Research Article Rapid Characterization of Tanshinone ...downloads.hindawi.com/journals/ijac/2015/704940.pdf · e particle size distribution was determined by the bt-laser particle

Research ArticleRapid Characterization of Tanshinone Extract Powder byNear Infrared Spectroscopy

Gan Luo12 Bing Xu12 Xinyuan Shi12 Jianyu Li12 Shengyun Dai12 and Yanjiang Qiao12

1Beijing University of Chinese Medicine Beijing 100029 China2The Key Laboratory of TCM Information Engineering of State Administration of Traditional ChineseMedicine Beijing 100029 China

Correspondence should be addressed to Bing Xu btcm163com and Yanjiang Qiao yjqiao263net

Received 28 December 2014 Revised 4 March 2015 Accepted 8 March 2015

Academic Editor Peter A Tanner

Copyright copy 2015 Gan Luo et alThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Chemical and physical quality attributes of herbal extract powders play an important role in the research and development ofChinese medicine preparations The active pharmaceutical ingredients have a direct impact on the herbal extractrsquos efficacy whilethe physical properties of raw material affect the pharmaceutical manufacturing process and the final productsrsquo quality In thisstudy tanshinone extract powders from Salvia miltiorrhiza which are widely used for the treatment of cardiovascular diseases inthe clinic are taken as the research object Both the chemical information and physical information of tanshinone extract powdersare analyzed by near infrared (NIR) spectroscopy The partial least squares (PLS) and least square support vector machine (LS-SVM) models are investigated to build the relationship between NIR spectra and reference values PLS models performed wellfor the content of crytotanshinone tanshinone IIA the moisture and average median particle size while for specific surface areaand tapped density the LS-SVM models performed better than the PLS models Results demonstrated NIR to be a valid and fastprocess analytical technology tool to simultaneously determine multiple quality attributes of herbal extract powders and indicatedthat there existed some nonlinear relationship between NIR spectra and physical quality attributes

1 Introduction

Pharmaceutical powders described as heterogeneous sys-tems with different chemical and physical attributes are themain source of oral solid preparations It is estimated thatmore than 80 of the drug production is based on powdersin a typical pharmaceutical industry [1] As the input ofthe pharmaceutical process powders have great impacts onthe whole production process Tiny quality fluctuations ofpowders may result in batch to batch variations of the finalproducts [2ndash4] For example the performance of the granuletableting process would be deteriorated significantly whenthe initial moisture content of microcrystalline cellulosewas increased from 26 to 49 the values of which areconsidered to be within normal variations of the moisturecontent (ie 3ndash5) for microcrystalline cellulose On theother hand the flow ability of the granule was improvedas the initial moisture content of microcrystalline cellulosewas increased [4] Therefore it is necessary to understandand control the critical material attributes of powders at thebeginning of pharmaceutical processes

Process analytical technology launched by the UnitedStates Food and Drug Administration [5] is often usedto monitor and control critical quality attributes of rawmaterials and in-process products to ensure the quality offinal products Process analytical technology approacheswhich are based on scientific knowledge and risk analysisafford the design and development of efficiently controlledprocess In this way it is possible to realize the preset targetof the product when the manufacturing process is finishedCommon process analytical technology tools used in rapidevaluation of chemical and physical properties of powderare as follows near infrared spectroscopy [6 7] Ramanspectroscopy [8] Raman chemical imaging [9] acousticemission [10 11] and so forth Among them near infraredspectroscopy (NIR) is themost widely used process analyticaltechnology tool in the pharmaceutical process monitoringand control [12 13] since it is fast nondestructive and of low-cost Compared with the imaging technology NIR is rapidand of low-cost And for the analysis of complex system withmany ingredients that is herbalmaterials [14 15] NIR shows

Hindawi Publishing CorporationInternational Journal of Analytical ChemistryVolume 2015 Article ID 704940 9 pageshttpdxdoiorg1011552015704940

2 International Journal of Analytical Chemistry

unique advantages over other spectroscopy technologiessuch as Raman spectroscopy which does well in the analysisof pure compounds [16]

NIR spectra carry abundant information not only onchemical compositions but also on physical properties (egparticle size) of the sample [17 18] In powder analysissome qualitative work with NIR has been reported suchas rapid identification of the production area [19] andbrand traceability [20] And some work about quantitativeanalysis has also been done in relating the NIR spectra todifferent quality attributes of powders such as content ofingredients [6] flow ability [21] content ofmoisture [22] andparticle size [23] And subsequent unit operations of soliddosage preparations could benefit from the quality control ofpowders

Herbal extract powders as raw materials play an impor-tant role in the research development and manufacturingof Chinese medicine preparations Currently the qualitycontrol of herbal extract powders mainly focuses on thecontent of active pharmaceutical ingredients according tothe Chinese Pharmacopoeia (ChP) 2010 [24] However likepowders of chemical materials it is far from enough tocontrol the quality of herbal extract powders only by thecontent of components In order to understand and controlthe quality of herbal extract powders as well as relatedmanufacturing processes and products physical propertiesof herbal extract powders should be paid more attentionGenerally classical methods for determination of physicalproperties of herbal extract powders were time-consumingAnd recently NIR spectroscopy has been reported to besuccessfully applied in the analysis of herbal powders wherethe contents of active pharmaceutical ingredients were theattractive part Quantification of contents of two or moreingredients in herbal extract powders could be carried outby NIR [15 25] Nevertheless the application of NIR toanalysis of physical properties of herbal extract powders isstill an untouched area Therefore the aim of this paperis to investigate the possibility of using NIR to predictboth the chemical and physical properties of herbal extractpowders at the same time To the best of our knowledgethis is the first report on the application of NIR spectroscopyin characterization of multiple quality attributes of herbalextract powders It is expected that the usage of NIR couldbe broadened in the quality control of rawmaterials of herbalproducts

The rest part of the paper is organized as followsfirstly contents of cryptotanshinone and tanshinone IIA of50 batches of tanshinone extract powders were determinedby high performance liquid chromatography (HPLC) andthe physical quality attributes were measured by classicalmethods Then the NIR spectra of tanshinone extract pow-ders were collected in diffuse reflection mode and differentdata pretreatment methods were screened After that partialleast squares (PLS) and least square support vector machine(LS-SVM) models for quantitative prediction of differentquality attributes were built and the performances of thesemodels were compared Finally a conclusion of this paper isprovided

