidence level for the F test.Figure 3shows the results when AR data are used as the training set for constructing a model.It was successfully performed without any failure in identification.All 20AR data in the test set were identified as AR and no ginseng or SR data in the test set were misidentified as AR.Figure 4shows the result when using ginseng data as the training set for constructing a model.All 20ginseng data were identified as ginseng.In addition,no AR or SR data in the test set were misidentified as ginseng.The results when SR data were used as a training set for constructing a model are shown in Fig.5.All 20AR data in the test set were identified as AR and all ginseng and SR data in the test set were not misidentified as AR.Since all these species could be 100%differentiated from the others,it shows that it is possible to use residual variance analysis to identify herbalmedicines.FIG.5.Prediction results using wavelength distance method,residual variance method,and Mahalanobis distance method when Smilacis Rhizoma was used as training set.67NIR REFLECTANCE SPECTRA OF HERBAL MEDICINESMahalanobis Distance AnalysisData for training set and test set were randomly selected for Mahalanobis distance analysis listed in Table 1.Full ranges from 400to 2500nm of NIR spectra were used for constructing the PCs used in the Mahalanobis distance analysis.Five,eleven,and fourteen principal components were used to construct a model for AR,ginseng,and SR,respec-tively.Mahalanobis distance analysis was performed in the 95%confidence level by Hotelling’s T 2statistics.Figure 3shows the results when AR data are used as the training set for constructing a model.Nineteen of twenty AR data in the test set were identified as AR and no ginseng or SR data in the test set were misidentified as AR.Figure 4shows the results when ginseng data were used as the training set for constructing a model.No AR or SR data in the test set were misidentified as ginseng.However,only 17of 20ginseng data were positively identified as ginseng.In Fig.5are shown the results whenTABLE 2Data Set Preparation for SIMCASample Number of spectrain training setNumber of spectra in test set Austragali Radix 5047Ginseng Radix 5050Smilacis Rhizoma5043FIG.6.Three-dimensional score plot using PC1,PC2,and PC3.68WOO,KIM,AND CHOSR data were used as the training set for constructing a model.All 20AR data in the test set were identified as AR and no ginseng or SR data in the test set were misidentified as AR.This method does not seem to be capable of differentiating between these three species 100%of the time,given the chosen test parameters.Soft Independent Modeling of Class AnalogyTypical modeling is carried out using some of the objects contained in a class training set,and the validity of the model can be tested on the remaining objects belonging to the same class (test set).Data set preparation for the SIMCA algorithm is given in Table 2.Data for training set and test set were randomly ing the SIMCA method,each class was modeled independently using the principal components listed in Table 3.Figure 7shows that all AR,ginseng,and SR data in the test set were correctly identified,with none misidentified.TABLE 3Optimal Parameters for Modeling aNumber of used principalcomponentsARGin SR Wavelength distanceNone Residual variance5812Mahalanobis distance51114SIMCA101214a Range:400–2500nm.FIG.7.Prediction result using SIMCA.69NIR REFLECTANCE SPECTRA OF HERBAL MEDICINES70WOO,KIM,AND CHOCONCLUSIONThese studies have indicated that NIR reflectance spectroscopy can be used to identify herbal medicines using pattern recognition techniques.It is shown that the residual variance method and soft independent modeling of class analogy based on PCA differ-entiate between these three herbal medicines better than other methods compared in this study.With simple preparation,rapid and nondestructive identification methods using NIR reflectance spectroscopy can be 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