Table 1 Factors and levels of central composite design (120572 = 1)

Name Units Low High minus120572 +120572Ethanolconcentration 80 100 80 100

Ethanolvolume L 4 6 4 6

Decoctiontime Hour 05 3 05 3

120572means the distance between the ldquostarrdquo point and the central point

2 Experimental

21 Materials Salvia miltiorrhiza (batch number 20120402)were purchased from Beijing Ben Cao Fang Yuan Phar-maceutical Co Ltd (Beijing China) Cryptotanshinone(batch number 1110852-200806) and tanshinone IIA (batchnumber 110766-200619) were obtained from National Insti-tute for the Control of Pharmaceutical and BiologicalProducts (Beijing China) Acetonitrile (Fisher scientificWaltham Massachusetts USA) phosphoric acid (Fisherscientific Waltham Massachusetts USA) and distilled water(HangzhouWahahaGroupCo Ltd Hangzhou China) wereofHPLC grade and all other reagents were of analytical grade

30 batches of alcohol extracts of Salvia miltiorrhizawere purchased from 5 different suppliers 20 batches ofalcohol extracts of Salvia miltiorrhizawere homemade undercentral composite experimental design in order to expand thevariation coverage of the sample sets Factors and levels ofthe alcohol extraction process are shown in Table 1 and theexperiment schedules are listed in Table 2 The alpha valuewas set to 1 and the replication of center points was 5 Theextraction process was carried out according to proceduresspecified in the Chinese Pharmacopoeia (ChP) 2010 [24]as follows pulverized powders of Salvia miltiorrhiza wereextracted by alcohol through heating reflux after which thealcohol was filtered and the filtrates were merged Then thefiltrate was vacuum evaporated to recover ethanol enrichedto 130sim135 of the relative density at 60∘Cwashed to colorlessby hot water dried at 80∘C and crushed to fine powdersfinally As a result 50 batches of alcohol extracts of Salviamiltiorrhiza were used in the following experiments

3 Method

31 HPLC Analysis 5 grams of tanshinone extract powderswas taken to a 5mL volumetric flask after a precise weighingdissolved by methanol and then diluted with methanolto volume All samples were filtered through a milliporemembrane filter with an average pore diameter of 045 120583mand 10 120583L filtrate was injected into the HPLC system foranalysis

The contents of cryptotanshinone and tanshinone IIAwere quantitated by the reverse phase HPLC according toCh P 2010 [24] An Agilent 1100 HPLC system (AgilentTechnologies Santa Clara California USA) with a vacuumdegasser a quaternary pump an autosampler a thermostaticcolumn compartment and a diode array detector were

International Journal of Analytical Chemistry 3

Table 2 Experiment schedules of Salviamiltiorrhiza alcohol extrac-tion (120572 = 1)

Run 119860 ethanolconcentration

119861 ethanolvolumeL

119862 decoctiontimehour

1 90 5 1752 90 5 3003 80 4 0504 80 4 3005 90 4 1756 90 6 1757 100 6 0508 80 6 3009 100 5 17510 90 5 17511 100 4 05012 90 5 17513 80 5 17514 100 4 30015 100 6 30016 80 6 05017 90 5 17518 90 5 05019 90 5 17520 90 5 175

used Separation was performed on Agilent SB C18

column(250mm times 46mm with 5120583m particle size) at 30∘C Themobile phase consisted of (A) acetonitrile and (B) 0026phosphoric acid water solution The gradient elution was asfollows linear change from (A) 0 to 60 at 0ndash20min andlinear change from (A) 60 to 80 at 20ndash50min The signalwas monitored at 270 nm The flow rate was maintained at08mLsdotminminus1 Reequilibration duration was 10min betweenindividual runs

32 NIR Spectroscopy The NIR spectra were collected inthe integrating sphere mode using an Antaris Nicolet FT-NIR system (Thermo Fisher Scientific Inc Waltham Mas-sachusetts USA) About 2 grams of powders was used withcompaction in each test Each sample spectrum was a resultof 64 scans in the range between 10000 and 4000 cmminus1 using8 cmminus1 resolution at ambient temperature and was recordedby log 1119877 with air as reference Every sample was scannedthree times and the final spectrum was an average of thethree All NIR spectra were collected and archived using theThermo Scientific Result software

33 Physical Attributes Determination The specific surfacearea was determined by the 3h-2000a automatic specific sur-face area analyzer (Beishide Instrument Technology (Beijing)Co Ltd Beijing China) according to the multimolecularlayer absorption theory In reference mode with nitrogen asthe absorbate and purge medium and helium as carrier gasthe test was purged for 60 minutes at 30∘C

The particle size distribution was determined by the bt-2001 laser particle size analyzer (Dandong Bettersize Instru-ments Ltd Dandong Liaoning China) Based on the lightscattering theory measurements were obtained using drydispersion with air as medium and the refractive index ofsample is 1520 The 119863

10 11986350 and 119863

90values are calculated

to represent the maximal particle size diameters that include10 50 and 90 of the particles respectively For examplethe11986390valuemeans that 90of particles are smaller than this

particle diameter whereas the remaining 10 of the particleshave larger diameters Each sample was tested for three timesand the average value was taken

The tapped density was analyzed by the hy-100 powderdensity tester (Dandong Hengyu Instruments Ltd Dan-dong Liaoning China) According to the European Phar-macopoeia 80 [26] 5 grams of powders was poured intothe measuring cylinder Afterwards the volumetric measure-mentwasmade following 1250 taps which has been describedas the number of taps sufficient to achieve maximum com-paction equilibrium [27] The final volume (Vt) was used tocompute the tapped density Each sample was measured intriplicate and the average value of density was taken

The moisture content of sample was determined by theSartorius ma-35 moisture analyzer (Sartorius AG GottingenGermany) This test needs about 2 grams of powders beingheated for 10 minutes at 105∘C

34 NIR Spectra Pretreatment A variety of preprocessingmethods for the spectroscopic data were compared to extractthe useful information from noise such as normalizationbaseline Savitzky-Golay smoothing Savitzky-Golay smooth-ing plus first-order derivatives Savitzky-Golay smoothingplus second-order derivatives spectroscopic transformationmultiplicative scatter correction standard normal variatetransformation and wavelet de-nosing of spectra SIMCAP +115 (Umetrics AB Umea Sweden) and Unscrambler 97(Camo software Oslo Norway) served as chemometric toolsfor data preprocessing

35 Model Building In order to build quantitative modelsthe samples were split into the calibration and validation setsby Kennard-Stone algorithm In this study 40 samples wereselected as the calibration set while the remaining 10 sampleswere kept as the validation set The whole spectra withwavenumber 10000ndash4000 cmminus1 were used to build models

PLS regression algorithm performed on Matlab version70 (Mathworks Inc Natick Massachusetts USA) with PLSToolbox 21 (Eigenvector Research Inc Wenatchee Wash-ington USA) was used to set up quantitative models Thenumber of latent variables was optimized by the leave-one-out cross validation method and predicted residual errorsum square (PRESS) The performances of PLS modelswere evaluated in terms of correlation coefficient 119903 forboth calibration and validation sets (119903cal and 119903pre resp) theroot mean square error of calibration (RMSEC) the rootmean square error of cross validation (RMSECV) the rootmean square error of prediction (RMSEP) BIAS for bothcalibration and validation (BIAScal and BIASpre resp) andthe relative predictive deviation (RPD) PLS model showed


good performance with large 119903 and RPD values while smallRMSEC RMSECV RMSEP and BIAS values The equationsof these indicators were as follows

119903 =

sum119899

119894=1(119862119894minus 119862119894) (119862119901119894minus 119862119901119894)

radicsum119899

119894=1(119862119894minus 119862119894)

2

radicsum119899

119894=1(119862119901119894minus 119862119901119894)

2

RMSE = radicsum119899

119894=1(119862119901119894minus 119862119894)

2

119899

BIAS =sum119899

119894=1

10038161003816100381610038161003816119862119901119894minus 119862119894

10038161003816100381610038161003816

119899

RPD =SDpre

RMSEP

SDpre =radicsum119899

119894=1(119862119894119901minus 119862119894119901)

2

119899 minus 1

(1)

where 119899 is the number of samples 119862119894is the reference value

of the sample of number 119894 119862119901119894

is the predictive value ofthe sample of number 119894 119862

119894is the average value of reference

value and 119862119901119894is the average value of predictive value SDpre

is the standard deviation of prediction set data 119862119894119901

is thereference value of prediction set and 119862

119894119901is the average value

of prediction setLS-SVM algorithms carried out by LS-SVM lab Toolbox

18 (Department of Electrical Engineering Leuven-HeverleeBelgium) [28] was also used to set up quantitative models Inorder to obtain the LS-SVM model two extra hyperparam-eters gam and sig2 need to be tuned by leave-one-out crossvalidation Gam is the regularization parameter determiningthe tradeoff between the training error minimization andsmoothness of the estimated function Sig2 is the GaussianRBF kernel function parameter The performance of theLS-SVM model was evaluated in terms of chemometricindicators the same as PLS regression

4 Results and Discussion

41 HPLC Determination of Cryptotanshinone and Tan-shinone IIA For quantitative consideration the calibra-tion curves of cryptotanshinone and tanshinone IIA wereestablished upon eleven consecutive injections of differ-ent concentrations The concentration range is from 073to 10248 120583gsdotmLminus1 for cryptotanshinone and from 083 to18256 120583gsdotmLminus1 for tanshinone IIA Regression equation cal-ibrated for cryptotanshinone was 119910 = 4695119909 + 2506 (1198772 =09999 119899 = 11) and 119910 = 4474119909+1993 (1198772 = 09999 119899 = 11)for tanshinone IIA Seen in Table 3 it is obvious that contentsof cryptotanshinone and tanshinone IIA varied considerablyamong different samples And large quality fluctuations couldbe observed among the commercial tanshinone extractsproduced under the same specifications according to the

Table 3 The contents of cryptotanshinone and tanshinone IIA oftanshinone extract powders

Type Index Contentsmgsdotgminus1Relativestandarddeviation

Homemade Cryptotanshinone 21 plusmn 22 10(20 batches) Tanshinone IIA 25 plusmn 27 11Commercial Cryptotanshinone 18 plusmn 18 10(30 batches) Tanshinone IIA 12 plusmn 28 23

ChP 2010 [24] For example contents of cryptotanshinonein commercial extract powders varied from 28 to 88mgsdotgminus1with the mean value of 12mgsdotgminus1 and the standard deviationof 28mgsdotgminus1 And for tanshinone IIA in commercial extractpowders the contents were from 085 to 14 times 102mgsdotgminus1with the average value and standard deviation being 25and 27mgsdotgminus1 respectively The possible reasons could beattributed to different sources of Salvia miltiorrhiza differentpreparation processes and various storage conditions

42 Analysis of Physical Attributes The results of physicalattributes tests are shown in Table 4 The relative standarddeviations of all physical attributes were smaller than 05It is clear that the variation coverage of physical propertieswere smaller than the contents of active pharmaceuticalingredients whose relative standard deviations values wereabove than 10

Generally the specific surface area of loose porous mate-rial is supposed to be large due to plenty of microporesBut values of specific surface area for the homemade andcommercial Salvia miltiorrhiza extract powders were allbelow 0500m2sdotgminus1 indicating that these samples were densewith little micropores If such extract powder was used asrawmaterial for dry granulation or direct tableting the densestructure might result negatively in the dissolution tests ofproduced granules or tablets

Homemade extract powders were treated by grindingwhile commercial extract powder was directly from spraydrying The two different preparation methods may lead tothe variation of particle size distribution between the twosample sets 119863

50values were 1534sim5717 120583m for commer-

cial samples and those were 3552sim8333 120583m for homemadesamples suggesting that spray drying powders were finerthan grinding ones In real pharmaceutical applications thatis granulation or tableting fine powders made from spraydrying as raw materials are a better choice than coarsepowder since fine powders deserve better uniformity ofdistribution

The tapped density values of homemade samples are closeto that of commercial samples Different from the liquiddensity solid density is not a unique ldquobandrdquo Tapped densityof herbal extract powders with different chemical composi-tions and contents may be the same For example as seenin Table 5 contents of cryptotanshinone for the first threesamples are 34 37 and 33mgsdotgminus1 and contents of tanshinoneIIA are 15 25 and 28mgsdotgminus1 respectivelyThe three samples


Table 4 Results of physical attributes tests of tanshinone extract powders

Type Index Values Relative standard deviation

Homemade (20 batches)

Specific surface aream2sdotgminus1 0240 plusmn 00663 0276

11986310120583m 1205 plusmn 5335 0442711986350120583m 5252 plusmn 1236 0235311986390120583m 1261 plusmn 1920 01523

Tapped densitygsdotcmminus3 072 plusmn 0060 0083Moisture 301 plusmn 122 0405

Commercial (30 batches)




The1198631011986350 and119863

90values indicate the maximal particle size diameter that includes 10 50 and 90 of particles respectively

Table 5 The contents of cryptotanshinone and tanshinone IIA bulk and tapped density of five samples exemplified

Sample Cryptotanshinone contentmgsdotgminus1 Tanshinone IIA contentmgsdotgminus1 Tapped densitygsdotcmminus3

1 34 plusmn 0040 15 plusmn 0015 074 plusmn 0032





had the same tapped density 074 gsdotcmminus3 while the formertwo samples had similar contents of tanshinone IIA and thelatter two samples had similar contents of cryptotanshinoneIn contrast tapped density of the extract powders withsimilar chemical compositionsmay be differentThe contentsof cryptotanshinone of the last two samples in Table 5 are40 and 43mgsdotgminus1 and the contents of tanshinone IIA ofthem are 20 and 29mgsdotgminus1 indicating that the chemicalcompositions and contents of these two samples are similarbut the tapped densities are 077 and 070 gsdotcmminus3 respectively

Similar to tapped density values values of moisturecontent did not show much difference Most of moisturecontents were below 5 except for two samples of homemadesample sets Moisture of extract powders could directly affectthe subsequent operations such as dry granulation and directcompaction If the moisture content is too high the storageof extract powders will be a challenge While if the moisturecontent is too low it will be difficult for direct compaction oftablet So moisture of tanshinone extract powder should bemonitored and controlled within a proper range

It could be summarized that for each quality attributethe values fluctuatedwithin a certain rangeThe differences oftapped density (the values of relative standard deviation being0083 for homemade and 0096 for commercial) were smallerthan other indexes (all values of relative standard deviationbeing more than 015 for the homemade and commercial)Tapped density is macroscopic while other quality attributesare microscopic which may lead to the different variationcoverage of measured indexes

43 Data Pretreatment Figure 1 shows the raw NIR spec-tra without any pretreatment In the region of wavelength

10000 9000 8000 7000 6000 5000 4000Wavenumbers (cmminus1)

12

10

08

06

04

02

Abso

rban

ce

Figure 1 The near infrared spectra of 50 samples without anypretreatment

7000sim4000 cmminus1 serious peak overlapping and great noisecould be observed suggesting that a great deal of informationmay be concealed

For different quality attributes the NIR spectra withdifferent data pretreatment methods were found to beardifferent capability in both calibration and validation Asshown in Tables 6 and 7 the best preprocessing methods inprediction of the contents of cryptotanshinone and tanshi-none IIA are normalization and Savitzky-Golay smoothingplus first-order derivatives respectively Normalization couldeliminate redundant information and increase the differenceamong samples Savitzky-Golay smoothing could clear high


Table 6 Comparison of different preprocessing methods of partial least squares model for content of cryptotanshinonemgsdotgminus1

Preprocessing method LVs Calibration set Validation set119903cal RMSEC RMSECV BIAScal 119903pre RMSEP RPD BIASpre

Raw 10 09922 00026 00049 00019 09798 00027 44 00021S-G smooth 10 09921 00026 00049 00019 09796 00027 44 00022Normalization 11 09963 00018 00033 00013 09969 00013 89 00011S-T 11 09955 00020 00041 00015 09922 00014 81 00011MSC 14 09983 00012 00029 00010 09921 00015 79 00012S-G 1st 6 09883 00032 00041 00023 09949 00020 59 00016S-G 2nd 6 09934 00024 00043 00016 09910 00015 77 00014Baseline 8 09845 00036 00055 00025 09882 00021 56 00017SNV 11 09962 00018 00037 00015 09963 00015 81 00010WDS 8 09799 00041 00059 00029 09814 00023 51 00020Raw means using the original spectra without any pretreatment LVs means numbers of latent factors of the PLS model 119903cal and 119903pre represent correlationcoefficients for calibration and validation sets respectively RMSEC RMSECV and RMSEP represent the rootmean square error of calibration cross validationand prediction respectively BIAScal and BIASpre represent bias for calibration and validation respectively RPD means relative predictive deviationS-G smooth means Savitzky-Golay smoothing S-T represents spectroscopic transformation MSC means multiplicative scatter correction S-G 1st is Savitzky-Golay smoothing plus first-order derivatives for short S-G 2nd means Savitzky-Golay smoothing plus first-order derivatives baseline means baselinecorrection SNV represents standard normal variate transformation and WDS is wavelet denoise of spectra for short

Table 7 Comparison of different preprocessing methods of the partial least squares model for the content of tanshinone IIAmgsdotgminus1

Preprocessingmethod LVs Calibration set Validation set

119903cal RMSEC RMSECV BIAScal 119903pre RMSEP RPD BIASpreRaw 10 09893 00043 00093 00036 09484 00060 20 00055S-G smooth 13 09952 00029 00091 00019 09932 00044 27 00029Normalization 10 09939 00033 00063 00027 09716 00043 27 00039S-T 12 09965 00025 00066 00020 09921 00034 81 00022MSC 14 09984 00017 00044 00014 09943 00024 50 00017S-G 1st 8 09953 00029 00060 00021 09957 00019 62 00015S-G 2nd 4 09915 00039 00056 00027 09955 00021 56 00016Baseline 10 09899 00022 00055 00033 09529 00048 25 00041SNV 11 09973 00018 00037 00018 09977 00015 36 00018WDS 9 09772 00063 00106 00045 09532 00059 20 00046

frequency noise by means of least square polynomial fittingto the data in the moving window And 1st derivative spec-trum could eliminate shift irrelevant to the wavelength Asshown in Table 8 the best preprocessing method for specificsurface area119863

1011986350 andmoisture content is Savitzky-Golay

smoothing plus first-order derivatives Figure 2 shows theNIR spectra after Savitzky-Golay smoothing plus first-orderderivatives where the shifted baselines of the raw spectra arecorrected For 119863

90and tapped density the best preprocess-

ing methods are spectroscopic transformation and waveletdenoising respectively Spectroscopic transformation is oftenused to switch between absorbance and reflectance dataand transform reflectance data into Kubelka-Munk unitsAnd wavelet denoising deals with high frequency noise ofspectrum

44 Calibration and Validation of Quantitative Models Thecalibration results of the content of cryptotanshinone (seeFigure 3) demonstrate that 11 latent factors with minimumRMSECV and PRESS values are enough to build the PLS

modelsThe correlation coefficients of calibration and valida-tion sets were 09963 and 09969 respectively The values ofroot mean square error for calibration cross validation andprediction were 00018 00033 and 00013mgsdotgminus1 respec-tively And the RPD value was 89

Gam and sig2 of the LSSVMmodel for the tapped densityare optimized by the standard simplex algorithm and resultedvalues are 69355 and 11163 respectively (see Figure 4) Thecorrelation coefficients of calibration and validation sets were09851 and 08875 respectively The values of root meansquare error of prediction were 0020 gsdotcmminus3 And the RPDvalue was 22

Furthermore the established PLS and LS-SVM modelsare compared as shown in Tables 8 and 9 It can be foundthat PLS models exhibited good performance in predictionof chemical properties particle size and moisture contentBut for specific surface area and tapped density LS-SVMmodels performed better than PLS models Take the tappeddensity for example the correlation coefficients of calibrationand validation sets for the PLS model were 08830 and


Table 8 The best preprocessing methods for near infrared spectra of partial least squares models of physical attributes

Index Processing method LVs Calibration Validation119903cal RMSEC RMSECV BIAScal 119903pre RMSEP RPD BIASpre

SSAm2sdotgminus1 S-G 1st 11 09591 0021 0045 0017 08282 0025 17 0020

11986310120583m S-G 1st 12 09867 074 24 056 09720 076 28 05411986350120583m S-G 1st 5 09392 60 71 46 09561 41 33 32811986390120583m S-T 11 09477 10 16 75 09058 87 25 63119863119905gsdotcmminus3 WDS 10 08830 0034 0038 0027 08940 0023 19 0019

Moisture S-G 1st 12 09679 026 068 018 09191 033 26 025LVs means numbers of latent factors of the PLS model 119903cal and 119903pre represent correlation coefficients for calibration and validation sets respectively RMSECRMSECV and RMSEP represent the root mean square error of calibration cross validation and prediction respectively BIAScal and BIASpre represent biasfor calibration and validation respectively RPD means relative predictive deviation

Table 9 The best preprocessing methods for near infrared spectra of the least squares support vector machine model for different qualityattributes

Index Processing method gam sig2 Calibration set Validation set119903cal BIAScal 119903pre RMSEP RPD BIASpre

Ccmgsdotgminus1 S-G 1st 22996 50195 09980 93 times 10minus4 09985 00020 15 65 times 10minus4

IIAmgsdotgminus1 S-G smooth 15042 59633 09996 69 times 10minus4 09978 00010 12 72 times 10minus4

SSAm2sdotgminus1 Normalization 174749 25414 09207 0023 09661 0017 25 0016

11986310120583m S-G 1st 21218 44769 09908 042 09723 067 32 04911986350120583m Raw 25830 22229 09604 39 09795 31 43 26911986390120583m Normalization 19285 21065 09835 38 09276 78 27 58119863119905gsdotcmminus3 S-g smooth 69355 11163 09851 0011 08875 0020 22 0018

Moisture Baseline 20423 19705 08900 028 09336 030 29 025Gam and sig2 are two tuned hyperparameters of LS-SVMmodel 119903cal and 119903pre represent correlation coefficient for calibration and validation sets respectivelyRMSEP represents the root mean square error of prediction BIAScal and BIASpre represent bias for calibration and validation respectively RPDmeans relativepredictive deviationCc and IIA mean the content of cryptotanshinone and tanshinone IIA respectively SSA 119863

119905mean specific surface area and tapped density respectively The

1198631011986350

and11986390

values represent the maximal particle size diameters that include 10 50 and 90 of the particles respectively

10000 9000 8000 7000 6000 5000 4000

0006

0004

0002

0000

minus0002

minus0004

minus0006

minus0008

minus0010

minus0012

Inte

nsity

Wavenumbers (cmminus1)

Figure 2 The near infrared spectra of 50 samples after Savitzky-Golay smoothing plus first-order derivatives

08940 respectively The root mean square error for cali-bration cross validation and prediction were 0034 0038and 0023 gsdotcmminus3 respectively The RPD value was only 19In contrast the correlation coefficients of calibration andvalidation sets for the LS-SVMmodel were 09851 and 08875respectively The root mean square error of prediction was

decreased to 0020 gsdotcmminus3 And the RPD value was increasedto 22

For all quality attributes the performances of LS-SVMmodels were slightly better than PLS models As stated in[29] the LS-SVMmodels could take into account some non-linearity between the dependent and independent variableswhile improved PLS models with the low prediction abilitiesThat is to say there may be some nonlinear relationshipbetween the NIR spectra and quality attributes However inprediction of the content of cryptotanshinone and tanshinoneIIA particle size and moisture PLS models were sufficientsince they are easy to be implemented While for physicalattributes such as the specific surface area and tapped densitywhere the prediction of PLSmodels did not performwell LS-SVMmodel may be a better choice

5 Conclusions

In this paper the chemical and physical quality attributes oftanshinone extract powders are determined simultaneouslyby near infrared spectroscopy for the first time The PLSand LS-SVM models are used to build quantitative modelsIt is found that PLS models exhibit good performance inprediction of the chemical properties particle size (119863

1011986350

and 11986390) and moisture content And the LS-SVM models


Number of factors0 1 2 3 4 5 6 7 8 9 10 11 12 13

0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV

RMSEPCumulative PRESS

Figure 3 Calibration characteristics versus number of latent factorsfor the content of cryptotanshinone RMSECRMSECV andRMSEPrepresent the root mean square error for calibration cross valida-tion and prediction respectively PRESS means predicted residualerror sum square

minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)

Figure 4 Hyperparameters optimization of the least squares sup-port vector machine model for tapped density RMSECV representsthe root mean square error of cross validation Gam is the regu-larization parameter and sig2 is the Gaussian RBF kernel functionparameter

are good at predicting the specific surface area and tappeddensity Results demonstrated that the massive informationconcealed in NIR spectra could be analyzed with the helpof a combination of process analytical technology tools andchemometric methods The subsequent process operationssuch as blending granulation and tableting and even the finalproducts could benefit from the understanding and control ofherbal extract powders

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors wish to acknowledge the research funding sup-ports from National Natural Science Foundation of Chinastudy on the quality transfer model and global optimizationmethod of the chained pharmaceutical process of Chinesemedicine products (no 81403112) the Joint DevelopmentProgram supported by Beijing Municipal Education Com-mission (Key Laboratory construction project study on theintegrated modeling and optimization technology of thepharmaceutical process of Chinese medicine preparations)and scientific research project of Beijing University of Chi-nese Medicine holographic and rapid evaluation study ofphysical and chemical properties of Flos Lonicerae extractpowders (no 2013-JYBZZ-XS-117)

References

[1] C V Navaneethan S Missaghi and R Fassihi ldquoApplication ofpowder rheometer to determine powder flow properties andlubrication efficiency of pharmaceutical particulate systemsrdquoAAPS PharmSciTech vol 6 no 3 pp E398ndashE404 2005

[2] M Cavinato E Franceschinis S Cavallari N Realdon andA Santomaso ldquoRelationship between particle shape and someprocess variables in high shear wet granulation using binders ofdifferent viscosityrdquo Chemical Engineering Journal vol 164 no2-3 pp 292ndash298 2010

[3] M Fonteyne H Wickstrom E Peeters et al ldquoInfluence ofraw material properties upon critical quality attributes ofcontinuously produced granules and tabletsrdquo European Journalof Pharmaceutics and Biopharmaceutics vol 87 pp 252ndash2632014

[4] L Shi Y Feng and C C Sun ldquoInitial moisture content in rawmaterial can profoundly influence high shear wet granulationprocessrdquo International Journal of Pharmaceutics vol 416 no 1pp 43ndash48 2011

[5] US Food and Drug Administration (FDA) Guidance forIndustry PAT 2004 httpwwwfdagovdownloadsDrugsGuidancesucm070305pdf

[6] I Tomuta D Dudas A L Vonica and S E Leucuta ldquoQuan-tification of ascorbic acid and sodium ascorbate in powderblends for tableting and in vitamin C chewable tablets by NIR-chemometryrdquo Acta Pharmaceutica vol 63 no 3 pp 373ndash3842013

[7] C Sırbu I TomutaMAchim L L Rus LVonica andEDinteldquoQuantitative characterization of powder blends for tablets withindapamide by near-infrared spectroscopy and chemometryrdquoFarmacia vol 62 no 1 pp 48ndash57 2013

[8] Y Hida T Fukami N Suzuki T Suzuki and K TomonoldquoProcessing and formulation of drug nanoparticles by ternarycogrinding with methacrylic copolymer and sucrose fatty acidestersrdquoAdvanced Powder Technology vol 24 no 1 pp 246ndash2512013


[9] A Kuriyama and Y Ozaki ldquoAssessment of active pharmaceuti-cal ingredient particle size in tablets by Raman chemical imag-ing validated using polystyrene microsphere size standardsrdquoAAPS PharmSciTech vol 15 no 2 pp 375ndash387 2014

[10] A Boschetto and F Quadrini ldquoPowder size measurement byacoustic emissionrdquo Measurement vol 44 no 1 pp 290ndash2972011

[11] A Bastari C Cristalli R Morlacchi and E Pomponi ldquoAcousticemissions for particle sizing of powders through signal process-ing techniquesrdquo Mechanical Systems and Signal Processing vol25 no 3 pp 901ndash916 2011

[12] B Xu G Luo Z Z Lin L Ai X Y Shi and Y J QiaoldquoEnd-point detection of the alcohol adding process in alcoholprecipitation of lonicerae japonicae based on design space andprocess analytical technologyrdquo Chemical Journal of ChineseUniversities (Chinese Edition) vol 34 pp 2284ndash2289 2013

[13] A D Karande P W S Heng and C V Liew ldquoIn-line quan-tification of micronized drug and excipients in tablets by nearinfrared (NIR) spectroscopy real time monitoring of tablettingprocessrdquo International Journal of Pharmaceutics vol 396 no 1-2 pp 63ndash74 2010

[14] W L Li Y F Wang and H B Qu ldquoNear infrared spectroscopyas a tool for the rapid analysis of the Honeysuckle extractsrdquoVibrational Spectroscopy vol 62 pp 159ndash164 2012

[15] W Li and H Qu Haibin ldquoRapid quantification of phenolicacids in Radix Salvia Miltrorrhiza extract solutions by FT-NIRspectroscopy in transflective moderdquo Journal of Pharmaceuticaland Biomedical Analysis vol 52 no 4 pp 425ndash431 2010

[16] X Jiang X Y Qin D Yin et al ldquoRapid monitoring of benzyl-penicillin sodium using Raman and surface enhanced Ramanspectroscopyrdquo Spectrochim Acta A vol 140 pp 474ndash478 2015

[17] W Kessler D Oelkrug and R Kessler ldquoUsing scattering andabsorption spectra as MCR-hard model constraints for diffusereflectance measurements of tabletsrdquo Analytica Chimica Actavol 642 no 1-2 pp 127ndash134 2009

[18] L K H Bittner N Heigl C H Petter et al ldquoNear-infraredreflection spectroscopy (NIRS) as a successful tool for simulta-neous identification and particle size determination of amox-icillin trihydraterdquo Journal of Pharmaceutical and BiomedicalAnalysis vol 54 no 5 pp 1059ndash1064 2011

[19] L Xu P T Shi Z H Ye S M Yan and X P Yu ldquoRapid analysisof adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric classmodeling techniquesrdquo Food Chemistry vol 141 no 3 pp 2434ndash2439 2013

[20] X Guan F-Q Gu J Liu and Y-J Yang ldquoStudies on thebrand traceability of milk powder based on NIR spectroscopytechnologyrdquo Spectroscopy and Spectral Analysis vol 33 no 10pp 2621ndash2624 2013

[21] A Porfire L Rus A L Vonica and I Tomuta ldquoHigh-throughput NIR-chemometric methods for determination ofdrug content and pharmaceutical properties of indapamidepowder blends for tablettingrdquo Journal of Pharmaceutical andBiomedical Analysis vol 70 pp 301ndash309 2012

[22] J G RosasM Blanco JMGonzalez andMAlcala ldquoReal-timedetermination of critical quality attributes using near-infraredspectroscopy a contribution for Process Analytical Technology(PAT)rdquo Talanta vol 97 pp 163ndash170 2012

[23] M C Sarraguca A V Cruz H R Amaral P C Costa and JA Lopes ldquoComparison of different chemometric and analytical

methods for the prediction of particle size distribution in phar-maceutical powdersrdquo Analytical and Bioanalytical Chemistryvol 399 no 6 pp 2137ndash2147 2011

[24] National Pharmacopoeia Committee Pharmacopoeia of PeoplesRepublic of China Part 1 Chemical Industry Press BeijingChina 2010

[25] Q-S Shao A-L Zhang W-W Ye H-P Guo and R-HHu ldquoFast determination of two atractylenolides in RhizomaAtractylodis Macrocephalae by Fourier transform near-infraredspectroscopy with partial least squaresrdquo Spectrochimica ActaPart A Molecular and Biomolecular Spectroscopy vol 120 pp499ndash504 2014

[26] Committee of European Pharmacopoeia European Pharma-copoeia 80 European Directorate for the Quality of Medicinesamp HealthCare Strasbourg France 2013

[27] E Guerin P Tchoreloff B Leclerc D Tanguy M Deleuil andG Couarraze ldquoRheological characterization of pharmaceuticalpowders using tap testing shear cell andmercury porosimeterrdquoInternational Journal of Pharmaceutics vol 189 no 1 pp 91ndash1031999

[28] K D Brabanter P Karsmakers F Ojeda et al LS-SVMlab AMatlabC Toolbox for Least Squares Support Vector Machines2002 httpwwwesatkuleuvenacbesistalssvmlab

[29] F Chauchard R Cogdill S Roussel J M Roger and V Bellon-Maurel ldquoApplication of LS-SVM to non-linear phenomena inNIR spectroscopy development of a robust and portable sensorfor acidity prediction in grapesrdquo Chemometrics and IntelligentLaboratory Systems vol 71 no 2 pp 141ndash150 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy


Carbohydrate Chemistry

International Journal of


Journal of

Chemistry


Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of


Chromatography Research International


Applied ChemistryJournal of



Theoretical ChemistryJournal of


Journal of

Spectroscopy

Analytical ChemistryInternational Journal of


Journal of


Quantum Chemistry


Organic Chemistry International

ElectrochemistryInternational Journal of



CatalystsJournal of


unique advantages over other spectroscopy technologiessuch as Raman spectroscopy which does well in the analysisof pure compounds [16]

NIR spectra carry abundant information not only onchemical compositions but also on physical properties (egparticle size) of the sample [17 18] In powder analysissome qualitative work with NIR has been reported suchas rapid identification of the production area [19] andbrand traceability [20] And some work about quantitativeanalysis has also been done in relating the NIR spectra todifferent quality attributes of powders such as content ofingredients [6] flow ability [21] content ofmoisture [22] andparticle size [23] And subsequent unit operations of soliddosage preparations could benefit from the quality control ofpowders

Herbal extract powders as raw materials play an impor-tant role in the research development and manufacturingof Chinese medicine preparations Currently the qualitycontrol of herbal extract powders mainly focuses on thecontent of active pharmaceutical ingredients according tothe Chinese Pharmacopoeia (ChP) 2010 [24] However likepowders of chemical materials it is far from enough tocontrol the quality of herbal extract powders only by thecontent of components In order to understand and controlthe quality of herbal extract powders as well as relatedmanufacturing processes and products physical propertiesof herbal extract powders should be paid more attentionGenerally classical methods for determination of physicalproperties of herbal extract powders were time-consumingAnd recently NIR spectroscopy has been reported to besuccessfully applied in the analysis of herbal powders wherethe contents of active pharmaceutical ingredients were theattractive part Quantification of contents of two or moreingredients in herbal extract powders could be carried outby NIR [15 25] Nevertheless the application of NIR toanalysis of physical properties of herbal extract powders isstill an untouched area Therefore the aim of this paperis to investigate the possibility of using NIR to predictboth the chemical and physical properties of herbal extractpowders at the same time To the best of our knowledgethis is the first report on the application of NIR spectroscopyin characterization of multiple quality attributes of herbalextract powders It is expected that the usage of NIR couldbe broadened in the quality control of rawmaterials of herbalproducts

The rest part of the paper is organized as followsfirstly contents of cryptotanshinone and tanshinone IIA of50 batches of tanshinone extract powders were determinedby high performance liquid chromatography (HPLC) andthe physical quality attributes were measured by classicalmethods Then the NIR spectra of tanshinone extract pow-ders were collected in diffuse reflection mode and differentdata pretreatment methods were screened After that partialleast squares (PLS) and least square support vector machine(LS-SVM) models for quantitative prediction of differentquality attributes were built and the performances of thesemodels were compared Finally a conclusion of this paper isprovided

Table 1 Factors and levels of central composite design (120572 = 1)

Name Units Low High minus120572 +120572Ethanolconcentration 80 100 80 100

Ethanolvolume L 4 6 4 6

Decoctiontime Hour 05 3 05 3

120572means the distance between the ldquostarrdquo point and the central point

2 Experimental

21 Materials Salvia miltiorrhiza (batch number 20120402)were purchased from Beijing Ben Cao Fang Yuan Phar-maceutical Co Ltd (Beijing China) Cryptotanshinone(batch number 1110852-200806) and tanshinone IIA (batchnumber 110766-200619) were obtained from National Insti-tute for the Control of Pharmaceutical and BiologicalProducts (Beijing China) Acetonitrile (Fisher scientificWaltham Massachusetts USA) phosphoric acid (Fisherscientific Waltham Massachusetts USA) and distilled water(HangzhouWahahaGroupCo Ltd Hangzhou China) wereofHPLC grade and all other reagents were of analytical grade

30 batches of alcohol extracts of Salvia miltiorrhizawere purchased from 5 different suppliers 20 batches ofalcohol extracts of Salvia miltiorrhizawere homemade undercentral composite experimental design in order to expand thevariation coverage of the sample sets Factors and levels ofthe alcohol extraction process are shown in Table 1 and theexperiment schedules are listed in Table 2 The alpha valuewas set to 1 and the replication of center points was 5 Theextraction process was carried out according to proceduresspecified in the Chinese Pharmacopoeia (ChP) 2010 [24]as follows pulverized powders of Salvia miltiorrhiza wereextracted by alcohol through heating reflux after which thealcohol was filtered and the filtrates were merged Then thefiltrate was vacuum evaporated to recover ethanol enrichedto 130sim135 of the relative density at 60∘Cwashed to colorlessby hot water dried at 80∘C and crushed to fine powdersfinally As a result 50 batches of alcohol extracts of Salviamiltiorrhiza were used in the following experiments

3 Method

31 HPLC Analysis 5 grams of tanshinone extract powderswas taken to a 5mL volumetric flask after a precise weighingdissolved by methanol and then diluted with methanolto volume All samples were filtered through a milliporemembrane filter with an average pore diameter of 045 120583mand 10 120583L filtrate was injected into the HPLC system foranalysis

The contents of cryptotanshinone and tanshinone IIAwere quantitated by the reverse phase HPLC according toCh P 2010 [24] An Agilent 1100 HPLC system (AgilentTechnologies Santa Clara California USA) with a vacuumdegasser a quaternary pump an autosampler a thermostaticcolumn compartment and a diode array detector were






1 90 5 1752 90 5 3003 80 4 0504 80 4 3005 90 4 1756 90 6 1757 100 6 0508 80 6 3009 100 5 17510 90 5 17511 100 4 05012 90 5 17513 80 5 17514 100 4 30015 100 6 30016 80 6 05017 90 5 17518 90 5 05019 90 5 17520 90 5 175






10 11986350 and 119863











119903 =

sum119899

119894=1(119862119894minus 119862119894) (119862119901119894minus 119862119901119894)

radicsum119899

119894=1(119862119894minus 119862119894)

2

radicsum119899

119894=1(119862119901119894minus 119862119901119894)

2


119894=1(119862119901119894minus 119862119894)

2

119899

BIAS =sum119899

119894=1

10038161003816100381610038161003816119862119901119894minus 119862119894

10038161003816100381610038161003816

119899

RPD =SDpre

RMSEP


119894=1(119862119894119901minus 119862119894119901)

2

119899 minus 1

(1)


































The1198631011986350 and119863














12

10

08

06

04

02

Abso

rban

ce


































1198631011986350

and11986390


10000 9000 8000 7000 6000 5000 4000

0006

0004

0002

0000

minus0002

minus0004

minus0006

minus0008

minus0010

minus0012

Inte

nsity






5 Conclusions


1011986350




0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV



minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)





Acknowledgments


References









































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of






1 90 5 1752 90 5 3003 80 4 0504 80 4 3005 90 4 1756 90 6 1757 100 6 0508 80 6 3009 100 5 17510 90 5 17511 100 4 05012 90 5 17513 80 5 17514 100 4 30015 100 6 30016 80 6 05017 90 5 17518 90 5 05019 90 5 17520 90 5 175






10 11986350 and 119863











119903 =

sum119899

119894=1(119862119894minus 119862119894) (119862119901119894minus 119862119901119894)

radicsum119899

119894=1(119862119894minus 119862119894)

2

radicsum119899

119894=1(119862119901119894minus 119862119901119894)

2


119894=1(119862119901119894minus 119862119894)

2

119899

BIAS =sum119899

119894=1

10038161003816100381610038161003816119862119901119894minus 119862119894

10038161003816100381610038161003816

119899

RPD =SDpre

RMSEP


119894=1(119862119894119901minus 119862119894119901)

2

119899 minus 1

(1)


































The1198631011986350 and119863














12

10

08

06

04

02

Abso

rban

ce


































1198631011986350

and11986390


10000 9000 8000 7000 6000 5000 4000

0006

0004

0002

0000

minus0002

minus0004

minus0006

minus0008

minus0010

minus0012

Inte

nsity






5 Conclusions


1011986350




0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV



minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)





Acknowledgments


References









































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of



119903 =

sum119899

119894=1(119862119894minus 119862119894) (119862119901119894minus 119862119901119894)

radicsum119899

119894=1(119862119894minus 119862119894)

2

radicsum119899

119894=1(119862119901119894minus 119862119901119894)

2


119894=1(119862119901119894minus 119862119894)

2

119899

BIAS =sum119899

119894=1

10038161003816100381610038161003816119862119901119894minus 119862119894

10038161003816100381610038161003816

119899

RPD =SDpre

RMSEP


119894=1(119862119894119901minus 119862119894119901)

2

119899 minus 1

(1)


































The1198631011986350 and119863














12

10

08

06

04

02

Abso

rban

ce


































1198631011986350

and11986390


10000 9000 8000 7000 6000 5000 4000

0006

0004

0002

0000

minus0002

minus0004

minus0006

minus0008

minus0010

minus0012

Inte

nsity






5 Conclusions


1011986350




0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV



minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)





Acknowledgments


References









































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of












The1198631011986350 and119863














12

10

08

06

04

02

Abso

rban

ce


































1198631011986350

and11986390


10000 9000 8000 7000 6000 5000 4000

0006

0004

0002

0000

minus0002

minus0004

minus0006

minus0008

minus0010

minus0012

Inte

nsity






5 Conclusions


1011986350




0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV



minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)





Acknowledgments


References









































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of































1198631011986350

and11986390


10000 9000 8000 7000 6000 5000 4000

0006

0004

0002

0000

minus0002

minus0004

minus0006

minus0008

minus0010

minus0012

Inte

nsity






5 Conclusions


1011986350




0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV



minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)





Acknowledgments


References









































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of















1198631011986350

and11986390


10000 9000 8000 7000 6000 5000 4000

0006

0004

0002

0000

minus0002

minus0004

minus0006

minus0008

minus0010

minus0012

Inte

nsity






5 Conclusions


1011986350




0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV



minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)





Acknowledgments


References









































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of



0020

0015

0010

0005

0000

Root

mea

n sq

uare

d er

ror

RMSECRMSECV



minus4

minus2

0

2

4

minus3minus2

minus10

12

34

0016

0014

0012

0010

0008

0006

0004

0002

0000

lg sig 2

lg ga

m

RMSE

CV (t

appe

d de

nsity

)





Acknowledgments


References









































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of










Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

Documents

Research Article Rapid Characterization of Tanshinone ...downloads.hindawi.com/journals/ijac/2015/704940.pdf · e particle size distribution was determined by the bt-laser particle